Reviewerly/exHarmony huggingface dataset:
Research on Signal Models and Recognition Method for Digital Modulation

Methods based on instantaneous statistical features and high-order cumulants are widely used in digital modulation recognition. However, the signal models used by the above two methods are different from each other, which lies in the difference of code elements. Specifically, for one method the code element ranges from 0 to M-1, while for the other method the code element ranges from 1 to M, where M denotes the modulation order. The former one is usually used in method based on instantaneous, while the latter one is usually used in method based on high-order cumulants. Unfortunately, the signal model is usually unknown for the receiver and demodulator, hence the digital modulation recognition method based on specific signal model may lead to high error in all probability, this may become obvious for MASK signals. This paper analyzed the two signal models with different code element ranges and high-order cumulants of the two signal models, and proposed a digital modulation recognition method suitable for above two signal models based on mixed features, including two features based on high-order cumulants, spectrum feature based on FFT and signal instantaneous feature. Simulation results showed that the method proposed in this paper can recognize six classical digital modulation patterns efficiently with satisfactory recognition rate.
Ethereum Blockchain Network-based Electrical Vehicle Charging Platform with Multi-Criteria Decision Support System

The following topics are dealt with: learning (artificial intelligence); data mining; pattern classification; feature extraction; social networking (online); neural nets; security of data; convolutional neural nets; Big Data; evolutionary computation.
Web Data Extraction Approach for Deep Web using WEIDJ

Data extraction is one of the most prominent areas in data mining analysis that is been extensively studied especially in the field of data requirements and reservoir. The main aim of data extraction with regards to semi-structured data is to retrieve beneficial information from the World Wide Web. The data from large web data also known as deep web is retrievable but it requires request through form submission because it cannot be performed by any search engines. Data mining applications and automatic data extraction are very cumbersome due to the diverse structure of web pages. Most of the previous data extraction techniques were dealing with various data types such as text, audio, video and etc. but research works that are focusing on image as data are still lacking. Document Object Model (DOM) is an example of the state of the art of data extraction technique that is related to research work in mining image data. DOM was the method used to solve semi-structured data extraction from web. However, as the HTML documents start to grow larger, it has been found that the process of data extraction has been plagued with lengthy processing time and noisy information. In this research work, we propose an improved model namely Wrapper Extraction of Image using DOM and JSON (WEIDJ) in response to the promising results of mining in a higher volume of web data from a various types of image format and taking the consideration of web data extraction from deep web. To observe the efficiency of the proposed model, we compare the performance of data extraction by different level of page extraction with existing methods such as VIBS, MDR, DEPTA and VIDE. It has yielded the best results in Precision with 100, Recall with 97.93103 and F-measure with 98.9547.
Scalable Bayesian variable selection regression models for count data

Variable selection, also known as feature selection in the machine learning literature, plays an indispensable role in scientific studies. In many research areas with massive data, finding a subset of representative features that best explain the outcome of interest has become a critical component in any researcher's workflow. In this chapter, we focus on Bayesian variable selection regression models for count data, and specifically on the negative binomial linear regression model and on the Dirichlet-multinomial regression model. We address the variable selection problem via spike-and-slab priors. For posterior inference, we review standard MCMC methods and also investigate computationally more efficient variational inference approaches that use data augmentation techniques and concrete relaxation methods. We investigate performance of the methods via simulation studies and benchmark data sets.
Understanding a public environment via continuous robot observations

This paper presents a study on a point cloud analysis captured by a robot navigating in a shopping mall environment. It investigates the type and how much information the robot could extract from the environment. For this purpose, information regarding environmental changes and the number of people in shops was extracted and analyzed. First, the robot was manually controlled to collect data in a typical shopping mall having different types of shops and a food court. As the robot navigated thoroughly around the environment, seven data recordings of data obtained from various onboard sensors were recorded during afternoon hours over three consecutive days. We built a composite map by overlaying 3D point clouds for each recording sharing the same coordinate frame, which reveals the changes in the environment's static objects. The number of humans at each shop in each recording was computed using a human tracker. Then, we computed a fourteen-dimensional vector for each shop: seven dimensions for environmental changes and seven for human density. Experimental results show that the environmental changes and the human density at each shop are consistent with the visual changes that occurred in the shops and the number of people who visited the shops. Correlation analysis was done among shop changes, shop open space, and human density where results suggest that change in shop configurations are often done in smaller shops and shops with larger open space tend to attract larger number of customers. Finally, information extracted from shops was used to categorize the shops according to similarity.
Local Sigmoid Method: Non-Iterative Deterministic Learning Algorithm for Automatic Model Construction of Neural Network

