Reviewerly/exHarmony huggingface dataset:
YOLOv3-DPFIN: A Dual-Path Feature Fusion Neural Network for Robust Real-Time Sonar Target Detection

Real-time detection of sonar target plays a vital role in the underwater research field. Conventionaldeep learning methods need large quantities of sonar images as the sample for model training, and they cannot ensure detection speed and feature extraction ability simultaneously. For sonar dataset with small effective sample and low Signal-to-Noise Ratios (SNR), an improved YOLOv3 algorithm for real-time detection called as YOLOv3-DPFIN is proposed. The objective of the proposed YOLOv3-DPFIN is to accomplish the accurate detection of noise-intensive multi-category sonar targets with minimum time consumption. The proposed model conducts efficient feature extraction via the Dual-Path Network (DPN) module and the fusion transition module, and adopts a dense connection method to improve multi-scale prediction, which can complete precise object classification and location. The experimental results show that the algorithm achieves 84.4% mAP75 with 56fps on a Nvidia Titan Xp, when testing on the sonar dataset and using the new VOC2012 mAP standard, which can meet the requirement of robust real-time detection for both raw and noised sonar targets. Moreover, the precision and speed of the proposed YOLOv3-DPFIN are superior to the original YOLOv3 model and state-of-the-art improved SSD models.
Overfitting and Underfitting Analysis for Deep Learning Based End-to-end Communication Systems

In this paper, we study the deep learning (DL) based end- to-end transmission systems, then we present the analysis for the underfitting and overfitting phenomena which happen during the training of the neural networks (NNs). Different from the DL based image processing, the transmission systems require the bit error rate to be as low as 10– <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> to achieve high reliability, thus the training errors induced by the underfitting and overfitting may greatly degrade the transmission reliability performances of DL-based communications. In considered DL-based communication systems, we propose to use the average, variance and minimum of transmitted signals' minimum Euclidean distance to estimate the effects of underfitting and overfitting on the error rate performances in terms of the energy per bit to noise power spectral ratio E <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</inf> / N <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</inf> of signals. Furthermore, we propose to apply the regularization scheme to alleviate the overfitting issue. Simulations are performed to demonstrate the underfitting and overfitting analysis for transmission systems over the additive white Gaussian noise (AWGN) channel, and validate the improved performances by applying the regularization.
Community detection in networks using bio-inspired optimization: Latest developments, new results and perspectives with a selection of recent meta-heuristics

Detecting groups within a set of interconnected nodes is a widely addressed problem that can model a diversity of applications. Unfortunately, detecting the optimal partition of a network is a computationally demanding task, usually conducted by means of optimization methods. Among them, randomized search heuristics have been proven to be efficient approaches. This manuscript is devoted to providing an overview of community detection problems from the perspective of bio-inspired computation. To this end, we first review the recent history of this research area, placing emphasis on milestone studies contributed in the last five years. Next, we present an extensive experimental study to assess the performance of a selection of modern heuristics over weighted directed network instances. Specifically, we combine seven global search heuristics based on two different similarity metrics and eight heterogeneous search operators designed ad-hoc. We compare our methods with six different community detection techniques over a benchmark of 17 Lancichinetti–Fortunato–Radicchi network instances. Ranking statistics of the tested algorithms reveal that the proposed methods perform competitively, but the high variability of the rankings leads to the main conclusion: no clear winner can be declared. This finding aligns with community detection tools available in the literature that hinge on a sequential application of different algorithms in search for the best performing counterpart. We end our research by sharing our envisioned status of this area, for which we identify challenges and opportunities which should stimulate research efforts in years to come.
PixelHop: A successive subspace learning (SSL) method for object recognition

A new machine learning methodology, called successive subspace learning (SSL), is introduced in this work. SSL contains four key ingredients: (1) successive near-to-far neighborhood expansion; (2) unsupervised dimension reduction via subspace approximation; (3) supervised dimension reduction via label-assisted regression (LAG); and (4) feature concatenation and decision making. An image-based object classification method, called PixelHop, is proposed to illustrate the SSL design. It is shown by experimental results that the PixelHop method outperforms the classic CNN model of similar model complexity in three benchmarking datasets (MNIST, Fashion MNIST and CIFAR-10). Although SSL and deep learning (DL) have some high-level concept in common, they are fundamentally different in model formulation, the training process and training complexity. Extensive discussion on the comparison of SSL and DL is made to provide further insights into the potential of SSL.
Hierarchical User Profiling for E-commerce Recommender Systems

