Reviewerly/exHarmony huggingface dataset:
Graph based encrypted malicious traffic detection with hybrid analysis of multi-view features

At present, the TLS cryptographic protocol is widely deployed. While protecting the security and integrity of transmitted information, it also makes the detection of malicious behavior more difficult. In recent years, researchers have proposed many encrypted malicious traffic detection methods. However, the existing approaches have some shortcomings. Firstly, although researchers have extracted multi-view features from different aspects, which can be divided into vectorized features based on feature engineering and image features based on original data, existing methods cannot fully integrate the features of different forms of expression. Secondly, most of the existing methods do not fully analyze the correlation between different encrypted traffic. Thirdly, the existing methods based on correlation analysis have low processing efficiency and cannot be applied to real networks. In the paper, we present MalDiscovery, a novel technique to discover encrypted malicious traffic to address all the above issues. For encrypted malicious traffic, MalDiscovery constructs an attribute KNN graph, in which encrypted sessions are used as nodes to construct a KNN graph according to the similarity of image features, and vectorized features are used as attributes of corresponding nodes. After that, the GraphSAGE model is used to collect relevant node information through correlation analysis to enrich the embeddings of each node. Finally, we achieve the accurate binary classification of nodes in the graph based on richer embeddings. We use extensive experiments to evaluate the proposed method, and the experiment results show that MalDiscovery can achieve an accuracy of about 99.9%, significantly outperforming all compared methods.
Study on Multi-UAV Cooperative Path Planning for Complex Patrol Tasks in Large Cities

Unmanned Aerial Vehicles (UAVs) are increasingly utilized for urban patrol and defense owing to their low cost, high mobility, and rapid deployment. This paper proposes a multi-UAV mission planning model that takes into account mission execution rates, flight energy consumption costs, and impact costs. A kinematics and dynamics model of a quadcopter UAV is established, and the UAV’s flight state is analyzed. Due to the difficulties in addressing 3D UAV kinematic constraints and poor uniformity using traditional optimization algorithms, a lightning search algorithm (LSA) based on multi-layer nesting and random walk strategies (MNRW-LSA) is proposed. The convergence performance of the MNRW-LSA algorithm is demonstrated by comparing it with several other algorithms, such as the Golden Jackal Optimization (GJO), Hunter–Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA), and the Golden Eagle Optimization (GEO) using optimization test functions, Friedman and Nemenyi tests. Additionally, a greedy strategy is added to the Rapidly-Exploring Random Tree (RRT) algorithm to initialize the trajectories for simulation experiments using a 3D city model. The results indicate that the proposed algorithm can enhance global convergence and robustness, shorten convergence time, improve UAV execution coverage, and reduce energy consumption. Compared with other algorithms, such as Particle Swarm Optimization (PSO), Simulated Annealing (SA), and LSA, the proposed method has greater advantages in addressing multi-UAV trajectory planning problems.
Blur Invariants for Image Recognition

Blur is an image degradation that is difficult to remove. Invariants with respect to blur offer an alternative way of a~description and recognition of blurred images without any deblurring. In this paper, we present an original unified theory of blur invariants. Unlike all previous attempts, the new theory does not require any prior knowledge of the blur type. The invariants are constructed in the Fourier domain by means of orthogonal projection operators and moment expansion is used for efficient and stable computation. It is shown that all blur invariants published earlier are just particular cases of this approach. Experimental comparison to concurrent approaches shows the advantages of the proposed theory.
Deep learning method for super-resolution reconstruction of the spatio-temporal flow field

Abstract The high-resolution (HR) spatio-temporal flow field plays a decisive role in describing the details of the flow field. In the acquisition of the HR flow field, traditional direct numerical simulation (DNS) and other methods face a seriously high computational burden. To address this deficiency, we propose a novel multi-scale temporal path UNet (MST-UNet) model to reconstruct temporal and spatial HR flow fields from low-resolution (LR) flow field data. Different from the previous super-resolution (SR) model, which only takes advantage of LR flow field data at instantaneous (SLR) or in a time-series (MTLR), MST-UNet introduces multi-scale information in both time and space. MST-UNet takes the LR data at the current frame and the predicted HR result at the previous moment as the model input to complete the spatial SR reconstruction. On this basis, a temporal model is introduced as the inbetweening model to obtain HR flow field data in space and time to complete spatio-temporal SR reconstruction. Finally, the proposed model is validated by the spatio-temporal SR task of the flow field around two-dimensional cylinders. Experimental results show that the outcome of the MST-UNet model in spatial SR tasks is much better than those of SLR and MTLR, which can greatly improve prediction accuracy. In addition, for the spatio-temporal SR task, the spatio-temporal HR flow field predicted by the MST-UNet model has higher accuracy either.
A hybridizing-enhanced differential evolution for optimization

