Reviewerly/exHarmony huggingface dataset:
A random walk Grey wolf optimizer based on dispersion factor for feature selection on chronic disease prediction

• Improvised Grey wolf optimizer for feature selection on medical data. • Proposed method ranks best over other Nature Inspired Algorithms. • Identification of significant features of chronic disease data. • Prediction of the output class for each data using significant features. In the field of Chronic disease prediction, identifying the relevant features plays an important role for early disease diagnosis. With a high dimensionality of data, search for an adequate subset feature increases exponentially and is categorised as NP hard problem. Nature Inspired Algorithm (NIA) have been famous to tackle this problem by finding an optimum solution in a reasonable amount of time. Grey wolf optimizer (GWO) is an emerging and powerful NIA used in wrapper feature selection method. It is well known for its flexibility, simplicity and efficient results. However, GWO have unsatisfactory results on local searching ability, and a slow convergence rate. To improve the local search and find a balance between exploration and exploitation, this paper proposes a Random Walk Grey Wolf Optimizer based on dispersion factor (RWGWO) approach to the feature selection problem. To demonstrate the methodology, a set of classification measures is evaluated and examined on eighteen different chronic disease data. For a fair comparison, RWGWO is compared with several recent state of the art method. Finding shows that RWGWO method ranks best over other NIAs and is able to drastically reduces the features size on each chronic disease data. Further, identification of significant set of features from each data is determined using obtained features of RWGWO. The significant features are able to enhance the classification accuracy of the presented data and solves the dimensionality reduction problem.
Blockchain Technology:– A New Technology for Creating Distributed and Trusted Computing Environment

In these (several) past decades (1950 to 2018), the world is moved from wired things to wireless devices or electronics devices/ technology. This increment or development in networking technology faces several incidents like issues of leaking privacy of users, issue of surveillance and security breaches in systems. This paper discusses about compromising users” privacy as various issues, challenges and questions (raised in existed models). Note that due to these issues or breaches or loopholes, unknown users/ third-parties collect and control large amounts of personal data, also they may use this data against respective user, for example, for financial use, for blackmailing, etc. Hence, this article discusses about a new technology (i.e., Blockchain), on which every industry is trusting on. How this new technology has been growing as fastest technology only in the past decade to build among users and organization? Such answers have been given in this paper. Blockchain provides decentralized, distributed personal data management system which ensures users own and control their data or protect user's data any kind of breaches.
FIDChain: Federated Intrusion Detection System for Blockchain-Enabled IoT Healthcare Applications

Recently, there has been considerable growth in the internet of things (IoT)-based healthcare applications; however, they suffer from a lack of intrusion detection systems (IDS). Leveraging recent technologies, such as machine learning (ML), edge computing, and blockchain, can provide suitable and strong security solutions for preserving the privacy of medical data. In this paper, FIDChain IDS is proposed using lightweight artificial neural networks (ANN) in a federated learning (FL) way to ensure healthcare data privacy preservation with the advances of blockchain technology that provides a distributed ledger for aggregating the local weights and then broadcasting the updated global weights after averaging, which prevents poisoning attacks and provides full transparency and immutability over the distributed system with negligible overhead. Applying the detection model at the edge protects the cloud if an attack happens, as it blocks the data from its gateway with smaller detection time and lesser computing and processing capacity as FL deals with smaller sets of data. The ANN and eXtreme Gradient Boosting (XGBoost) models were evaluated using the BoT-IoT dataset. The results show that ANN models have higher accuracy and better performance with the heterogeneity of data in IoT devices, such as intensive care unit (ICU) in healthcare systems. Testing the FIDChain with different datasets (CSE-CIC-IDS2018, Bot Net IoT, and KDD Cup 99) reveals that the BoT-IoT dataset has the most stable and accurate results for testing IoT applications, such as those used in healthcare systems.
Innovative System for Water Level Management using IoT to prevent Water Wastage

Some countries face water shortages, and transmission wastage has been identified as a significant contributor; this research is motivated by the desire for increased financial gains while also benefiting the environment and the water cycle, so that a cutting-edge technology can be ensured that alerts users of the liquid's level and keep it from overflowing. Using containers with ultrasonic sensors above them, the system demonstrates this by comparing the liquid level in the container to the container's depth. For data transmission, a Wi-Fi modem and a buzzer are used in conjunction with an A VR microprocessor from the A VR family. A 12 V transformer provides the electricity for this system. In order to keep track of how much liquidis remaining in the containers, an LCD monitor is used. A web page shows the user how much liquid is in the container by highlighting the liquid level. The buzzer starts to sound when the liquid's specified limit is surpassed. Thus, this technology contributes to water conservation by providing information about the liquid levels in the containers.
Category-aware Multi-relation Heterogeneous Graph Neural Networks for session-based recommendation

