Reviewerly/exHarmony huggingface dataset:
Fast predictive visual servoing: A reference governor-based approach

In the robotic domain, the well-known advantages of using predictive controllers have to be carefully balanced with the fast execution needs of the low-level motion control loops. In this work, we propose a control architecture with a reference governor to enhance visual servoing with a predictive behavior, without giving up on the fast execution of the original reactive controller. The reference governor, implemented as a model predictive control, acts as an outer loop running at a relatively low frequency over the original visual servoing loop. This strategy takes advantage of the model prediction to improve the performance of the reactive controller and makes the whole structure more flexible. The proposed approach has been validated through simulations with a 7-axis robotic manipulator, a humanoid robot, and a hybrid wheeled-legged quadruped robot. Experiments on a real 7-axis manipulator show the validity of the approach in real-world scenarios.
VGAE-AMF: A Novel Topology Reconstruction Algorithm for Invulnerability of Ocean Wireless Sensor Networks Based on Graph Neural Network

Ocean wireless sensor networks (OWSNs) play an important role in marine environment monitoring, underwater target tracking, and marine defense. OWSNs not only monitor the surface information in real time but also act as an important relay layer for underwater sensor networks to establish data communication between underwater sensors and ship-based base stations, land-based base stations, and satellites. The destructive resistance of OWSNs is closely related to the marine environment where they are located. Affected by the dynamics of seawater, the location of nodes is extremely easy to shift, resulting in the deterioration of the connectivity of the OWSNs and the instability of the network topology. In this paper, a novel topology optimization model of OWSNs based on the idea of link prediction by cascading variational graph auto-encoders and adaptive multilayer filter (VGAE-AMF) was proposed, which attenuates the extent of damage after the network is attacked, extracts the global features of OWSNs by graph convolutional network (GCN) to obtain the graph embedding vector of the network so as to decode and generate a new topology, and finally, an adaptive multilayer filter (AMF) is used to achieve topology control at the node level. Simulation experiment results show that the robustness index of the optimized network is improved by 39.65% and has good invulnerability to both random and deliberate attacks.
Efficient False Positive Control Algorithms in Big Data Mining

The typical hypothesis testing issue in statistical analysis is determining whether a pattern is significantly associated with a specific class label. This usually leads to highly challenging multiple-hypothesis testing problems in big data mining scenarios, as millions or billions of hypothesis tests in large-scale exploratory data analysis can result in a large number of false positive results. The permutation testing-based FWER control method (PFWER) is theoretically effective in dealing with multiple hypothesis testing issues. In reality, however, this theoretical approach confronts a serious computational efficiency problem. It takes an extremely long time to compute an appropriate FWER false positive control threshold using PFWER, which is almost impossible to achieve in a reasonable amount of time using human effort on medium- or large-scale data. Although some methods for improving the efficiency of the FWER false positive control threshold calculation have been proposed, most of them are stand-alone, and there is still a lot of space for efficiency improvement. To address this problem, this paper proposes a distributed PFWER false-positive threshold calculation method for large-scale data. The computational effectiveness increases significantly when compared to the current approaches. The FP-growth algorithm is used first for pattern mining, and the mining process reduces the computation of invalid patterns by using pruning operations and index optimization for merging patterns with index transactions. The distributed computing technique is introduced on this basis, and the constructed FP tree is decomposed into a set of subtrees, each corresponding to a subtask. All subtrees (subtasks) are distributed to different computing nodes. Each node independently calculates the local significance threshold according to the designated subtasks. Finally, all local results are aggregated to compute the FWER false positive control threshold, which is completely consistent with the theoretical result. A series of experimental findings on 11 real-world datasets demonstrate that the distributed algorithm proposed in this paper can significantly improve the computation efficiency of PFWER while ensuring its theoretical accuracy.
AN EFFICIENT ENGLISH TEACHING DRIVEN BY ENTERPRISE-SOCIAL MEDIA BIG DATA: A NEURAL NETWORK-BASED SOLUTION

