Guowu Yang

Bio: Guowu Yang is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Quantum circuit & Toffoli gate. The author has an hindex of 23, co-authored 145 publications receiving 1718 citations. Previous affiliations of Guowu Yang include Portland State University & Chinese Academy of Sciences.

Optimal synthesis of multiple output Boolean functions using a set of quantum gates by symbolic reachability analysis

Abstract: This paper proposes an approach to optimally synthesize quantum circuits by symbolic reachability analysis, where the primary inputs and outputs are basis binary and the internal signals can be nonbinary in a multiple-valued domain. The authors present an optimal synthesis method to minimize quantum cost and some speedup methods with nonoptimal quantum cost. The methods here are applicable to small reversible functions. Unlike previous works that use permutative reversible gates, a lower level library that includes nonpermutative quantum gates is used here. The proposed approach obtains the minimum cost quantum circuits for Miller gate, half adder, and full adder, which are better than previous results. This cost is minimum for any circuit using the set of quantum gates in this paper, where the control qubit of 2-qubit gates is always basis binary. In addition, the minimum quantum cost in the same manner for Fredkin, Peres, and Toffoli gates is proven. The method can also find the best conversion from an irreversible function to a reversible circuit as a byproduct of the generality of its formulation, thus synthesizing in principle arbitrary multi-output Boolean functions with quantum gate library. This paper constitutes the first successful experience of applying formal methods and satisfiability to quantum logic synthesis

Identifying APT Malware Domain Based on Mobile DNS Logging

Abstract: Advanced Persistent Threat (APT) is a serious threat against sensitive information. Current detection approaches are time-consuming since they detect APT attack by in-depth analysis of massive amounts of data after data breaches. Specifically, APT attackers make use of DNS to locate their command and control (C&C) servers and victims’ machines. In this paper, we propose an efficient approach to detect APT malware C&C domain with high accuracy by analyzing DNS logs. We first extract 15 features from DNS logs of mobile devices. According to Alexa ranking and the VirusTotal’s judgement result, we give each domain a score. Then, we select the most normal domains by the score metric. Finally, we utilize our anomaly detection algorithm, called Global Abnormal Forest (GAF), to identify malware C&C domains. We conduct a performance analysis to demonstrate that our approach is more efficient than other existing works in terms of calculation efficiency and recognition accuracy. Compared with Local Outlier Factor (LOF), -Nearest Neighbor (KNN), and Isolation Forest (iForest), our approach obtains more than 99% and for the detection of C&C domains. Our approach not only can reduce data volume that needs to be recorded and analyzed but also can be applicable to unsupervised learning.

An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures

Abstract: Computer-aided diagnosis (CAD) in the medical field has received more and more attention in recent years. One important CAD application is to detect and classify breast lesions in ultrasound images. Traditionally, the process of CAD for breast lesions classification is mainly composed of two separated steps: i) locate the lesion region of interests (ROI); ii) classify the located region of interests (ROI) to see if they are benign or not. However, due to the complex structure of breast and the existence of noise in the ultrasound images, traditional handcrafted feature based methods usually can not achieve satisfactory result. With the recent advance of deep learning, the performance of object detection and classification has been boosted to a great extent. In this paper, we aim to systematically evaluate the performance of several existing state-of-the-art object detection and classification methods for breast lesions CAD. To achieve that, we have collected a new dataset consisting of 579 benign and 464 malignant lesion cases with the corresponding ultrasound images manually annotated by experienced clinicians. We evaluate different deep learning architectures and conduct comprehensive experiments on our newly collected dataset. For the lesion regions detecting task, Single Shot MultiBox Detector with the input size as 300×300 (SSD300) achieves the best performance in terms of average precision rate (APR), average recall rate (ARR) and F1 score. For the classification task, DenseNet is more suitable for our problems. Our experiments reveal that better and more efficient detection and convolutional neural network (CNN) frameworks is one important factor for better performance of detecting and classification task of the breast lesion. Another significant factor for improving the performance of detecting and classification task, which is transfer learning from the large-scale annotated ImageNet to classify breast lesion.

Quantum logic synthesis by symbolic reachability analysis

Abstract: Reversible quantum logic plays an important role in quantum computing. In this paper, we propose an approach to optimally synthesize quantum circuits by symbolic reachability analysis where the primary inputs are purely binary. we use symbolic reachability analysis, a technique most commonly used in model checking (a way of formal verification), to synthesize the optimum quantum circuits. We present an exact synthesis method with optimal quantum cost and a speedup method with non-optimal quantum cost. Both our methods guarantee the synthesizeability of all reversible circuits. Unlike previous works which use permutative reversible gates, we use a lower level library which includes non-permutative quantum gates. For the first time, problems in quantum logic synthesis have been reduced to those of multiple-valued logic synthesis thus reducing the search space and algorithm complexity. We synthesized quantum circuits for gate, half-adder, full-adder, etc. with the smallest cost.. Our approach obtains the minimum cost quantum circuits for Miller's gate, half-adder, and full-adder, which are better than previous results. In addition, we prove the minimum quantum cost (using our elementary quantum gates) for Fredkin, Peres, and Toffoli gates. Our work constitutes the first successful experience of applying satisfiability with formal methods to quantum logic synthesis.

File-Centric Multi-Key Aggregate Keyword Searchable Encryption for Industrial Internet of Things

Abstract: Cloud storage has been used to reduce the cost and support convenient collaborations for industrial Internet of things (IIoT) data management. When data owners share IIoT data with authorized parties for data interaction, secure cloud data searching and file access control are fundamental security requirements. In this paper, first we discuss a new insider attack to the Cui's multi-key aggregate searchable encryption scheme, where the unauthorized inside users can guess the other users private keys. Then, we propose a novel file-centric multi-key aggregate keyword searchable encryption (Fc-MKA-KSE) system for the IIoT data in the file-centric framework. Specifically, we present two formal security models, namely, the security models of the indistinguishable selective-file chosen keyword attack and the indistinguishable selective-file keyword guessing attack, which can satisfy the security requirements. Our experimental results show that the proposed scheme achieves computational efficiency.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.