Differential evolution (DE) belongs to the most usable optimization algorithms, presented in many improved and modern versions in recent years. Generally, the low convergence rate is the main drawback of the DE algorithm. In this article, the gray wolf optimizer (GWO) is used to accelerate the convergence rate and the final optimal results of the DE algorithm. The new resulting algorithm is called Hunting Differential Evolution (HDE). The proposed HDE algorithm deploys the convergence speed of the GWO algorithm as well as the appropriate searching capability of the DE algorithm. Furthermore, by adjusting the crossover rate and mutation probability parameters, this algorithm can be adjusted to pay closer attention to the strengths of each of these two algorithms. The HDE/current-to-rand/1 performed the best on CEC-2019 functions compared to the other eight variants of HDE. HDE/current-to-best/1 is also chosen as having superior performance to other proposed HDE compared to seven improved algorithms on CEC-2014 functions, outperforming them in 15 test functions. Furthermore, jHDE performs well by improving in 17 functions, compared with jDE on these functions. The simulations indicate that the proposed HDE algorithm can provide reliable outcomes in finding the optimal solutions with a rapid convergence rate and avoiding the local minimum compared to the original DE algorithm.
Deep learning-based image super-resolution restoration for mobile infrared imaging system

Mobile infrared imaging system (MIIS) has important applications in the areas of the military affairs, medical diagnosis, and industrial detection. However, MIIS usually suffers the problems of the low resolution, low contrast, and noise interference. To solve these problems, we propose a deep learning-based image super-resolution restoration method for the MIIS. We design a multiscale feature distillation residual network to retain image features at various stages during the training process. The network uses dilated convolution to expand the receptive field and outputs the super-resolution (SR) infrared image with a sub-pixel method. Many infrared images that are captured by a MIIS are used for experiments. Experimental results indicate that the proposed method can realize the infrared image SR restoration and performs outstandingly against the four recently published SR methods both in visual quality and indicator performance. The proposed method is helpful for the practical applications of the MIIS.
A Secure Big Data Storage Framework Based on Blockchain Consensus Mechanism With Flexible Finality

Data security and integrity are becoming increasingly important as the volume of data being created and stored grows.A controlled third party that provides most of the existing big data security systems makes them susceptible to several security risks.By resolving current technology challenges, including scalability, non-tampering, trustworthiness, data governance, and transparency, blockchain technology plays a vital role and has a significant potential to safeguard personal information.Therefore, this work focuses on addressing real-time big data storage issues based on a transdisciplinary research approach.This study introduces a brand-new approach to big data storage security that leverages blockchain technology and applies highway protocol to generate new blocks.The proposed highway protocol works based on the flexible finality condition to overcome issues of baseline models.The highway allows blocks to run the consensus mechanism to configure security thresholds more freely.The proposed protocol also allows blocks with lower thresholds to reach finality more quickly than blocks requiring greater degrees of confidence.Therefore, the proposed big data framework can dynamically control data manipulation and continuously support individuals to participate in the data-sharing process.A comparison was performed with the number of data requests in terms of hit ratio, and a highway protocol provides better results than baseline models.The proposed model provides a data processing period of 13 to 30 ms and an energy consumption of 32 to 41 mJ.
Switching Text-Based Image Encoders for Captioning Images With Text

Visual understanding, such as image caption generation, has received extensive attention.Describing images with textual information is one way to help people achieve barrier-free visibility.This study focuses on the text-based image captioning (TextCaps) task.The TextCaps task is more complex than the traditional image captioning task because it depends on optical character recognition (OCR) and the textual information that appears in the image.It also requires consideration of the relationship between recognized objects and OCR's linguistic part in the image.In this study, we propose maximizing the use of multiple modalities in an image to improve TextCaps performance.We enrich the image and OCR linguistic features using pre-trained Contrastive Language-Image Pre-training (CLIP) models.We then introduce using two additional attention models in a transformer architecture to strengthen the representation of the image modality.The experimental results demonstrate that our proposed method, which introduces a multimodal transformer with four image-related modalities, outperforms existing methods for the TextCaps dataset.
Accurately Predicting Quality of Services in IoT via Using Self-Attention Representation and Deep Factorization Machines

