Edge-Nodes Representation Neural Machine for Link Prediction

Link prediction is a task predicting whether there is a link between two nodes in a network. Traditional link prediction methods that assume handcrafted features (such as common neighbors) as the link’s formation mechanism are not universal. Other popular methods tend to learn the link’s representation, but they cannot represent the link fully. In this paper, we propose Edge-Nodes Representation Neural Machine (ENRNM), a novel method which can learn abundant topological features from the network as the link’s representation to promote the formation of the link. The ENRNM learns the link’s formation mechanism by combining the representation of edge and the representations of nodes on the two sides of the edge as link’s full representation. To predict the link’s existence, we train a fully connected neural network which can learn meaningful and abundant patterns. We prove that the features of edge and two nodes have the same importance in link’s formation. Comprehensive experiments are conducted on eight networks, experiment results demonstrate that the method ENRNM not only exceeds plenty of state-of-the-art link prediction methods but also performs very well on diverse networks with different structures and characteristics.

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Weighted Channel Dropout for Regularization of Deep Convolutional Neural Network

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33018425 ◽

2019 ◽

Vol 33 ◽

pp. 8425-8432 ◽

Cited By ~ 7

Author(s):

Saihui Hou ◽

Zilei Wang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Random Number ◽

Random Number Generator ◽

Test Phase ◽

Deep Convolutional Neural Network ◽

Multiple Datasets ◽

Novel Method ◽

Fully Connected ◽

Activation Status

In this work, we propose a novel method named Weighted Channel Dropout (WCD) for the regularization of deep Convolutional Neural Network (CNN). Different from Dropout which randomly selects the neurons to set to zero in the fully-connected layers, WCD operates on the channels in the stack of convolutional layers. Specifically, WCD consists of two steps, i.e., Rating Channels and Selecting Channels, and three modules, i.e., Global Average Pooling, Weighted Random Selection and Random Number Generator. It filters the channels according to their activation status and can be plugged into any two consecutive layers, which unifies the original Dropout and Channel-Wise Dropout. WCD is totally parameter-free and deployed only in training phase with very slight computation cost. The network in test phase remains unchanged and thus the inference cost is not added at all. Besides, when combining with the existing networks, it requires no re-pretraining on ImageNet and thus is well-suited for the application on small datasets. Finally, WCD with VGGNet-16, ResNet-101, Inception-V3 are experimentally evaluated on multiple datasets. The extensive results demonstrate that WCD can bring consistent improvements over the baselines.

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Path-based extensions of local link prediction methods for complex networks

Scientific Reports ◽

10.1038/s41598-020-76860-2 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Furqan Aziz ◽

Haji Gul ◽

Irfan Uddin ◽

Georgios V. Gkoutos

Keyword(s):

Complex System ◽

Link Prediction ◽

Network Science ◽

State Of The Art ◽

Superior Performance ◽

Prediction Methods ◽

Local Similarity ◽

Fundamental Interest ◽

Current State ◽

Similarity Indices

AbstractLink prediction in a complex network is a problem of fundamental interest in network science and has attracted increasing attention in recent years. It aims to predict missing (or future) links between two entities in a complex system that are not already connected. Among existing methods, local similarity indices are most popular that take into account the information of common neighbours to estimate the likelihood of existence of a connection between two nodes. In this paper, we propose global and quasi-local extensions of some commonly used local similarity indices. We have performed extensive numerical simulations on publicly available datasets from diverse domains demonstrating that the proposed extensions not only give superior performance, when compared to their respective local indices, but also outperform some of the current, state-of-the-art, local and global link-prediction methods.