Session-based recommendation (SBR) is one of the hot research areas in recent years. Various SBR models have been proposed, of which graph neural network (GNN)-based models are shown to have the state-of-the-art performance. Items’ category information is an important piece of information and should be utilized in SBR models to improve model performance. In this paper, we introduce a principle way to incorporate items’ category information for SBR. More specifically, we propose a new SBR model, Category-aware Multi-relation Heterogeneous Graph Neural Networks (CM-HGNN). In CM-HGNN, we first propose to construct an item–category heterogeneous graph (ICHG) to model both category–category relation and item–category relation. More specifically, we propose to transform the sequential information contained in a session into a heterogeneous graph with both item nodes and category nodes, by which items and categories can learn from each other and the items belonging to the same category can also perceive one another. As a result, multiple interests in a session could be more effectively captured. Then, a multi-relation heterogeneous graph convolution method is proposed to extract the multiple relation information contained in the ICHG. Extensive experiments are conducted on three widely used real-world datasets, and the results suggest that the proposed CM-HGNN outperforms the state-of-the-art SBR models.
An efficient differential evolution with fitness-based dynamic mutation strategy and control parameters

It is known that the performance of the differential evolution (DE) algorithm highly depends on the mutation strategy and its control parameters. However, it is arduous to choose an appropriate mutation strategy and control parameters for a given optimization problem. Therefore, in this paper, an efficient framework of the DE named EFDE is proposed with a novel fitness-based dynamic mutation strategy and control parameters. This algorithm avoids the burden of selecting appropriate mutation strategy and control parameters and tries to maintain an appropriate balance between diversity and convergence. In the EFDE, the proposed mutation strategy adopts a dynamic number of fitness-based leading individuals to utilize the evolutionary state of the EFDE population for the evolution procedure. Furthermore, a new way of defining the control parameters is introduced based on the evolutionary state of each individual involved during the trial vector generation process. A comprehensive comparison of the proposed EFDE over challenging sets of problems from a well-known benchmark set of 23 problems, CEC2014, and CEC2017 real parameter single objective competition against several state-of-the-art algorithms is performed. The proposed EFDE is also used to solve four engineering design problems. Comparison and analysis of results confirm that the EFDE provides very competitive and better solution accuracy as compared to the other state-of-the-art algorithms.
ByteGNN

Graph neural networks (GNNs) have shown excellent performance in a wide range of applications such as recommendation, risk control, and drug discovery. With the increase in the volume of graph data, distributed GNN systems become essential to support efficient GNN training. However, existing distributed GNN training systems suffer from various performance issues including high network communication cost, low CPU utilization, and poor end-to-end performance. In this paper, we propose ByteGNN, which addresses the limitations in existing distributed GNN systems with three key designs: (1) an abstraction of mini-batch graph sampling to support high parallelism, (2) a two-level scheduling strategy to improve resource utilization and to reduce the end-to-end GNN training time, and (3) a graph partitioning algorithm tailored for GNN workloads. Our experiments show that ByteGNN outperforms the state-of-the-art distributed GNN systems with up to 3.5--23.8 times faster end-to-end execution, 2--6 times higher CPU utilization, and around half of the network communication cost.
The mechanism influencing the residents’ support of the government policy for accelerating tourism recovery under COVID-19

For accelerating the tourism recovery under COVID-19, gaining residents' support is essential. However, residents' negative emotion and their perception of the governments' policies in responding to the COVID-19 may affect their attitude toward this support. The study aims to explore the mechanism of how Quality of Government (QoG) ultimately influences residents' support of the government policy for accelerating tourism recovery. The results of two empirical surveys in Macau and South Korea find that the two components of QoG (policy responsiveness and information transparency) positively influence residents' support of the government policy for accelerating tourism recovery through satisfaction with policy response and trust in government. On the other hand, these two QoG components negatively influence residents’ anxiety, and anxiety further negatively influences trust, but anxiety does not have any effect on satisfaction. The study contributes a satisfaction-trust of the governance model for researchers to taking tourism risk research. Recommendations are provided for governments in responding to the COVID-19 pandemic.
How can we manage Offensive Text in Social Media - A Text Classification Approach using LSTM-BOOST