The past decade has witnessed a widespread adoption of IoT devices and services in various applications such as intelligent transportation systems. It is crucial for IoT applications to select high-quality services to boost their reliability and efficiency. The prediction for Quality of Service (QoS) can be used to address this issue. Although a number of QoS prediction approaches have been proposed, their performance may be limited in the IoT environment where context features can significantly impact QoS predictions. Based on the historical QoS records of services, this paper proposes a collaborative QoS prediction approach using self-attention representation and deep factorization machine. The approach first leverages the global and local contextual information of services and users to learn personalized representations. Then, based on the personalized representations, it utilizes a deep factorization machine to make QoS predictions. Extensive experiments conducted on a real-world dataset show that the proposed QoS prediction approach significantly outperforms the other state-of-the-art approaches in terms of prediction accuracy.
Tensor-Empowered Federated Learning for Cyber-Physical-Social Computing and Communication Systems

The deep fusion of human-centered Cyber-Physical-Social Systems (CPSSs) has attracted widespread attention worldwide and big data as the blood of CPSSs could lay a solid data cornerstone for providing more proactive and accurate wisdom services. However, due to concerns about data privacy and security, traditional data centralized learning paradigm is no longer suitable. Federated Learning (FL) as an emerging distributed privacy-preserving machine learning paradigm would have great research significance and application values. Although few survey papers on FL already exist in the literature, the survey about FL from the perspective of human-centered CPSSs and tensor theory is lacking. Toward this end, we first introduce the CPSSs and deeply analyze the correlations among humans, cyber space, physical space and social space, as well as the opportunities brought by it. Afterwards, we present an overview of FL and then review extensive researches on FL in terms of resources management, communication, security and privacy protection, which provides a shortcut for readers to quickly understand and learn FL. Furthermore, the theory about tensor representation, operation and decomposition for handling massive, multi-source heterogeneous big data and corresponding applications are described. By leveraging the advantages of tensor in unified modeling, dimensionality reduction, and feature extraction, a framework and three tensor-empowered solutions are provided to solve these challenges about heterogeneous resource management, communication overhead together with security and privacy. Finally, some future research directions are listed for looking forward to inspiring more readers to devote themselves to researching tensor-empowered FL for human-centered CPSSs in the future.
Ebola optimization with modified DarkNet‐53 model for scene classification and security on Internet of Things in smart cities

Remote sensing (RS) has developed significantly with the progress of the Internet of Things (IoT) which is allowed the cheap and fast acquisition of data in millions and billions of interrelated devices utilized throughout the whole world. RS scene classifier that purposes for classifying scene types for RS images has wide applications in several domains like urban planning, national defence security, environmental monitoring, and natural hazard detection. State-of-the-art deep learning (DL) successes are performed in a novel wave of RS scene classification applications, but it is the absence of explainability and trustworthiness. An intrusion detection system (IDS) plays a vital role to ensure security in the RS-based IoT environment. In this aspect, this study presents an ebola optimization algorithm with deep learning-based scene classification and intrusion detection (EOADL-SCID) technique on IoT-enabled remote sensing images. The aim of the EOADL-SCID system lies in the effectual scene classification of remote sensing images and intrusion detection. It involves a two-stage procedure. In the initial stage, the EOADL-SCID algorithm involves a modified DarkNet-53 feature extractor, EOA-based hyperparameter tuning, and graph convolution network (GCN) based classification. Next, in the second stage, the intrusion detection process takes place via two subprocesses namely variational autoencoder (VAE) based intrusion detection and skill optimization algorithm (SOA) based parameter tuning. The simulation outcomes of the EOADL-SCID approach are tested utilizing two benchmark databases and the experimental outcomes highlighted the improved performance of the EOADL-SCID algorithm on scene classification and intrusion classification processes.
Adversarial and Random Transformations for Robust Domain Adaptation and Generalization

Data augmentation has been widely used to improve generalization in training deep neural networks. Recent works show that using worst-case transformations or adversarial augmentation strategies can significantly improve accuracy and robustness. However, due to the non-differentiable properties of image transformations, searching algorithms such as reinforcement learning or evolution strategy have to be applied, which are not computationally practical for large-scale problems. In this work, we show that by simply applying consistency training with random data augmentation, state-of-the-art results on domain adaptation (DA) and generalization (DG) can be obtained. To further improve the accuracy and robustness with adversarial examples, we propose a differentiable adversarial data augmentation method based on spatial transformer networks (STNs). The combined adversarial and random-transformation-based method outperforms the state-of-the-art on multiple DA and DG benchmark datasets. Furthermore, the proposed method shows desirable robustness to corruption, which is also validated on commonly used datasets.
Cooperative coevolutionary competition swarm optimizer with perturbation for high-dimensional multi-objective optimization

