Reviewerly/exHarmony huggingface dataset:
Real-Time Performance Comparison of Vision-Based Autonomous Landing of Quadcopter on a Ground Moving Target

The tremendous applications of unmanned aerial vehicles (UAVs), such as inspection in complex environments, search, and rescue missions, have established this area of research. The domain of UAVs autonomous navigation and landing has received considerable amount of interest of robotics researchers in the past decades. Nevertheless, the limited capability of UAVs for autonomous landing severely hampers the use of aerial vehicles specially in GPS-denied areas. Fortunately, with the rapid development of computer vision, vision-based techniques have become an important and cheap tool to be used in UAVs for moving target detection and landing. In this paper, four major vision-based techniques namely PID controller, fuzzy logic, sliding mode control and model predictive control have been investigated and their landing performance is compared to each other. These landing techniques are implemented on a quadcopter for the purpose of ground moving target detection and then landing on it. The Raspberry Pi board with two Pi cameras for 3D scene construction and depth estimation along with ultrasonic sensor have been used in the quadcopter. A wheeled mobile robot with landing platform is taken as the target. The landing performance of different techniques has been observed and compared in terms of landing displacement error, time taken and the distance travelled to finish the operation in an open and obstacle-free environment. The simulation results are further verified by the hardware experiments.
Self-adjusting offspring population sizes outperform fixed parameters on the cliff function

In the discrete domain, self-adjusting parameters of evolutionary algorithms (EAs) has emerged as a fruitful research area with many runtime analyses showing that self-adjusting parameters can out-perform the best fixed parameters. Most existing runtime analyses focus on elitist EAs on simple problems, for which moderate performance gains were shown. Here we consider a much more challenging scenario: the multimodal function Cliff, defined as an example where a (1, λ) EA is effective, and for which the best known upper runtime bound for standard EAs is O(n25).
On the state of V3 onion services

Tor onion services are a challenging research topic because they were designed to reveal as little metadata as possible which makes it difficult to collect information about them. In order to improve and extend privacy protecting technologies, it is important to understand how they are used in real world scenarios. We discuss the difficulties associated with obtaining statistics about V3 onion services and present a way to monitor V3 onion services in the current Tor network that enables us to derive statistically significant information about them without compromising the privacy of individual Tor users. This allows us to estimate the number of currently deployed V3 onion services along with interesting conclusions on how and why onion services are used.
Fuzzy artificial bee colony‐based <scp>CNN‐LSTM</scp> and semantic feature for fake product review classification

Summary In recent years, online reviews are considered as the most significant resource for consumers to make a decision regarding the purchase of a particular product. The reviews can either encourage or relegate a product; therefore posting fake reviews has turned into a money‐spinning business in the modern period. The detection of fake reviews has become a center of attraction for various business people. This research study aims in detecting fake product reviews using four significant phases namely the data pre‐processing, feature extraction, feature selection, and classification. The features obtained in the pre‐processing phase are extracted and selected using chi‐squared technique to obtain a delegate subset among all data and to reduce the complication issues. Then a CNNLSTM‐FABC approach classifies and detects the review as fake or real. Finally, the performance evaluation and the comparative analysis are carried out to determine the effectiveness of the proposed approach. The results reveal that the proposed approach performs well irrespective of the product type and sentiment polarity.
Synopsis on Arabic speech recognition

With the advancement and increased usage of intelligible smart devices, researchers have an intensified interest in the field of large-vocabulary speaker-independent continuous speech recognition. Although considerable research has been devoted to English speech recognition, less attention has been paid to the Arabic speech recognition. This paper aims to highlight the achievements that have been made during the last several decades of Arabic speech recognition. The paper also discusses speech recognition components such as corpora, phonemes, language models, acoustic models, and performance evaluation. For an empirical evaluation of Arabic speech recognition, the free, off-the-shelf Mac Soundflower tool was employed to evaluate the recognition performance using a continuous speech corpus that contains 2.63 h (by 10 male and 10 female speakers) of modern standard Arabic (MSA) broadcast news. The experimental results indicate recognition accuracy at 54.02%, and the accuracies for the male and female speakers are almost the same. This result promotes the need for further research to expose the practical range of accuracy. The performance's decline might be an indication of the necessity for further research to boost the overall recognition accuracy.
The Structure of Personnel Training within Getting Higher Education Meets the Needs of Economy: Problems and Solutions