The cultivation of creativity is closely related to language learning. How to design the creativity promotion mechanism of English teaching in the public environment is the challenge faced by English teachers. With the advent of the era of big data, English teachers can apply the latest research results to classroom teaching. For example, the rational use of social media helps students to learn and communicate in the language, cultivate students’ creativity in learning English, and improves the quality of teaching. Corporate social media has become the most important way for corporate employees to record their lives, express opinions, share and communicate, and it is also one of the reliable and real-time sources of big data that reflects the true state of English learners. Real, accurate, and timely enterprise social media big data samples contain an enormous educational value, providing more possibilities for educational research. From the perspective of value, through sentiment analysis, topic mining, social network analysis, etc. on social media big data, learner portraits can be realized, thereby providing decision-making reference and support for stakeholders. This paper first builds a learning interest classification model based on TCNN-GRU deep learning, collects experimental data sets from an online English learner’s social media platform and performs learning interest classification and labeling, and then uses the TCNN-GRU model to determine the user’s learning interest tendency. On this basis, the concept of learning interest index is further proposed, and a neural network-based corporate social platform English learner portrait model is established. The experimental results show that, compared with the traditional machine learning model, convolutional neural network model, and recurrent neural network model, the TCNN-GRU model can obtain better results in learning interest classification.
A Novel AB-CNN Model for Multi-Classification Sentiment Analysis of e-Commerce Comments

Despite the success of dichotomous sentiment analysis, it does not encompass the various emotional colors of users in reality, which can be more plentiful than a mere positive or negative association. Moreover, the complexity and imbalanced nature of Chinese text presents a formidable obstacle to overcome. To address prior inadequacies, the three-classification method is employed and a novel AB-CNN model is proposed, incorporating an attention mechanism, BiLSTM, and a CNN. The proposed model was tested on a public e-commerce dataset and demonstrated a superior performance compared to existing classifiers. It utilizes a word vector model to extract features from sentences and vectorize them. The attention layer is used to calculate the weighted average attention of each text, and the relevant representation is obtained. BiLSTM is then employed to read the text information from both directions, further enhancing the emotional level. Finally, softmax is used to classify the emotional polarity.
Blockchain-Based Security Configuration Management for ICT Systems

The world has become increasingly dependent on large-scale and distributed information and communication technology (ICT) infrastructures and systems in sectors such as energy, transport, banking, healthcare, water supply, and digital services, while their protection is considered of paramount importance and has already drawn remarkable attention from governments and key industry players. Establishing common approaches by leveraging existing frameworks and cyber security practices for improving the security postures of those systems is one of the major objectives for ensuring an adequate level of protection and avoiding the detrimental effects of disruptions on society and citizens. Configuration management (CM) is one of those common practices for establishing and maintaining the integrity and consistency of a system and its elements with regard to the function, performance, and status of technical and physical attributes, and it contributes to a desirable security posture throughout the lifecycle of a system. This study addresses the importance of CM, and while considering the corresponding frameworks, standards, and best practices, it proposes a permissioned blockchain-based approach, that inherits the benefits of the blockchain technology and ensures the integrity of the systems’ configuration across the complete lifecycle management of its products and services as an underlying model for mapping and integrating CM functions. Furthermore, this study briefly presents the benefits and challenges of the application of permissioned blockchain models and proposes a smart-contract-based role-based access control mechanism, in addition to presenting an operating concept based on brief but real-life lifecycle requirements of organizational configuration management.
AIDA: Analytic isolation and distance-based anomaly detection algorithm

Many unsupervised anomaly detection algorithms rely on the concept of nearest neighbours to compute the anomaly scores. Such algorithms are popular because there are no assumptions about the data, making them a robust choice for unstructured datasets. However, the number (k) of nearest neighbours, which critically affects the model performance, cannot be tuned in an unsupervised setting. Hence, we propose the new and parameter-free Analytic Isolation and Distance-based Anomaly (AIDA) detection algorithm, that combines the metrics of distance with isolation. Based on AIDA, we also introduce the Tempered Isolation-based eXplanation (TIX) algorithm, which identifies the most relevant features characterizing an outlier, even in large multi-dimensional datasets, improving the overall explainability of the detection mechanism. Both AIDA and TIX are thoroughly tested and compared with state-of-the-art alternatives, proving to be useful additions to the existing set of tools in anomaly detection.
A Multi-Feature Fusion and Attention Network for Multi-Scale Object Detection in Remote Sensing Images

