Reviewerly/exHarmony huggingface dataset:
A novel discrete firefly algorithm for Bayesian network structure learning

As an effective tool for the representation and reasoning of uncertain theories, Bayesian networks are widely used in various fields of artificial intelligence. However, learning the optimal Bayesian network structure is an NP-hard problem. The score-and-search approach is a common method for solving the problems associated with Bayesian network structure learning. This paper presents a novel method for learning the structure of a Bayesian network using a discrete firefly optimization algorithm, which has been successfully applied to solve various optimization problems. In the proposed algorithm, each firefly moves in a discrete space based on a redefined movement strategy. Then, the mutation operator is employed to prevent the algorithm from stopping prematurely and falling into a local optimum. Finally, a local optimizer is used to enhance the exploitation ability of the firefly to obtain the best feasible solution. We compared the proposed algorithm with state-of-the-art algorithms on well-known benchmark networks. The experimental results show that the proposed algorithm has better convergence accuracy and higher scores in most cases, compared to other algorithms, indicating that the proposed algorithm can be used as an effective and feasible method for learning Bayesian network structures.
Thorough Review Analysis of Safe Control of Autonomous Vehicles: Path Planning and Navigation Techniques

Mobile robot path planning has passed through multiple phases of development and took up several challenges. Up to now and with the new technology in hands, it becomes less complicated to conduct path planning for mobile robots and avoid both static and dynamic obstacles, so that collision-free navigation is ensured. Thorough state of the art review analysis with critical scrutiny of both safe and optimal paths for autonomous vehicles is addressed in this study. Emphasis is given to several developed techniques based using sampling algorithms, node-based optimal algorithms, mathematic model-based algorithms, bio-inspired algorithms, which includes neural network algorithms, and then multi-fusion-based algorithms, which combine different methods to overcome the drawbacks of each. All of these approaches consider different conditions and they are used for multiple domains.
Predict-then-optimize or predict-and-optimize? An empirical evaluation of cost-sensitive learning strategies

Predictive models are increasingly being used to optimize decision-making and minimize costs. A conventional approach is predict-then-optimize: first, a predictive model is built; then, this model is used to optimize decision-making. A drawback of this approach, however, is that it only incorporates costs in the second stage. Conversely, the predict-and-optimize approach proposes learning a predictive model by directly minimizing the cost of the downstream decision-making task. This is achieved by using a task-specific loss function incorporating the costs of different outcomes in the first stage, with the eventual aim of obtaining more cost-effective decisions in the second stage. This work compares both approaches in the context of cost-sensitive classification. Conceptually, we use the two-stage framework to categorize existing cost-sensitive learning methodologies by differentiating between methodologies for cost-sensitive model training and decision-making. Empirically, we compare and evaluate both approaches using different cost-sensitive training and decision-making methodologies, as well as both class-dependent and instance-dependent cost-sensitive methods. This is achieved using real-world data from a range of application areas and a combination of cost-sensitive and cost-insensitive performance measures. The key finding is that the decision-making strategy is generally found to be more effective than training with a task-specific loss or their combination.
FEATURES OF CREATIVE BURNOUT AMONG EDUCATIONAL WORKERS IN PUBLIC ADMINISTRATION SYSTEM

The purpose of the study is to form and determine the main ways to reduce creative burnout among employees of educational institutions. The object of the research is the employees of the educational service in Ukraine and their creative burnout. through the methods of analysis and synthesis of scientific and practical literature, we came to the conclusion that the following three methods should be used, as they will allow us to see which group of educators is estimated to have creative burnout. Due to the application of the IDEF0 methodology, the main ways to reduce their creative burnout have been identified. The reflected results received their practical application for the group of respondents in which, according to the assessment, a state of creative burnout was found. The value of this research lies in the systematization of existing methods for the formation of a certain methodological approach to assessing creative burnout and practical methods for reducing it. The results obtained showed that in a certain number of respondents, creative burnout decreased. The study has limitations and they relate primarily to the fact that, in addition to the methods we have chosen for assessing creative burnout, there are others that should be used in future research.
K-nearest neighbors rule combining prototype selection and local feature weighting for classification

K-Nearest Neighbors (KNN) rule is a simple yet powerful classification technique in machine learning. Nevertheless, it suffers from some drawbacks such as high memory consumption, low time efficiency, class overlapping and difficulty of setting an appropriate K value. In this study, we propose an Improved K-Nearest Neighbor rule combining Prototype Selection and Local Feature Weighting (IKNN_PSLFW) to address the above issues in one framework. Differing from conventional prototype selection, IKNN_PSLFW not only selects the representative instances as prototypes, but also preserves the information of instances that are not selected. To deal with the class overlapping problem, IKNN_PSLFW explores the feature relevance in local regions by assigning different weights to different features. For an instance with unknown class label, IKNN_PSLFW uses three classification rules corresponding to three scenarios, according to the distance between the instance and each prototype, for classification. To evaluate the performance of IKNN_PSLFW, we conduct experimental study on 20 benchmark datasets. The experimental results show that compared with the conventional KNN rule, some state-of-the-art prototype selection methods and other machine learning algorithms, the proposed IKNN_PSLFW achieves promising classification performance with high time efficiency.
Choosing the Best of Both Worlds

