Reviewerly/exHarmony huggingface dataset:
Leveraging Large-scale Multimedia Datasets to Refine Content Moderation Models

The sheer volume of online user-generated content has rendered content moderation technologies essential in order to protect digital platform audiences from content that may cause anxiety, worry, or concern. Despite the efforts towards developing automated solutions to tackle this problem, creating accurate models remains challenging due to the lack of adequate task-specific training data. The fact that manually annotating such data is a highly demanding procedure that could severely affect the annotators' emotional well-being is directly related to the latter limitation. In this paper, we propose the CM-Refinery framework that leverages large-scale multimedia datasets to automatically extend initial training datasets with hard examples that can refine content moderation models, while significantly reducing the involvement of human annotators. We apply our method on two model adaptation strategies designed with respect to the different challenges observed while collecting data, i.e. lack of (i) task-specific negative data or (ii) both positive and negative data. Additionally, we introduce a diversity criterion applied to the data collection process that further enhances the generalization performance of the refined models. The proposed method is evaluated on the Not Safe for Work (NSFW) and disturbing content detection tasks on benchmark datasets achieving 1.32 % and 1.94 % accuracy improvements compared to the state of the art, respectively. Finally, it significantly reduces human involvement, as 92.54 % of data are automatically annotated in case of disturbing content while no human intervention is required for the NSFW task.
The UAV Swarm Mission Planning Based on Adaptive Genetic Algorithm

With the rapid development of Unmanned Aerial Vehicle(UAV) technology, the application fields of the UAV are becoming more and more wide. Especially in the field of military operations, as a new type of weapon with high cost performance, the application prospect of the UAV in modern war will be very broad. However, due to the complexity of the battlefield environment and the diversity of mission forms, the mission capability that a single UAV can perform is limited and its survivability is also challenged. The mission mode of the UAV has gradually developed from single platform mission execution to swarm collaborative mission execution. As one of the key technologies for multiple UAVs cooperative operations, mission planning technology has received extensive attention. This paper conducts research on the field of UAV swarm mission planning. Firstly, the multi-objective optimization model is established with the goal of the shortest path and the least airborne time for the UAV swarm to complete the mission. Secondly, the above model is optimized with constructing an adaptive genetic algorithm by improving the genetic operator and the crossover operator. Finally, the corresponding algorithm is simulated and analyzed through a specific example.
Improved Shallow-UWnet for Underwater Image Enhancement

Underwater image enhancement technology is significant to the development of underwater monitoring and autonomous underwater robots, and has always been one of the hotspots of underwater image enhancement research. At present, deep learning methods have been utilized to process image, in which heavy models such as GANs and deep CNNs will consume a lot of memory and numerical cost in the calculation task due to the large volume of them, thus lowering the efficiency in underwater tasks. In this paper, on the basis of Shallow-UWnet, an improved model is proposed which contains a series of convolutional blocks, batch normalization layers and LeakyReLU activation function. Further, a combination of mean squared error loss, perceptual loss and structural similarity loss is used. Based on the trained model, we compare the performance with three other advanced methods on three evaluation metrices and demonstrate our model's superior performance and generalization ability. Moreover, we test the proposed model's effect on two engineering cases and highlight its practical meaning.
Combining Regularization and Dropout Techniques for Deep Convolutional Neural Network

Deep learning techniques face the problem of overfitting due to their complex layer structure. Regularization methods are used to overcome this problem and improve the designed models. In this article, we use the combination of L1 regularization, L2 regularization, Elastic Net-regularization, and Dropout methods. The designed deep model using combination of these methods is considered with different rates. The deep network model using a combination of these methods is designed with different rates. Finally, the performance of all combination methods is compared with the Convolutional Neural Network model which does not use regularization methods. Experiments are performed using the Gold price per ounce data set and linear simulation model. The obtained results show that the performance of the combination model of Dropout and Elastic Net regularization is better than the other models.
Detecting Malicious URLs using Lexical Analysis and Network Activities