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A Novel Architecture to Classify Histopathology Images Using Convolutional Neural Networks

Applied Sciences ◽

10.3390/app10082929 ◽

2020 ◽

Vol 10 (8) ◽

pp. 2929 ◽

Cited By ~ 2

Author(s):

Ibrahem Kandel ◽

Mauro Castelli

Keyword(s):

Neural Network ◽

Neural Networks ◽

State Of The Art ◽

Treatment Plan ◽

Tissue Structure ◽

Activation Functions ◽

Proposed Model ◽

Histopathology Images ◽

Fully Connected

Histopathology is the study of tissue structure under the microscope to determine if the cells are normal or abnormal. Histopathology is a very important exam that is used to determine the patients’ treatment plan. The classification of histopathology images is very difficult to even an experienced pathologist, and a second opinion is often needed. Convolutional neural network (CNN), a particular type of deep learning architecture, obtained outstanding results in computer vision tasks like image classification. In this paper, we propose a novel CNN architecture to classify histopathology images. The proposed model consists of 15 convolution layers and two fully connected layers. A comparison between different activation functions was performed to detect the most efficient one, taking into account two different optimizers. To train and evaluate the proposed model, the publicly available PatchCamelyon dataset was used. The dataset consists of 220,000 annotated images for training and 57,000 unannotated images for testing. The proposed model achieved higher performance compared to the state-of-the-art architectures with an AUC of 95.46%.

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Pedestrian Attributes Recognition in Surveillance Scenarios Using Multi-Task Lightweight Convolutional Neural Network

Applied Sciences ◽

10.3390/app9194182 ◽

2019 ◽

Vol 9 (19) ◽

pp. 4182 ◽

Cited By ~ 2

Author(s):

Pu Yan ◽

Li Zhuo ◽

Jiafeng Li ◽

Hui Zhang ◽

Jing Zhang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

State Of The Art ◽

Cross Entropy ◽

Semantic Features ◽

Recognition Method ◽

Relationship Model ◽

High Level ◽

Fully Connected ◽

Video Structuring

Pedestrian attributes (such as gender, age, hairstyle, and clothing) can effectively represent the appearance of pedestrians. These are high-level semantic features that are robust to illumination, deformation, etc. Therefore, they can be widely used in person re-identification, video structuring analysis and other applications. In this paper, a pedestrian attributes recognition method for surveillance scenarios using a multi-task lightweight convolutional neural network is proposed. Firstly, the labels of the attributes for each pedestrian image are integrated into a label vector. Then, a multi-task lightweight Convolutional Neural Network (CNN) is designed, which consists of five convolutional layers, three pooling layers and two fully connected layers to extract the deep features of pedestrian images. Considering that the data distribution of the datasets is unbalanced, the loss function is improved based on the sigmoid cross-entropy, and the scale factor is added to balance the amount of various attributes data. Through training the network, the mapping relationship model between the deep features of pedestrian images and the integration label vector of their attributes is established, which can be used to predict each attribute of the pedestrian. The experiments were conducted on two public pedestrian attributes datasets in surveillance scenarios, namely PETA and RAP. The results show that, compared with the state-of-the-art pedestrian attributes recognition methods, the proposed method can achieve a superior accuracy by 91.88% on PETA and 87.44% on RAP respectively.

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Finding Missing Links in Complex Networks: A Multiple-Attribute Decision-Making Method

Complexity ◽

10.1155/2018/3579758 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 3

Author(s):

Longjie Li ◽

Shenshen Bai ◽

Mingwei Leng ◽

Lu Wang ◽

Xiaoyun Chen

Keyword(s):

Decision Making ◽

Complex Networks ◽

Similarity Measure ◽

Real World ◽

Link Prediction ◽

State Of The Art ◽

Multiple Attribute Decision Making ◽

Ideal Solution ◽

Multiple Attribute ◽

Novel Method

Link prediction, which aims to forecast potential or missing links in a complex network based on currently observed information, has drawn growing attention from researchers. To date, a host of similarity-based methods have been put forward. Usually, one method harbors the idea that one similarity measure is applicable to various networks, and thus has performance fluctuation on different networks. In this paper, we propose a novel method to solve this issue by regarding link prediction as a multiple-attribute decision-making (MADM) problem. In the proposed method, we consider RA, LP, and CAR indices as the multiattribute for node pairs. The technique for order performance by similarity to ideal solution (TOPSIS) is adopted to aggregate the multiattribute and rank node pairs. The proposed method is not limited to only one similarity measure, but takes separate measures into account, since different networks may have different topological structures. Experimental results on 10 real-world networks manifest that the proposed method is superior in comparison to state-of-the-art methods.

