Reviewerly/exHarmony huggingface dataset:
Improved Image Synthesis with Attention Mechanism for Virtual Scenes via UAV Imagery

Benefiting from the development of unmanned aerial vehicles (UAVs), the types and number of datasets available for image synthesis have greatly increased. Based on such abundant datasets, many types of virtual scenes can be created and visualized using image synthesis technology before they are implemented in the real world, which can then be used in different applications. To achieve a convenient and fast image synthesis model, there are some common issues such as the blurred semantic information in the normalized layer and the local spatial information of the feature map used only in the generation of images. To solve such problems, an improved image synthesis model, SYGAN, is proposed in this paper, which imports a spatial adaptive normalization module (SPADE) and a sparse attention mechanism YLG on the basis of generative adversarial network (GAN). In the proposed model SYGAN, the utilization of the normalization module SPADE can improve the imaging quality by adjusting the normalization layer with spatially adaptively learned transformations, while the sparsified attention mechanism YLG improves the receptive field of the model and has less computational complexity which saves training time. The experimental results show that the Fréchet Inception Distance (FID) of SYGAN for natural scenes and street scenes are 22.1, 31.2; the Mean Intersection over Union (MIoU) for them are 56.6, 51.4; and the Pixel Accuracy (PA) for them are 86.1, 81.3, respectively. Compared with other models such as CRN, SIMS, pix2pixHD and GauGAN, the proposed image synthesis model SYGAN has better performance and improves computational efficiency.
Vision-Based HAR in UAV Videos Using Histograms and Deep Learning Techniques

Activity recognition in unmanned aerial vehicle (UAV) surveillance is addressed in various computer vision applications such as image retrieval, pose estimation, object detection, object detection in videos, object detection in still images, object detection in video frames, face recognition, and video action recognition. In the UAV-based surveillance technology, video segments captured from aerial vehicles make it challenging to recognize and distinguish human behavior. In this research, to recognize a single and multi-human activity using aerial data, a hybrid model of histogram of oriented gradient (HOG), mask-regional convolutional neural network (Mask-RCNN), and bidirectional long short-term memory (Bi-LSTM) is employed. The HOG algorithm extracts patterns, Mask-RCNN extracts feature maps from the raw aerial image data, and the Bi-LSTM network exploits the temporal relationship between the frames for the underlying action in the scene. This Bi-LSTM network reduces the error rate to the greatest extent due to its bidirectional process. This novel architecture generates enhanced segmentation by utilizing the histogram gradient-based instance segmentation and improves the accuracy of classifying human activities using the Bi-LSTM approach. Experimental outcomes demonstrate that the proposed model outperforms the other state-of-the-art models and has achieved 99.25% accuracy on the YouTube-Aerial dataset.
Chains-BERT: A High-Performance Semi-Supervised and Contrastive Learning-Based Automatic Question-and-Answering Model for Agricultural Scenarios

Achieving automatic question-and-answering for agricultural scenarios based on machine reading comprehension can facilitate production staff to query information and process data efficiently. Nevertheless, when studying agricultural question-and-answer classification, there are barriers, such as small-scale corpus, narrow content range of corpus, or the need for manual annotation. In the context of such production needs, this paper proposed a text classification model based on text-relational chains and applied it to machine reading comprehension and open-ended question-and-answer tasks in agricultural scenarios. This paper modified the BERT network based on semi-supervised and contrastive learning to enhance the model’s performance. By incorporating the text-relational chains with the BERT network, the Chains-BERT model is constructed. Our efficient mode method outperformed other methods on the CAIL2018 dataset. Ultimately, we developed an automatic question-and-answering application to embed the contrastive-learning information aggregation model in this paper. The accuracy of the proposed model exceeded that of several contrasting mainstream models in many open-source datasets. In agricultural scenarios, the model has achieved state-of-the-art levels and is the best in efficiency.
Data Exfiltration through Electromagnetic Covert Channel of Wired Industrial Control Systems

Industrial control systems (ICS) often contain sensitive information related to the corresponding equipment being controlled and their configurations. Protecting such information is important to both the manufacturers and users of such ICSs. This work demonstrates an attack vector on industrial control systems where information can be exfiltrated through a electromagnetic (EM) radiation covert channel from the wired Ethernet connections commonly used by these devices. The attack leverages compromised firmware for the controller—capable of encoding sensitive/critical information into the wired network as packet transmission patterns. The EM radiation from the wired network’s communication is captured without direct physical interaction using a portable software-defined radio, and subsequently demodulated on the attacker’s computer. This covert channel facilitates the exfiltration of data from a distance of up to two metres with a data rate of 10 bps without any significant data loss. The nature of this covert channel demonstrates that having strong firewalls and network security.
Aligned Matching: Improving Small Object Detection in SSD