A non-iterative learning algorithm for artificial neural networks is an alternative to optimize the neural network parameters with extremely fast convergence time. Extreme learning machine (ELM) is one of the fastest learning algorithms based on a non-iterative method for a single hidden layer feedforward neural network (SLFN) model. ELM uses a randomization technique that requires a large number of hidden nodes to achieve the high accuracy. This leads to a large and complex model, which is slow at the inference time. Previously, we reported analytical incremental learning (AIL) algorithm, which is a compact model and a non-iterative deterministic learning algorithm, to be used as an alternative. However, AIL cannot grow its set of hidden nodes, due to the node saturation problem. Here, we describe a local sigmoid method (LSM) that is also a sufficiently compact model and a non-iterative deterministic learning algorithm to overcome both the ELM randomization and AIL node saturation problems. The LSM algorithm is based on “divide and conquer” method that divides the dataset into several subsets which are easier to optimize separately. Each subset can be associated with a local segment represented as a hidden node that preserves local information of the subset. This technique helps us to understand the function of each hidden node of the network built. Moreover, we can use such a technique to explain the function of hidden nodes learned by backpropagation, the iterative algorithm. Based on our experimental results, LSM is more accurate than other non-iterative learning algorithms and one of the most compact models.
Automatic speaker recognition from speech signal using bidirectional long‐short‐term memory recurrent neural network

Abstract Speaker recognition is a major challenge in various languages for researchers. For programmed speaker recognition structure prepared by utilizing ordinary speech, shouting creates a confusion between the enlistment and test, henceforth minimizing the identification execution as extreme vocal exertion is required during shouting. Speaker recognition requires more time for classification of data, accuracy is optimized, and the low root‐mean‐square error rate is the major problem. The objective of this work is to develop an efficient system of speaker recognition. In this work, an improved method of Wiener filter algorithm is applied for better noise reduction. To obtain the essential feature vector values, Mel‐frequency cepstral coefficient feature extraction method is used on the noise‐removed signals. Furthermore, input samples are created by using these extracted features after the dimensions have been reduced using probabilistic principal component analysis. Finally, recurrent neural network‐bidirectional long‐short‐term memory is used for the classification to improve the prediction accuracy. For checking the effectiveness, the proposed work is compared with the existing methods based on accuracy, sensitivity, and error rate. The results obtained with the proposed method demonstrate an accuracy of 95.77%.
Data Poisoning Attacks on Graph Convolutional Matrix Completion

Recommender systems have been widely adopted in many web services. As the performance of the recommender system will directly affect the profitability of the business, driving bad merchants to boost revenue for themselves by conducting adversarial attacks to compromise the effectiveness of such systems. Several studies have shown that recommender systems are vulnerable to adversarial attacks, e.g. data poisoning attack. Since different recommender systems adopt different algorithms, existing attacks are designed for specific systems. In recent years, with the development of graph deep learning, recommender systems have been also starting to use new methods, like graph convolutional networks. More recently, graph convolutional networks have also been found to be affected by poisoning attacks. However, characteristics of data sources in recommender systems, such as heterogeneity of nodes and edges, will bring challenge to solve attack problem. To overcome this challenge, in this paper, we propose data poisoning attacks on graph convolutional matrix completion (GCMC) recommender system by adding fake users. The key point of the method is to make fake users mimicrking rating behavior of normal users, then pass the information of thier rating behaviors towards the target item back to related normal users, attempting to interfere with the prediction of the recommender system. Futhermore, on two real-world datasets ML-100K and Flixster, the results show that our method significantly overmatches three baseline methods: (i) random attack, (ii) popular item based attack, (iii) and mimicry with random scores based attack.
Channel Coding of a Quantum Measurement

In this work, we consider the preservation of a measurement for quantum systems interacting with an environment. Namely, a method of preserving an optimal measurement over a channel is devised, what we call channel coding of a quantum measurement in that operations are applied before and after a channel in order to protect a measurement. A protocol that preserves a quantum measurement over an arbitrary channel is shown only with local operations and classical communication without the use of a larger Hilbert space. Therefore, the protocol is readily feasible with present day's technologies. Channel coding of qubit measurements is presented, and it is shown that a measurement can be preserved for an arbitrary channel for both i) pairs of qubit states and ii) ensembles of equally probable states. The protocol of preserving a quantum measurement is demonstrated with IBM quantum computers.
Detecting computer-generated random responding in online questionnaires: An extension of Dupuis, Meier &amp; Cuneo (2019) on dichotomous data

