Reviewerly/exHarmony huggingface dataset:
FedUSC: Collaborative Unsupervised Representation Learning From Decentralized Data for Internet of Things

Federated learning (FL) lately has shown much promise in improving the shared model and preserving data privacy. However, these existing methods are only of limited utility in the Internet of Things (IoT) scenarios, as they either heavily depend on high-quality labeled data or only perform well under idealized conditions, which typically cannot be found in practical applications. In this article, we propose a novel federated unsupervised learning method for image classification without the use of any ground truth annotations. In IoT scenarios, a big challenge is that decentralized data among multiple clients is normally nonindependent and identically distributed (non-IID), leading to performance degradation. To address this issue, we further propose a dynamic update mechanism that can decide how to update the local model based on weights divergence. Extensive experiments show that our method outperforms all baseline methods by large margins, including +6.67% on CIFAR-10, +5.15% on STL-10, and +8.44% on SVHN in terms of classification accuracy. In particular, we obtain promising results on Mini-ImageNet and COVID-19 data sets and outperform several federated unsupervised learning methods under non-IID settings.
Anomaly Detection in Industrial Multivariate Time-Series Data With Neutrosophic Theory

The Industrial Internet of Things (IIoT) plays an important role in the digital management process of industrial enterprises. It improves the productivity of enterprises and reduces the management cost of enterprises. Anomaly detection has gradually become an important tool in the field of IIoT security. Due to the complex topology network and randomness of IIoT, the increase in data dimension and information distortion makes it difficult to extract data neighborhood information effectively. This is a challenge for current detection methods. In this article, a new multivariate time-series (MTSs) anomaly detection framework is proposed. The framework involves an intuition-based neutrosophic representation method for MTS and an automatic learning graph structure. First, the data preprocessing and the data representation are performed. Then, the fuzzy features of the MTS data after dimensionality reduction and co-frequency processing are captured by using the intuition-based neutrosophic model. Finally, the graph model is used to identify rare events in the data and interpret the sensor-level anomalies. Experiments on two public and available anomaly detection data sets prove that our method has better robustness and interpretability in the anomaly detection tasks of IIoT.
An Infrared Pedestrian Detection Method Based on Improved YOLOX

In order to solve a series of problems of slow speed and low accuracy encountered by traditional infrared pedestrian detection methods, we improved the YOLOX detection network and proposed a new model—G-YOLOX. In order to extract deeper information from the image, we changed the backbone of YOLOX from CSPDarknet to Ghostnet. The CBAM is added to Ghost Bottleneck which is the base block of Ghostnet, and the original activation function ReLU is replaced by Leaky ReLU to improve the rate of convergence. After the above improvement, the parameters of G-YOLOX model is reduced by 80% and the training speed of the model was significantly improved. The G-YOLOX model realizes lightweight. The study results show that the G-YOLOX network improves the detection speed of the model by 43%, its fps increases from 17 to 30, and its AP (@0.5) is 2.35% higher than that of YOLOX.
Hybrid Low-Rank and Sparsity Constraint With Hankel Structure Preservation for Simultaneous Seismic Reconstruction and Denoising

As acquired seismic data is usually incomplete and noisy, simultaneous reconstruction and denoising is an extremely important step for the accurate interpretation of seismic data and subsequent processing. We propose a hybrid low-rank and sparsity constraint method with Hankel structure preservation to improve the performance of simultaneous reconstruction and denoising. The proposed method combines the advantages of high efficiency pertaining to sparsity-promoting transforms and the strong data adaptability of rank reduction methods. Meanwhile, a structure-preserving matrix is constructed to preserve the predefined Hankel structure of the twofold Hankel matrix to further improve the accuracy and efficiency of simultaneous reconstruction and denoising. Moreover, weighted nuclear norm minimization (WNNM) is introduced to adaptively assign weights to different singular values. Experimental results in both synthetic and field seismic data compared with other state-of-the-art methods demonstrate the superior performance of the proposed method.
Deep Convolutional Neural Network Based Cervical Cancer Exfoliated Cell Detection

Cervical cancer is a type of malignant tumor, seriously and extensively endangering women’s life. Early diagnosis and treatment can effectively reduce its morbidity and mortality. Therefore, regular screening for cervical cancer is a necessary mean for women to maintain their health. Traditional cervical cancer detection uses a combined method of cervical smear and manual reading. However, multiple factors such as different film producing technology, disparate sampling techniques, the knowledge and attitude of a doctor, can easily affect the accuracy of the test results. In this paper, we propose several convolutional neural networks with different architectures to classify cervical cells on the SIPAKMeD dataset into five categories. The result shows that the accuracy of the VGG16 with SENET module obtains a classification accuracy of 96.57% on SIPAKMeD dataset; adopting attention modules is an efficacious way to improve the classification accuracy of a CNN.
Photovoltaic Power Forecast Considering the Effect of Atmospheric Particulate Matter on Irradiance

