In the past few years, there has been an increasing focus on deep learning-based watermarking techniques. Many existing methods do not impose constraints to guide the embedding of watermarking, which leads to random embedding positions and makes watermarks vulnerable to detection and attack. In this paper, an adaptive robust watermarking technique is proposed as a solution to this issue. The proposed method employs a new embedding-guided end-to-end architecture, introducing the Embedding Guide component that utilizes attention mechanism and edge information to embed the secret message into regions that are visually insensitive and inconspicuous. This component enables adaptive embedding of the secret message in each cover image, resulting in high-quality watermarked images with improved imperceptibility. To enhance robustness, this study integrates the Efficient Channel Attention (ECA) block into both the message preprocessor and decoder, facilitating more effective secret message embedding and extraction. Furthermore, UNet++ is applied to improve performance against combined noise. The experimental findings demonstrate that the suggested algorithm surpasses current approaches.

Differential privacy (DP) provides a robust model to achieve privacy guarantees for released information. We examine the protection potency of sanitized multi-dimensional frequency distributions (FDs) via DP mechanisms against homogeneity attack (HA). Adversaries can obtain the exact values on sensitive attributes of their targets through HA without having to identify them from released data. We propose measures for disclosure risk (DR) from HA and derive closed-form relations between the privacy loss parameters and DR from HA. The availability of the closed-form relations will assist practitioners in understanding the abstract concepts of DP and privacy loss parameters by putting them in the context of a concrete privacy attack and offer a perspective for choosing privacy loss parameters when employing DP mechanisms. We apply the derived mathematical relations in real data to demonstrate the assessment of DR from HA on differentially privately sanitized FDs at various privacy loss parameters. The results suggest that relations between DR from HA and privacy loss are S-shaped; the former may not disappear even when privacy loss approaches 0.

Differential privacy (DP) provides a robust model to achieve privacy guarantees in released information. We examine the robustness of the protection against homogeneity attack (HA) in multi-dimensional frequency distributions sanitized via DP randomization mechanisms. We propose measures for disclosure risk from HA and derive closed-form relationships between privacy loss parameters in DP and disclosure risk from HA. We also provide a lower bound to the disclosure risk on a sensitive attribute when all the cells formed by quasi-identifiers are homogeneous for the sensitive attribute. The availability of the closed-form relationships helps understand the abstract concepts of DP and privacy loss parameters by putting them in the context of a concrete privacy attack and offers a perspective for choosing privacy loss parameters when employing DP mechanisms to release information in practice. We apply the closed-form mathematical relationships on real-life datasets to assess disclosure risk due to HA in differentially private sanitized frequency distributions at various privacy loss parameters.

Differential privacy (DP) is a state-of-the-art concept that formalizes privacy guarantees. We derive a new bound for the privacy loss from releasing Bayesian posterior samples in the setting of DP. The new bound is tighter than the existing bounds for common Bayesian models and is also consistent with the likelihood principle. We apply the privacy loss quantified by the new bound to release differentially private synthetic data from Bayesian models in several experiments and show the improved utility of the synthetic data compared to those generated from explicitly designed randomization mechanisms that privatize posterior distributions.

Interlinear Glossed Text (IGT) is a widely used format for encoding linguistic information in language documentation projects and scholarly papers.Manual production of IGT takes time and requires linguistic expertise.We attempt to address this issue by creating automatic glossing models, using modern multi-source neural models that additionally leverage easy-to-collect translations.We further explore cross-lingual transfer and a simple output length control mechanism, further refining our models.Evaluated on three challenging low-resource scenarios, our approach significantly outperforms a recent, state-of-the-art baseline, particularly improving on overall accuracy as well as lemma and tag recall.

Cross-lingual word embedding (CWE) algorithms represent words in multiple languages in a unified vector space.Multi-Word Expressions (MWE) are common in every language.When training word embeddings, each component word of an MWE gets its own separate embedding, and thus, MWEs are not translated by CWEs.We propose a simple method for word translation of MWEs to and from English in ten languages: we first compile lists of MWEs in each language and then tokenize the MWEs as single tokens before training word embeddings.CWEs are trained on a wordtranslation task using the dictionaries that only contain single words.In order to evaluate MWE translation, we created bilingual word lists from multilingual WordNet that include single-token words and MWEs, and most importantly, include MWEs that correspond to single words in another language.We show that the pre-tokenization of MWEs as single tokens performs better than averaging the embeddings of the individual tokens of the MWE.We can translate MWEs at a top-10 precision of 30-60%.The tokenization of MWEs makes the occurrences of single words in a training corpus more sparse, but we show that it does not pose negative impacts on single-word translations.

Label embedding has been widely used as a method to exploit label dependency with dimension reduction in multilabel classification tasks. However, existing embedding methods intend to extract label correlations directly, and thus they might be easily trapped by complex label hierarchies. To tackle this issue, we propose a novel Two-Stage Label Embedding (TSLE) paradigm that involves Neural Factorization Machine (NFM) to jointly project features and labels into a latent space. In encoding phase, we introduce a Twin Encoding Network (TEN) that digs out pairwise feature and label interactions in the first stage and then efficiently learn higherorder correlations with deep neural networks (DNNs) in the second stage. After the codewords are obtained, a set of hidden layers is applied to recover the output labels in decoding phase. Moreover, we develop a novel learning model by leveraging a max margin encoding loss and a label-correlation aware decoding loss, and we adopt the mini-batch Adam to optimize our learning model. Lastly, we also provide a kernel insight to better understand our proposed TSLE. Extensive experiments on various real-world datasets demonstrate that our proposed model significantly outperforms other state-ofthe-art approaches.

In this work, we propose an end-to-end approach for robust remote heart rate (HR) measurement gleaned from facial videos. Specifically the approach is based on remote photoplethysmography (rPPG), which constitutes a pulse triggered perceivable chromatic variation, sensed in RGB-face videos. Consequently, rPPGs can be affected in less-constrained settings. To unpin the shortcoming, the proposed algorithm utilizes a spatio-temporal attention mechanism, which places focus on the salient features included in rPPG-signals. In addition, we propose an effective rPPG augmentation approach, generating multiple rPPG signals with varying HRs from a single face video. Experimental results on the public datasets VIPL-HR and MMSE-HR show that the proposed method outperforms state-of-the-art algorithms in remote HR estimation.