Some authors recently underlined the existence of programs generating invalid responses in online surveys as an emerging threat for the different crowdsourced research fields (e.g., botnets, form fillers or survey bots). Accordingly, online data research might include computer-generated sets of responses representing invalid data at risk of largely distorting study results. Several statistical indices exist in order to detect problematic data. In line with a previous study that compared these indices in Likert-type scale questionnaire data, this study purported to extend the analyses with dichotomous-itemed questionnaires. Three samples of about more than 2,000 participants were mixed with different proportions (i.e., 5% to 50%) of simulated data to mimic their effect. Then, seven indices were compared in terms of correct detections of non-human response sets. Consistent with former findings, three indices resulted in superior correct detection rates: response coherence, the Mahalanobis distance and the person-total correlation. Two of them can easily be computed using basic statistical software. The current study findings represent an encouragement to use them in priority as routine for data screening.
A Dual-Chain Digital Copyright Registration and Transaction System Based on Blockchain Technology

The increasing demand for digital copyright transactions in the big data era causes the emergence of piracy and infringement incidents. The traditional centralized digital copyright protection system based on centralized authority management of authoritative organizations has high registration cost. Blockchain, as a decentralized protocol, has the characteristics of decentralization, anonymity, auditability, security and persistency, which provide a solution to the current problems in the field of digital copyright. Combining with blockchain technology, this paper proposes a digital copyright registration and transaction system with double chain architecture. The blockchain based on digital copyright registration and management chain (RMC) and digital copyright transaction and subscription chain (TSC) can prevent information disclosure and improve the privacy protection by segregating account information and transaction information. Meanwhile, transforming the previous one-chain architecture into multiple chains in parallel on the RMC and TSC can reduce the redundant amount of computation, the consensus efficiency and improve the throughput rate. Experimental analysis shows that the system has the advantages of short registration time, high throughput and good scalability.
Local relation network with multilevel attention for visual question answering

With the tremendous success of the visual question answering (VQA) tasks, visual attention mechanisms have become an indispensable part of VQA models. However, these attention-based methods do not consider any relationship among regions, which is crucial for the thorough understanding of the image by the model. We propose local relation networks for generating context-aware image features for each image region, which contain information on the relationship among the other image regions. Furthermore, we propose a multilevel attention mechanism to combine semantic information from the LRNs and the original image regions, rendering the decision of the model more reasonable. With these two measures, we improve the region representation and achieve better attentive effect and VQA performance. We conduct numerous experiments on the COCO-QA dataset and the largest VQA v2.0 benchmark dataset. Our model achieves competitive results, proving the effectiveness of our proposed LRNs and multilevel attention mechanism through visual demonstrations.
Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling

The article presents a discriminative approach to complement the unsupervised probabilistic nature of topic modelling. The framework transforms the probabilities of the topics per document into class-dependent deep learning models that extract highly discriminatory features suitable for classification. The framework is then used for sentiment analysis with minimum feature engineering. The approach transforms the sentiment analysis problem from the word/document domain to the topics domain making it more robust to noise and incorporating complex contextual information that are not represented otherwise. A stacked denoising autoencoder (SDA) is then used to model the complex relationship among the topics per sentiment with minimum assumptions. To achieve this, a distinct topic model and SDA per sentiment polarity is built with an additional decision layer for classification. The framework is tested on a comprehensive collection of benchmark datasets that vary in sample size, class bias and classification task. A significant improvement to the state of the art is achieved without the need for a sentiment lexica or over-engineered features. A further analysis is carried out to explain the observed improvement in accuracy.
Data presentation in security operations centres: exploring the potential for sonification to enhance existing practice