Recently, offensive content has become increasingly popular for harassing and criticizing people on numerous social media platforms. This paper proposes an offensive text classification algorithm named LSTM-BOOST employing Long Short-Term Memory(LSTM) model with ensemble learning to recognize offensive Bengali texts in various social media platforms. The proposed LSTM-BOOST model uses the modified AdaBoost algorithm employing principal component analysis(PCA) along with LSTM networks. In the LSTM-Boost model, the dataset is divided into three categories, and PCA and LSTM networks are applied to each part of the dataset to obtain the most significant variance and reduce the weighted error of the weak hypothesis of the model. Furthermore, different classifiers are used for baseline experiment and the model is evaluated on various word embedding vector methods. Our investigation found that the LSTM-BOOST algorithms outperform most of the baseline architecture, leading F1-score of 92.61% on the Bengali offensive text from Social Platforms(BHSSP) dataset.
Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering

Recently, graph neural networks (GNN) have been successfully applied to recommender systems as an effective collaborative filtering (CF) approach. However, existing GNN-based CF models suffer from noisy user-item interaction data, which seriously affects the effectiveness and robustness in real-world applications. Although there have been several studies on data denoising in recommender systems, they either neglect direct intervention of noisy interaction in the message-propagation of GNN, or fail to preserve the diversity of recommendation when denoising.
BARS

The past two decades have witnessed the rapid development of personalized recommendation techniques. Despite the significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field. Many of the existing studies perform model evaluations and comparisons in an ad-hoc manner, for example, by employing their own private data splits or using a different experimental setting. However, such conventions not only increase the difficulty in reproducing existing studies, but also lead to inconsistent experimental results among them. This largely limits the credibility and practical value of research results in this field. To tackle these issues, we present an initiative project aimed for open benchmarking for recommender systems. In contrast to some earlier attempts towards this goal, we take one further step by setting up a standardized benchmarking pipeline for reproducible research, which integrates all the details about datasets, source code, hyper-parameter settings, running logs, and evaluation results. The benchmark is designed with comprehensiveness and sustainability in mind. It spans both matching and ranking tasks, and also allows anyone to easily follow and contribute. We believe that our benchmark could not only reduce the redundant efforts of researchers to re-implement or re-run existing baselines, but also drive more solid and reproducible research on recommender systems.
A Triple-Step Asynchronous Federated Learning Mechanism for Client Activation, Interaction Optimization, and Aggregation Enhancement

Federated Learning in asynchronous mode (AFL) is attracting much attention from both industry and academia to build intelligent cores for various Internet of Things (IoT) systems and services by harnessing sensitive data and idle computing resources dispersed at massive IoT devices in a privacy-preserving and interaction-unblocking manner. Since AFL is still in its infancy, it encounters three challenges that need to be resolved jointly, namely: 1) how to rationally utilize AFL clients, whose local data grow gradually, to avoid overlearning issues; 2) how to properly manage the client-server interaction with both communication cost reduced and model performance improved; and finally, 3) how to effectively and efficiently aggregate heterogeneous parameters received at the server to build a global model. To fill the gap, this article proposes a triple-step asynchronous federated learning mechanism (TrisaFed), which can: 1) activate clients with rich information according to an informative client activating strategy (ICA); 2) optimize the client–server interaction by a multiphase layer updating strategy (MLU); and 3) enhance the model aggregation function by a temporal weight fading strategy (TWF), and an informative weight enhancing strategy (IWE). Moreover, based on four standard data sets, TrisaFed is evaluated. As shown by the result, compared with four state-of-the-art baselines, TrisaFed can not only dramatically reduce the communication cost by over 80% but also can significantly improve the learning performance in terms of model accuracy and training speed by over 8% and 70%, respectively.
A Stochastic Simulation Optimization-Based Range Gate Pull-Off Jamming Method

Range gate pull-off (RGPO) jamming is an electronic countermeasure widely used to fool radar tracking systems. Nevertheless, the research on its strategy optimization has only been tepid. The optimization model, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. The algebraic description for the objective function of the optimization of the RGPO jamming strategy is difficult to obtain, and the jamming results are not deterministic under the influence of the input noise. To address these issues, this article models the generation of the RGPO jamming strategy as a stochastic simulation optimization (SSO) problem, and proposes an optimization algorithm of the RGPO jamming strategy with the help of SSO technologies. We propose the committee-based active learning (CAL) assisted optimal computing budget allocation-based particle swarm optimization (CALPSO-OCBA) to alleviate the competition between solution space search and candidate solution performance evaluation by embedding CAL into PSO-OCBA. To improve the feasibility of the proposed algorithm, a scoring scheme that does not rely on the internal knowledge of the radar tracking system (i.e., the tracking model, tracking method, and tracking parameters) is designed. In addition, we use four most widely used tracking problems as the benchmarks for testing the proposed optimization algorithm of the RGPO jamming strategy. Experimental results demonstrate that the proposed algorithm is highly competitive in solving the optimization problem of the RGPO jamming strategy.
Hybrid Feature Aligned Network for Salient Object Detection in Optical Remote Sensing Imagery

