Reviewerly/exHarmony huggingface dataset:
Consistent sensing algorithm for distributed PV station area line loss anomaly fluctuation based on spectral clustering

The current conventional line loss anomaly sensing algorithm mainly realizes line loss anomaly sensing by calculating the station area line loss rate and comparing it with the set threshold value, which leads to poor sensing effect due to the lack of clustering analysis of line loss characteristics. In this regard, a consistent sensing algorithm based on spectral clustering of line loss abnormal fluctuations in distributed PV station areas is proposed. The line loss data is assimilated with time granularity and data cleaning, and combined with the spectral clustering algorithm to cluster the line loss abnormal data features and set the highest threshold of the Euclidean distance of the line loss rate to realize the line loss abnormal sensing. In the experiments, the proposed method is verified for the sensing effect. The experimental results show that when the proposed method is used to sense line loss data, the algorithm can accurately sense different types of line loss data and has a more desirable sensing performance.
Ontology as an Open World Assumptions and Its Futuristic View

Everyone in the 22nd century moves around the next-generation technologies to extract meaningful information gathered from multiple sources of the informative world of the web to form the knowledge base. The informative world of the web is connected through the open world assumptions since it is necessary to make assumptions about the known information as well as unknown information of entities. The entities are interpreted as objects or concepts which are described using their data and properties. The entities are related to each other because logical relationships exist among them. The logical relation and its meaning are interpreted as semantic meanings. As human beings, it may happen that, interpretation of semantically similar kinds of entities may be interpreted differently which has resulted in the generation of heterogeneous meanings. This leads to duplication of data and it is very difficult to integrate such heterogeneous and duplicated information to form a knowledge base. For this purpose, the heart-touching technology of the semantic web called "Ontology", is attracting researchers to provide solutions to resolve issues of data heterogeneity. Recently, ontology integration is used as a dominant process to handle heterogeneous data properly. Integrating heterogeneous ontologies is a challenging job and implemented using either classical approaches or using modern approaches based on metaheuristic algorithms of nature inspired computing.
Scrambler type recognition based on shallow neural networks

In this paper, we propose a scrambler type recognition method using shallow neural networks to identify the scrambling type under the misleading code condition. Firstly, the inter-correlation properties of scrambled code elements are derived based on the scrambling principle and the characteristics of error-correcting codes, then a biased autocorrelation function is composed, and the superiority of the biased autocorrelation function over the unbiased autocorrelation function is demonstrated, and the two are combined as the real part and the imaginary part respectively to form the set of correlated feature samples. To enhance the extraction capability of correlated features, a correlated feature extraction network is constructed, and the purpose of scrambler type identification is accomplished by learning and training the set of correlated feature samples. Simulations show that the algorithm has a wider range of application and can identify a variety of scrambler types, while the anti-bug performance is stronger, laying the foundation for further scrambler parameter identification.
Comparative Study on Differential Evolution: Effect of the Ratio Between Binomial Crossover and Exponential One on Results

Among directions of intelligent computing, single objective optimization with real parameter is an important research focus. In recent, setting in single objective optimization with real parameter changes dramatically. Among types of population-based metaheuristic, differential evolution performs very outstandingly. So far, among the existing three crossover strategies, the binomial and exponential crossover is rarely used. However, IMODE, a DE algorithm suitable for the latter setting, is with the binomial and exponential crossover. In this paper, we optimize the parameter of the crossover strategy by experiment. According to our results, the original value of the parameter is not the best choice.
Siamese Tracker with Multi-Stage Unified Backbone Network

Backbone network plays an essential role in Siamese-based trackers, which can provide rich representational features for the subsequent target state prediction. Among the current mainstream backbones, convolution-based ones often fail to obtain global long-range dependencies, and Transformer-based ones tend to have high computational complexity. In this paper, a Multi-Stage Unified Backbone is proposed for the feature extraction of Siamese tracker. It organically unifies convolution and Transformer, can extract both local detail information and global semantic information, and run with low memory consumption. Experiments on multiple tracking benchmarks, GOT-10K, LaSOT, OTB-100, demonstrate the advantages of this backbone, which can extract more robust target features and effectively address the challenges of target occlusion, deformation, and fast motion.
Semi-supervised Learning of Graphs with Joint Node Similarity and Topology