Since the inception of Recommender Systems (RS), the accuracy of the recommendations in terms of relevance has been the golden criterion for evaluating the quality of RS algorithms. However, by focusing on item relevance, one pays a significant price in terms of other important metrics: users get stuck in a "filter bubble" and their array of options is significantly reduced, hence degrading the quality of the user experience and leading to churn. Recommendation, and in particular session-based/sequential recommendation, is a complex task with multiple - and often conflicting objectives - that existing state-of-the-art approaches fail to address. In this work, we take on the aforementioned challenge and introduce Scalarized Multi-Objective Reinforcement Learning (SMORL) for the RS setting, a novel Reinforcement Learning (RL) framework that can effectively address multi-objective recommendation tasks. The proposed SMORL agent augments standard recommendation models with additional RL layers that enforce it to simultaneously satisfy three principal objectives: accuracy, diversity, and novelty of recommendations. We integrate this framework with four state-of-the-art session-based recommendation models and compare it with a single-objective RL agent that only focuses on accuracy. Our experimental results on two real-world datasets reveal a substantial increase in aggregate diversity, a moderate increase in accuracy, reduced repetitiveness of recommendations, and demonstrate the importance of reinforcing diversity and novelty as complementary objectives.
Dynamic Incremental Ensemble Fuzzy Classifier for Data Streams in Green Internet of Things

Due to the fast, dynamic, and continuous arrival of data streams in the green Internet of Things (IoT) environment, the probability distribution of data streams changes over time. In real IoT scenarios such as unmanned aerial vehicle (UAV) detection and smart light switch control, data distribution changes have reduced the trained model’s accuracy for data streams problems classification, making it challenging to detect UAV intruders and predict whether energy-saving lamps in smart buildings are on or off. In this paper, an incremental ensemble classification method is proposed to improve prediction accuracy for green IoT. Specifically, a fuzzy rule-based classifier is combined with a dynamic weighting algorithm for improving classification accuracy. Moreover, the model is updated by incrementally learning the characteristics of data streams, which can effectively handle concept drift caused by data distribution changes in data streams. Experimental evaluations of UAV intrusion detection, smart buildings, and other datasets show that the proposed approach yields 2% higher area under the curve (AUC) and geometric mean (G-mean) than existing methods on UAV Detection and Occupancy datasets and 5% higher AUC and G-mean on five benchmarking datasets. For all datasets, the proposed approach yields 50% faster average training time than other methods.
Few-shot Link Prediction in Dynamic Networks

Dynamic link prediction, which aims at forecasting future edges of a node in a dynamic network, is an important problem in network science and has a wide range of real-world applications. A key property of dynamic networks is that new nodes and links keep coming over time and these new nodes usually have only a few links at their arrivals. However, how to predict future links for these few-shot nodes in a dynamic network has not been well studied. Existing dynamic network representation learning methods were not specialized for few-shot scenarios and thus would lead to suboptimal performances. In this paper, we propose a novel model based on a meta-learning framework, dubbed as MetaDyGNN, for few-shot link prediction in dynamic networks. Specifically, we propose a meta-learner with hierarchical time interval-wise and node-wise adaptions to extract general knowledge behind this problem. We also design a simple and effective dynamic graph neural network (GNN) module to characterize the local structure of each node in meta-learning tasks. As a result, the learned general knowledge serves as model initializations, and can quickly adapt to new nodes with a fine-tuning process on only a few links. Experimental results show that our proposed MetaDyGNN significantly outperforms state-of-the-art methods on three publicly available datasets.
Super-resolution guided knowledge distillation for low-resolution image classification

With the development of deep convolutional neural networks, the high-resolution image classification has achieved excellent classification results. However, in natural scenes, low-resolution images are very common, such as images taken by a webcam or images taken with a lens far away from the target object. Low-resolution image classification is a very difficult problem, because low-resolution images have small size and contain fewer discriminative features, which lead to a sharp decline in classification performance. In order to solve the above problem, this paper proposes a Super-Resolution guided Knowledge Distillation (SRKD) framework, which consists of two sub-networks: one is the super-resolution sub-network used to enhance the features of low-resolution images, and the other is the knowledge distillation sub-network used to minimize the difference between the features of high-resolution images and the features of the images output by the super-resolution sub-network. Extensive experiments on the Pascal VOC 2007 and CUB-200-2011 datasets show that the proposed method has a great improvement compared to the benchmark which is trained on high-resolution images. Especially in the case of very low resolution, the proposed method improves the mAP on Pascal VOC 2007 test set by 30.4% and improves the classification accuracy on CUB-200-2011 test set by 60.37% compared with the benchmark model.
SAR Ship Detection Based on End-to-End Morphological Feature Pyramid Network

