Reviewerly/exHarmony huggingface dataset:
Saliency-Guided Single Shot Multibox Detector for Target Detection in SAR Images

The single shot multibox detector (SSD), a proposal-free method based on convolutional neural network (CNN), has recently been proposed for target detection and has found applications in synthetic aperture radar (SAR) images. Moreover, the saliency information reflected in the saliency map can highlight the target of interest while suppressing clutter, which is beneficial for better scene understanding. Therefore, in this article, we propose a saliency-guided SSD (S-SSD) for target detection in SAR images, in which we effectively integrate the saliency into the SSD network not only to suggest where to focus on but also to improve the representation capability in complex scenes. The proposed S-SSD contains two separated convolutional backbone subnetwork architectures, one with the original SAR image as input to extract features, and the other with the corresponding saliency map obtained from the modified Itti's method as input to acquire refined saliency information under supervision. In addition, the dense connection structure, instead of the plain structure used in original SSD, is applied in the two convolutional backbone architectures to utilize multiscale information with fewer parameters. Then, for integrating saliency information to guide the network to emphasize informative regions, multilevel fusion modules are utilized to merge the two streams into a unified framework, thereby making the whole network end-to-end jointly trained. Finally, the convolutional predictors are used to predict targets. The experimental results on the miniSAR real data demonstrate that the proposed S-SSD can achieve better detection performance than state-of-the-art methods.
Revisiting metric learning for few-shot image classification

The goal of few-shot learning is to recognize new visual concepts with just a few amount of labeled samples in each class. Recent effective metric-based few-shot approaches employ neural networks to learn a feature similarity comparison between query and support examples. However, the importance of feature embedding, i.e., exploring the relationship among training samples, is neglected. In this work, we present a simple yet powerful baseline for few-shot classification by emphasizing the importance of feature embedding. Specifically, we revisit the classical triplet network from deep metric learning, and extend it into a deep K-tuplet network for few-shot learning, utilizing the relationship among the input samples to learn a general representation learning via episode-training. Once trained, our network is able to extract discriminative features for unseen novel categories and can be seamlessly incorporated with a non-linear distance metric function to facilitate the few-shot classification. Our result on the miniImageNet benchmark outperforms other metric-based few-shot classification methods. More importantly, when evaluated on completely different datasets (Caltech-101, CUB-200, Stanford Dogs and Cars) using the model trained with miniImageNet, our method significantly outperforms prior methods, demonstrating its superior capability to generalize to unseen classes.
Data sharing in the era of COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to test the capacity of world health systems. Since the outbreak started, the global community has learned about coronavirus disease 2019 (COVID-19), the disease resulting from SARS-CoV-2. In the first few weeks of the pandemic, knowledge about the disease and its treatment was generated from sharing of anecdotal observations and small case series. Although health-care professionals use modern technology to communicate, never before has the failure to build robust data-sharing systems for large-scale near real-time analysis in health care been more obvious.
Sarcasm detection using machine learning algorithms in Twitter: A systematic review

Recognizing both literal and figurative meanings is crucial to understanding users’ opinions on various topics or events in social media. Detecting the sarcastic posts on social media has received much attention recently, particularly because sarcastic comments in the form of tweets often include positive words that represent negative or undesirable characteristics. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used to understand the application of different machine learning algorithms for sarcasm detection in Twitter. Extensive database searching led to the inclusion of 31 studies classified into two groups: Adapted Machine Learning Algorithms (AMLA) and Customized Machine Learning Algorithms (CMLA). The review results revealed that Support Vector Machine (SVM) was the best and the most commonly used AMLA for sarcasm detection in Twitter. In addition, combining Convolutional Neural Network (CNN) and SVM was found to offer a high prediction accuracy. Moreover, our result showed that using lexical, pragmatic, frequency, and part-of-speech tagging can contribute to the performance of SVM, whereas both lexical and personal features can enhance the performance of CNN-SVM. This work also addressed the main challenges faced by prior scholars when predicting sarcastic tweets. Such knowledge can be useful for future researchers or machine learning developers to consider the major issues of classifying sarcastic posts in social media.
cmSalGAN: RGB-D Salient Object Detection With Cross-View Generative Adversarial Networks