In the context of semi-supervised learning, graph convolutional networks have become a hot research topic with their excellent ability to handle graph data. Existing graph convolutional network models have achieved success in classifying nodes on graphs, but they are also very limited by the preprocessing of the graph structure. Such networks face two problems: (1) the problem of confusing feature aggregation and node representation distortion due to too many dissimilar nodes around the nodes; (2) the problem of insufficient node representation learning due to the limited amount of information transmitted and received by the nodes with low degree. In this paper, we propose a joint node similarity and topology approach to solve these problems. We design a dual-view network to learn the similarity information of the nodes and the structural similarity information of the graph, and use interpolation perturbation in the Auxiliary View network to enhance the toughness of the model, and both of them perform joint learning by sharing parameters. Experimental results show that this method is more effective than traditional methods which only rely on structural information, and while achieving better classification accuracy on node classification, its learned node representation exhibits better inter-class separation and intra-class aggregation.
Cover selection for image steganography determined by distinct characteristics.

Security in digital communication is becoming necessary to protect secret messages during transmission over insecure channels of the internet. Steganography, an ingenious technique, offers a discrete solution to this challenge. It is used as an art of hiding data within a seemingly safe digital medium such as an image, audio, or video file without arousing suspicion if communication is taking place. One of the primary goals of the key aspect of steganography is selecting a medium to embed or hide secret messages in terms of text or images to obtain the effectiveness of data hiding in sensitive communications, generating a new image called a stego-image, which is concerned with maximizing embedding capacity while minimizing being noticeable in the resulting stego-image against steganalysis. In this paper, we mainly focus on finding the characteristics of an image that make it suitable for steganography. We analyze several image features such as color distribution, brightness, contrast, sharpness, entropy, and resolution. We have used Random Pixel Value Insertion (RPVI) to embed the secret information into an image to analyze the performance parameters like PSNR, MSE, RMSE, and SSIM and evaluated their suitability for designing steganographic systems based on the evaluation results which vary with certain image features.
Multi-Camera Multi-Person Tracking in Surveillance System

Surveillance systems have become an integral part of modern security infrastructure. The ability to track multiple individuals across multiple cameras in real-time is crucial for the effectiveness of such systems. In this paper, we propose a multi-camera multi-person tracking system capable of accurately tracking multiple individuals across a network of cameras. Our proposed system utilizes a combination of computer vision and machine learning techniques to perform robust tracking in complex environments with varying lighting conditions, occlusions, and camera views. The system employs a deep learning-based object detection algorithm to detect individuals in each camera view and a multi-object tracking algorithm to associate and track the individuals across the camera network. To evaluate the performance of our proposed system, we conducted experiments on a publicly available dataset, and the results show the effectiveness of our system in achieving high accuracy and efficiency in multi-camera multi-person tracking. The proposed system is scalable and can be easily integrated with existing surveillance systems to enhance their tracking capabilities. Overall, our proposed multi-camera multi-person tracking system can provide an effective solution for the real-time tracking of individuals across a network of cameras, which can significantly enhance security and safety in various domains, such as transportation, public spaces, and critical infrastructure.
Research on vehicle trajectory anomaly detection algorithm based on GRU and WGAN

The uncertainty of vehicle trajectories and the existence of anomalous data lead to challenges in their application in the field of digital transportation. In this paper, GRU-WGAN deep learning model based on GAN is proposed for vehicle trajectory feature extraction and anomalous trajectory detection. Firstly, VAE utilises the GRU neural network as the Encoder and Decoder part, which can deeply extract features from the original data at the encoding layer and do variational inference. At the same time, learning deep feature extraction helps the VAE model to restore the approximate probability distribution of the initial data at the coding layer to the maximum extent, thus improving the efficiency of anomaly detection. The GRU-WGAN model combining GRU and WGAN is then proposed to learn the output of the feature extraction part and the potential features of the real data to complete the task of anomaly detection of vehicle track data. In addition, comparative experiments were set up to validate the proposed model. The experiments demonstrate that the GRU-WGAN model outperforms the conventional algorithm in terms of accuracy, recall and F1 metrics. Therefore, the proposed model can be effectively applied to feature extraction and vehicle trajectory anomaly detection tasks.
Detection of Insulator Strings in Aerial Photography Based on Lightweight CenterNet

