摘要 :
Convolutional neural networks have been shown to extract features better than traditional algorithms in the fields such as image classification, object detection, and speech recognition. In parallel, a variational quantum algorith...
展开
Convolutional neural networks have been shown to extract features better than traditional algorithms in the fields such as image classification, object detection, and speech recognition. In parallel, a variational quantum algorithm incorporating parameterized quantum circuits has higher performance on near-term quantum processors. In this paper, we propose a classification algorithm called variational convolutional neural networks (VCNN), allowing for efficient training and implementation on nearterm quantum devices. The VCNN algorithm combines the multi-scale entanglement renormalization ansatz. We deploy the VCNN algorithm on the TensorFlow Quantum platform with the numerical simulator backends using the MNIST and Fashion MNIST datasets. Experimental results show that the average accuracy of VCNN on classification tasks can reach up to 96.41%. Our algorithm has higher learning accuracy and fewer training epochs than quantum neural network algorithms. Moreover, we conclude that circuit-based models have excellent resilience to noise by numerical simulations.(c) 2022 Elsevier B.V. All rights reserved.
收起
摘要 :
Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware. As this...
展开
Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware. As this trend is expected to continue, it should come as no surprise that an increasing number of machine learning researchers are investigating the possible advantages of quantum computing. The scientific literature on Quantum Machine Learning is now enormous, and a review of its current state that can be comprehended without a physics background is necessary. The objective of this study is to present a review of Quantum Machine Learning from the perspective of conventional techniques. Departing from giving a research path from fundamental quantum theory through Quantum Machine Learning algorithms from a computer scientist's perspective, we discuss a set of basic algorithms for Quantum Machine Learning, which are the fundamental components for Quantum Machine Learning algorithms. We implement the Quanvolutional Neural Networks (QNNs) on a quantum computer to recognize handwritten digits, and compare its performance to that of its classical counterpart, the Convolutional Neural Networks (CNNs). Additionally, we implement the QSVM on the breast cancer dataset and compare it to the classical SVM. Finally, we implement the Variational Quantum Classifier (VQC) and many classical classifiers on the Iris dataset to compare their accuracies.
收起
摘要 :
Nowadays, numerous attacks made by the malware (e.g., viruses, backdoors, spy ware, trojans and worms) have presented a major security threat to computer users. Currently, the most significant line of defense against malware is an...
展开
Nowadays, numerous attacks made by the malware (e.g., viruses, backdoors, spy ware, trojans and worms) have presented a major security threat to computer users. Currently, the most significant line of defense against malware is anti-virus products which focus on authenticating valid software from a whitelist, blocking invalid software from a blacklist, and running any unknown software (i.e., the gray list) in a controlled manner. The gray list, containing unknown software programs which could be either normal or malicious, is usually authenticated or rejected manually by virus analysts. Unfortunately, along with the development of the malware writing techniques, the number of file samples in the gray list that need to be analyzed by virus analysts on a daily basis is constantly increasing. The gray list is not only large in size, but also has an imbalanced class distribution where malware is the minority class. In this paper, we describe our research effort on building automatic, effective, and interpretable classifiers resting on the analysis of Application Programming Interfaces (APIs) called by Windows Portable Executable (PE) files for detecting malware from the large and unbalanced gray list. Our effort is based on associative classifiers due to their high interpretability as well as their capability of discovering interesting relationships among API calls. We first adapt several different post-processing techniques of associative classification, including rule pruning and rule re-ordering, for building effective associative classifiers from large collections of training data. In order to help the virus analysts detect malware from the unbalanced gray list, we then develop the Hierarchical Associative Classifier (HAC). HAC constructs a two-level associative classifier to maximize precision and recall of the minority (malware) class: in the first level, it uses high precision rules of majority (benign file samples) class and low precision rules of minority class to achieve high recall; and in the second level, it ranks the minority class files and optimizes the precision. Finally, since our case studies are based on a large and real data collection obtained from the Anti-virus Lab of Kingsoft corporation, including 8,000,000 malware, 8,000,000 benign files, and 100,000 file samples from the gray list, we empirically examine the sampling strategy to build the classifiers for such a large data collection to avoid over-fitting and achieve great effectiveness as well as high efficiency. Promising experimental results demonstrate the effectiveness and efficiency of the HAC classifier. HAC has already been incorporated into the scanning tool of Kingsoft's Anti-Virus software.
收起
摘要 :
With smartphones increasingly becoming ubiquitous and being equipped with various sensors, nowadays, there is a trend towards implementing HAR (Human Activity Recognition) algorithms and applications on smartphones, including heal...