Image salient object detection (SOD) is an active research topic in computer vision and multimedia area. Fusing complementary information of RGB and depth has been demonstrated to be effective for image salient object detection which is known as RGB-D salient object detection problem. The main challenge for RGB-D salient object detection is how to exploit the salient cues of both intra-modality (RGB, depth) and cross-modality simultaneously which is known as cross-modality detection problem. In this paper, we tackle this challenge by designing a novel cross-modality Saliency Generative Adversarial Network (cmSalGAN). cmSalGAN aims to learn an optimal view-invariant and consistent pixel-level representation for RGB and depth images via a novel adversarial learning framework, which thus incorporates both information of intra-view and correlation information of cross-view images simultaneously for RGB-D saliency detection problem. To further improve the detection results, the attention mechanism and edge detection module are also incorporated into cmSalGAN. The entire cmSalGAN can be trained in an end-to-end manner by using the standard deep neural network framework. Experimental results show that cmSalGAN achieves the new state-of-the-art RGB-D saliency detection performance on several benchmark datasets.
Understanding and Visualizing Data Iteration in Machine Learning

Successful machine learning (ML) applications require iterations on both modeling and the underlying data. While prior visualization tools for ML primarily focus on modeling, our interviews with 23 ML practitioners reveal that they improve model performance frequently by iterating on their data (e.g., collecting new data, adding labels) rather than their models. We also identify common types of data iterations and associated analysis tasks and challenges. To help attribute data iterations to model performance, we design a collection of interactive visualizations and integrate them into a prototype, Chameleon, that lets users compare data features, training/testing splits, and performance across data versions. We present two case studies where developers apply \system to their own evolving datasets on production ML projects. Our interface helps them verify data collection efforts, find failure cases stretching across data versions, capture data processing changes that impacted performance, and identify opportunities for future data iterations.
Overview of two-stage object detection algorithms

Abstract Nowadays, object detection has gradually become a quite popular field. From the traditional methods to the methods used at this stage, object detection technology has made great progress, and is still continuously developing and innovating. This paper reviews two-stage object detection algorithms used at this stage, explaining in detail the working principles of Faster R-CNN, R-FCN, FPN, and Casecade R-CNN and analyzing the similarities and differences between these four two-stage object detection algorithms. Then we used HSRC2016 ship dataset to perform experiments with Faster R-CNN, R-FCN, FPN, and Casecade R-CNN and compared the effectiveness of them with experimental results.
FeatureFlow: Robust Video Interpolation via Structure-to-Texture Generation

Video interpolation aims to synthesize non-existent frames between two consecutive frames. Although existing optical flow based methods have achieved promising results, they still face great challenges in dealing with the interpolation of complicated dynamic scenes, which include occlusion, blur or abrupt brightness change. This is mainly because these cases may break the basic assumptions of the optical flow estimation (i.e. smoothness, consistency). In this work, we devised a novel structure-to-texture generation framework which splits the video interpolation task into two stages: structure-guided interpolation and texture refinement. In the first stage, deep structure-aware features are employed to predict feature flows from two consecutive frames to their intermediate result, and further generate the structure image of the intermediate frame. In the second stage, based on the generated coarse result, a Frame Texture Compensator is trained to fill in detailed textures. To the best of our knowledge, this is the first work that attempts to directly generate the intermediate frame through blending deep features. Experiments on both the benchmark datasets and challenging occlusion cases demonstrate the superiority of the proposed framework over the state-of-the-art methods. Codes are available on https://github.com/CM-BF/FeatureFlow.
Data-Free Network Quantization With Adversarial Knowledge Distillation

Network quantization is an essential procedure in deep learning for development of efficient fixed-point inference models on mobile or edge platforms. However, as datasets grow larger and privacy regulations become stricter, data sharing for model compression gets more difficult and restricted. In this paper, we consider data-free network quantization with synthetic data. The synthetic data are generated from a generator, while no data are used in training the generator and in quantization. To this end, we propose data-free adversarial knowledge distillation, which minimizes the maximum distance between the outputs of the teacher and the (quantized) student for any adversarial samples from a generator. To generate adversarial samples similar to the original data, we additionally propose matching statistics from the batch normalization layers for generated data and the original data in the teacher. Furthermore, we show the gain of producing diverse adversarial samples by using multiple generators and multiple students. Our experiments show the state-of-the-art data-free model compression and quantization results for (wide) residual networks and MobileNet on SVHN, CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets. The accuracy losses compared to using the original datasets are shown to be very minimal.
Improving Chinese Word Segmentation with Wordhood Memory Networks