This research article studies the problem of inconsistency of higher-educated personnel training structure to the current and future sectoral specialization of the Russian economy. One of the key tasks of the higher education system in any country is to train personnel who would be demanded by economy in the future. The responsibility for balancing the structure of training higher-educated personnel with the needs of organizations and various industries lies both on the government authorities and on the universities themselves. The authors analyze the correspondence of the structure of training personnel with higher education to the current and future needs of the socio-economic development of Russia in general and its regions in particular. In the course of the study, the structure of training personnel with higher education by enlarged groups of specialties and training programs has been compared with the current and prospective structure of the gross regional product, as well as with the distribution of the employed by sectors of the economy of all Russian Federation constituent entities. The results of the analysis by federal districts are based on the data obtained for the constituent entities, and make it possible to assess the imbalance in the structure of training personnel with higher education, to formulate possible reasons for the existing imbalances at the regional, federal, and universities’ levels. The study is based on the documents of the Ministry of Education and Science of Russia concerning the distribution of admission control figures by the Russian Federation entities and enlarged groups of specialties and training programs, aswell as on the information from the Federal State Statistics Service. For most regions of Russia, the results of the analysis revealed the problem of inconsistency between higher-educated personnel training structure and the structure of the need for personnel as determined by the goals and objectives of socio-economic development. A possible way to solve this problem is to revise the universities’ development programs with due consideration of the long-term regional development prospects supported by a system of institutional measures on the part of education authorities, industries, and regions.
Learning Dynamic Interpolation for Extremely Sparse Light Fields with Wide Baselines

In this paper, we tackle the problem of dense light field (LF) reconstruction from sparsely-sampled ones with wide baselines and propose a learnable model, namely dynamic interpolation, to replace the commonly-used geometry warping operation. Specifically, with the estimated geometric relation between input views, we first construct a lightweight neural network to dynamically learn weights for interpolating neighbouring pixels from input views to synthesize each pixel of novel views independently. In contrast to the fixed and content-independent weights employed in the geometry warping operation, the learned interpolation weights implicitly incorporate the correspondences between the source and novel views and adapt to different image content information. Then, we recover the spatial correlation between the independently synthesized pixels of each novel view by referring to that of input views using a geometry-based spatial refinement module. We also constrain the angular correlation between the novel views through a disparity-oriented LF structure loss. Experimental results on LF datasets with wide baselines show that the reconstructed LFs achieve much higher PSNR/SSIM and preserve the LF parallax structure better than state-of-the-art methods. The source code is publicly available at https://github.com/MantangGuo/DI4SLF.
Blockchain-Based Auditable Privacy-Preserving Data Classification for Internet of Things

Internet of Things (IoT) connects massive physical devices to capture and collect useful data, which are used to make accurate decisions by taking advantage of the machine learning techniques. However, the collected data may contain users’ sensitive information. When guaranteeing the utility of data, we need to consider privacy of users’ data. To balance the utility and the privacy of data, the existing approaches usually adopt the privacy-preserving signature technology, where the privacy-preserving data are classified by a designated converter (data processor) interacting with a semihonest verifier (data center). However, for the malicious behavior of the data center and data processor, this kind of approach is insufficient. To prevent the malicious data center/data processor while guaranteeing the utility and privacy of data, we propose blockchain-based auditable privacy-preserving data classification ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PPDC</i> ) scheme for IoT. We put forth a new controllably linkable group signature ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CL-GS</i> ) to balance the utility and privacy of data and take advantage of blockchain to audit the correctness of privacy-preserving data classification against malicious data processor/data center. We formalize the system model of the auditable privacy-preserving data classification in the blockchain setting and its security model. Then, we present a concrete construction and prove its security in the random oracle model. Finally, we deploy a prototype system to evaluate the performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PPDC</i> .
RGBT tracking via cross-modality message passing

Many RGBT trackers utilize adaptive weighting mechanism to treat dual modalities differently and obtain more robust feature representations for tracking. Although these trackers work well under certain conditions, however, they ignore the information interactions in feature learning, which might limit tracking performance. In this paper, we propose a novel cross-modality message passing model to interactively learn robust deep representations of dual modalities for RGBT tracking. Specifically, we extract features of dual modalities by backbone network and take each channel of these features as a node of a graph. Therefore, all channels of dual modalities can explicitly communicate with each other by the graph learning, and the outputted features are thus more diverse and discriminative. Moreover, we introduce the gate mechanism to control the propagation of information flow to achieve more intelligent fusion. The features generated from the interactive cross-modality message passing model will be passed selectively through the gate layer and concatenated with original features as the final representation. We extend the ATOM tracker into its dual-modality version and combine it with our proposed module for final tracking. Extensive experiments on two RGBT benchmark datasets validate the effectiveness and efficiency of our proposed algorithm.
Segmentation of Cervical Cell Images Based on Generative Adversarial Networks