展开
With smartphones increasingly becoming ubiquitous and being equipped with various sensors, nowadays, there is a trend towards implementing HAR (Human Activity Recognition) algorithms and applications on smartphones, including health monitoring, self-managing system and fitness tracking. However, one of the main issues of the existing HAR schemes is that the classification accuracy is relatively low, and in order to improve the accuracy, high computation overhead is needed. In this paper, an efficient Group-based Context-aware classification method for human activity recognition on smartphones, GCHAR is proposed, which exploits hierarchical group-based scheme to improve the classification efficiency, and reduces the classification error through context awareness rather than the intensive computation. Specifically, GCHAR designs the two-level hierarchical classification structure, i.e., inter-group and inner-group, and utilizes the previous state and transition logic (so-called context awareness) to detect the transitions among activity groups. In comparison with other popular classifiers such as RandomTree, Bagging, J48, BayesNet, KNN and Decision Table, thorough experiments on the realistic dataset (UCI HAR repository) demonstrate that GCHAR achieves the best classification accuracy, reaching 94.1636%, and time consumption in training stage of GCHAR is four times shorter than the simple Decision Table and is decreased by 72.21% in classification stage in comparison with BayesNet.
收起
摘要 :
The research related to age estimation using face images has become increasingly important, due to the fact it has a variety of potentially useful applications. An age estimation system is generally composed of aging feature extra...
展开
The research related to age estimation using face images has become increasingly important, due to the fact it has a variety of potentially useful applications. An age estimation system is generally composed of aging feature extraction and feature classification; both of which are important in order to improve the performance. For the aging feature extraction, the hybrid features, which are a combination of global and local features, have received a great deal of attention, because this method can compensate for defects found in individual global and local features. As for feature classification, the hierarchical classifier, which is composed of an age group classification (e.g. the class of less than 20 years old, the class of 2039 years old, etc.) and a detailed age estimation (e.g. 17, 23 years old, etc.), provide a much better performance than other methods. However, both the hybrid features and hierarchical classifier methods have only been studied independently and no research combining them has yet been conducted in the previous works. Consequently, we propose a new age estimation method using a hierarchical classifier method based on both global and local facial features. Our research is novel in the following three ways, compared to the previous works. Firstly, age estimation accuracy is greatly improved through a combination of the proposed hybrid features and the hierarchical classifier. Secondly, new local feature extraction methods are proposed in order to improve the performance of the hybrid features. The wrinkle feature is extracted using a set of region specific Gabor filters, each of which is designed based on the regional direction of the wrinkles, and the skin feature is extracted using a local binary pattern (LBP), capable of extracting the detailed textures of skin. Thirdly, the improved hierarchical classifier is based on a support vector machine (SVM) and a support vector regression (SVR). To reduce the error propagation of the hierarchical classifier, each age group classifier is designed so that the age range to be estimated is overlapped by consideration of false acceptance error (FAE) and false rejection error (FRE) of each classifier. The experimental results showed that the performance of the proposed method was superior to that of the previous methods when using the BERC, PAL and FG-Net aging databases.
收起
摘要 :
Classification is a predictive modelling problem that involves assigning a class label to an instance correctly. There exist several strategies in machine learning to deal with the multi-class classification problems for attack de...
展开
Classification is a predictive modelling problem that involves assigning a class label to an instance correctly. There exist several strategies in machine learning to deal with the multi-class classification problems for attack detection. One of the popular strategies is the one-vs-one that decomposes the multi-class problem into multiple binary ones. The approach has been applied in many popular supervised learning algorithms, such as support vector machines. A possible problem of the standard multi-class classification problem is that it lacks correlation between different classes, which can increase overfitting problems and hinder generalization performance. Thus, a possible solution to the problem is to use a hierarchical classification that captures the relationship between classes by dividing the multi-class classification problem into a tree. However, one possible challenge in this approach is selecting parent and child nodes of the tree. The selected nodes should be informative to recognize and then classify different attack classes. One way is by looking at specific domain knowledge to train and build classifiers of the base learners for effective prediction. Thus, a soft marking scheme is introduced to assess a set of binary classifiers to ensure the best overall predictive base learners. Finally, we validate and compare the proposed approach to the standard NSL-KDD dataset. The results show that the proposed method outperforms the standard classifier on the intrusion attack classification.
收起
摘要 :
Ni(OH)(2), as a promising supercapacitor electrode material, has attracted numerous research attention to design well-organized structures and hybrid materials. Herein, carbon quantum dots (CQDs) decorated hierarchical Ni(OH)(2) w...