Contextual features always play an important role in Chinese word segmentation (CWS).Wordhood information, being one of the contextual features, is proved to be useful in many conventional character-based segmenters.However, this feature receives less attention in recent neural models and it is also challenging to design a framework that can properly integrate wordhood information from different wordhood measures to existing neural frameworks.In this paper, we therefore propose a neural framework, WMSEG, which uses memory networks to incorporate wordhood information with several popular encoder-decoder combinations for CWS.Experimental results on five benchmark datasets indicate the memory mechanism successfully models wordhood information for neural segmenters and helps WMSEG achieve state-ofthe-art performance on all those datasets.Further experiments and analyses also demonstrate the robustness of our proposed framework with respect to different wordhood measures and the efficiency of wordhood information in cross-domain experiments.
Learning to Cluster Faces via Confidence and Connectivity Estimation

Face clustering is an essential tool for exploiting the unlabeled face data, and has a wide range of applications including face annotation and retrieval. Recent works show that supervised clustering can result in noticeable performance gain. However, they usually involve heuristic steps and require numerous overlapped subgraphs, severely restricting their accuracy and efficiency. In this paper, we propose a fully learnable clustering framework without requiring a large number of overlapped subgraphs. Instead, we transform the clustering problem into two sub-problems. Specifically, two graph convolutional networks, named GCN-V and GCN-E, are designed to estimate the confidence of vertices and the connectivity of edges, respectively. With the vertex confidence and edge connectivity, we can naturally organize more relevant vertices on the affinity graph and group them into clusters. Experiments on two large-scale benchmarks show that our method significantly improves clustering accuracy and thus performance of the recognition models trained on top, yet it is an order of magnitude more efficient than existing supervised methods.
Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers’ review

Abstract Big data analytics plays a major role in various industries using computing applications such as E-commerce and real-time shopping. Big data are used for promoting products and provide better connectivity between retailers and shoppers. Nowadays, people always use online promotions to know about best shops for buying better products. This shopping experience and opinion about the shopper’s shop can be observed by the customer-experience shared across social media platforms. A new customer when searching a shop needs information about manufacturing date (MRD) and manufacturing price (MRP), offers, quality, and suggestions which can only be provided by the previous customer experience. The MRP and MRD are already available in the product cover or label. Several approaches have been used for predicting the product details but not providing accurate information. This paper is motivated towards applying Machine Learning algorithms for learning, analysing and classifying the product information and the shop information based on the customer experience. The product data with customer reviews is collected from benchmark Unified computing system (UCS) which is a server for data based computer product lined up for evaluating hardware, support to visualization, software management. From the results and comparison, it has been found that machine learning algorithms outperform than other approaches. The proposed HRS system has higher values of MAPE which is 96% and accuracy is nearly 98% when compared to other existing techniques. Mean absolute error of proposed HRS system is nearly 0.6 which states that the performance of the system is significantly effective.
Multi-Scale Dynamic Convolutional Network for Knowledge Graph Embedding

Knowledge graphs are large graph-structured knowledge bases with incomplete or partial information. Numerous studies have focused on knowledge graph embedding to identify the embedded representation of entities and relations, thereby predicting missing relations between entities. Previous embedding models primarily regard (subject entity, relation, and object entity) triplet as translational distance or semantic matching in vector space. However, these models only learn a few expressive features and hard to handle complex relations, i.e., 1-to-N, N-to-1, and N-to-N, in knowledge graphs. To overcome these issues, we introduce a multi-scale dynamic convolutional network (M-DCN) model for knowledge graph embedding. This model features topnotch performance and an ability to generate richer and more expressive feature embeddings than its counterparts. The subject entity and relation embeddings in M-DCN are composed in an alternating pattern in the input layer, which helps extract additional feature interactions and increase the expressiveness. Multi-scale filters are generated in the convolution layer to learn different characteristics among input embeddings. Specifically, the weights of these filters are dynamically related to each relation to model complex relations. The performance of M-DCN on the five benchmark datasets is tested via experiments. Results show that the model can effectively handle complex relations and achieve state-of-the-art link prediction results on most evaluation metrics.
Fine-Grained Spatial Alignment Model for Person Re-Identification With Focal Triplet Loss

