Reviewerly/exHarmony huggingface dataset:
Mobile wireless sensor network lifetime maximization by using evolutionary computing methods

Due to the continuous development and progress of wireless communication technology and sensor network technology, wireless sensor networks (WSNs) have gradually become an attractive technology that facilitates people's lives. Due to the extensive use of WSNs, maximizing the lifetime of WSNs to obtain real-time and effective information has become a critical concern. This paper studies the life of mobile wireless sensor networks (MWSNs). MWSNs are a special type of WSN in that the sensor nodes are movable within a certain area. A system model is developed to prolong the network lifetime of MWSNs. This paper uses five evolutionary computing (EC) algorithms to develop the MWSN lifetime optimization model. Numerical simulations are performed to study the advantages and disadvantages of the five algorithms for solving the model. The comparison and discussion can provide advice for using EC algorithms to solve MWSN lifetime maximization problems.
Multi-level supervised hashing with deep features for efficient image retrieval

Image hashing based on deep convolutional neural networks (CNN), deep hashing, has acquired breakthrough in image retrieval. Although deep features from various CNN layers have various levels of information, most of the existing deep hashing methods extract the feature vector only from the output of the penultimate fully-connected layer, focusing primarily on semantic information whilst ignoring detailed structure information. This calls for research on multi-level hashing, utilizing multi-level features to exploit different levels of CNN characteristics. To fill this gap, a novel image hashing method, Multi-Level Supervised Hashing with deep feature (MLSH), is proposed in this paper to further exploit multiple levels of deep image features. It uses a multiple-hash-table mechanism to integrate multi-level features extracted from an individual deep convolutional neural network. It takes advantage of the complementarity among multi-level features from various layers of a single deep network. High-level features reveal the semantic content of the image, while low-level features provide the structural information that is missing in high-level features. Instead of simple concatenation, several hash tables are trained individually using different levels of features from different layers, which are then integrated for efficient image retrieval. The method has been systematically evaluated through experiments on three image databases, including CIFAR-10, MNIST and NUSWIDE, and has thus been demonstrated to set a new state of the art in image hashing, outperforming several state-of-the-art hashing methods. Furthermore, the recall and precision can be balanced and improved simultaneously.
Review of Blockchain based IoT application and its security issues

The always increasing number of Internet net of Things (IoT) devices in the society, it makes a secure, accessible, and reliable infrastructure to process and store the computed data. The present IoT model uses a central cloud server model which leads to single point failures. To avoid this Blockchain technology is integrated with IoT because Blockchain uses a distributed network. Blockchain technology has tremendous development in the field of IoT security. In this paper, we conducted a systematic review of Blockchain based IoT application and its security issues. Comparison between IoT and Blockchain technology discussed. Based on an organized, evaluation and efficient content analysis of the academic literature, we discussed the broad classification of blockchain-based IoT applications through various segments such as Industrial, smart Health care, smart city, smart home, ad-hoc vehicle networks (VANET), eBusiness model, and supply chain management. We also pointed out the security issues of Blockchain based IoT application, particularly limitations the blockchain technology in IoT. This paper will help the academician and researcher further develop the IoT based Blockchain application.
Scaling Blockchains to Support Electronic Health Records for Hospital Systems

Electronic Health Records (EHRs) have improved many aspects of healthcare and allowed for easier patient management for medical providers. Blockchains have been proposed as a promising solution for supporting Electronic Health Records (EHRs), but have also been linked to scalability concerns about supporting real-world healthcare systems. This paper quantifies the scalability issues and bottlenecks related to current blockchains and puts into perspective the limitations blockchains have with supporting healthcare systems. Particularly we show that well known blockchains such as Bitcoin, Ethereum, and IOTA cannot support transactions of a large scale hospital system such as the University of Kentucky HealthCare system and leave over 7.5M unsealed transactions per day. We then discuss how bottlenecks of blockchains can be relieved with sidechains, enabling well-known blockchains to support even larger hospital systems of over 30M transactions per day. We then introduce the Patient-Healthchain architecture to provide future direction on how scaling blockchains for EHR systems with sidechains can be achieved.
Adversarial Attack Against DoS Intrusion Detection: An Improved Boundary-Based Method

Denial of Service (DoS) attacks pose serious threats to network security. With the rapid development of machine learning technologies, artificial neural network (ANN) has been used to classify DoS attacks. However, ANN models are vulnerable to adversarial samples: inputs that are specially crafted to yield incorrect outputs. In this work, we explore a kind of DoS adversarial attacks which aim to bypass ANN-based DoS intrusion detection systems. By analyzing features of DoS samples, we propose an improved boundary-based method to craft adversarial DoS samples. The key idea is to optimize a Mahalanobis distance by perturbing continuous features and discrete features of DoS samples respectively. We experimentally study the effectiveness of our method in two trained ANN classifiers on KDDcup99 dataset and CICIDS2017 dataset. Results show that our method can craft adversarial DoS samples with limited queries.
Phoneme Boundary Detection Using Learnable Segmental Features