Intelligent ship detection based on high-precision synthetic aperture radar (SAR) images plays a vital role in ocean monitoring and maritime management.Denoising is an effective preprocessing step for target detection.Morphological network based denoising can effectively remove speckle noise, while the smoothing effect of which blurs the edges of the image and reduce the detection accuracy.The fusion of edge extraction and morphological network can improve detection accuracy by compensating for the lack of edge information caused by smoothing.This paper proposes an end-to-end lightweight network called morphological feature-pyramid (Mor-FP) Yolo v4-tiny for SAR ship detection.First, a morphological network is introduced to preprocess the SAR images for speckle noise suppression and edge enhancement, providing spatial high-frequency information for target detection.Then, the original and preprocessed images are combined into the multi-channel as an input for the convolution layer of the network.The feature pyramid fusion structure is used to extract the high-level semantic features and shallow detailed features from the image, improving the performance of multi-scale target detection.Experiments on the public SAR ship detection data set (SSDD) and AIR SARShip-1.0show that the proposed method performs better than the other CNN-based methods.
InfographicVQA

Infographics communicate information using a combination of textual, graphical and visual elements. This work explores the automatic understanding of infographic images by using a Visual Question Answering technique. To this end, we present InfographicVQA, a new dataset comprising a diverse collection of infographics and question-answer annotations. The questions require methods that jointly reason over the document layout, textual content, graphical elements, and data visualizations. We curate the dataset with an emphasis on questions that require elementary reasoning and basic arithmetic skills. For VQA on the dataset, we evaluate two Transformer-based strong baselines. Both the baselines yield unsatisfactory results compared to near perfect human performance on the dataset. The results suggest that VQA on infographics—images that are designed to communicate information quickly and clearly to human brain—is ideal for benchmarking machine understanding of complex document images. The dataset is available for download at docvqa.org
DarknetSec: A novel self-attentive deep learning method for darknet traffic classification and application identification

Darknet traffic classification is crucial for identifying anonymous network applications and defensing cyber crimes. Although notable research efforts have been dedicated to classifying darknet traffic by combining machine learning algorithms and elaborately designed features, current methods either heavily depend on hand-crafted features or overlook the global intrinsic relationships among the local features automatically extracted from different data positions, leading to limited classification performance. To tackle this issue, we propose DarknetSec, a novel self-attentive deep learning method for darknet traffic classification and application identification. Concretely, DarknetSec utilizes a cascaded model with a 1-dimensional Convolutional Neural Network (1D CNN) and a bidirectional Long Short-Term Memory (Bi-LSTM) network to capture local spatial-temporal features from the payload content of packets, while the self-attention mechanism is integrated into the abovementioned feature extraction network to mine the intrinsic relationships and hidden connections among the previously extracted content features. In addition, DarknetSec extracts side-channel features from payload statistics to enhance its classification performance. We evaluate DarknetSec on the CICDarknet2020 dataset, which is a representative of darknet traffic covering both Virtual Private Network (VPN) and The Onion Router (Tor) applications. Thorough experiments show that DarknetSec is superior to other state-of-the-art methods, achieving a multiclass accuracy of 92.22% and a macro-F1-score of 92.10%. Additionally, DarknetSec maintains its high accuracy when applied to other encrypted traffic classification tasks.
GaRuDa: A Blockchain-Based Delivery Scheme Using Drones for Healthcare 5.0 Applications

Over the years, the healthcare industry has transformed from being hospital-centric to patient-centric. This shift is mainly due to the convergence of emergent technologies such as Internet of Things (IoT)-based healthcare, fifth generation (5G)-assisted networking, and data-driven analytics through artificial intelligence. The convergence, referred to as Healthcare 5.0, has improved healthcare services to support real-time analytics, user mobility, remote monitoring, and personalized user experience through decentralized applications. However, the medical supply chain systems and delivery operations among healthcare stakeholders suffer from limitations of harsh environmental conditions due to restricted zones, rough terrains, war-prone areas, poor road conditions, congested traffic, and remote locations. Thus, the Internet of Drones (IoD) is deployed in Healthcare 5.0 supply chains to streamline and expedite the medical delivery process through open channels (i.e. the Internet). However, the Internet is an open channel that is prone to malicious activities which can violate the privacy and confidentiality of patient data. Blockchain is a promising technology that can handle the security and reliability of drone delivery among untrusted open channels. Motivated by the aforementioned facts, we propose GaRuDa, a blockchain-based drone delivery scheme for Healthcare 5.0 applications. The proposed scheme integrates IoD and blockchain through 5G-enabled tactile Internet to facilitate low-latency responsive delivery of medical supplies that can be chronologically monitored and tracked among different stakeholders. We compared the proposed scheme with the traditional medical delivery scheme with payment gateways to demonstrate its effectiveness in terms of data storage, computation, and communication costs. The simulation results obtained show that the average transaction cost has been reduced by &#x2248; 44.56 percent, and 5G tactile Internet has a reduced latency of 0.051 ms compared to 18.3 ms in 4G LTE service. The average communication overheads have reduced by &#x2248; 67.34 percent compared to traditional 4G LTE and orthogonal frequency-division multiplexing channels, which indicates the scheme&#x0027;s viability.
Two-Phase Industrial Manufacturing Service Management for Energy Efficiency of Data Centers