Hierarchical user profiling that aims to model users' real-time interests in different granularity is an essential issue for personalized recommendations in E-commerce. On one hand, items (i.e. products) are usually organized hierarchically in categories, and correspondingly users' interests are naturally hierarchical on different granularity of items and categories. On the other hand, multiple granularity oriented recommendations become very popular in E-commerce sites, which require hierarchical user profiling in different granularity as well. In this paper, we propose HUP, a Hierarchical User Profiling framework to solve the hierarchical user profiling problem in E-commerce recommender systems. In HUP, we provide a Pyramid Recurrent Neural Networks, equipped with Behavior-LSTM to formulate users' hierarchical real-time interests at multiple scales. Furthermore, instead of simply utilizing users' item-level behaviors (e.g., ratings or clicks) in conventional methods, HUP harvests the sequential information of users' temporal finely-granular interactions (micro-behaviors, e.g., clicks on components of items like pictures or comments, browses with navigation of the search engines or recommendations) for modeling. Extensive experiments on two real-world E-commerce datasets demonstrate the significant performance gains of the HUP against state-of-the-art methods for the hierarchical user profiling and recommendation problems. We release the codes and datasets at https://github.com/guyulongcs/WSDM2020_HUP.
CNN-Based Automatic Prioritization of Bug Reports

Software systems often receive a large number of bug reports. Triagers read through such reports and assign different priorities to different reports so that important and urgent bugs could be fixed on time. However, manual prioritization is tedious and time-consuming. To this end, in this article, we propose a convolutional neural network (CNN) based automatic approach to predict the multiclass priority for bug reports. First, we apply natural language processing (NLP) techniques to preprocess textual information of bug reports and covert the textual information into vectors based on the syntactic and semantic relationship of words within each bug report. Second, we perform the software engineering domain specific emotion analysis on bug reports and compute the emotion value for each of them using a software engineering domain repository. Finally, we train a CNN-based classifier that generates a suggested priority based on its input, i.e., vectored textual information and emotion values. To the best of our knowledge, it is the first CNN-based approach to bug report prioritization. We evaluate the proposed approach on open-source projects. Results of our cross-project evaluation suggest that the proposed approach significantly outperforms the state-of-the-art approaches and improves the average F1-score by more than 24%.
Blockchain Security Attack: A Brief Survey

Blockchain technology has attracted various areas like corporations, education, government, and healthcare because of its unhackable security features. In spite of common arguments about the occurrence of Blockchain technology in terms of security and privacy, in reality, several attacks can be launched against them. This paper presents a comprehensive survey of the most vulnerable Blockchain attacks and the different approaches proposed against these attacks. This paper also provides taxonomy of attacks, which are most challengeable to Blockchain security key features. Additionally, a review of the countermeasures, which have offered solutions to avoid these attacks, has been presented.
Automatic Modulation Classification for MIMO Systems via Deep Learning and Zero-Forcing Equalization

Automatic modulation classification (AMC) is one of the most critical technologies for non-cooperative communication systems. Recently, deep learning (DL) based AMC (DL-AMC) methods have attracted significant attention due to their preferable performance. However, the study of most of DL-AMC methods are concentrated in the single-input and single-output (SISO) systems, while there are only a few works on DL-based AMC methods in multiple-input and multiple-output (MIMO) systems. Therefore, we propose in this work a convolutional neural network (CNN) based zero-forcing (ZF) equalization AMC (CNN/ZF-AMC) method for MIMO systems. Simulation results demonstrate that the CNN/ZF-AMC method achieves better performance than the artificial neural network (ANN) with high order cumulants (HOC)-based AMC method under the condition of the perfect channel state information (CSI). Moreover, we also explore the impact of the imperfect CSI on the performance of the CNN/ZF-AMC method. Simulation results demonstrated that the classification performance is not only influenced by the imperfect CSI, but also associated with the number of the transmit and receive antennas.
Adaptive weighted over-sampling for imbalanced datasets based on density peaks clustering with heuristic filtering