Although detecting small objects is critical in various applications, neural network models designed and trained for generic object detection struggle to do so with precision. For example, the popular Single Shot MultiBox Detector (SSD) tends to perform poorly for small objects, and balancing the performance of SSD across different sized objects remains challenging. In this study, we argue that the current IoU-based matching strategy used in SSD reduces the training efficiency for small objects due to improper matches between default boxes and ground truth objects. To address this issue and improve the performance of SSD in detecting small objects, we propose a new matching strategy called aligned matching that considers aspect ratios and center-point distance in addition to IoU. The results of experiments on the TT100K and Pascal VOC datasets show that SSD with aligned matching detected small objects significantly better without sacrificing performance on large objects or requiring extra parameters.
Intelligent Adaptive PID Control for the Shaft Speed of a Marine Electric Propulsion System Based on the Evidential Reasoning Rule

To precisely and timely control the shaft speed for a marine electric propulsion system under normal sea conditions, a new shaft speed control technique combining the evidential reasoning rule with the traditional PID controller was proposed in this study. First, an intelligent adaptive PID controller based on the evidential reasoning rule was designed for a marine electric propulsion system to obtain the PID parameters KP, KI, and KD. Then, a local iterative optimization strategy for model parameters was proposed. Furthermore, the parameters of the adaptive PID controller model were optimized in real time by using the sequential linear programming algorithm, which enabled the adaptive adjustment of KP, KI, and KD. Finally, the performance of the adaptive PID controller regarding the shaft speed control was compared with that of other controllers. The results showed that the adaptive PID controller designed in this study had better control performance, and the shaft speed control method based on the adaptive PID controller could better control the shaft speed of the marine electric propulsion system.
Explainable human activity recognition based on probabilistic spatial partitions for symbiotic workplaces

In recent years, smart workplaces that adapt to human activity and motion behavior have been proposed for cognitive production systems. In this respect, methods for identifying the feelings and activities of human workers are being investigated to improve the cognitive capability of smart machines such as robots in shared working spaces. Recognizing human activities and predicting the possible next sequence of operations may simplify robot programming and improve collaboration efficiency. However, human activity recognition still requires explainable models that are versatile, robust, and interpretative. Therefore, recognizing and analyzing human action details using continuous probability density estimates in different workplace layouts is essential. Three scenarios are considered: a standalone, a one-piece flow U-form, and a human-robot hybrid workplace. This work presents a novel approach to human activity recognition based on a probabilistic spatial partition (HAROPP). Its performance is compared to the geometric-bounded activity recognition method. Results show that spatial partitions based on probabilistic density contain 20% fewer data frames and 10% more spatial areas than the geometric bounding box. The approach, on average, detects human activities correctly for 81% of the cases for a pre-known workplace layout. HAROPP has scalability and applicability potential for cognitive workplaces with a digital twin in the loop for pushing the cognitive capabilities of machine systems and realizing human-centered environments.
Fuzzy TOPSIS-based privacy measurement in multiple online social networks

Abstract Information leakage has become an urgent problem in multiple Online Social Networks (OSNs). The interactive communication of users has raised several privacy concerns. However, the current related work on privacy measurement only considers the privacy disclosure of user profile settings, ignoring the importance of profile attributes. To solve the efficient measurement problem, we consider the influence of attribute weight on privacy disclosure scores and propose a privacy measurement method by quantifying users’ privacy disclosure scores in social networks. Through introducing Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), we propose a Privacy Scores calculation model based on Fuzzy TOPSIS decision method (PSFT), that is more accurate calculate users’ privacy disclosure scores and that can improve users’ privacy awareness in multiple OSNs. Users can reasonably set the attribute file configuration based on privacy scores and attribute weight. We conduct extensive experiments on synthetic data set and real data set. The results of the experiments demonstrate the effectiveness of our model.
Bug severity prediction using <scp>LDA</scp> and sentiment scores: A <scp>CNN</scp> approach