Solar radiation intensity and photovoltaic power generation power are affected by atmospheric particulate matter concentration. Considering that atmospheric particulate matter concentration is conducive to improving the forecast accuracy of solar amplitude strength and PV power generation power. Using K-means algorithm to cluster historical data such as large particulate matter concentrations. Based on cluster data, an optical thickness prediction model of atmospheric aerosols is established by radial base neural network. The optical thickness of the atmospheric aerosol is used as an importation to the two-band solar radiation atmospheric transport model. Combined with the inclined surface radiation model, estimate the intensity of solar radiation getting by the PV panel surface. Finally, use the photoelectric conversion model to forecast the PV power generation. The results verification shows that the average difference of the established prediction model is 6.87%, and PV power generation power forecast model considering the impact of atmospheric particle concentration on the solar amplitude strength with high accuracy.
Split Dynamic Target Counting Framework Based on Yolov5 +Deepsort Optimization

Objective counting is a widely used application and has achieved excellent results. At present, most of the research on target counting is limited to the improvement of accuracy, but for engineering, the main demand is the improvement of speed performance. In order to speed up the time efficiency of picture processing, we propose a split object counting method, which is divided into three independent modules: target detection module, signal transmission module and target tracking module. In the end, we achieved excellent performance with the same precision, decreasing from 207 ms a frame to 65 ms a frame in the parallel processing of three counting tasks.
Denoising Supervision Based Generative Adversarial Networks for MRI Super-Resolution Reconstruction

High-resolution magnetic resonance images can display richer texture details and facilitate doctors' diagnosis of the disease. However, limited by hardware equipment, the resolution of clinical magnetic resonance images is close to the physical limit. Many studies use super-resolution reconstruction to enhance magnetic resonance images, but they ignore the image noise due to voltage fluctuations and patient jitter. Direct super-resolution reconstruction of the image will amplify these noises, and denoising first will blur the image to a certain extent. To address this issue, we propose a denoising-supervised Generative Adversarial Network for super-resolution reconstruction of magnetic resonance images. The generator of the network uses the residual information distillation module to separate the features so that the discriminator can guide it to restore the image. The network contains two discriminators, a sample discriminator and a noise discriminator. The sample discriminator assists the generator to fit the real sample distribution, and the noise discriminator assists the generator to suppress noise. Qualitative and quantitative experiments show that the noise discriminator can effectively suppress the generation of noise in the super-resolution task. Compared with advanced methods on the IXI dataset, the proposed network achieves the highest reconstruction quality (PSNR and SSIM).
The Disambiguation Strategy of Knowledge Fusion for Domain Knowledge Graph

In the knowledge fusion stage of Knowledge Graph construction, there are problems of entity redundancy and polysemy. The quality of Knowledge Graph construction is improved through disambiguation of knowledge fusion. The thesis uses Shenyang University of Technology graduate admissions related information as a dataset to construct a domain Knowledge Graph and adopts the knowledge extraction method of the BERT word vector pre-training model combined with the BilLSTM-CRF model. In the knowledge fusion stage, a disambiguation strategy is proposed, the related algorithm of the SED strategy is implemented, the merging of synonymous entities and the problem of polysemy are solved, and the Neo4j graph database is used for storage. The effectiveness of entity disambiguation is evaluated by the Purity indicator, and the quality of Knowledge Graph construction is evaluated by the Mean Rank and Hits@10 indicators. The SED strategy solves the problem of ambiguity between synonyms and entities with the same name, shows the association logic inside the knowledge graph, lays the foundation for the question answering system in the intelligent field, and is a necessary prerequisite for the intelligent question answering business process.
Text Classification Model Based on Artificial Intelligence Algorithm

The existing text classification algorithms can not effectively use the instance information in the training set for feature extraction, so a text classification model based on artificial intelligence algorithm is proposed. Firstly, KNN is used to extract K training text instances adjacent to the original text. Then Attention is used to calculate the attention score of the adjacent text, and the weighted labels and instance information of the training text are obtained according to the score value. Secondly, GCNN is used to extract semantic information from the original text to obtain contextual semantic information and local key information. Finally, the output of KNN-Attention is fused with the output of GCNN, so as to realize the purpose of effectively utilizing the instance information of training text in text classification task. Experimental results show that the proposed model performs better than the benchmark model and improves the performance of classification tasks effectively.
A Self-Adaptive Approach to Exploit Topological Properties of Different GAs’ Crossover Operators

