Reviewerly/exHarmony huggingface dataset:
Photovoltaic power station identification using refined encoder–decoder network with channel attention and chained residual dilated convolutions

The quantity and distribution of photovoltaic power stations play an important role in policymaking related to clean energy. High-resolution satellite imagery with a wide field of view enables photovoltaic power station monitoring at low cost. The separability varies greatly due to distinct backgrounds in different regions, which makes photovoltaic power station identification a challenging task. We propose an end-to-end semantic segmentation network to identify photovoltaic power stations under multiple complex backgrounds in Gaofen 1 imageries. Our network adopts the encoder–decoder architecture in which three modules between the encoder and decoder are added, i.e., feature refinement residual module (FRRM), chained dilation attention module (CDAM), and global channel attention module (GCAM). FRRM uses the structure of residual connection to refine the features from each stage of the encoder. CDAM consisting of chained residual dilated convolutions with different dilation rates and channel attention can enlarge the receptive field without reducing image resolution and select useful features. GCAM utilizes high-level features containing rich semantic information as a guide to select more discriminative low-level features. Experiments demonstrate the effectiveness of each module and the ability of the proposed network to improve photovoltaic power station identification under complex backgrounds.
Implementation of Blockchain Based Distributed Web Attack Detection Application

In last decades' web application security has become one of the most important case study of information security studies. Business processes are transferred to web platforms. So web application usage is increased very fast. Web-based attacks have also increased due to the increased use of web applications. In order to ensure the security of web applications, intrusion detection and prevention systems and web application firewalls are used against web based attacks. Blockchain technology, which has become popular in recent years, enables reliable and transparent sharing of data with all stakeholders. In this study, in order to detect web-based attacks, a blockchain based web attack detection model that uses the signature based detection method is proposed. The signature based detection refers to the detection of attacks by looking for specific patterns against known web based attack types, such as Structured Query Language (SQL) Injection, Cross Site Scripting (XSS), Command Injection. Three web servers were used for the experimental study. A blockchain node has been installed with the MultiChain application for each server. Attacks on web applications are detected using the signature list found in the web application as well as detected using the signature list updated on the blockchain. According to the experimental results, the attacks signature detected and defined by a web application are updated in the blockchain lists and used by all web applications.
End‐to‐end learning interpolation for object tracking in low frame‐rate video

In many scenarios, where videos are transmitted through bandwidth-limited channels for subsequent semantic analytics, the choice of frame rates has to balance between bandwidth constraints and analytics performance. Faced with this practical challenge, this study focuses on enhancing object tracking at low frame rates and proposes a learning Interpolation for tracking framework. This framework embeds an implicit video frame interpolation sub-network, which is concatenated and jointly trained with another object tracking sub-network. Once a low frame-rate video is an input, it is first mapped into a high frame-rate latent video, based on which the tracker is learned. Novel strategies and loss functions are derived to ensure the effective end-to-end optimisation of the authors’ network. On several challenging benchmarks and settings, their method achieves a highly competitive tradeoff between frame rate and tracking accuracy. As is known, the implications of interpolation on semantic video analytics and tracking remain unexplored, and the authors expect their method to find many applications in mobile embedded vision, Internet of Things and edge computing.
Research on Government Information Sharing Model Using Blockchain Technology

Research Purpose: The distributed, traceable and security of blockchain technology are applicable to the construction of new government information resource models, which could eliminate the barn effect and trust in government information sharing, as well as promoting the transformation of government affairs from management to service, it is also of great significance to the sharing of government information and construction of service-oriented e-government. Propose Methods: By analyzing the current problems of government information sharing, combined with literature research, this paper proposes the theoretical framework and advantages of blockchain technology applied to government information management and sharing, expounds the blockchain-based solution, it also constructs a government information sharing model based on blockchain, and gives implementation strategies at the technical and management levels. Results and Conclusion: The government information sharing model based on the blockchain solution and the transparency of government information can be used as a research framework for information interaction analysis between the government and users. It can also promote the construction and development of information sharing for Chinese government, as well as providing unified information sharing solution at the departmental and regional levels for e-government.
Digital image watermarking based on ANN and least significant bit