The majority of unlawful actions have utilised web URLs. This behaviour has a significant financial impact on businesses, as well as a loss of intellectual or copyright property and a negative reputation. This effect is a major loss for any organization, which may be small or large depending on the loss. This research work has introduced a lightweight approach for the detection and categorization of malicious URLs according to their type, and finally it has been shown that the lexical analysis is effective and efficient for the proactive detection of these URLs. The existing research works have used three multi-class classification algorithms: K-nearest neighbours (KNN), Random Forest, and C4.5 to classify the network intrusion into four categories like benign, defacement, malware, and phishing; 78 different lexical features are used and one more obfuscating technique across 70,000 URLs. The proposed research work has used four different algorithms: Random Forest, K-Nearest Neighbour (KNN), Decision Tree, and Extra Tree Classifier, and utilized the top 15 lexical features, which are sufficient and necessary to classify all the four categories over 700,000 URLs.
Combined Local Pattern (CLP): A Novel Descriptor for Face Recognition

LBP is renowned as most powerful texture descriptor. But major issue which LBP possesses is the noisy thresholding function. This sacrifices the discriminativity of the descriptor. To complement that three LBP variants are introduced and these are Mean LBP (MLBP), Median LBP (MnLBP) and Combined Local Pattern (CLP). In MLBP, the mean of the whole patch (3×3) is used for neighborhoods comparison. In MnLBP, the median of whole patch (3×3) is used for neighborhood comparison. The Mean and Median value proves out very effective in contrast to LBP, in which center pixel is used for the neighborhoods comparison. Both MLBP and MnLBP comprehensively conquer the performance of the LBP. To build more productive descriptor features of LBP, MLBP and MnLBP are integrated into one framework. This joined descriptor is called as CLP. The CLP conquer the results of all alone descriptors. Additionally it outrun the performance of the various literature methods. The compression in feature size is accomplished by PCA and classification is assisted from SVMs. Experiments are conducted on ORL and GT face datasets.
Segmentation of White Blood Cells Based on CBAM-DC-UNet

Monitoring the morphology of blood leukocytes, plays an important role in medical research, especially in the treatment of diseases such as immunodeficiency. Traditional manual detection methods are susceptible to numerous interference factors. Therefore, blood cells are often segmented using deep-learning algorithms. This study proposes a U-Net model based on a combination of an attention mechanism and dilated convolutions. First, the traditional convolution in a double convolutional module in a network is replaced by dilated convolution, and multi-scale features are obtained by expanding the receptive field. Second, after each convolution layer in the upsampling layer, an attention mechanism module is combined to refine the adaptive features and improve the segmentation performance of the model. Finally, the RAdam optimizer was used to enhance the robustness of the learning rate variations. Through the ablation experiment of the three improvement directions, it was concluded that all three improvement directions had a positive effect on the segmentation result, and the improvement was the most effective when the three improvements were combined. The experimental results show that compared with the original U-Net model, the segmentation indicators of blood leukocytes, intersection over union (IOU), recall and accuracy were increased by 5.1%, 5.7% and 1.2%, respectively, which more accurately segmented blood leukocytes, which may be used for a greater degree of auxiliary leukocyte detection in the application of immunodeficiency and other diseases.
Deep Constraints Space via Channel Alignment for Visible-Infrared Person Re-identification

Reducing the inter-modality gap has been the core of visible-infrared person re-identification (VI-ReID). Most of the existing methods directly constrain the features to reduce the gap between the different modalities. However, the consistency of channel semantic information across different modalities is ignored. In this letter, channel alignment along with severe semantic constraints is proposed to alleviate the significant distribution differences between modalities. To obtain the optimum channel matching mode, a novel channel alignment mechanism termed Channel Instance Level Alignment (CILA) is proposed at the shallow layer, and matched channel features are constrained by the proposed Channel Instance Alignment (CIA) Loss. In the middle layer, we divide the features into multiple hierarchically-aware channel clusters and align the channel clusters by Channel Cluster Alignment (CCA) Loss. The proposed method is validated on SYSU-MM01 and RegDB, and extensive experiments show that the proposed method achieves competitive performance compared with the state of the arts.
Privacy‐preserving hands‐free voice authentication leveraging edge technology

Abstract Although speech recognition technology has improved significantly over the past few years, the absence of reliable voice authentication methods has negatively affected the Internet of Things (IoT). Voice‐activated devices, by design, rely on personal voice biometrics for access‐level security, and insecure voice authentication techniques limit the development of voice interfaces that can deal with multiple users with different privileges. Furthermore, public concerns surrounding biometric systems have been reported. Outsourcing biometric data are liable to various privacy breaches, increasing vulnerability to cyberattacks from both criminal organizations and government agencies. In this article, we address the security and privacy challenges of voice authentication for IoT by presenting an edge‐based, hands‐free, single‐factor authentication scheme that consists of three security‐based features: random phrase verification, anti‐spoof verification, and text‐independent speaker recognition. The use of edge technology allows the integration of this authentication scheme with resource contained IoT devices such as voice assistants. Additionally, privacy is ensured by a novel security protocol that uses chaffing and encryption techniques to protect the users' biometric data stored locally on the edge device. Finally, the scheme does not compromise the user experience as it requires only two short voice inputs.
ACUTE: Attentional Communication Framework for Multi-Agent Reinforcement Learning in Partially Communicable Scenarios