Recent advances of person re-identification have well advocated the usage of human body cues to boost performance. However, most existing methods still retain on exploiting a relatively coarse-grained local information. Such information may include redundant backgrounds that are sensitive to the apparently similar persons when facing challenging scenarios like complex poses, inaccurate detection, occlusion and misalignment. In this paper we propose a novel Fine-Grained Spatial Alignment Model (FGSAM) to mine fine-grained local information to handle the aforementioned challenge effectively. In particular, we first design a pose resolve net with channel parse blocks (CPB) to extract pose information in pixel-level. This network allows the proposed model to be robust to complex pose variations while suppressing the redundant backgrounds caused by inaccurate detection and occlusion. Given the extracted pose information, a locally reinforced alignment mode is further proposed to address the misalignment problem between different local parts by considering different local parts along with attribute information in a fine-grained way. Finally, a focal triplet loss is designed to effectively train the entire model, which imposes a constraint on the intra-class and an adaptively weight adjustment mechanism to handle the hard sample problem. Extensive evaluations and analysis on Market1501, DukeMTMC-reid and PETA datasets demonstrate the effectiveness of FGSAM in coping with the problems of misalignment, occlusion and complex poses.
Correntropy-Based Hypergraph Regularized NMF for Clustering and Feature Selection on Multi-Cancer Integrated Data

Non-negative matrix factorization (NMF) has become one of the most powerful methods for clustering and feature selection. However, the performance of the traditional NMF method severely degrades when the data contain noises and outliers or the manifold structure of the data is not taken into account. In this article, a novel method called correntropy-based hypergraph regularized NMF (CHNMF) is proposed to solve the above problem. Specifically, we use the correntropy instead of the Euclidean norm in the loss term of CHNMF, which will improve the robustness of the algorithm. And the hypergraph regularization term is also applied to the objective function, which can explore the high-order geometric information in more sample points. Then, the half-quadratic (HQ) optimization technique is adopted to solve the complex optimization problem of CHNMF. Finally, extensive experimental results on multi-cancer integrated data indicate that the proposed CHNMF method is superior to other state-of-the-art methods for clustering and feature selection.
Unsupervised log message anomaly detection

Log messages are now broadly used in cloud and software systems. They are important for classification and anomaly detection as millions of logs are generated each day. In this paper, an unsupervised model for log message anomaly detection is proposed which employs Isolation Forest and two deep Autoencoder networks. The Autoencoder networks are used for training and feature extraction, and then for anomaly detection, while Isolation Forest is used for positive sample prediction. The proposed model is evaluated using the BGL, Openstack and Thunderbird log message data sets. The results obtained show that the number of negative samples predicted to be positive is low, especially with Isolation Forest and one Autoencoder. Further, the results are better than with other well-known models.
Credit Card Fraud Detection using Pipeling and Ensemble Learning

Financial fraud is a problem that has proved to be a menace and has a huge impact on the financial industry. Data mining is one of the techniques which has played an important role in credit card fraud detection in transactions which are online. Credit card fraud detection has proved to be a challenge mainly due to the 2 problems that it poses - both the profiles of fraudulent and normal behaviours change and data sets used are highly skewed. The performance of fraud detection is affected by the variables used and the technique used to detect fraud. This paper compares the performance of logistic regression, K-nearest neighbors, random forest, naive bayes, multilayer perceptron, ada boost, quadrant discriminative analysis, pipelining and ensemble learning on the credit card fraud data.
HAGERec: Hierarchical Attention Graph Convolutional Network Incorporating Knowledge Graph for Explainable Recommendation

Knowledge graph (KG) can provide auxiliary information for recommender system to alleviate the sparsity and cold start problems, while graph convolutional networks (GCN) has recently been established as the state-of-the-art representation learning method. The combination of them is a promising perspective to improve the performance of graph-structured recommendation. However, most of GCN-based recommendations focus on homogeneous graph or user/item-similarity graph, fail to fully make use of the complex and rich semantics between entities in heterogeneous knowledge graph. In this paper, we develop Hierarchical Attention Graph Convolutional Network Incorporating Knowledge Graph for Explainable Recommendation (HAGERec) to explore users’ potential preferences from the high-order connectivity structure of heterogeneous knowledge graph. To exploit semantic information, HAGERec simultaneously learn the representations of users and items via a bi-directional information propagation strategy. Specifically, the entity’s representation can be aggregated through messages passing from its local proximity structure, and a hierarchical attention mechanism is developed to adaptively characterize and adjust collaborative signals. With the help of the attention mechanism, an attentive entity sampling strategy is proposed to select relevant neighbor entities, and the explainability is endowed to the model by building knowledge-aware connectivity. Experiments conducted on four real-world public datasets demonstrate the state-of-the-art performance and the strong explainability of HAGERec.