The tremendous AI benefits, machine learning and deep learning, have led to the adoption of advanced technology and modern applications. Especially in an image, Video processing, natural language processing, speech recognition. AI algorithms have recently overcome many drawbacks, thanks to the DNN models, that have contributed to delivering state-of-the-art results in computing and other areas. But safety and security are always challenging tasks.Our proposed approach provides a secure and efficient watermarking method based on a neural network for digital images using the least significant method. First, we used the least significant bit (LSB) to insert a watermark for the image pixel. Because only LSB -based methods are not robust; they are not sufficient in an attack-free environment and lossless compression. We used an Artificial Application Neural Network (ANN) to detect the presence of sensitive information and extract information from the source image. It is inherently unstable when the proper machine learning algorithm is trained, re-trained, and adapted to a few new applications. The standard solution would have a digital signature there as there are very simple ways to change the neural network model so that it still does the same thing as before, but the overall presentation will be different. This paper highlights the essential needs of the ANN model in watermarking.
Chi-Square Feature Selection Effect On Naive Bayes Classifier Algorithm Performance For Sentiment Analysis Document

The main problem in using a sentiment analysis algorithm Naïve Bayes is sensitivity to the selection of features. There exist Chi-Square feature selections to eliminate features that are not very influential. This study aimed to determine the effect of Chi-Square feature selection on the performance Naïve Bayes algorithm in analyzing sentiment documents. Data were taken from Corpus v1.0 Indonesian Movie Review 700 training data and 30 test data. Testing was done by analyzing sentiment documents with and without a Chi-Square feature selection. The evaluated subsequently by the method of accuracy, precision, and recall. The result from the analysis of sentiment without feature selection obtained 73.33% accuracy, precision 100.00%, 65.21% recall. While the Chi-Square feature selection (significance level a 0.1) obtained 93.33% accuracy results, Precision 93.33%, and 93.33% recall. From these results, it can be seen that the selection of Chi-Square features affects performance Naïve Bayes algorithm in analyzing sentiment documents.
Automated Classification for Visual-Only Postmortem Inspection of Porcine Pathology

Several advantages would arise from the automated detection of pathologies of pig carcasses, including avoidance of the inherent risks of subjectivity and variability between human observers. Here, we develop a novel automated classification of two porcine offal pathologies at abattoir: a focal, localized pathology of the liver and a diffuse pathology of the heart, as cases in point. We develop a pattern recognition system based on machine learning to identify those organs that exhibit signs of the pathology of interest. Specifically, deep neural networks are trained to produce probability heat maps, highlighting regions on the surface of an organ potentially affected by a given condition. A final classification stage then decides whether a given organ is affected by the condition in question based on statistics computed from the heat map. We compare outcomes of automated classification with classification by expert pathologists. Results show the classification of liver and heart pathologies in agreement with an expert at levels comparable to, or exceeding, interexpert agreement. A system using methods such as those presented here has potential to overcome the limitations of human-based abattoir inspection, especially if this is based on visual-only inspection, and ultimately to provide a new gold standard for pathology.
An Attention-Enhanced Recurrent Graph Convolutional Network for Skeleton-Based Action Recognition

Dynamic movements of human skeleton have attracted more and more attention as a robust modality for action recognition. As not all temporal stages and skeleton joints are informative for action recognition, and the irrelevant information often brings noise which can degrade the detection performance, extracting discriminative temporal and spatial features becomes an important task. In this paper, we propose a novel end-to-end attention-enhanced recurrent graph convolutional network (AR-GCN) for skeleton-based action recognition. An attention-enhanced mechanism is employed in AR-GCN to pay different levels of attention to different temporal stages and spatial joints. This approach overcomes the information loss caused by only using keyframes and key joints. In particular, AR-GCN combines the graph convolutional network (GCN) with the bidirectional recurrent neural network (BRNN), which retains the irregular joints expressive power of the original GCN, while promoting its sequential modeling ability by introducing a recurrent network. Experimental results demonstrate the effectiveness of our proposed model on the widely used NTU and Kinetics datasets.
A Vision-based Human Action Recognition System for Moving Cameras Through Deep Learning

This study presents a vision-based human action recognition system using a deep learning technique. The system can recognize human actions successfully when the camera of a robot is moving toward the target person from various directions. Therefore, the proposed method is useful for the vision system of indoor mobile robots.
Making Better Risk Decisions in a New Technological Environment

Emerging technologies such as artificial intelligence, the Internet of Things, and distributed ledger continue to transform businesses, enabling new value creation in transformative ways. Their potential social impacts create risks. These risks are not easily identified or evaluated making it difficult to address them. There are upside and downside risks. Business leaders from companies deploying these technologies are initially interviewed. Development and preliminary testing of three different risk optimization models using scenario analysis data are informed by this information. Using this analysis, we introduce the foundations for a risk model and potentially a dashboard that executives could use to consider these technologies.
Sentiment Analysis of School Zoning System On Youtube Social Media Using The K-Nearest Neighbor With Levenshtein Distance Algorithm