Learning from imbalanced datasets poses a major challenge in data mining community. When dealing with imbalanced datasets, conventional classification algorithms generally perform poorly as they are originally designed to work under balanced class distribution scenarios. Although there exist different methods to addressing this issue, sampling methods especially over-sampling techniques have shown great potentials as they aim to improve datasets itself rather than the classifiers, which can allow them to be used for any classifier. In this paper, we propose a novel adaptive weighted over-sampling for imbalanced datasets based on density peaks clustering with heuristic filtering. Unlike other clustering-based over-sampling methods, the proposed approach applies modified density peaks clustering rather than traditional k-means clustering techniques to cluster the minority instances due to its capability of accurately identifying sub-clusters with different sizes and densities, which is beneficial for the proposed method to simultaneously accommodate for between-class and within-class imbalance issues caused by various reasons. Subsequently, the size for each identified sub-cluster to be oversampled is adaptively determined according to its own size and density and then the minority instances within each sub-cluster are oversampled based on their probabilities inversely proportional to their distances to the majority class and their densities with the aim of generating more synthetic minority instances for borderline and sparser ones. Finally, in order to avoid the generation of overlapping, a heuristic filtering strategy is also developed to iteratively move the possibly overlapped minority instances away from the majority class. The extensive experimental results on the different imbalanced datasets demonstrate that the proposed approach can achieve better classification performance in most datasets as compared to the other existing over-sampling techniques.
Facial expression recognition based on a multi-task global-local network

Facial expression recognition plays an important role in intelligent human-computer interaction. The clues for understanding facial expressions lie not in global facial appearance, but also in local informative dynamics among different but confusing expressions. In this paper, we design a multi-task learning framework for global-local representation of facial expressions. First, a shared shallow module is designed to learn information from local regions and the global image. Then we construct a part-based module, which processes critical local regions including the eyes, the nose, and the mouth to extract local informative dynamics related to facial expressions. A global face module is proposed to extract global appearance features related to expressions. The proposed network extracts both local-global and spatio-temporal information for a discriminative and robust representation of facial expressions. Through properly fusing these modules into a system, we have achieved competitive results on the CK+ and Oulu-CASIA databases.
Graininess-Aware Deep Feature Learning for Robust Pedestrian Detection

In this paper, we propose a graininess-aware deep feature learning method for pedestrian detection. Unlike most existing methods which utilize the convolutional features without explicit distinction, we appropriately exploit multiple convolutional layers and dynamically select most informative features. Specifically, we train a multi-scale pedestrian attention via pixel-wise segmentation supervision to efficiently identify the pedestrian of particular scales. We encodes the fine-grained attention map into the feature maps of the detection layers to guide them to highlight the pedestrians of specific scale and avoid the background interference. The graininess-aware feature maps generated with our attention mechanism are more focused on pedestrians, and in particular on the small-scale and occluded targets. We further introduce a zoom-in-zoom-out module to enhances the features by incorporating local details and context information. Extensive experimental results on five challenging pedestrian detection benchmarks show that our method achieves very competitive or even better performance with the state-of-the-arts and is faster than most existing approaches.
An Efficient NIZK Scheme for Privacy-Preserving Transactions Over Account-Model Blockchain

We introduce the abstract framework of decentralized smart contracts system with balance and transaction amount hiding property over account-model blockchain. To build a concrete system with such properties, we utilize a homomorphic public-key encryption scheme and construct a highly efficient non-interactive zero knowledge (NIZK) argument based upon the encryption scheme to ensure the validity of the transactions. Our NIZK scheme is perfect zero knowledge in the common reference string model, while its soundness holds in the random oracle model. Compared to previous similar constructions, our proposed NIZK argument dramatically improves the time efficiency in generating a proof, at the cost of relatively longer proof size.
Security Of Cryptocurrency Using Hardware Wallet And QR Code

Today, the privacy and the security of any organization are the key requirement, the digital online transaction of money or coins also needed a certain level of security not only during the broadcasting of the transaction but before the sending of the transaction. In this research paper we proposed and implemented a cryptocurrency (Bitcoin) wallet for the android operating system, by using the QR code-based android application and a secure private key storage (Cold Wallet). Two android applications have been implemented one of them is called cold wallet and the other one is hot wallet. Cold wallet (offline) is to store and generate the private key addresses for secure transaction confirmation and the hot wallet is used to send bitcoin to the network. Hot wallet application gives facility to the user view history of performed transactions, to send and compose a new bitcoin transaction, receive bitcoin, sign it and send it to the network. By using the process of cross QR code scanning of the hot and cold wallet to the identification, validation and authentication of the user made it secure.
Securing and authenticating healthcare records through blockchain technology

Healthcare data is important in making critical policy decisions, patients care, and medical diagnostics to name a few. Due to the importance and market demand, healthcare data is also vulnerable to cyber attacks. The centralized record keeping systems expose a single node for the attackers to attack. A decentralized system is computationally expensive but has the ability to be revolutionary by keeping the patient at the core and providing security, transparency, privacy, and interoperability of the electronic healthcare data. A blockchain is such an implementation of a distributed and decentralized system using reliable cryptographic algorithms. This paper proposes a secure blockchain based architecture tailored specifically to cater to the needs of e-healthcare systems.
Genetic Programming With a New Representation to Automatically Learn Features and Evolve Ensembles for Image Classification