Evolutionary algorithms (EAs) are a family of optimization algorithms inspired by the Darwinian theory of evolution, and Genetic Algorithm (GA) is a popular technique among EAs. Similar to other EAs, common limitations of GAs have geometrical origins, like premature convergence, where the final population's convex hull might not include the global optimum. Population diversity maintenance is a central idea to tackle this problem but is often performed through methods that constantly diminish the search space's area. This work presents a self-adaptive approach, where the non-geometric crossover is strategically employed with geometric crossover to maintain diversity from a geometrical/topological perspective. To evaluate the performance of the proposed method, the experimental phase compares it against well-known diversity maintenance methods over well-known benchmarks. Experimental results clearly demonstrate the suitability of the proposed self-adaptive approach and the possibility of applying it to different types of crossover and EAs.
Unsupervised Domain Adaptation via Class Aggregation for Text Recognition

Cross-domain text recognition is a very challenging task due to the domain drift problem. One solution is aligning feature distributions between domains through Unsupervised Domain Adaptation (UDA). All existing methods perform feature alignment based on the whole image or semantic character features. However, visual character features without contextual semantics also contain much valuable information, e.g., stroke features of individual characters, which also benefits domain transfer. To this end, we propose a dual intra-Class Aggregation based unsupervised Domain Adaptation method (CADA) for text recognition, which aligns both visual and semantic character feature distributions. To our knowledge, CADA is the first to consider visual character features without contextual semantics in cross-domain text recognition tasks. Accordingly, a Single-head Self-Attention (SSA) mechanism is introduced for extracting visual character features. Thereafter, a dual intra-class aggregation strategy is designed, which performs class aggregations in both visual and semantic spaces. We test the proposed method on widely-used datasets by combining it with representative text recognition models with various decoding methods. Extensive experimental results demonstrate the superiority and generality. Moreover, there is no additional inference time introduced compared to the baselines.
Convolutional Neural Network Design for Single Image Super-Resolution

Single image super-resolution (SR) is designed to recover high-resolution (HR) images from a single low-resolution (LR) image, which has important applications in surveillance equipment, satellite imagery, mobile phone cameras and medical images. Skip-connection is widely used in SR networks (e.g., ESRGAN and RDN), and have led to further performance improvements. However, SR models with many skip-connections are often accompanied by extremely high computation, which hinders their deployment on mobile devices. To solve this problem, we propose a connection pruning algorithm that automatically prunes redundant connections to make the network more compact. By connection pruning, a lightweight skip-connection structure is generated, called Residual-in-Residual Sparse Block (RRSB), which improves the performance of the network while reducing the computation. Our proposed lightweight network is evaluated on Set14, BSD100 and Urban100 datasets. The experimental results show that our network performance does not degrade in the case of a 50% reduction in the amount of parameters. The textures generated by our network are close to baseline and are sharper and more natural than other methods.
A Multi-blockchain Based Reliable Noise Adding Method for Privacy Preservation in Cyber-Physical Systems

Privacy preservation is an enabling technology for applications in cyber-physical systems. Noise adding is one of the popular methods to efficiently achieve effective privacy preservation. However, the strength of privacy preservation may be degraded for the noise-adding methods since the historical information of the malicious nodes cannot be tracked during the noise-adding process. To address this issue, in this paper, we apply a multi-blockchain to design a reliable noise-adding (RNA) method to strengthen the privacy preservation level. First, a multi-blockchain architecture is developed to ensure the traceability of the noised information, and avoid the information being impersonated, tampered with, and denied. The nodes are clustered into multiple groups so as to integrate the multi-blockchain architecture with zero-sum noise adding mechanism. Then, the proposed RNA method is analyzed in terms of correctness, privacy, and efficiency. The performance evaluations show that the proposed RNA method has advantages in protecting privacy, ensuring correctness, and reducing computational and communication costs.
LMSA: Low-Relation Multi-head Self-attention Mechanism in Visual Transformer

The Transformer backbone network with self-attention mechanism as the core has achieved great success in the field of natural language processing and computer vision. However, compared with classical visual feature extraction methods, the self-attention mechanism needs more training data to capture the relationship between tokens, which makes it challenging to train the transformer effectively on small datasets. We design a novel lightweight self-attention mechanism: Low-relation Multi-head Self-Attention (LMSA), which is superior to the recent self-attention and can fully explore the relationship between rare tokens. Specifically, the proposed self-attention mechanism breaks the barrier of the dimensional consistency of the traditional self-attention mechanism, making the feature relationship focus on a small number of dimensions, thereby reducing computational complexity and occupying less storage space. Experimental results show that the dimensional consistency inside the traditional self-attention mechanism is unnecessary. In particular, using Swin as the backbone model for training, the accuracy of the CIFAR-10 image classification task is improved by 0.43%, in the meanwhile, the consumption of a single self-attention resource is reduced by 64.58%, and the number of model parameters and model size is reduced by more than 15%. By appropriately condensing the self-attention relationship variables, the Transformer network can be more efficient and even perform better.
Axial Transformer U-Net for Cell Nuclei Segmentation in Gastrointestinal Cancer Pathological Image