In order to realize the rapid and accurate detection of transmission line insulator strings in aerial images, an improved detection method based on lightweight CenterNet was proposed. First, the lightweight network EfficientNetB0 was used as the backbone network of CenterNet to reduce the number of parameters of the model; Second, the feature enhancement structure was constructed to make full use of the global information; Then, the weight-based attention feature fusion modules was adopted to replace the common feature fusion mode in the upper sampling process; Finally, based on transfer learning and efficient network training methods, the generalization ability and training efficiency of the model were improved. The experimental results showed that compared with the original model, the average accuracy of the model on the public dataset (CPLID) was 93.9%, which was increased by 4.2 percentage points, and the image prediction speed was raised from 53.3 frames / s to 7.7 frames / s.
PCMA signal modulation identification method based on transfer learning

PCMA (Paired carrier multiple access, PCMA) signal is a new type of digital modulation for satellite communication, which has been widely used because it can save up to 50% of the frequency band resources by mixing and superimposing the transmit and receive carriers of duplex link, and has excellent performance in interception resistance. Blind separation of PCMA signals is an important part of radio spectrum monitoring, high-precision modulation parameter information is the necessary basis for separation. In order to process the PCMA signal normally, it is necessary to distinguish the PCMA signal from the common single-channel satellite digital modulation signal, and then identify the modulation mode of the PCMA signal. The modulation identification of the PCMA signal provides the necessary guidance for parameter estimation and blind processing. In this paper, a PCMA signal modulation type recognition algorithm based on a fusion model of time-frequency analysis, residual network and transfer learning is proposed to address the problems of small data volume, difficulty in extracting feature parameters, and complexity of classifier model parameters of the current PCMA signal modulation type recognition algorithm.
Attention Mechanism Based Technical Phrase Extraction for Patent Text

Technological innovation is becoming one of the critical factors in promoting social development all over the world. The vigorous development of patent applications in recent years provides an opportunity to reveal the inherent laws of innovation, but it also puts forward higher requirements for patent mining technology. An essential step in patent text mining is to build a technical portrait for each patent, that is, to identify the technical phrases involved, which can summarize and represent the patent from a technical perspective. Previous technical phrase extraction methods thoroughly used technical phrases' characteristics and the relationship between technical phrases. Regarding our observations, the relationship between patent texts and technical phrases is also essential. Specifically, critical technical phrases are more relevant to the patent text and can be discovered by the attention mechanism. Motivated by this, we propose an unsupervised technical phrase extraction method based on the attention mechanism named UTESC. Self-attention captures the importance of technical phrases in sentences, and cross-attention captures the relevance between technical phrases and patents. Extensive experiments and algorithm comparisons on patent datasets have proven the effectiveness of our algorithm.
Domestic waste image classification algorithm based on improved EfficientNetV2

Aiming at the problem of low efficiency and poor real-time performance of domestic waste classification, this paper proposes a domestic waste classification model based on improved EfficientNetV2. The model was trained by introducing transfer learning, and ECA (Efficient Channel Attention) attention mechanism was introduced to enhance the feature extraction of the model. LeakyReLU activation function and Adam optimization algorithm were selected to improve the generalization ability of the model. Thus, a high-performance and lightweight garbage classification model ECAE-Net (Efficient Channel Attention EfficientNetV2) is obtained. Experimental results show that the accuracy of the proposed model reaches 96.8%, which is 1.3%higher than that of the initial model EfficientNetV2-S. At the same time, the number of parameters and floating point calculations are reduced by 57.3%and 52.3%respectively. The improved model has achieved excellent performance in the domestic waste classification task.
An Efficient Workflow Scheduling Approach for Enhancing the Performance in Cloud Environment

Workflow Scheduling (WS) is a technique to arrange the jobs in an order of workflow, map the tasks on distributed system and manage the execution process of interdependent tasks in a workflow. It presents to allocate the proper resources for particular tasks of workflow, therefore, execution process can be done with the objectives defined by the end users and fulfill the end user’s request. This research targets to organize the given workflow that describes an accurate sequence of implementation of tasks. When the number of incoming tasks is maximized, the response time is also raised and performance becomes low. To handle a large number of consumers and their applications, an efficient workflow scheduling approach is necessary. The existing workflow scheduling algorithms are analyzed, that algorithms do not consider incoming request variations and performance variations. An efficient workflow scheduling procedure is proposed to arrange the incoming consumer’s requests according to the priority of the consumer, assign the suitable virtual machines, virtual machines are mapped to the tasks; it succeeds implementation process, offer resources within the allotted period and enhances system performance.
A Video Captioning Method Based on Visual-Text Semantic Association