Abstract Security practitioners working in Security Operations Centres (SOCs) are responsible for detecting and mitigating malicious computer network activity. This work requires both automated tools that detect and prevent attacks, and data presentation tools that can present pertinent network security monitoring information to practitioners in an efficient and comprehensible manner. In recent years, advances have been made in the development of visual approaches to data presentation, with some uptake of advanced security visualization tools in SOCs. Sonification in which data are represented as sound, is said to have potential as an approach that could work alongside existing visual data presentation approaches to address some of the unique challenges faced by SOCs. For example, sonification has been shown to enable peripheral monitoring of processes, which could aid practitioners multitasking in busy SOCs. The perspectives of security practitioners on incorporating sonification into their actual working environments have not yet been examined, however. The aim of this article, therefore, is to address this gap by exploring attitudes to using sonification in SOCs and by identifying the data presentation approaches currently used. We report on the results of a study consisting of an online survey (N = 20) and interviews (N = 21) with security practitioners working in a range of different SOCs. Our contributions are (i) a refined appreciation of the contexts in which sonification could aid in SOC working practice, (ii) an understanding of the areas in which sonification may not be beneficial or may even be problematic, (iii) an analysis of the critical requirements for the design of sonification systems and their integration into the SOC setting and (iv) evidence of the visual data presentation techniques currently used and identification of how sonification might work alongside and address challenges to using them. Our findings clarify insights into the potential benefits and challenges of introducing sonification to support work in this vital security monitoring environment. Participants saw potential value in using sonification systems to aid in anomaly detection tasks in SOCs (such as retrospective hunting), as well as in situations in which peripheral monitoring is desirable: while multitasking with multiple work tasks, or while outside of the SOC.
Radial-Based Undersampling Algorithm for Classification of Breast Cancer Histopathological Images Affected by Data Imbalance

The problem of data imbalance remains one of the most widespread challenges of the contemporary machine learning, affecting most of the real datasets. It is especially ubiquitous in the medical domain, in which some of the classes used during classification can be naturally under-represented. At the same time it can significantly affect the classification performance of most standard algorithms, such as convolutional neural networks. One of the approaches for dealing with data imbalance is artificially reducing the number of objects from the over-represented class, an approach called undersampling. In this paper we present a novel undersampling technique, Radial-Based Undersampling (RBU), and apply it to the classification of breast cancer histopathological images. The results of the conducted experimental analysis not only indicate the usefulness of the RBU algorithm in that task, but also shed light on some of the drawbacks of that method, which could potentially be used in the future to improve upon the proposed algorithm.
A Novel Double Layered Weighted Potential Field Framework for Multi-USV Navigation towards Dynamic Obstacle Avoidance in a Constrained Maritime Environment

Motion planning of multiple unmanned surface vehicles (USVs) towards increased autonomy and wider coverage of the maritime environment is a pertinent requirement. Given the numerous types of USVs currently available with a wide spectrum of maneuvering capabilities, we present a generalized multi-USV navigation framework adaptable to specific USV maneuvering response capabilities for dynamic obstacle avoidance. The present paper integrates an optimal path planning with safety distance constrained A* algorithm and a proposed adaptively weighted potential field based path following approach with collision avoidance based on USV maneuvering response times. The system allows USVs with fast maneuvering abilities to react late and slow USVs to react sooner to oncoming moving obstacles gradually such that a smooth path is followed by the USV group with reduced cross track error. Simulation results validate reduced cross track error for slow and fast maneuvering response time multi-USV teams.
A New Password Cracking Model with Generative Adversarial Networks

Owing to the generality and importance of the password as a means of authentication, many studies have addressed password-strength evaluation methods and password cracking methods. Recently, the generative adversarial networks approach to enhance password guessing (PassGAN) has been proposed as a password cracking method in research that is based on generative adversarial networks (GAN). The results of this study have received substantial attention. In this paper, we propose the use of a recurrent neural networks-based (RNN) GAN, which comprises the use of the improved Wasserstein GAN (IWGAN) cost function. These models that combine the RNN with IWGAN perform better than PassGAN. We have conducted experiments to compare the performance of our proposed model with that of PassGAN and analyzed the results. Using these analyses, we confirmed that our proposed models exhibited a password cracking performance improvement of 5–10% more than that of PassGAN.
A QoE-oriented Saliency-aware Approach for 360-degree Video Transmission

The tradeoff between bandwidth efficiency and quality of experience (QoE) is a key issue in 360 video transmission. In this paper, we propose a QoE-oriented saliency-aware 360 video transmission framework to balance this tradeoff. The target is to reduce the bandwidth demand without declining the QoE. Specifically, the proposed model is based on the decision-making process. We use Lyapunov optimization to solve the decisionmaking problem. Furthermore, we integrate saliency information into the model to influence the decision policy, so that the model has the advantage of bandwidth efficiency. The simulation results show that the tradeoff parameter of Lyapunov optimization can balance the tradeoff between QoE and bandwidth efficiency, and 360 video saliency entropy limits the upper and lower bounds of QoE and bandwidth efficiency.