DG-CNN: Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images
Although using convolutional neural networks (CNNs) for computer-aided diagnosis (CAD) has made tremendous progress in the last few years, the small medical datasets remain to be the major bottleneck in this area. To address this problem, ...
Document-Level Neural Machine Translation with Hierarchical Modeling of Global Context
Document-level machine translation (MT) remains challenging due to its difficulty in efficiently using document-level global context for translation. In this paper, we propose a hierarchical model to learn the global context for document-level ...
Intent-Slot Correlation Modeling for Joint Intent Prediction and Slot Filling
Slot filling and intent prediction are basic tasks in capturing semantic frame of human utterances. Slots and intent have strong correlation for semantic frame parsing. For each utterance, a specific intent type is generally determined with the ...
Imputing DNA Methylation by Transferred Learning Based Neural Network
DNA methylation is one important epigenetic type to play a vital role in many diseases including cancers. With the development of the high-throughput sequencing technology, there is much progress to disclose the relations of DNA methylation with ...
Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture
COVID-19 is a contagious infection that has severe effects on the global economy and our daily life. Accurate diagnosis of COVID-19 is of importance for consultants, patients, and radiologists. In this study, we use the deep learning network ...
Quality of Service Support in RPL Networks: Standing State and Future Prospects
The development of IP-based Internet of Things (IoT) networks would facilitate more effective end-to-end IP network architectures, but it remains a challenge. Network routing needs to be effectively addressed in the IoT environments of scarce ...
Differential Privacy via a Truncated and Normalized Laplace Mechanism
When querying databases containing sensitive information, the privacy of individuals stored in the database has to be guaranteed. Such guarantees are provided by differentially private mechanisms which add controlled noise to the query responses. ...
Test-Driven Feature Extraction of Web Components
With the growing requirements of web applications, web components are developed to package the implementation of commonly-used features for reuse. In some cases, the developer may want to reuse some features which cannot be customized by the ...
ovAFLow: Detecting Memory Corruption Bugs with Fuzzing-Based Taint Inference
Grey-box fuzzing is an effective technology to detect software vulnerabilities, such as memory corruption. Previous fuzzers in detecting memory corruption bugs either use heavy-weight analysis, or use techniques which are not customized for memory ...
Byte Frequency Based Indicators for Crypto-Ransomware Detection from Empirical Analysis
File entropy is one of the major indicators of crypto-ransomware because the encryption by ransomware increases the randomness of file contents. However, entropy-based ransomware detection has certain limitations; for example, when distinguishing ...
Unconditionally Secure Oblivious Polynomial Evaluation: A Survey and New Results
Oblivious polynomial evaluation (OPE) is a two-party protocol that allows a receiver, ℛ to learn an evaluation f(α), of a sender, 𝒮’s polynomial (f(x)), whilst keeping both α and f(x) private. This protocol has attracted a lot of attention ...
Generalized Goldwasser and Micali’s Type Cryptosystem
In 1982, Goldwasser and Micali proposed the first probabilistic public key cryptosystem with indistinguishability under chosen plaintext attack security based on the quadratic residuosity assumption. Ciphertext expansion of Goldwasser’s scheme is ...
Unified Enclave Abstraction and Secure Enclave Migration on Heterogeneous Security Architectures
Nowadays, application migration becomes more and more attractive. For example, it can make computation closer to data sources or make service closer to end-users, which may significantly decrease latency in edge computing. Yet, migrating ...
Conjugate-Gradient Progressive-Iterative Approximation for Loop and Catmull-Clark Subdivision Surface Interpolation
Loop and Catmull-Clark are the most famous approximation subdivision schemes, but their limit surfaces do not interpolate the vertices of the given mesh. Progressive-iterative approximation (PIA) is an efficient method for data interpolation and ...