Abstract The crucial part of the software development cycle is software maintenance. The demands included in the software management are fault fixes and request to change or bring a new feature. If priority is not given to these demands, then it may lead to customer dissatisfaction, inefficient planning, and software failure as well. Therefore, it is important to study the severity of the bug reports to maintain the efficiency of the software. Various research has been conducted in the past to predict the severity of the paper using text mining focusing only on the content of the bug reports. The sentiment of the user while reporting a bug also plays a vital role. In this study, we will be focusing on two aspects, that is, sentiment and content to improve the prediction. We propose a prediction model based on LDA to study the content aspect and emotion analysis to study the sentiment aspect. The model is validated on the datasets collected from the Eclipse project using Convolutional Neural Network (CNN). The results show that the CNN model effectively utilizes the content and sentiment aspect of the data to handle the severity prediction. CNN has weight sharing feature that decreases the number of parameters used for training. It also improves generalization and overfitting is avoided The Accuracy, Precision, Recall, and F‐measure are improved when both aspects are taken into account rather than considering only content.
A resource efficient pseudo random number generator based on sawtooth maps for <scp>Internet of Things</scp>

Abstract The strength of cryptographic keys rely on the random number generators (RNGs) to produce random seed values. Unfortunately there are not many RNGs options suitable for Internet of Things (IoTs) scenario, due to limited processing resources and bulk quantity of IoT data that needs to be secured. In this article, we studied sawtooth map which is a chaotic map. However, when implemented on a computer, the sawtooth map results on a non‐chaotic orbit due to the finite precision of computation. This can be avoided if we use the sawtooth map as the local map in a coupled map lattice (CML) system. We explore such coupled map systems for randomness through entropy and statistical analysis. Based on the results, we propose a lightweight hybrid pseudo random number generator (PRNG) based on sawtooth based CML system and SPONGENT hashing. The proposed PRNG is thoroughly tested against statistical attacks, entropy analysis, key space analysis and compared with existing state of the art solutions. The results provide evidence that the proposed PRNG produces random numbers that could produce sufficiently strong cryptographic keys for resource constrained IoT devices.
Pet dog facial expression recognition based on convolutional neural network and improved whale optimization algorithm

Pet dogs are our good friends. Realizing the dog's emotions through the dog's facial expressions is beneficial to the harmonious coexistence between human beings and pet dogs. This paper describes a study on dog facial expression recognition using convolutional neural network (CNN), which is a representative algorithm model of deep learning. Parameter settings have a profound impact on the performance of a CNN model, improper parameter setting will make the model exposes several shortcomings, such as slow learning speed, easy to fall into local optimal solution, etc. In response to these shortcomings and improve the accuracy of recognition, a novel CNN model based on the improved whale optimization algorithm (IWOA) called IWOA-CNN is applied to complete this recognition task. Unlike human face recognition, a dedicated face detector in Dlib toolkit is utilized to recognize the facial region, and the captured facial images are augmented to build an expression dataset. The random dropout layer and L2 regularization are introduced into the network to reduce the number of transmission parameters of network and avoid over fitting. The IWOA optimizes the keep probability of the dropout layer, the parameter λ of L2 regularization and the dynamic learning rate of gradient descent optimizer. Carry out a comparative experiment of IWOA-CNN, Support Vector Machine, LeNet-5 and other classifiers for facial expression recognition, its results demonstrate that the IWOA-CNN has better recognition effect in facial expression recognition and also explain the efficiency of the swarm intelligence algorithm in dealing with model parameter optimization.
Dual adaptive learning multi-task multi-view for graph network representation learning

Graph network analysis, which achieves widely application, is to explore and mine the graph structure data. However, existing graph network analysis methods with graph representation learning technique ignore the correlation between multiple graph network analysis tasks, and they need massive repeated calculation to obtain each graph network analysis results. Or they cannot adaptively balance the relative importance of multiple graph network analysis tasks, that lead to weak model fitting. Besides, most of existing methods ignore multiplex views semantic information and global graph information, which fail to learn robust node embeddings resulting in unsatisfied graph analysis results. To solve these issues, we propose a multi-task multi-view adaptive graph network representation learning model, called M2agl. The highlights of M2agl are as follows: (1) Graph convolutional network with the linear combination of the adjacency matrix and PPMI (positive point-wise mutual information) matrix is utilized as encoder to extract the local and global intra-view graph feature information of the multiplex graph network. Each intra-view graph information of the multiplex graph network can adaptively learn the parameters of graph encoder. (2) We use regularization to capture the interaction information among different graph views, and the importance of different graph views are learned by view attention mechanism for further inter-view graph network fusion. (3) The model is trained oriented by multiple graph network analysis tasks. The relative importance of multiple graph network analysis tasks are adjusted adaptively with the homoscedastic uncertainty. The regularization can be considered as an auxiliary task to further boost the performance. Experiments on real-worlds attributed multiplex graph networks demonstrate the effectiveness of M2agl in comparison with other competing approaches.
Meta-path based graph contrastive learning for micro-video recommendation