The nuclear shape and features are crucial in determining the development of pathological alterations. The automatic segmentation of the nucleus from pathological tissue images can provide important reference information for diagnosing diseases. However, the large number, different morphology, and overlapping nuclei make the segmentation task very difficult. We adopted the deep learning model to complete nuclei segmentation to overcome the above problems. First, we collected pathological images of gastrointestinal cancer from clinical hospitals and constructed a disease-specific database. Subsequently, the images were color normalized using the spectral matching method to alleviate the problem of color inconsistencies. Then, the axial transformer U-net model was constructed and trained by the training dataset so that it could automatically recognize nuclear features. Finally, the trained model is used to segment the test dataset. The results reveal that the model described in this research outperforms the traditional image segmentation approach.
Automatic Liver Segmentation and 3D Imaging for CT Images Based on FANU-Net

The liver is an important organ in the human body. As the information provided by 2D images is limited, the 3D liver model is needed to allow doctors to observe the size and shape of the organ more clearly. Most of the traditional 3D modeling methods are based on the difference in CT values of organs. But organs with small differences in CT values cannot be constructed in high quality. To solve this problem, we proposed a 3D modeling method for the liver based on image segmentation. The liver is segmented and then the segmented images are used for 3D modeling, which can effectively reduce the influence of other organs. However, the existing segmentation methods cannot well adapt to the changes in liver shape, size, and position, and the segmentation effect for livers of different shapes is very unsatisfactory. To improve the liver segmentation effect, we added a fully attentional network (FAN) block to the classical U-Net network to enhance the extraction of liver features. Our model achieves 83.56% and 92.53% on MIoU and MPA metrics, respectively, which are 5.51% and 0.69% higher than the classical U-Net.
AI-to-Human Actuation

Imagine a near-future smart home. Home-embedded visual AI sensors continuously monitor the resident, inferring her activities and internal states that enable higher-level services. Here, as home-embedded sensors passively monitor a free person, good inferences happen randomly. The inferences' confidence highly depends on how congruent her momentary conditions are to the conditions favored by the AI models, e.g., front-facing or unobstructed. We envision new strategies of AI-to-Human Actuation (AHA) that empower the sensory AIs with proactive actuation so that they induce the person's conditions to be more favorable to the AIs. In this light, we explore the initial feasibility and efficacy of AHA in the context of home-embedded visual AIs. We build a taxonomy of actuations that could be issued to home residents to benefit visual AIs. We deploy AHA in an actual home rich in sensors and interactive devices. With 20 participants, we comprehensively study their experiences with proactive actuation blended with their usual home routines. We also demonstrate the substantially improved inferences of the actuation-empowered AIs over the passive sensing baseline. This paper sets forth an initial step towards interweaving human-targeted AIs and proactive actuation to yield more chances for high-confidence inferences without sophisticating the model, in order to improve robustness against unfavorable conditions.
The Emergence and Evolution of Parallel Coordinates Plots

Using parallel coordinates plots to visualize and analyze high-dimensional data is a very useful and efficient geometry-based visualization approach, which aims at intuitively understanding and analyzing high-dimensional data which is frequently used in two-dimensional space. In this paper, we discuss the emergence and evolution of parallel coordinates plots and outline the importance of the emergence of parallel coordinates plots visualization methods in facilitating our analysis of high-dimensional data. Parallel coordinates are inherently limited in two points, with cross-overlapping lines and weak inter-dimensional relationships. The growth of data dimension and data volume made the research in parallel coordinates plots very crucial. Many researchers have carried out influentially technological innovation methods, while focusing on four influential directions: combining data mining, combining with dimensionality reduction, combining with human-computer interaction and combining with other visualization methods, making parallel coordinates plots a representative and significant tool of geometric visualization of high-dimensional data. Realizing the deep integration with data mining, human-computer interaction, computer graphics, cognitive science, psychology and many other disciplines is the biggest challenge faced by parallel coordinates plots in the future. More artificial intelligence factors will be introduced, and a truly efficient and intuitive data visualization interfaces will be developed to intelligently visualize and analyze high-dimensional data.