24 research outputs found
NRPA: Neural Recommendation with Personalized Attention
Existing review-based recommendation methods usually use the same model to
learn the representations of all users/items from reviews posted by users
towards items. However, different users have different preference and different
items have different characteristics. Thus, the same word or similar reviews
may have different informativeness for different users and items. In this paper
we propose a neural recommendation approach with personalized attention to
learn personalized representations of users and items from reviews. We use a
review encoder to learn representations of reviews from words, and a user/item
encoder to learn representations of users or items from reviews. We propose a
personalized attention model, and apply it to both review and user/item
encoders to select different important words and reviews for different
users/items. Experiments on five datasets validate our approach can effectively
improve the performance of neural recommendation.Comment: 4 pages, 4 figure
Wound Segmentation with Dynamic Illumination Correction and Dual-view Semantic Fusion
Wound image segmentation is a critical component for the clinical diagnosis
and in-time treatment of wounds. Recently, deep learning has become the
mainstream methodology for wound image segmentation. However, the
pre-processing of the wound image, such as the illumination correction, is
required before the training phase as the performance can be greatly improved.
The correction procedure and the training of deep models are independent of
each other, which leads to sub-optimal segmentation performance as the fixed
illumination correction may not be suitable for all images. To address
aforementioned issues, an end-to-end dual-view segmentation approach was
proposed in this paper, by incorporating a learn-able illumination correction
module into the deep segmentation models. The parameters of the module can be
learned and updated during the training stage automatically, while the
dual-view fusion can fully employ the features from both the raw images and the
enhanced ones. To demonstrate the effectiveness and robustness of the proposed
framework, the extensive experiments are conducted on the benchmark datasets.
The encouraging results suggest that our framework can significantly improve
the segmentation performance, compared to the state-of-the-art methods
PrivateRec: Differentially Private Training and Serving for Federated News Recommendation
Privacy protection is an essential issue in personalized news recommendation,
and federated learning can potentially mitigate the privacy concern by training
personalized news recommendation models over decentralized user data.For a
theoretical privacy guarantee, differential privacy is necessary. However,
applying differential privacy to federated recommendation training and serving
conventionally suffers from the unsatisfactory trade-off between privacy and
utility due to the high-dimensional characteristics of model gradients and
hidden representations. In addition, there is no formal privacy guarantee for
both training and serving in federated recommendation. In this paper, we
propose a unified federated news recommendation method for effective and
privacy-preserving model training and online serving with differential privacy
guarantees. We first clarify the notion of differential privacy over users'
behavior data for both model training and online serving in the federated
recommendation scenario. Next, we propose a privacy-preserving online serving
mechanism under this definition with differentially private user interest
decomposition. More specifically, it decomposes the high-dimensional and
privacy-sensitive user embedding into a combination of public basic vectors and
adds noise to the combination coefficients. In this way, it can avoid the
dimension curse and improve the utility by reducing the required noise
intensity for differential privacy. Besides, we design a federated
recommendation model training method with differential privacy, which can avoid
the dimension-dependent noise for large models via label permutation and
differentially private attention modules. Experiments on real-world news
recommendation datasets validate the effectiveness of our method in achieving a
good trade-off between privacy protection and utility for federated news
recommendations
SMART APPLIANCE SYSTEM
The invention describes a smart appliance system. The system receives instructions from a user over a network to control an appliance. The system then transmits these instructions to the appliance to control the appliance. The system causes the appliance to operate according to the transmitted instructions
A Modularity Degree Based Heuristic Community Detection Algorithm
A community in a complex network can be seen as a subgroup of nodes that are densely connected. Discovery of community structures is a basic problem of research and can be used in various areas, such as biology, computer science, and sociology. Existing community detection methods usually try to expand or collapse the nodes partitions in order to optimize a given quality function. These optimization function based methods share the same drawback of inefficiency. Here we propose a heuristic algorithm (MDBH algorithm) based on network structure which employs modularity degree as a measure function. Experiments on both synthetic benchmarks and real-world networks show that our algorithm gives competitive accuracy with previous modularity optimization methods, even though it has less computational complexity. Furthermore, due to the use of modularity degree, our algorithm naturally improves the resolution limit in community detection
Protecting Intellectual Property of Language Generation APIs with Lexical Watermark
Nowadays, due to the breakthrough in natural language generation (NLG), including machine translation, document summarization, image captioning, etc NLG models have been encapsulated in cloud APIs to serve over half a billion people worldwide and process over one hundred billion word generations per day. Thus, NLG APIs have already become essential profitable services in many commercial companies. Due to the substantial financial and intellectual investments, service providers adopt a pay-as-you-use policy to promote sustainable market growth. However, recent works have shown that cloud platforms suffer from financial losses imposed by model extraction attacks, which aim to imitate the functionality and utility of the victim services, thus violating the intellectual property (IP) of cloud APIs. This work targets at protecting IP of NLG APIs by identifying the attackers who have utilized watermarked responses from the victim NLG APIs. However, most existing watermarking techniques are not directly amenable for IP protection of NLG APIs. To bridge this gap, we first present a novel watermarking method for text generation APIs by conducting lexical modification to the original outputs. Compared with the competitive baselines, our watermark approach achieves better identifiable performance in terms of p-value, with fewer semantic losses. In addition, our watermarks are more understandable and intuitive to humans than the baselines. Finally, the empirical studies show our approach is also applicable to queries from different domains, and is effective on the attacker trained on a mixture of the corpus which includes less than 10% watermarked samples