Multi-agent reinforcement learning (MARL) aims to study the behavior of multiple agents in a shared environment. Existing communication-based MARL methods seldom consider the case of communication interference. However, such situations are not rare in real-world inter-agent communication. The majority of previous MARL methods struggle to design effective communication techniques for better cooperation between agents without considering communication reliability or channel capacity constraints. In addition, these models are typically not ready to be extended to large-scale multi-agent systems. To address these issues, in this paper, we propose a method named the Attentional CommUnicaTion FramEwork (ACUTE), which enables efficient communication between agents in a dynamic environment and improves the effectiveness of decision-making by using the most useful information from other agents. Specifically, we introduce an attention mechanism for the feature extraction of information during communication which determines the importance of messages received by agents. We evaluate the performance of our approach under different channel capacity constraints. Experimental results show that our model can efficiently exploit messages transmitted in unreliable channels for higher returns when compared to existing methods and can be applied to large-scale multi-agent systems.
The Application of modern multimedia Technology in English Teaching

In order to improve students' interest in English teaching, the author puts forward the research hypothesis that the new method of teaching assisted by multimedia technology can stimulate students' interest in reading and improve their reading level, and verified by experimental study. The experiment takes two parallel classes taught by the same teacher in grade two of A middle school in A city as the research object. Before the experiment, a questionnaire was sent to the teachers to understand the effects of multimedia technology and the main reasons affecting their reading level. Through classroom observation, interview, questionnaire and other methods, statistical methods were used to analyze the reading scores and comprehensive language ability test scores of the two classes, the results showed that before the experiment, there was very little difference in English scores between the experimental class and the comparison class, the average score of the experimental class was only 0.3 points higher than that of the comparison class, and the P value was greater than 0.05, indicating that there was no significant difference between the two samples. But after a semester of changes in teaching methods, the results show that, although the English scores of the two classes have improved, the English scores of the experimental class are 1.97 points higher than that of the comparison class, and the P value is less than 0.05, indicating that there is a significant difference between the two samples. It is proved that the application of multimedia technology to the new teaching method can improve students' interest in learning and reading level.
Dynamic Soft Label Assignment for Arbitrary-Oriented Ship Detection

Ship detection with several military and civilian applications has drawn considerable attention in recent years. In remote sensing images, ships have the characteristics of arbitrary orientation. Based on the characteristics, many arbitrary-oriented ship detectors have been proposed. Most of these detectors preset many horizontal or rotated anchors and determine the positive and negative samples based on the intersection over union (IoU) between the anchor and ground-truth bounding box, in what is called the label assignment process. However, IoU performance is limited as it can only reflect the quality of the anchor to a certain extent. In addition, the manually fixed IoU threshold to separate the positive and negative limits the flexibility of the method, as different ships may have different optimal thresholds. Moreover, the equally weighted training samples cause a misalignment between the classification and regression heads. Therefore, we propose a dynamic soft label assignment method for arbitrary-oriented ship detection. First, we design a novel anchor quality score function that takes into account both prior and prediction information of the anchor and enables the model to participate in the label assignment process. Second, we propose a dynamic anchor quality score threshold instead of a fixed IoU threshold for dividing positive and negative samples. Third, in contrast to assigning equal weights, we propose a soft label assignment strategy to weigh the training samples in the loss function. The proposed method offers superior detection performances for arbitrary-oriented ships with only one horizontal preset anchor. Experimental results on HRSC2016, FGSD, and ShipRSImageNet datasets demonstrate the effectiveness of our proposed dynamic soft label assignment for arbitrary-oriented ship detection.
Interaction-Aware Labeled Multi-Bernoulli Filter with Road Constraints