Recently, salient object detection in optical remote sensing images (RSI-SOD) has attracted great attention. Benefiting from the success of deep learning and the inspiration of natural SOD task, RSI-SOD has achieved fast progress over the past two years. However, existing methods usually suffer from the intrinsic problems of optical RSIs, 1) cluttered background; 2) scale variation of salient objects; 3) complicated edges and irregular topology. To remedy these problems, we propose a hybrid feature aligned network (HFANet) jointly modeling boundary learning to detect salient objects effectively. Specifically, we design a hybrid encoder by unifying two components to capture global context for mitigating the disturbance of complex background. Then, to detect multiscale salient objects effectively, we propose a Gated Fold-ASPP (GF-ASPP) to extract abundant context in the deep semantic features. Furthermore, an adjacent feature aligned module (AFAM) is presented for integrating adjacent features with unparameterized alignment strategy. Finally, we propose a novel interactive guidance loss (IGLoss) to combine saliency and edge detection, which can adaptively perform mutual supervision of the two sub-tasks to facilitate detection of salient objects with blurred edges and irregular topology. Adequate experimental results on three optical RSI-SOD datasets reveal that the presented approach exceeds 18 state-of-the-art ones. All codes and detection results are available at <i>https://github.com/lyf0801/HFANet</i>.
Cycle-SNSPGAN: Towards Real-World Image Dehazing via Cycle Spectral Normalized Soft Likelihood Estimation Patch GAN

Image dehazing is a common operation in autonomous driving, traffic monitoring and surveillance. Learning-based image dehazing has achieved excellent performance recently. However, it is nearly impossible to capture pairs of hazy/clean images from the real world to train an image dehazing network. Most of existing dehazing models that are learnt from synthetically generated hazy images generalize poorly on real-world hazy scenarios due to the obvious domain shift. To deal with this unpaired problem arisen by real-world hazy images, we present Cycle Spectral Normalized Soft likelihood estimation Patch Generative Adversarial Network (Cycle-SNSPGAN) for image dehazing. Cycle-SNSPGAN is an unsupervised dehazing framework to boost the generalization ability on real-world hazy images. To leverage unpaired samples of real-world hazy images without relying on their clean counterparts, we design an SN-Soft-Patch GAN and exploit a new cyclic self-perceptual loss which avoids using the ground-truth image to compute the perceptual similarity. Moreover, a significant color loss is adopted to brighten the dehazed images as human expects. Both visual and numerical results show clear improvements of the proposed Cycle-SNSPGAN over state-of-the-arts in terms of hazy-robustness and image detail recovery, with even only a small dataset training our Cycle-SNSPGAN. Code has been available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yz-wang/Cycle-SNSPGAN</uri> .
An Explainable Deep Learning Framework for Resilient Intrusion Detection in IoT-Enabled Transportation Networks

The security of safety-critical IoT systems, such as the Internet of Vehicles (IoV), has a great interest, focusing on using Intrusion Detection Systems (IDS) to recognise cyber-attacks in IoT networks. Deep learning methods are commonly used for the anomaly detection engines of many IDSs because of their ability to learn from heterogeneous data. However, while this type of machine learning model produces high false-positive rates and the reasons behind its predictions are not easily understood, even by experts. The ability to understand or comprehend the reasoning behind the decision of an IDS to block a particular packet helps cybersecurity experts validate the system’s effectiveness and develop more cyber-resilient systems. This paper proposes an explainable deep learning-based intrusion detection framework that helps improve the transparency and resiliency of DL-based IDS in IoT networks. The framework employs a SHapley Additive exPlanations (SHAP) mechanism to interpret decisions made by deep learning-based IDS to experts who rely on the decisions to ensure IoT networks’ security and design more cyber-resilient systems. The proposed framework was validated using the ToN_IoT dataset and compared with other compelling techniques. The experimental results have revealed the high performance of the proposed framework with a 99.15% accuracy and a 98.83% F1 score, illustrating its capability to protect IoV networks against sophisticated cyber-attacks.
Recommendation Systems: Content-Based Filtering vs Collaborative Filtering

A job seeker will spend hours searching for the most useful information while dealing with the vast volume of recruiting information available on the Internet. In today's technologically evolved world, the majority of internet users are continuously searching for various goods on the internet, and we normally do it via search engines. When searching, it's important to get the most relevant results possible, which recommender systems can help with. Users can easily find and evaluate items of interest when confronted with a huge number of possibilities. The goal of recommender systems is to establish a connection between products and consumers based on the users' interests. We evaluate and contrast the two basic approaches to recommendation systems in this paper. The first is referred to as Collaborative Filtering, and the second is referred to as Content-based Filtering.