Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition

Current state-of-the-art few-shot learners focus on developing effective training procedures for feature representations, before using simple, e.g. nearest centroid, classifiers. In this paper we take an orthogonal approach that is agnostic to the features used, and focus exclusively on meta-learning the actual classifier layer. Specifically, we introduce MetaQDA, a Bayesian meta-learning generalisation of the classic quadratic discriminant analysis. This setup has several benefits of interest to practitioners: meta-learning is fast and memory efficient, without the need to fine-tune features. It is agnostic to the off-the-shelf features chosen, and thus will continue to benefit from advances in feature representations. Empirically, it leads to robust performance in cross-domain few-shot learning and, crucially for real-world applications, it leads to better uncertainty calibration in predictions

Molecular dynamics simulation of surfactant induced wettability alteration of shale reservoirs

Shale oil has recently received considerable attention as a promising energy source due to its substantial reserves. However, the recovery of shale oil presents numerous challenges due to the low-porosity and low-permeability characteristics of shale reservoirs. To tackle this challenge, the introduction of surfactants capable of modifying wettability has been employed to enhance shale oil recovery. In this study, we perform molecular dynamics simulations to investigate the influence of surfactants on the alteration of wettability in shale reservoirs. Firstly, surfaces of kaolinite, graphene, and kerogen are constructed to represent the inorganic and organic constituents of shale reservoirs. The impact and underlying mechanisms of two types of ionic surfactants, namely, the anionic surfactant sodium dodecylbenzene sulfonate (SDBS) and cationic surfactant dodecyltrimethylammonium bromide (DTAB), on the wettability between oil droplets and surfaces are investigated. The wettability are analyzed from different aspects, including contact angle, centroid ordinates, and self-diffusion coefficient. Simulation results show that the presence of surfactants can modify the wetting characteristics of crude oil within shale reservoirs. Notably, a reversal of wettability has been observed for oil-wet kaolinite surfaces. As for kerogen surfaces, it is found that an optimal surfactant concentration exists, beyond which the further addition of surfactant may not enhance the efficiency of wettability alteration

Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition

The audio-video based emotion recognition aims to classify a given video into basic emotions. In this paper, we describe our approaches in EmotiW 2019, which mainly explores emotion features and feature fusion strategies for audio and visual modality. For emotion features, we explore audio feature with both speech-spectrogram and Log Mel-spectrogram and evaluate several facial features with different CNN models and different emotion pretrained strategies. For fusion strategies, we explore intra-modal and cross-modal fusion methods, such as designing attention mechanisms to highlights important emotion feature, exploring feature concatenation and factorized bilinear pooling (FBP) for cross-modal feature fusion. With careful evaluation, we obtain 65.5% on the AFEW validation set and 62.48% on the test set and rank third in the challenge.Comment: Accepted by ACM ICMI'19 (2019 International Conference on Multimodal Interaction

A Multi-User Steganographic File System on Untrusted Shared Storage

Existing steganographic file systems enable a user to hide the existence of his secret data by claiming that they are (static) dummy data created during disk initialization. Such a claim is plausible if the adversary only sees the disk content at the point of attack. In a multi-user computing environment that employs untrusted shared storage, however, the adversary could have taken multiple snapshots of the disk content over time. Since the dummy data are static, the differences across snapshots thus disclose the locations of user data, and could even reveal the user passwords. In this paper, we introduce a Dummy-Relocatable Stegano-graphic (DRSteg) file system to provide deniability in multi-user environments where the adversary may have multi-ple snapshots of the disk content. With its novel tech-niques for sharing and relocating dummy data during run-time, DRSteg allows a data owner to surrender only some data and attribute the unexplained changes across snapshots to the dummy operations. The level of deniability offered by DRSteg is configurable by the users, to balance against the resulting performance overhead. Additionally, DRSteg guarantees the integrity of the protected data, except where users voluntarily overwrite data under duress. 1