Nowadays, micro-video sharing platforms have become popular tools for people creating and viewing micro-videos in daily life. The micro-video recommendation task has attracted significant attention from researchers, recently. The key to a high-quality micro-video recommendation system is learning meaningful user and micro-video representations. Recently, many graph neural networks (GNNs) are proposed to learn node representations in graphs, which could be useful for recommendation tasks. However, we argue that directly utilizing existing GNNs in micro-video recommendations is ill-posed. The reasons are: (1) most previous GNNs fail to capture the heterogeneity of heterogeneous graphs since they are designed for homogeneous graphs; (2) they overemphasize node proximity and may hurt the robustness of node embeddings. In contrast to previous work, we propose a meta-path-based graph contrastive learning network (MPGCL) that learns more meaningful user and video embeddings for recommendation. Specifically, we yield homogeneous graphs for user type and video type to better capture heterogeneity based on a well-designed meta-path-based random walk strategy. Furthermore, to learn more robust node embeddings that are less sensitive to noise, we propose a graph contrastive learning network on different types of homogeneous graphs, which maximizes the consistency between graph representations with different views. We do extensive experiments on three real-world datasets and the results show that our model can learn more meaningful embeddings for users and micro-videos and outperforms the strong baselines.
The L2 convergence of stream data mining algorithms based on probabilistic neural networks

This paper concerns a new incremental approach to mining data streams. It is known that patterns in a data stream may evolve over time. In many cases, we need to track and analyze the nature of these changes. In the paper, the probabilistic neural networks are considered as basic models for tracking changes in data streams. We present globally convergent stream data mining algorithms applied to problems of regression, classification, and density estimation in a time-varying (drifting) environment. The algorithms are derived from the Parzen kernel-based probabilistic neural networks working in the online mode. For each problem, a theorem is presented ensuring the L2 convergence of the algorithm designed for tracking drifting regression, density, or discriminant functions. Illustrative examples explain in detail how to choose the bandwidth of the Parzen kernel and the learning rate of the online algorithm. The performance of all algorithms is shown in exemplary simulations. It should be noted that this paper is one of very few, in the existing literature, presenting mathematically justified stream data mining algorithms.
Flexible model weighting for one-dependence estimators based on point-wise independence analysis

Recent studies have shown that Bayesian network classifiers (BNCs) are powerful tools for knowledge representation and classification, and averaged one-dependence estimators (AODE) is one of the most popular and effective BNCs since it can achieve the tradeoff between bias and variance due to its independence assumptions and ensemble learning strategy. However, unverified independence assumptions may result in biased estimates of probability distribution and then degradation in classification performance. In this paper, we prove theoretically the uncertainty of probability-theoretic independence and propose to measure the independence between attribute values implicated in specific instance. The estimates of conditional probability can be finely tuned based on point-wise independence analysis. Point-wise log likelihood function is then applied as weighting metric for committee members of AODE to improve the estimate of joint probability. Extensive experiments on 36 benchmark datasets show that, compared to other state-of-the-art classifiers, weighted one-dependence estimators using point-wise independence analysis can achieve competitive classification performance in terms of zero-one loss, RMSE, bias-variance decomposition and conditional log likelihood.
Secure quantum remote sensing without entanglement

Quantum metrology and quantum communications are typically considered as distinct applications in the broader portfolio of quantum technologies. However, there are cases where we might want to combine the two, and recent proposals have shown how this might be achieved in entanglement-based systems. Here, we present an entanglement-free alternative that has advantages in terms of simplicity and practicality, requiring only individual qubits to be transmitted. We demonstrate the performance of the scheme in both the low and high data limits, showing quantum advantages both in terms of measurement precision and security against a range of possible attacks.
An Efficient Spectral Clustering Algorithm Based on Granular-Ball

In order to solve the problem that the traditional spectral clustering algorithm is time-consuming and resource consuming when applied to large-scale data, resulting in poor clustering effect or even unable to cluster, this paper proposes a spectral clustering algorithm based on granular-ball(GBSC). The algorithm changes the construction method of the similarity matrix. Based on granular-ball, the size of the similarity matrix is greatly reduced, and the construction of the similarity matrix is more reasonable. Experimental results show that the proposed algorithm achieves better speedup ratio, less memory consumption and stronger anti noise performance while achieving similar clustering results to the traditional spectral clustering algorithm. Suppose the number of granular-balls is <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of points in the dataset, and <inline-formula><tex-math notation="LaTeX">$m&lt; &lt; n$</tex-math></inline-formula> , the time complexity of GBSC is <inline-formula><tex-math notation="LaTeX">$O(m^{3})$</tex-math></inline-formula> . It is proved that GBSC has good adaptability to large-scale datasets. All codes have been released at <uri>https://github.com/xjnine/GBSC</uri> .