展开
Ni(OH)(2), as a promising supercapacitor electrode material, has attracted numerous research attention to design well-organized structures and hybrid materials. Herein, carbon quantum dots (CQDs) decorated hierarchical Ni(OH)(2) with lamellar structure (similar to a dessert of dragon eye pastry in Sichuan, China) was successfully fabricated by a facile and cost-effective hydrothermal method. The CQDs induces a great enhancement in electrochemical energy storage. The hybrid exhibits a specific capacitance of 2900 F g(-1) at 1 A g(-1) and good rate capability. Such remarkable electrochemical properties can be primarily ascribed to the uniform structure of hierarchical Ni(OH)(2) and the significantly enhanced utilization of CQDs.
收起
摘要 :
Pattern classification methods have been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer's disease (AD) and its early stage such as mild cognitive impairment (MCI). By considering the nature o...
展开
Pattern classification methods have been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer's disease (AD) and its early stage such as mild cognitive impairment (MCI). By considering the nature of pathological changes, a large number of features related to both local brain regions and interbrain regions can be extracted for classification. However, it is challenging to design a single global classifier to integrate all these features for effective classification, due to the issue of small sample size. To this end, we propose a hierarchical ensemble classification method to combine multilevel classifiers by gradually integrating a large number of features from both local brain regions and interbrain regions. Thus, the large-scale classification problem can be divided into a set of small-scale and easier-to-solve problems in a bottom-up and local-to-global fashion, for more accurate classification. To demonstrate its performance, we use the spatially normalized grey matter (GM) of each MR brain image as imaging features. Specifically, we first partition the whole brain image into a number of local brain regions and, for each brain region, we build two low-level classifiers to transform local imaging features and the inter-region correlations into high-level features. Then, we generate multiple high-level classifiers, with each evaluating the high-level features from the respective brain regions. Finally, we combine the outputs of all high-level classifiers for making a final classification. Our method has been evaluated using the baseline MR images of 652 subjects (including 198 AD patients, 225 MCI patients, and 229 normal controls (NC)) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our classification method can achieve the accuracies of 92.0% and 85.3% for classifications of AD versus NC and MCI versus NC, respectively, demonstrating very promising classification performance compared to the state-of-the-art classification methods.
收起
摘要 :
Designing low-cost and high-activity electrocatalysts is significant to enhance the slow kinetic process of oxygen evolution reaction (OER). Inspired by the Lego game, hierarchical CdP_(2)–CDs–CoP nanoarrays were fabricated by c...
展开
Designing low-cost and high-activity electrocatalysts is significant to enhance the slow kinetic process of oxygen evolution reaction (OER). Inspired by the Lego game, hierarchical CdP_(2)–CDs–CoP nanoarrays were fabricated by combining negatively charged carbon quantum dots (CDs) with positively charged Co and Cd ions. Because of the low evaporation temperature of Cd and relatively high solubility product (K _(sp)) of Cd(OH)_(2), trace CdP_(2) is trapped on the surface of CdP_(2)–CDs–CoP nanoarrays. When coupled with CDs, CdP_(2) enriches the defects and active sites of catalysts. The CdP_(2)–CDs–CoP nanoarray, as a robust OER electrocatalyst, delivers an overpotential of 285 mV to drive a current density of 10 mA cm~(–2), demonstrating its superiority over commercial RuO_(2).
收起
摘要 :
Hybrid hierarchical architectures possess significant possibility in construction of anode materials for advanced sodium ion batteries (SIBs). Herein, ZnS nanoparticles embedded in N-doped-carbon polyhedra and modified by Co4S3 na...
展开
Hybrid hierarchical architectures possess significant possibility in construction of anode materials for advanced sodium ion batteries (SIBs). Herein, ZnS nanoparticles embedded in N-doped-carbon polyhedra and modified by Co4S3 nanoparticles (ZnS ? N-C@Co4S3) with a hierarchical porous architecture, is designed using a selfassembly and associative sulfuration method. This ingenious nanoarchitecture frameworks possess several prominent merits. First, the N-doped-carbon skeleton as a scaffold for Co4S3 and ZnS can inhibit the agglomeration and buffer the volume expansion of the electrodes during cycling processes. Second, the abundant channels, rich interfaces and better conductivity for this architecture can benefit for the electrolyte permeation into the whole structure and shorten the diffusion pathway of the sodium ions. Impressively, the as-obtained hybrid frameworks deliver a stable capacity of 255.9 mAh g-1 in the 300th cycle at 2.0 A g-1, and superior rate capabilities when cycled at different currents. The enhanced sodium storage performance of this hierarchical multicomponent hybrid electrode indicates the importance of the advanced structure design with higher complexity for the energy storage.
收起
原文获取