At present, video captioning methods based on codec frameworks often rely too much on the information of a single visual modality, which makes it difficult for the model to understand the video content accurately. To address this problem, this paper proposes a video captioning method based on visual-text semantic association (VC-VTSA) from the perspective of multimodal association. In the encoding stage, the method extracts 2D static features, 3D motion features, and object-level regional features of the video and integrates them into global visual features. In the semantic association phase, the generated vocabulary is combined into phrases with contextual semantic dependencies using a self-attentive mechanism, and they are associated with the visual features extracted in the encoding phase to create a bi-modal semantic region with visual content and textual information. By exploiting the potential associative complementary relationships between different modalities in the semantic region, the video content information is better characterized. In addition, a visual noise filtering strategy(VNFS) is designed in this paper to help the lexical phrases in the semantic zone and the corresponding visual content to be accurately associated. Finally, the constructed semantic regions are fed into the LSTM decoder for the next lexical prediction until the complete video captioning is generated.
Deep Fake Face Detection using Convolutional Neural Networks

More fake face image generators have emerged worldwide owing to the growth of Face Image Modification (FIM) tools like Face2Face and Deepfake, which pose a severe threat to public trust. Although there have been significant advancements in the identification of certain FIM, a reliable false face detector is still lacking. Convolutional Neural Network (CNN) tends to learn picture content representations because of the structure's relative stability. A deep fake face detection model is developed by analyzing the visual features in a face. By the use of deep learning techniques, a CNN model is developed to identify deep fakes.
Traffic sign detection based on depth improved YOLOV5

Traffic sign detection is an important part of the intelligent transportation system, which is of great significance to assisted driving, unmanned driving and intelligent driving. The sign detection algorithm needs to be lightweight. In order to solve these problems, reduce the amount of network calculations and improve the detection speed of the network without affecting the accuracy of the algorithm, this paper proposes an algorithm using the lightweight network model Ghost net as the backbone network. By embedding the CBAM attention mechanism in the network, the influence of interference information is reduced, and the feature extraction ability of the network is improved. At the same time, the weight-removed BIFPN module is introduced to strengthen the feature fusion ability of the network and improve the detection effect of small targets. Experiments show that the improved algorithm has achieved an excellent level of 80.2% in the Map_0.5 performance index on the CCTSDB China traffic sign detection dataset, and the FPS index has increased by 37% compared with the classic algorithm.
Copy-Move Forgery Detection Based on Quaternion and LBP

With the development of image processing technology, multimedia images can be easily tampered. Copy-move forgery, abbreviated as CMF, is one of the most common methods of image forgery, among which copy-move forgery detection belongs to passive image forgery detection.In order to detect copy-move forgery and identify forgery areas in color images, this paper proposes a new algorithm for copy-move forgery detection and positioning based on quaternion and LBP. Firstly, represent the color image being tampered with the normalized quaternion and get the modulus of each pixel to form a new matrix. Secondly,apply the LBP transformation to the new matrix. Thirdly,based on the above results, filter out independent blocks by the framework of extended block sorting. Fourthly,apply the SVD transformation to the new matrix. Fifthly, the second filtration is carried out to filter out more blocks using the above results which can detect and locate the image tampered.The experimental results show that the new method can admit the replicated region effectively.
Effective Identification And Diagnosis Of Malaria Parasite In Blood Cell Images Through Deep Learning Approach

Malaria is a disease touching more than million’s of lifes in tropical regions all over the world. While benign in most cases, certain variants of malaria are known to be fatal if not treated at the right time. As such, this is a serious threat and has received attention from researchers far and wide. Due to the long, delicate, and labor-intensive process of pathological diagnosis, malaria is often erroneously diagnosed or not diagnosed early enough to prevent the ill effects. Therefore, this study based on trying to devise a method of computer aided diagnosis of malaria with high accuracy rates, using machine learning and deep learning methods. With the enormous research already dedicated to the subject, we have found several previously created models available for image based diagnosis of the same. Here the objective is to increase the accuracy of the tests while decreasing the time taken for the individual testing. In the process, also seek to verify, through replication of process, the possibility of data leak and overfitting of the models. Further methodology of quantization and sparsity checks on Deep Learning models, and transfer learning will be applied for betterment of accuracy rates to reduce the size of the model and make these models ready to apply on edge devices. Here the used methods provides more than 90 % accuracy for classifying malaria disease properly.