Phoneme boundary detection plays an essential first step for a variety of speech processing applications such as speaker diarization, speech science, keyword spotting, etc. In this work, we propose a neural architecture coupled with a parameterized structured loss function to learn segmental representations for the task of phoneme boundary detection. First, we evaluated our model when the spoken phonemes were not given as input. Results on the TIMIT and Buckeye corpora suggest that the proposed model is superior to the baseline models and reaches state-of-the-art performance in terms of F1 and R-value. We further explore the use of phonetic transcription as additional supervision and show this yields minor improvements in performance but substantially better convergence rates. We additionally evaluate the model on a He-brew corpus and demonstrate such phonetic supervision can be beneficial in a multi-lingual setting.
Performance evaluation of containers and virtual machines when running Cassandra workload concurrently

Summary NoSQL distributed databases are often used as Big Data platforms. To provide efficient resource sharing and cost effectiveness, such distributed databases typically run concurrently on a virtualized infrastructure that could be implemented using hypervisor‐based virtualization or container‐based virtualization. Hypervisor‐based virtualization is a mature technology but imposes overhead on CPU, networking, and disk. Recently, by sharing the operating system resources and simplifying the deployment of applications, container‐based virtualization is getting more popular. This article presents a performance comparison between multiple instances of VMware VMs and Docker containers running concurrently. Our workload models a real‐world Big Data Apache Cassandra application from Ericsson. As a baseline, we evaluated the performance of Cassandra when running on the nonvirtualized physical infrastructure. Our study shows that Docker has lower overhead compared with VMware; the performance on the container‐based infrastructure was as good as on the nonvirtualized. Our performance evaluations also show that running multiple instances of a Cassandra database concurrently affected the performance of read and write operations differently; for both VMware and Docker, the maximum number of read operations was reduced when we ran several instances concurrently, whereas the maximum number of write operations increased when we ran instances concurrently.
Global Relation Reasoning Graph Convolutional Networks for Human Pose Estimation

We explore the importance of global relation reasoning in Human Pose Estimation (HPE). Global relation reasoning aims to globally learn relations among regions of images or videos. For HPE, if we can globally model the relations among different body joints, we may mitigate some challenges such as occlusion. Most existing human pose estimation methods rely on building Convolutional Neural Networks (CNNs). Because convolution operations can only model local relations, in order to capture global relations, they must inefficiently stack multiple convolution layers to enlarge the receptive fields to cover all the body joints in the image. In this paper, we propose to utilize Global Relation Reasoning Graph Convolutional Networks (GRR-GCN) to efficiently capture the global relations among different body joints. GRR-GCN projects all the features in the original coordinate space to a graph space. In the graph space, these features are represented by a set of nodes to form a fully-connected graph, on which global relation reasoning is performed by graph convolution. After reasoning, node features are projected back to the coordinate space for further processing. GRR-GCN is a plug-and-play module, and can be integrated into current state-of-the-art networks. Experiments on human pose estimation benchmark, MPII and COCO keypoint detection dataset, show that GRR-GCN can boost the performance of state-of-the-art human pose estimation networks including SimpleBaseline and HRNet (High-Resolution Net).
Multi-Feature View-Based Shallow Convolutional Neural Network for Road Segmentation

This study presents a shallow and robust road segmentation model.The computer-aided real-time applications, like driver assistance, require real-time and accurate processing.Current studies use Deep Convolutional Neural Networks (DCNN) for road segmentation.However, DCNN requires high computational power and lots of labeled data to learn abstract features for deeper layers.The deeper the layer is, the more abstract information it tends to learn.Moreover, the prediction time of the DCNN network is an important aspect of autonomous vehicles.To overcome these issues, a Multi-feature View-based Shallow Convolutional Neural Network (MVS-CNN) is proposed that utilizes the abstract features extracted from the explicitly derived representations of the input image.Gradient information of the input image is used as additional channels to enhance the learning process of the proposed deep learning architecture.The multi-feature views are fed to a fully-connected neural network to accurately segment the road regions.The testing accuracy demonstrates that the proposed MVS-CNN achieved an improvement of 2.7% as compared to baseline CNN consisting of only RGB inputs.Furthermore, the comparison of the proposed method with the popular semantic segmentation network (SegNet) has shown that the proposed scheme performs better while being more efficient during training and evaluation.Unlike traditional segmentation techniques, which are based on the encoder-decoder architecture, the proposed MVS-CNN consists of only the encoder network.The proposed MVS-CNN has been trained and validated with two well-known datasets: the KITTI Vision Benchmark and the Cityscapes dataset.The results have been compared with the state-ofthe-art deep learning architectures.The proposed MVS-CNN outperforms and shows supremacy in terms of model accuracy, processing time, and segmentation accuracy.Based on the experimental results, the proposed architecture can be considered as an efficient road segmentation architecture for autonomous vehicle systems.
An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network