The segmentation of cervical cell in liquid-based smear image plays an important role in cervical cancer detection.Despite of research for many years, it is still a challenge for the complexity of cell images such as poor contrast, cell irregularity, and overlapping.To solve this problem, a novel method is proposed based on Cell-GAN -a generative adversarial network.Firstly, the Cell-GAN is trained to learn a probability distribution of cell morphology by comparing the difference between the generated single-cell images and annotated single-cell images.Thus, the Cell-GAN has the ability to judge the integrity of a cell and treat other cellular information of a cell image, except for overlapping parts, as the background.Then, a complete single-cell image is generated by the trained Cell-GAN for each cell, which is located by a guide factor.The guide factor is constructed by a part of the cell to be segmented, such as the nucleus, to help Cell-GAN locate the cell and avoid generating a multi-cell image in the presence of overlapping, which means the contours of cells still cannot be distinguished.Finally, the segmentation line is defined by the contour of the generated cell, and the input image is cropped using the cell size information.The cropped image is reused for image generation until the area of generated cell varies within a small range.The proposed method is evaluated on the segmentation of single-cell images and overlapping cell images and obtained significant values of 94.3% DC, 7.9% FNR o for single-cell images and 89.9% DC, 6.4% FNR o for overlapping cell images respectively.The experimental results indicate that the proposed method can adaptively approach the boundary lines of cells to handle with different cases of overlapping in cervical cell images through what learned by the Cell-GAN.The proposed method outperforms most current methods in both segmentation accuracy and robustness.
Deep Learning-Based Short Story Generation for an Image Using the Encoder-Decoder Structure

Research that applies artificial intelligence (AI) to generate the captions for an image has been extensively studied in recent years.However, the length of these captions was short, and the number of generated captions was limited.In addition, it is unknown whether a short story can be generated based on the image, because many sentences have to be connected to create a fluent short story.As a result, this study introduces an encoder-decoder framework structure to generate a short story captioning (SSCap) using a common image caption dataset and a manually collected story corpus.This manuscript has three main contributions, which include 1) an unsupervised deep learning-based framework that combines a recurrent neural network (RNN) structure and encoder-decoder model for composing a short story for an image, 2) a huge story corpus, which includes two different genres (horror and romantic), manually collected and validated.Extensive experiments demonstrated that short stories created by the proposed model show creative content compared to existing systems that can only make concise sentences.Therefore, the demonstrated framework has the potential to motivate the development of a more robust AI story writer and motivates the integration of the suggested model into practical applications to help the story writers find a new idea.
pcapStego: A Tool for Generating Traffic Traces for Experimenting with Network Covert Channels

The increasing diffusion of malware endowed with steganographic and cloaking capabilities requires tools and techniques for conducting research activities, testing real deployments and elaborating mitigation mechanisms. To investigate attacks targeting network and appliances, a core requirement concerns the availability of suitable traffic traces, which can be used to derive mathematical models for simulation or to develop machine-learning-based countermeasures. Unfortunately, the young nature of threats injecting secrets or cloaking their presence within network traffic, the high protocol-dependent nature of the various embedding processes, and privacy issues, prevent the vast diffusion of datasets to perform research. Therefore, in this paper we present pcapStego, a tool for creating network covert channels within .pcap files. This approach has two major advantages: it allows to prepare large datasets starting from real network traces, and it generates "replayable" conversations useful for both emulating attacks or conduct pentesting campaigns. To prove the effectiveness of the tool, we showcase the generation of network covert channels targeting IPv6 traffic, which is gaining momentum and it is expected to be a major target for future attacks.
Ontology-based Cyber Risk Monitoring Using Cyber Threat Intelligence