In the realm of high-dimensional problem spaces, particle swarm optimizers have been found to exhibit unnecessary roaming behavior. In response, this paper proposes a cooperative coevolutionary competition swarm optimizer with perturbation (CPCSO) that reduces computational resource consumption. The CPCSO is both simple and effective. Specifically, this optimizer divides the swarm into two sub-swarms, denoted NP1 and NP2. A modified CSO algorithm is used in NP1 to facilitate search space exploration while ensuring that the swarm is well diversified. In NP2, perturbation is introduced to each loser particle to guide it along a smooth granular trajectory, thereby avoiding unnecessary oscillations and improving its capacity to exploit the search space. The two sub-swarms exchange information to balance convergence and distribution, with excellent particles shared between them. Finally, we demonstrate the efficacy of the proposed CPCSO algorithm and several state-of-the-art high-dimensional multi-objective optimizers on the high-dimensional benchmark set LSMOP. Our experimental results indicate that the proposed CPCSO outperforms other algorithms regarding solution quality, convergence speed, and computational cost. Notably, the proposed optimizer demonstrates robust performance across various landscape problems.
Memento 3.0: An Enhanced Lifelog Search Engine for LSC’23

In this work, we present our system Memento 3.0 for participation in the Lifelog Search Challenge 2023, which is a successor to the previous 2 iterations of our system called Memento 1.0 [1] and Memento 2.0 [2]. Memento 3.0 employs image-text embeddings derived from OpenAI CLIP models as well as larger OpenCLIP models trained on ∼ 5x more data. Our system also significantly reduces the query processing time by almost 75% when compared to its predecessor systems by employing a cluster-based search technique. We additionally make important updates to the system’s user interface to offer more flexibility to the user and at the same time be better suited to efficiently handle new query types introduced in the Lifelog Search Challenge.
LifeLens: Transforming Lifelog Search with Innovative UX/UI Design

One of the important components of the lifelog systems is the user interface which provides the ability to quickly and easily find a specific image or set of images. Although lifelogging is a mature field in the information retrieval domain, the focus on user interfaces is not explored extensively. We start by identifying the common issues with existing lifelog systems from the user interface and user experience perspective. Following the exploration, we present a set of guidelines for designing a user interface for Lifelog systems. We introduce LifeLens- a novel minimalist user interface design specifically designed to improve the usability and ease of use of an interactive lifelog system. The initial version of the LifeLens system provides several improvements over existing lifelog systems addressing the design issues identified during the exploration. The proposed system presents several features that not only enable the users of the system to easily navigate the interface with minimal effort on the user’s part to learn and understand the features offered but also provide a minimal way to gather user feedback.
Smart education in the prospective teachers' training

The article reveals the features of smart education as a leading concept in the development of professional training of future teachers. The main components of smart education, such as a smart student, smart pedagogy and smart environment were characterized. The main principles of smart education and the ideas that formed the basis of this concept of education (mobile access, formation of new knowledge, creation of a smart environment) were defined. The features of smart education were substantiated. The peculiarities of the implementation of smart education in the conditions of the COVID-19 pandemic and military events in Ukraine were revealed. The functions of the smart system (site management system) in the process of studying the disciplines of the pedagogical cycle, its content and technological components, and facilities of the smart complexes for students and teachers in the process of training future teachers were defined. The criteria of smart complexes (automation, sequencing, assessment, data collection in real time, self-organisation) were singled out. The distance learning systems for creating smart complexes in the process of training prospective teachers were considered. The results of students’ survey as for using smart complexes in the educational process were analyzed. Due to the results, the advantages and disadvantages of using smart technologies in educational process were determined. The ways of further research work regarding the introduction of smart education into the educational process were outlined.
Evaluating digital work instructions with augmented reality versus paper-based documents for manual, object-specific repair tasks in a case study with experienced workers

Abstract Manual repair tasks in the industry of maintenance, repair, and overhaul require experience and object-specific information. Today, many of these repair tasks are still performed and documented with inefficient paper documents. Cognitive assistance systems have the potential to reduce costs, errors, and mental workload by providing all required information digitally. In this case study, we present an assistance system for object-specific repair tasks for turbine blades. The assistance system provides digital work instructions and uses augmented reality to display spatial information. In a user study with ten experienced metalworkers performing a familiar repair task, we compare time to task completion, subjective workload, and system usability of the new assistance system to their established paper-based workflow. All participants stated that they preferred the assistance system over the paper documents. The results of the study show that the manual repair task can be completed 21% faster and with a 26% lower perceived workload using the assistance system.