Author gender detection (AGD) is a serious and crucial issue in Internet security applications, in particular in email, messenger, and social network communications. Detecting the gender of communication partner helps preventing massive fraud and abuses happening through social media such as email, blogs, forums. Text and writings of people on the Internet have valuable information that can be used to identify the gender of an author. Machine learning and meta-heuristic algorithms are valuable techniques to extract hidden patterns useful for detecting gender of a text. In this paper, an artificial neural network (ANN) is employed as a classifier to detect the gender of an email author and the whale optimization algorithm (WOA) is used to find optimal weights and biases for improving the accuracy of the ANN classification. Through this combination of ANN and WOA an accuracy of 98%, precision of 97.16%, and recall of 99.67% were achieved, which indicates the superiority of the proposed method on Bayesian networks, regression, decision tree, support vector machine, and ANN examined.
Automatic soccer field of play registration

This paper proposes a strategy for the automatic registration of soccer images on a model of the field of play. First, a robust and efficient preprocessing is applied to discard the areas of the image that do not belong to the field of play and eliminate most edge points that are not part of the line marks. Then, a novel probabilistic decision tree is used to identify the most probable classification for the set of all the straight lines in the image. Additionally, the line surrounding the center circle is also modeled for providing results when only few straight lines are visible. Finally, a three-step analysis stage is applied to ensure the validity of the results. To assess its quality, the strategy has been tested on several sequences corresponding to three stadiums with different characteristics. The results obtained have shown that the registration is successful in most images (94%).
Regression-Based Neuro-Fuzzy Network Trained by ABC Algorithm for High-Density Impulse Noise Elimination

Salt and pepper (SAP) noise elimination is a crucial step for further image processing and pattern recognition applications. The main aim of this article is to propose a novel SAP noise elimination method which employs a regression-based neuro-fuzzy network for highly corrupted gray scale and color images. In the proposed method, multiple neuro-fuzzy filters trained with artificial bee colony algorithm is combined with a decision tree algorithm. The performance of the proposed filter is compared with a number of well known methods with respect to popular metrics including, structural similarity index, peak signal-to-noise ratio, and correlation on well known test images. The results reveal that the proposed filter has superior performance in terms of all comparison metrics.
Threshold changeable secret sharing with secure secret reconstruction

(t,n) threshold secret sharing (SS) is an important cryptographic primitive in which a secret is divided into n shares, and then any t or more shareholders can exchange shares to reconstruct the secret without help of any trusted third party. However, if an illegal participant, without any valid share, impersonates a shareholder to recover the secret with m (m≥t) legal shareholders in a traditional (t,n) threshold SS scheme, it may obtain the secret. Therefore, this paper utilizes a bivariate symmetry polynomial to propose a basic threshold changeable secret sharing (TCSS) scheme which is cheating immune and thwarts the above illegal participant attack. In the basic TCSS scheme, threshold is allowed to increase from t to the exact number of all participants during secret reconstruction. In this way, the secret can be recovered only if all the participants have valid shares. However, each shareholder has to keep t coefficients of its share. Then, an improved TCSS scheme based on both univariate polynomial and bivariate symmetry polynomial is proposed to reduce coefficients of shares for each shareholder.
An Overview of the Latest Progress and Core Challenge of Autonomous Vehicle Technologies

The mass production of autonomous vehicle is coming, thanks to the rapid progress of autonomous driving technology, especially the recent breakthroughs in LiDAR sensors, GPUs, and deep learning. Many automotive and IT companies represented by Waymo and GM are constantly promoting their advanced autonomous vehicles to hit public roads as early as possible. This paper systematically reviews the latest development and future trend of the autonomous vehicle technologies, discusses the extensive application of AI in ICV, and identifies the key problems and core challenges facing the commercialization of autonomous vehicle. Based on the review, it forecasts the prospects and conditions of autonomous vehicle’s mass production and points out the arduous, long-term and systematic nature of its development.
IoMT: A Blockchain Perspective