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Algorithm for Detecting Characteristic Points on a Three-Dimensional, Whole-Body Human Scan

Applied Sciences ◽

10.3390/app10041342 ◽

2020 ◽

Vol 10 (4) ◽

pp. 1342

Author(s):

Michał Koźbiał ◽

Łukasz Markiewicz ◽

Robert Sitnik

Keyword(s):

Neural Network ◽

State Of The Art ◽

Characteristic Point ◽

Three Dimensional ◽

Whole Body ◽

Data Preparation ◽

Surface Projection ◽

Characteristic Points ◽

Novel Method ◽

One Machine

Anthropometric landmarks obtained from three-dimensional (3D) body scans are widely used in medicine, civil engineering, and virtual reality. For all those fields, an acquisition of certain and accurate landmark positions is crucial for obtaining satisfying results. Manual marking is time-consuming and is affected by the subjectivity of the human operator. Therefore, an automatic approach has become increasingly popular. This paper provides a short survey of different attempts for automatic landmark localization, from which one machine learning-based method was further analyzed and extended in the case of input data preparation for a convolutional neural network (CNN). A novel method of data processing is presented which utilize a mid-surface projection followed by further unwrapping. The article emphasizes its significance and the way it affects the outcome of a deep neural network. The workflow and the detailed description of algorithms used are included in this paper. To validate the method, it was compared with the orthogonal projection used for the state-of-the-art approach. Datasets consisting of 200 specimens, acquired using both methods, were used for convolutional neural networks training and 20 for validation. In this paper, we used YOLO v.3 architecture for detection and ResNet-152 for classification. For each approach, localizations of 22 normalized body landmarks for 10 male and 10 female subjects of different ages and various postures were obtained. To compare the accuracy of approaches, errors and their distribution were measured for each characteristic point. Experiments confirmed that the mid-surface projections resulted, on average, in a 14% accuracy improvement and up to 15% enhancement of resistance on errors related to scan imperfections.

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A protection method of trained CNN model with a secret key from unauthorized access

APSIPA Transactions on Signal and Information Processing ◽

10.1017/atsip.2021.9 ◽

2021 ◽

Vol 10 ◽

Author(s):

AprilPyone Maungmaung ◽

Hitoshi Kiya

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

State Of The Art ◽

Network Models ◽

The State ◽

Secret Key ◽

Neural Network Models ◽

Unauthorized Access ◽

Protection Method ◽

Novel Method

In this paper, we propose a novel method for protecting convolutional neural network models with a secret key set so that unauthorized users without the correct key set cannot access trained models. The method enables us to protect not only from copyright infringement but also the functionality of a model from unauthorized access without any noticeable overhead. We introduce three block-wise transformations with a secret key set to generate learnable transformed images: pixel shuffling, negative/positive transformation, and format-preserving Feistel-based encryption. Protected models are trained by using transformed images. The results of experiments with the CIFAR and ImageNet datasets show that the performance of a protected model was close to that of non-protected models when the key set was correct, while the accuracy severely dropped when an incorrect key set was given. The protected model was also demonstrated to be robust against various attacks. Compared with the state-of-the-art model protection with passports, the proposed method does not have any additional layers in the network, and therefore, there is no overhead during training and inference processes.

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RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist

10.26434/chemrxiv.11869692.v5 ◽

2020 ◽

Author(s):

Chaochao Yan ◽

Qianggang Ding ◽

Peilin Zhao ◽

Shuangjia Zheng ◽

Jinyu Yang ◽

...