Image classification is a popular task in machine learning and computer vision, but it is very challenging due to high variation crossing images. Using ensemble methods for solving image classification can achieve higher classification performance than using a single classification algorithm. However, to obtain a good ensemble, the component (base) classifiers in an ensemble should be accurate and diverse. To solve image classification effectively, feature extraction is necessary to transform raw pixels into high-level informative features. However, this process often requires domain knowledge. This article proposes an evolutionary approach based on genetic programming to automatically and simultaneously learn informative features and evolve effective ensembles for image classification. The new approach takes raw images as inputs and returns predictions of class labels based on the evolved classifiers. To achieve this, a new individual representation, a new function set, and a new terminal set are developed to allow the new approach to effectively find the best solution. More important, the solutions of the new approach can extract informative features from raw images and can automatically address the diversity issue of the ensembles. In addition, the new approach can automatically select and optimize the parameters for the classification algorithms in the ensemble. The performance of the new approach is examined on 13 different image classification datasets of varying difficulty and compared with a large number of effective methods. The results show that the new approach achieves better classification accuracy on most datasets than the competitive methods. Further analysis demonstrates that the new approach can evolve solutions with high accuracy and diversity.
Relation Structure-Aware Heterogeneous Graph Neural Network

Heterogeneous graphs with different types of nodes and edges are ubiquitous and have immense value in many applications. Existing works on modeling heterogeneous graphs usually follow the idea of splitting a heterogeneous graph into multiple homogeneous subgraphs. This is ineffective in exploiting hidden rich semantic associations between different types of edges for large-scale multi-relational graphs. In this paper, we propose Relation Structure-Aware Heterogeneous Graph Neural Network (RSHN), a unified model that integrates graph and its coarsened line graph to embed both nodes and edges in heterogeneous graphs without requiring any prior knowledge such as metapath. To tackle the heterogeneity of edge connections, RSHN first creates a Coarsened Line Graph Neural Network (CL-GNN) to excavate edge-centric relation structural features that respect the latent associations of different types of edges based on coarsened line graph. After that, a Heterogeneous Graph Neural Network (H-GNN) is used to leverage implicit messages from neighbor nodes and edges propagating among nodes in heterogeneous graphs. As a result, different types of nodes and edges can enhance their embedding through mutual integration and promotion. Experiments and comparisons, based on semi-supervised classification tasks on large scale heterogeneous networks with over a hundred types of edges, show that RSHN significantly outperforms state-of-the-arts.
Institutional Quality and Financial Development: Evidence from Developing and Emerging Economies

Using a panel data set of 189 countries by employing dynamic models OLS, fixed effect, random effect and generalized method of moments (GMM) estimators. The study revisits the relationship between institutional quality and financial development in developing and emerging countries. This study is ever the first comprehensive and wide-ranging that categorizes the countries into developing and emerging countries using multiple dynamic approaches. Experimental findings of the study are based on GMM, which indicates that better institutions are important for financial development, specifically political stability, control of corruption and regulatory quality positively affect financial development in the global panel of the study. Rule of law negatively affects financial development, which reveals that in most of the global countries, the rule of law is very feeble. Control of corruption index is positively effecting financial development in emerging and global panel which indicates that most of the countries have reduced corruption to low level. The current study also found that emerging countries have reduced corruption, but other institutional indicators are found to be insignificant. The overall result concludes that good quality institutions are the main drivers of financial development and it stimulates financial development. The present study suggests that developing and emerging countries should focus to improve the institutional quality by re-examining the rules of law, government effectiveness and voice accountability institutional factors to make it better.
Violence Detection in Videos by Combining 3D Convolutional Neural Networks and Support Vector Machines

Video-surveillance has always been a vital tool to enforce safety in both public and private environments.Even though (smart) cameras are nowadays relatively widespread and cheap, such monitoring systems lack effectiveness in most scenarios.In addition, there is no guarantee about a human operator who monitors rare events in live video footages, forcing the use of such systems after unwanted events already took their undisturbed course, as a mere tool for investigations.Having an intelligent software to perform the task would allow to unlock the full potential of video-surveillance systems.To this end, in this paper we propose a solution based on a 3D Convolutional Neural Network that can effectively detect fights, aggressive motions and violence scenes in live video streams.Compared to state-of-the-art techniques, our method showed very promising performance on three challenging benchmark datasets: Hockey Fight, Crowd Violence and Movie Violence.