This chapter presents several aspects concerning the evolution and importance of the Internet of Medical Devices (IoMT) applications and blockchain technology in healthcare for patients' data analysis and research about the adequate medication for diseases such as cancer, Alzheimer or epilepsy. Blockchain can help in creating a single database to collect data during clinical trials and allow patients' data security. In the healthcare system, blockchain is based on P2P (peer to peer) network computers (nodes), which can optimize the efficiency of the Internet of Things (IoT) and can ensure a safe connection of the medical equipment. According to statistics, spending on blockchain solutions worldwide from 2019 to 2022 is increasing from $2.9 billion to $12.4 billion. The novelty of the chapter consists of providing a comparison between cloud computing and blockchain, together with a connection between two of the essential radical innovation breakthroughs, i.e. blockchain. In this chapter, there are also presented the IoMT applications based on blockchain for healthcare and aspects concerning data security in blockchain.
Overhead View Person Detection Using YOLO

In video surveillance system, one of the important task is to detect person. In recent years, different computer vision and deep learning algorithms have been developed, which provides robust person detection results. Majority of these developed techniques focused on frontal and asymmetric views. Therefore, in this paper, person detection has been performed from a significantly changed perspective i.e. overhead view. A deep learning model i.e. YOLO (You Look Only Once) has been explored in the context of person detection from overhead view. The model is trained on frontal view data set and tested on overhead view person data set. Furthermore, overhead view person counting has been performed using information of classified bounding box. The YOLO model produces significantly good results with TPR of 95% and FPR up to 0.2%.
sMRT: Multi-Resident Tracking in Smart Homes with Sensor Vectorization

Smart homes equipped with anonymous binary sensors offer a low-cost, unobtrusive solution that powers activity-aware applications, such as building automation, health monitoring, behavioral intervention, and home security. However, when multiple residents are living in a smart home, associating sensor events with the corresponding residents can pose a major challenge. Previous approaches to multi-resident tracking in smart homes rely on extra information, such as sensor layouts, floor plans, and annotated data, which may not be available or inconvenient to obtain in practice. To address those challenges in real-life deployment, we introduce the sMRT algorithm that simultaneously tracks the location of each resident and estimates the number of residents in the smart home, without relying on ground-truth annotated sensor data or other additional information. We evaluate the performance of our approach using two smart home datasets recorded in real-life settings and compare sMRT with two other methods that rely on sensor layout and ground truth-labeled sensor data.
BTM and GloVe Similarity Linear Fusion-Based Short Text Clustering Algorithm for Microblog Hot Topic Discovery

Microblog hot topic discovery is one of the research hotspots in the field of text mining.The distance function of traditional K-means leads to low clustering accuracy, which leads to poor hot topic discovery.Three definitions are proposed in this paper: title words and body words, positional contribution-based weight and fusion similarity-based distance.The short text clustering algorithm based on BTM and GloVe similarity linear fusion (BG & SLF-Kmeans) is further proposed.BTM and GloVe are used to model the preprocessed microblog short texts.JS divergence is adopted to calculate the text similarity based on BTM topic modeling.WMD of improved word weight (IWMD) is used to calculate the text similarity based on GloVe word vector modeling.Finally, the two similarities are linearly fused and used as the distance function to realize K-means clustering.Specific word sets of 6 hot topics can be obtained, and microblog hot topics can be discovered.The experimental results show that BG & SLF-Kmeans significantly improves clustering accuracy compared with TF-IDF & K-means, BTM & K-means, and BTF & SLF-Kmeans.
Design of Nonsubsampled Graph Filter Banks via Lifting Schemes

Graph filter banks play a crucial role in the vertex and spectral representation of graph signals.The notion of twochannel nonsubsampled graph filter banks (NSGFBs) on an undirected graph was introduced recently.The absence of downsampling/upsampling operators allows greater flexibility in the design of NSGFBs that achieve perfect reconstruction.However the design of NSGFBs that take the spectral response into account has not been adequately addressed yet.Based on the polynomial/rational lifting scheme, this letter presents a simple method to design NSGFBs with good spectral response and perfect reconstruction.Experimental results will demonstrate the effectiveness of the proposed method in tailoring the spectral responses of the lifted NSGFBs.Application of the NSGFB to denoising will also be considered.
Design and analysis of random multiple access quantum key distribution

This article proposes a potential application of quantum key distribution in multiuser networks. In modern networks, variety of multiple access technologies are used for multiuser access purposes. In this article, we focus on a type of widely used media access control protocol—carrier-sense multiple access (CSMA) and analyze the use of carrier-sense multiple access with collision avoidance (CSMA/CA) for the quantum key distribution in the network. The quantum key generation is based on a successful experiment that implemented the quantum key distribution by using the decoy-state method. The secret key generation rate as the main indicator of the performance is given, including the relationship between it and the number of stations in the network, and the relationship between the key generation rate and the transmission distance under the multiple access condition as well. In addition, a multiple access quantum key distribution with channel detection protocol is also proposed.