Keyword(s):

Neural Network ◽

Reaction Center ◽

Organic Synthesis ◽

State Of The Art ◽

Building Blocks ◽

Prediction Methods ◽

Generation Model ◽

Synthesis Planning ◽

Target Molecules ◽

Template Free

<div>Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. It plays an important role in solving problems in organic synthesis planning. To automate the retrosynthesis analysis, many retrosynthesis prediction methods have been proposed.</div><div>However, most of them are cumbersome and lack interpretability about their predictions.</div><div>In this paper, we devise a novel template-free algorithm, RetroXpert, for automatic retrosynthetic expansion by automating the procedure that chemists used to do.</div><div>Our method disassembles retrosynthesis into two steps: i) we identify the potential reaction center within the target molecule through a graph neural network and generate intermediate synthons; and ii) we predict the associated reactants based on the obtained synthons via a reactant generation model. </div><div>While outperforming the state-of-the-art baselines by a significant margin, our model also provides chemically reasonable interpretation.</div>

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Xatu: Richer Neural Network Based Prediction for Video Streaming

Proceedings of the ACM on Measurement and Analysis of Computing Systems ◽

10.1145/3491056 ◽

2021 ◽

Vol 5 (3) ◽

pp. 1-26

Author(s):

Yun Seong Nam ◽

Jianfei Gao ◽

Chandan Bothra ◽

Ehab Ghabashneh ◽

Sanjay Rao ◽

...

Keyword(s):

Neural Network ◽

Reinforcement Learning ◽

Video Streaming ◽

Real World ◽

State Of The Art ◽

Clustering Method ◽

Download Time ◽

Abr Algorithm ◽

Fully Connected ◽

Prediction Approach

The performance of Adaptive Bitrate (ABR) algorithms for video streaming depends on accurately predicting the download time of video chunks. Existing prediction approaches (i) assume chunk download times are dominated by network throughput; and (ii) apriori cluster sessions (e.g., based on ISP and CDN) and only learn from sessions in the same cluster. We make three contributions. First, through analysis of data from real-world video streaming sessions, we show (i) apriori clustering prevents learning from related clusters; and (ii) factors such as the Time to First Byte (TTFB) are key components of chunk download times but not easily incorporated into existing prediction approaches. Second, we propose Xatu, a new prediction approach that jointly learns a neural network sequence model with an interpretable automatic session clustering method. Xatu learns clustering rules across all sessions it deems relevant, and models sequences with multiple chunk-dependent features (e.g., TTFB) rather than just throughput. Third, evaluations using the above datasets and emulation experiments show that Xatu significantly improves prediction accuracies by 23.8% relative to CS2P (a state-of-the-art predictor). We show Xatu provides substantial performance benefits when integrated with multiple ABR algorithms including MPC (a well studied ABR algorithm), and FuguABR (a recent algorithm using stochastic control) relative to their default predictors (CS2P and a fully connected neural network respectively). Further, Xatu combined with MPC outperforms Pensieve, an ABR based on deep reinforcement learning.

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A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

Applied Sciences ◽

10.3390/app11199023 ◽

2021 ◽

Vol 11 (19) ◽

pp. 9023

Author(s):

Najam-ur Rehman ◽

Muhammad Sultan Zia ◽

Talha Meraj ◽

Hafiz Tayyab Rauf ◽

Robertas Damaševičius ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

State Of The Art ◽

Detection System ◽

Classification Methods ◽

Single Class ◽

X Ray ◽

Computer Aided ◽

Chest X Ray ◽

Fully Connected

Chest diseases can be dangerous and deadly. They include many chest infections such as pneumonia, asthma, edema, and, lately, COVID-19. COVID-19 has many similar symptoms compared to pneumonia, such as breathing hardness and chest burden. However, it is a challenging task to differentiate COVID-19 from other chest diseases. Several related studies proposed a computer-aided COVID-19 detection system for the single-class COVID-19 detection, which may be misleading due to similar symptoms of other chest diseases. This paper proposes a framework for the detection of 15 types of chest diseases, including the COVID-19 disease, via a chest X-ray modality. Two-way classification is performed in proposed Framework. First, a deep learning-based convolutional neural network (CNN) architecture with a soft-max classifier is proposed. Second, transfer learning is applied using fully-connected layer of proposed CNN that extracted deep features. The deep features are fed to the classical Machine Learning (ML) classification methods. However, the proposed framework improves the accuracy for COVID-19 detection and increases the predictability rates for other chest diseases. The experimental results show that the proposed framework, when compared to other state-of-the-art models for diagnosing COVID-19 and other chest diseases, is more robust, and the results are promising.

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