Vehicle tracking is vital in many applications related to vehicle automation and surveillance. However, the realistic information regarding external influences on a vehicle’s motion is often disregarded. Many applications assume the motion of vehicles to be independent of all external factors. However, vehicles often continuously interact with other close vehicles, especially with their front vehicle. The interaction-aware labeled multi-Bernoulli (IA-LMB) filter has been explicitly designed to modify the LMB filter to incorporate distance-based interactions among targets, which is the most commonly occurring form of vehicle interaction. In this paper, we propose a new method to incorporate road-related information within the target state in the IA-LMB filter to depict further the efficacy of including realistic constraints on target motion for multi-object tracking. For a carefully designed synthetic scenario with multiple vehicle interactions and location influence on the road, we have depicted the advantages of the proposed method. Performance comparison has been conducted regarding the optimal sub-pattern assignment (OSPA) metric for the LMB filter, IA-LMB filter and the proposed method. Results show that including road information within the filtering process further enhances tracking accuracy.
An Improved Robust Estimation Method for GNSS/SINS under GNSS-Challenged Environment

The outlier measurement affects the accurate estimation effect of tightly coupled global navigation satellite system (GNSS) and strapdown inertial navigation system (SINS) integrated navigation parameters under GNSS-challenged environment. To improve the convergence speed and robustness of nonlinear filter used in tightly coupled GNSS/INS under GNSS-challenged environment, an iterated cubature Kalman filter (CKF) based on the improved robust estimation method is proposed. Firstly, the mind of nonlinear least squares regression is included into CKF framework, and multiple iterations are used to improve the convergence speed of the filter and the error compensation effect. Then, a simplified iterated update structure is developed to reduce the computational cost for integrated navigation system. Moreover, the Geman McClure (GM) loss function is introduced to reduce the weight of outlier measurement, which improves the robust estimation ability of the filter. The field experiment indicates that the proposed method has better compensation effect than traditional methods on navigation errors in the case of frequent signal outages.
Three-channel Cascade Network for Underwater Image Enhancement

In underwater scenes, the absorption and scattering of underwater light reduces the quality of underwater images, resulting in blurring and color distortion in underwater images, which affect the accuracy of underwater target detection, tracking, and navigation. In order to address these problems, we have built an underwater three-channel cascade convolutional neural network (UTC-Net) for underwater image enhancement in this paper. Specifically, the UTC-Net consists of a three-channel image restoration network (T-Net) and a cascade optimization network (C-Net). The T-Net recovers the gray images under different color channels respectively using an underwater optical physics imaging model and a dense connection structure, according to the attenuation ratio of light with different wavelengths in underwater images, and the C-Net consists of locally residual block and dense connection, which is used to better recover color and texture details. The UTC-Net has been quantitatively and qualitatively tested by Galdran2015 and RUIE datasets, the experimental results showed that the method achieves satisfactory results in enhancing images quality, such as color correcting, dehazing and feature restoring.
Physical Locomotion Detection using pre- classification for Muscles monitoring

Revolutions in physical locomotion detection have aided in living a protected and effortless life. Physical locomotion can be referred to as both indoor and outdoor human motion. However, the applications of physical locomotion are not limited to smart homes or smart societies and expand over the security systems, healthcare gadgets, sensing devices, and motion detection tactics. For this purpose, multiple sensors support attaining comprehensive learning of the physical locomotion in order to avoid confidentiality violations. In this paper, a model has been recommended to contribute toward muscles monitoring during exercising for locomotion detection. The objective of this research is to assist in recognizing locomotion based on a pre-classification technique for patterns identification, which includes static and kinematic patterns cognition. The proposed system consists of different phases including data acquisition, filtering, windowing, pre- classification, cues extraction, cues optimization, and locomotion detection. Experimental outcomes of the research performed indicated that the proposed locomotion detection model has performed reliably for both simple and complex physical locomotion patterns by achieving 92.33% accuracy.
Secure Smart Grids: Based on Post-Quantum Blockchain

Smart Grids are an emerging technology and are significant for widespread industries and domains. In smart-grid-based applications, data is collected by various sensors that assist in effective decision-making. Currently, such systems are centralised and inherit multiple users’ privacy and security concerns. To overcome these security concerns, blockchain-based security schemes are in place. However, the rapid emergence of quantum computers is threatening the security of existing designs. This paper proposes a blockchain architecture for smart grids based on ring signatures that will be secure in the post-quantum era. Furthermore, comprehensive security and performance analysis make it evident that the proposed scheme is well suited for smart grids.