With the rapid development of speech synthesis and voice conversion technologies, Audio Deepfake has become a serious threat to the Automatic Speaker Verification (ASV) system. Numerous countermeasures are proposed to detect this type of attack. In this paper, we report our efforts to combine the self-supervised WavLM model and Multi-Fusion Attentive classifier for audio deepfake detection. Our method exploits the WavLM model to extract features that are more conducive to spoofing detection for the first time. Then, we propose a novel Multi-Fusion Attentive (MFA) classifier based on the Attentive Statistics Pooling (ASP) layer. The MFA captures the complementary information of audio features at both time and layer levels. Experiments demonstrate that our methods achieve state-of-the-art results on the ASVspoof 2021 DF set and provide competitive results on the ASVspoof 2019 and 2021 LA set.

Audio and visual signals usually coexist in realistic scenes, and human brains can learn this multi-modal perception easily. So, it is crucial for the computer to learn how human brains work for solving multi-modal tasks. The Audio-visual event localization (AVEL) task involves two sub-tasks: find video segments that contain Audio-visual events, and determine the category of the events. However, the AVEL task remains challenging due to the severe background noise. Additionally, processing information from both modalities simultaneously is also a tough issue. The current approaches have two main problems. One is that the network tends to be influenced by noise and predicts unreasonable events for consecutive segments within the same video clip. The other is that the model will oscillate between the local and global targets due to the multi-objective learning. To address these problems, we propose a decoupling multi-modal fusion network, which not only suppresses the complex noise but also learns the local and global information exclusively. The proposal consists of two sub-networks: the Which-event sub-network for predicting the event category and the Is-event sub-network for determining the time boundary for the event. We evaluate our method on the standard AVE Dataset in both fully and weakly supervised settings, and the results verify the effectiveness of our method.

Peer influence and homophily are two entangled forces underlying social influences. However, distinguishing homophily from peer influence is difficult, particularly when there is latent homophily caused by unobservable features. This paper proposes a novel data-driven framework that combines the advantages of latent homophily identification and causal inference. Specifically, the approach first utilizes scalable network representation learning algorithms to obtain node embeddings, which are extracted from social network structures. Then, the embeddings are used to control latent homophily in a quasi-experimental design for causal inference. The simulation experiments show that the proposed approach can estimate peer influence more accurately than existing parameterized approaches and data-driven methods. We applied the proposed framework in an empirical study of players’ online gaming behaviors. First, our approach can achieve improved model fitness for estimating peer influence in online games. Second, we discover a heterogeneous effect of peer influence: players with higher tenure and playing levels receive stronger peer influence. Finally, our results suggest that the homophily effect has a stronger influence on players’ behavior than peer influence. Summary of Contribution: The study proposes a novel computational method to separate peer influence from homophily in an online network. Using network embeddings learned from data to control latent homophily, the approach effectively addresses the challenge of correctly identifying peer effects in the absence of randomized experimental conditions. While simplifying the computational process, the method achieves good computational performance, thus effectively helping researchers and practitioners extract useful network information in various online service contexts.

To remain competitive in the present dynamic environment, ‘governance for agility’ has become a key solution. Past literature paid little attention to understanding how governance for agility, particularly in regard to the delivery of Information Technology (IT) implementation. Using agile organisation and IT-governance theory as lenses to analyse data from a hospital case study, a strategic process model of governance for agility is empirically derived. This model suggests that agile healthcare information technology implementation is achievable via phase-based IT-governance strategies and forms which authorise decision makers to maneuver resources strategically in a dynamic environment. Theoretically, this study contributes to the dearth of empirical understanding of IT governance in the Healthcare IT literature and advances knowledge by making a conceptual distinction through introducing the use of phase-based IT-governance strategies and forms to generate agile organisational capabilities to achieve agile Healthcare IT implementation. The findings serve as a foundation for future research within the information systems (IS) discipline. Practitioners could plan an agile Healthcare IT implementation by referring to the model—a systematic roadmap for governing and strategising hospital resources and capabilities.

With the rapid development of information technology, Social Networking Sites (SNSs) as important communication platforms have provided more and more individuals with an effective approach to communicate with friends and share information. Based on the Disclosure Decision Model (DDM) and Communication Privacy Management Theory (CPMT), this research investigates how perceived privacy risk and perceived utility affect self-disclosure behavior simultaneously under the circumstance of SNSs in a risk-utility decision model. Theoretical arguments and propositions are developed.

Abstract In this paper, a new method for fuzzy clustering is proposed that combines generative topographic mapping (GTM) and Fuzzy c‐means (FCM) clustering. GTM is used to generate latent variables and their posterior probabilities. These two provide the distribution of the input data in the latent space. FCM determines the seeds of clusters, as well as the resultant clusters and the corresponding membership functions of the input data, based on the latent variables obtained from GTM. Experiments are conducted to compare the results obtained using FCM and the Gustafson‐Kessel (GK) algorithm with the proposed method in terms of four cluster‐validity indexes. Using simulated and benchmark data sets, it is observed that the hybrid method (GTMFCM) performs better than FCM and GK algorithms in terms of these indexes. It is also found that the superiority of GTMFCM over FCM and GK algorithms becomes more pronounced with the increase in the dimensionality of the input data set.