Efficient cyber risk assessment needs to consider all security alerts provided by cybersecurity solutions deployed in a network. To build a reliable overview of cyber risk, there is a need to adopt continuous monitoring of emerged cyber threats related to that risk. Indeed, the integration of Cyber Threat Intelligence (CTI) into cybersecurity solutions provides valuable information about threats, targets, and potential vulnerabilities. Structured Threat Information eXpression (STIX), as a language for expressing information about cyber threats in a structured and unambiguous manner, is becoming a de facto standard for sharing information about cyber threats. In addition, ontology-based semantic knowledge modeling has become a promising solution that provides a machine-readable language for downstream work in cybersecurity problem-solving. In this paper, we propose an ontology using CTI for risk monitoring. This latter improves an existing ontology, originally proposed to be used within a SIEM (Security Information Event Management), by extending it and aligning it with the STIX concepts.
Graph transformer networks based text representation

Graph Neural Networks (GNN) has been used to exploit global features in text representation learning for natural language processing (NLP) tasks, including text classification, sequence tagging, neural machine translation and relational reasoning. However, GNN based models usually build a graph for the entire corpus, they have high memory consumption, ignoring the order of words and containing test documents in the training graph. Thus, these models are inherently transductive and have difficulties in inductive learning. In order to solve the above problems, we propose a Graph Transformer Networks based Text representation (GTNT) model. It first constructs a degree-centric text graph, which generates a text graph for each document in the corpus. Then it adopts a graph transformer network to model the graph to obtain node embeddings. When we apply our proposed GTNT model to citation recommendation and text classification tasks, the experimental results show that our model outperforms other state-of-the-art models.
Constraint-Objective Cooperative Coevolution for Large-scale Constrained Optimization

Large-scale optimization problems and constrained optimization problems have attracted considerable attention in the swarm and evolutionary intelligence communities and exemplify two common features of real problems, i.e., a large scale and constraint limitations. However, only a little work on solving large-scale continuous constrained optimization problems exists. Moreover, the types of benchmarks proposed for large-scale continuous constrained optimization algorithms are not comprehensive at present. In this article, first, a constraint-objective cooperative coevolution (COCC) framework is proposed for large-scale continuous constrained optimization problems, which is based on the dual nature of the objective and constraint functions: modular and imbalanced components. The COCC framework allocates the computing resources to different components according to the impact of objective values and constraint violations. Second, a benchmark for large-scale continuous constrained optimization is presented, which takes into account the modular nature, as well as both imbalanced and overlapping characteristics of components. Finally, three different evolutionary algorithms are embedded into the COCC framework for experiments, and the experimental results show that COCC performs competitively.
Benchmarking machine learning algorithms for adaptive quantum phase estimation with noisy intermediate-scale quantum sensors

Quantum phase estimation is a paradigmatic problem in quantum sensing andmetrology. Here we show that adaptive methods based on classical machinelearning algorithms can be used to enhance the precision of quantum phase estimation when noisy non-entangled qubits are used as sensors. We employ the Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms to this task and we identify the optimal feedback policies which minimize the Holevo variance. We benchmark these schemes with respect to scenarios that include Gaussian and Random Telegraph fluctuations as well as reduced Ramsey-fringe visibility due to decoherence. We discuss their robustness against noise in connection with real experimental setups such as Mach-Zehnder interferometry with optical photons and Ramsey interferometry in trapped ions,superconducting qubits and nitrogen-vacancy (NV) centers in diamond.
Training Neural Networks by Enhance Grasshopper Optimization Algorithm for Spam Detection System

A significant negative impact of spam e-mail is not limited only to the serious waste of resources, time, and efforts, but also increases communications overload and cybercrime.Perhaps the most damaging aspect of spam email is that it has become such a major tool for attacks of cross-site scripting, malware infection, phishing, and cross-site request forgery, etc. Due to the nature of the adaptive unsolicited spam, it has been weakening the effect of the previous discovery techniques.This article proposes a new Spam Detection System (SDS) framework, by using a series of six different variants of enhanced Grasshopper Optimization Algorithm (EGOAs), which are investigated and combined with a Multilayer Perceptron (MLP) for the purpose of advanced spam email detection.In this context, the combination of MLP and EGOAs produces Neural Network (NN) models, referred to (EGOAMLPs).The main idea of this research is to use EGOAs to train the MLP to classify the emails as spam and non-spam.These models are applied to SpamBase, SpamAssassin, and UK-2011 Webspam benchmark datasets.In this way, the effectiveness of our models on detecting diverse forms of spam email is evidenced.The results showed that the proposed MLP model trained by EGOAs achieves a higher performance compared to other optimization methods in terms of accuracy, detection rate, and false alarm rate.