Accurate multi-scale object detection in remote sensing images poses a challenge due to the complexity of transferring deep features to shallow features among multi-scale objects. Therefore, this study developed a multi-feature fusion and attention network (MFANet) based on YOLOX. By reparameterizing the backbone, fusing multi-branch convolution and attention mechanisms, and optimizing the loss function, the MFANet strengthened the feature extraction of objects at different sizes and increased the detection accuracy. The ablation experiment was carried out on the NWPU VHR-10 dataset. Our results showed that the overall performance of the improved network was around 2.94% higher than the average performance of every single module. Based on the comparison experiments, the improved MFANet demonstrated a high mean average precision of 98.78% for 9 classes of objects in the NWPU VHR-10 10-class detection dataset and 94.91% for 11 classes in the DIOR 20-class detection dataset. Overall, MFANet achieved an mAP of 96.63% and 87.88% acting on the NWPU VHR-10 and DIOR datasets, respectively. This method can promote the development of multi-scale object detection in remote sensing images and has the potential to serve and expand intelligent system research in related fields such as object tracking, semantic segmentation, and scene understanding.
An optimized Belief-Rule-Based (BRB) approach to ensure the trustworthiness of interpreted time-series decisions

The accuracy and reliability of XAI methods are important to establish their credibility and use in complex decision-making tasks. Existing XAI methods provide little information about the correctness and reliability of their outputs. Furthermore, post-hoc explanation approaches explain the outcomes after producing them, not in a step-by-step glass-box manner to explain how an output is reached. Our proposed approach addresses these drawbacks by designing a Belief-Rule-Based (BRB) framework that interprets in a glass-box manner why a particular decision has been reached. It does that by determining the chance of different output classes occurring for a specific time period by considering the different possible permutations of the inputs along with their influence. This also assists the user to determine if the given input dataset is incomplete, vague, imprecise or inconsistent before trusting the analysis emanating from it. We compare the performance of the proposed BRB approach against the different eXplainable artificial intelligence (XAI) methods, such as SHAP, LIME and LINDA-BN to ensure the users of the trustworthiness of its analysis. This also enables users to determine the extent to which each of the XAI techniques meets the requirements of XAI and the gaps that need to be addressed.
Explanations in warning dialogs to help users defend against phishing attacks

Phishing, the deceptive act of stealing personal and sensitive information by sending messages that seem to come from trusted entities, is one of the most widespread and effective cyberattacks. Automated defensive techniques against these attacks have been widely investigated. These solutions often exploit AI-based systems that, when a suspect website is detected, show a dialog that warns users about the potential risk. Despite significant advances in creating warning dialogs for phishing, this type of attack is still very effective. To overcome the limitations of existing warning dialogs and better defend users from phishing attacks, this article presents a novel technique to create warning dialogs that not only warn users about a possible attack, as in traditional solutions, but also explain why a website is suspicious, addressing in the explanation the most malicious feature of the suspect website. An experimental study that consisted of a remote survey and analyzed data from 150 participants is reported. The goal was to evaluate the proposed warning dialogs with explanations and to compare them with the dialogs presented by Chrome, Firefox, and Edge. The study revealed interesting results: most explanations were understandable and familiar to users; they also showed some potential of diverting users from visiting malicious sites. However, more attention should be devoted to aspects such as features to be explained, as well as user interest and trust in warning dialogs. The lessons learned that might drive the design of more powerful warning dialogs are provided.
Event-triggered distributed cooperative extended Kalman filter based on formation estimation

The existing distributed filters focus on the consensus estimation of multi-sensor information, but ignore the cooperative characteristics of formation targets, resulting in information loss and limited estimation accuracy. To address the problem of formation target tracking, a novel event-triggered distributed cooperative extended Kalman filter based on formation estimation is proposed, which improves the estimation performance without additional information by mining formation characteristics. Different from previous works, the formation estimation based on the best-matched model is developed to establish the connection between different targets, by which the unknown formation evolution can be modeled in the sense of minimum innovation. Considering the communication topology and the formation detection, an event-triggered mechanism is constructed to detect formation status reliably and reduce the communication burden. Afterwards, a novel distributed filter is designed by employing the multi-agent consensus theory. The cooperative term can be considered as a pseudo measurement, which forces local estimations to reach a consensus. By virtue of the variance-constrained approach, the filter gain is derived by minimizing the upper bound of the estimation error covariance. The proposed filter can also realize optimal estimation in the absence of cooperation and the multi-sensor fusion function for a single target. Moreover, the exponential stochastic boundness of the proposed filter is analyzed based on the stochastic stability theory. Finally, numerical experiments are carried out in three different simulation scenarios, and simulation results show that estimation errors of the proposed filter are significantly decreased compared to existing filters. The advantages of the proposed filter become more apparent as the number of formation members increases or the information quality of formation members improves.
Digital Technology for Constructing the Acoustic Field of Reactor Plants of the VVER Type