Data-driven industrial manufacturing services are proliferating.They use large amounts of data generated from Industrial-Internet-of-Things (IIoT) devices for intelligent services to end-service-users.However, cloud data centers hosting these services consume a huge amount of energy, resulting in a high operational cost.To address this issue, an energy-efficient resource allocation framework is proposed in this paper for cloud services.It operates in two phases.Firstly, a multi-threshold-based host CPU utilization classification scheme is developed to classify hosts into four groups for improved CPU resource allocation.It is designed through analyzing CPU utilization data by using the least median squares regression technique.Thereby, the scheme limits search space, thus reducing time complexity.In the second phase, with a metaheuristic search, an energy-and thermal-aware resource allocation method is developed to find an energy-efficient host for allocating resources to services.From real data center workload traces, extensive experiments show that our framework outperforms existing baseline approaches with 6.9%, 33.75%, and 34.1% on average in terms of temperature, energy consumption, and service-level-agreement violation, respectively.
Federated Learning with Blockchain Assisted Image Classification for Clustered UAV Networks

The evolving “Industry 4.0” domain encompasses a collection of future industrial developments with cyber-physical systems (CPS), Internet of things (IoT), big data, cloud computing, etc. Besides, the industrial Internet of things (IIoT) directs data from systems for monitoring and controlling the physical world to the data processing system. A major novelty of the IIoT is the unmanned aerial vehicles (UAVs), which are treated as an efficient remote sensing technique to gather data from large regions. UAVs are commonly employed in the industrial sector to solve several issues and help decision making. But the strict regulations leading to data privacy possibly hinder data sharing across autonomous UAVs. Federated learning (FL) becomes a recent advancement of machine learning (ML) which aims to protect user data. In this aspect, this study designs federated learning with blockchain assisted image classification model for clustered UAV networks (FLBIC-CUAV) on IIoT environment. The proposed FLBIC-CUAV technique involves three major processes namely clustering, blockchain enabled secure communication and FL based image classification. For UAV cluster construction process, beetle swarm optimization (BSO) algorithm with three input parameters is designed to cluster the UAVs for effective communication. In addition, blockchain enabled secure data transmission process take place to transmit the data from UAVs to cloud servers. Finally, the cloud server uses an FL with Residual Network model to carry out the image classification process. A wide range of simulation analyses takes place for ensuring the betterment of the FLBIC-CUAV approach. The experimental outcomes portrayed the betterment of the FLBIC-CUAV approach over the recent state of art methods.
Graph-Enhanced Spatial-Temporal Network for Next POI Recommendation

The task of next Point-of-Interest (POI) recommendation aims at recommending a list of POIs for a user to visit at the next timestamp based on his/her previous interactions, which is valuable for both location-based service providers and users. Recent state-of-the-art studies mainly employ recurrent neural network (RNN) based methods to model user check-in behaviors according to user’s historical check-in sequences. However, most of the existing RNN-based methods merely capture geographical influences depending on physical distance or successive relation among POIs. They are insufficient to capture the high-order complex geographical influences among POI networks, which are essential for estimating user preferences. To address this limitation, we propose a novel Graph-based Spatial Dependency modeling (GSD) module, which focuses on explicitly modeling complex geographical influences by leveraging graph embedding. GSD captures two types of geographical influences, i.e., distance-based and transition-based influences from designed POI semantic graphs. Additionally, we propose a novel Graph-enhanced Spatial-Temporal network (GSTN), which incorporates user spatial and temporal dependencies for next POI recommendation. Specifically, GSTN consists of a Long Short-Term Memory (LSTM) network for user-specific temporal dependencies modeling and GSD for user spatial dependencies learning. Finally, we evaluate the proposed model using three real-world datasets. Extensive experiments demonstrate the effectiveness of GSD in capturing various geographical influences and the improvement of GSTN over state-of-the-art methods.