In this paper, the existing results of this study have been carried out at NRU MPEI to create digital technologies for constructing the acoustic field of primary circuit equipment designed to manage the life cycle of nuclear power plants (NPPs) with VVER and develop their digital twins. The purpose of this paper is to study the influence of non-design dynamic loads on equipment and the causes of which are currently unknown and not provided for by regulatory documentation. The research methodology is based on using digital modeling of the acoustic field to predict its dependence on operating conditions, and also on the verification of the forecast at an operating NPP. The obtained results have been tested to the creation of methods and algorithms for calculating the acoustic field of VVER, which have no analogues in nuclear and thermal power engineering. The adequacy of the acoustic field of the VVER primary circuit to a group of simultaneously functioning Helmholtz resonators has been proved.
Wi-Fi Sensing based Real-Time Activity Detection in Smart Home Environment

Wi-Fi sensing technology is being used extensively for different sensing applications, mostly for human activity recognition in the recent past. The Channel State Information (CSI) identifies the frequency shifts in the wireless medium caused by movements and changes in the region of interest, which can be analyzed using the amplitude data of various frequency channels. However, the existing systems are not suitable for real-time implementation due to the three layer cloud based architecture used. With IoT devices, edge computing provides a suitable solution with negligible communication latency and reduced network traffic. The proposed work mainly focused on real-time human activity information extraction in smart home environments using CSI values of low-cost ESP32 WiFi device. The center point of this work is to implement the IoT and edge layers of the three layered architecture to extracts, processes and visualize the sensing data in real-time with low-latency. We use simple statistical features and light weight machine learning algorithms for human activity recognition in real-time instead of complex and computationally heavy algorithms which is not suitable for edge computing.
Automatic SAR Ship Detection Based on Multifeature Fusion Network in Spatial and Frequency Domains

SAR ship detection is sensitive to the interference of inshore background, disturbance of strong wind and waves. The similar textures of the neighbor objects in SAR images affect the detection performance. As a remarkable indicator, textural information in the frequency domain characterizes the subtle textural differences between an object and its surroundings. Inspired by this, a multi-feature fusion network (MFFN) for SAR ship detection is constructed in this paper, which can obtain contour and detail information of a SAR image for detecting ships from their background. Firstly, spatial and frequency information of ship targets, which characterizes the whole and subtle textural information of ship targets, are extracted by a double-backbone network with Haar wavelet transform. Afterward, a binary domain feature pyramid network (BDFPN) with feature fusion block (FFB) is applied to fuse the spatial, frequency textural information of ship targets to obtain the fused feature maps with a top-down structure. Finally, those feature maps are adopted through the region proposal network for detecting ship targets from original images. The experimental results show that the proposed method achieves greater performance and more accurate detection results in unique situations in the state-of-the-art SAR ship detection data set (SSDD).
Adaptive Image Dehazing and Object Tracking in UAV Videos Based on the Template Updating Siamese Network

Visual object tracking for unmanned aerial vehicles (UAVs) is extensively used in civil and military applications, such as intelligent transportation, disaster rescue, and military reconnaissance. However, when a UAV encounters haze weather, its tracking performance deteriorates greatly because of problems, such as blurring and quality degradation of the collected images. Most trackers are designed to operate in good weather conditions, and this greatly limits the robustness of UAV tracking in haze weather. To solve this problem, this article proposes an adaptive dehazing Siamese network, which consists of an adaptive dehazing module, a dynamic template update branch, and feature extraction and bounding box prediction subnetworks. The dehazing module yields haze-free images and preserves the edge information of the object, so that the tracker can extract the object features with better characterization. Subsequently, to mitigate the risk of tracking drift caused by the changing appearance of the object, the dynamic template update branch dynamically selects more reliable template images during tracking. In addition, to solve the problem of insufficient UAV object tracking testing datasets in haze scenes, a haze testing dataset for UAV video object tracking is obtained through artificial synthesis and data collection in real-world haze scenes. Furthermore, experiments are conducted on haze testing datasets, namely, UAV123-AH, UAV20L-AH, UAVDT-AH, DTB70-AH, and UAVhaze; the results verified that the proposed tracker yields more competitive performance and more robustness in haze scenes compared with the existing algorithms.