The role of the education effect on the progress of each nation and State. The Ministry of education and culture (Kemendikbud) published a regulation of the Minister of education and culture (Permendikbud) No. 14 Year 2018 on the acceptance of New Learners (PPDB). Replace the previous rules, one of which is using a system of zoning for equalization learners. New Learner Acceptance Policy (PPDB) reaps the pros and cons of the zoning system. This research was conducted on the analysis of the sentiments of the public comments against the policy. The data were analyzed taken from youtube as much as 160 comments. This research uses the K-Nearest Neighbor algorithm to the value k = 3, k = 5, k = 7, k = 9 in classifying the test data and the Levenshtein Distance to fix incorrect type. This research used a combination of K-N Nearest Neighbor algorithm and Levenshtein Distance that aims to find out the level of accuracy from a combination of the algorithm. Testing was done using the confusion matrix on test data. Conclusions from testing the combination algorithm for K-Nearest Neighbor and Levenshtein Distance can increase the accuracy of classification. Accuracy results from K-Nearest Neighbor algorithm, namely in the amount of 50% to the value k = 3 and k = 7 whereas the results accuracy the combination of K-Nearest Neighbor and Levenshtein Distance of 65.625% with a value of k = 3.
Classification of Cervical Cancer Using Hybrid Multi-layered Perceptron Network Trained by Genetic Algorithm

Cervical cancer is well known as the third killer for women in Malaysia. The precancerous stage for detection can be determine by screening test, known Pap smear test for avoiding occurrence of cervical cancer. The problem face in the test is the human error in reading the data analysis and also lack number of pathologists to interpret the data analysis. Creating a computer-aided diagnosis system is one of the solutions that can interpret the data. Most of the research available used artificial neural network for diagnosis system to classify the cervical cancer cells data into normal and abnormal. This research creates a neural network (NN) using Hybrid Multi-layered Perceptron (HMLP) trained by Genetic Algorithm (GA) to diagnose the data. The data is extracted from cervical cells and divided into four features as the input, which are size of nucleus, size of cytoplasm, grey level of nucleus, and grey level of cytoplasm. The data is interpreted into three categories; are normal, Low-grade Squamous Intraepithelial Lesion (LSIL) and High-grade Squamous Intraepithelial Lesion (HSIL). These categories will be inserted in to the algorithm to calculate and determined the neural network performance. The data is randomly separated into dataset using 5-fold cross validation technique. The performance is compared with Hybrid Radial Basis Function (HRBF) trained with Adaptive Fuzzy K-means and Moving K-means Clustering Algorithm. This researched shows HMLP trained with GA create a better performance of the network in the accuracy, sensitivity, and specificity to be implemented in the cervical cancer for test the performance improvement.
A Doubly Sparse Multiclass Support Vector Machine With Simultaneous Feature and Sample Screening

KSVCR is an effective algorithm to handle multiclass problems. But it cannot do variable selection and is time-consuming on large datasets. In this article, we propose a doubly sparse multiclass model DKSVCR which employs elastic net regularization term to improve the performance of KSVCR. And then, motivated by the sparsity of DKSVCR, a simultaneous safe feature and sample screening rule MFSS is further constructed to accelerate the solving speed of DKSVCR, which is termed as MFSS-DKSVCR. It has two major benefits. First, both classification and variable selection could be realized, the highly correlated features tend to be selected or removed together. Second, by using feature screening and sample screening rule alternatively rather than using each of them individually, our MFSS-DKSVCR can delete more redundant features and samples before the training stage. Hence, the solving speed is improved a lot. And our MFSS-DKSVCR is safe in the sense that the solutions obtained from the reduced problem and original problem are identical. Besides, a fast algorithm SDCA is used to solve the problem more efficiently. The experimental results on one artificial dataset, 28 benchmark datasets, and an image dataset verify the validity of our method.
Single View 3D Reconstruction Based on Improved RGB-D Image

Image-based 3D reconstruction is a fundamental task in computer vision and computer graphics. Although many existing approaches have obtained excellent results, they all dependent on multi-view images, then these methods are very complex. To reduce the complexity of the existing approaches, in this paper we fist propose a novel 3D reconstruction method based on single RGB image and the corresponding depth image, and design a unified framework called Single3D. Second, we trained a neural network model to reduce noises for improving the quality of the input RGB image, then leading a desirable RGB image. Third, we combine the deep guided filter and fast guided filter to fill holes on the depth image for improving the quality of 3D model. Fourth, we recover high-quality 3D model from the RGB-D image based on the proposed Single3D framework. Finally, the comprehensive experiments were conducted on the most challenging benchmarking dataset with variant light, occlusion, and clustering. Experimental results show that our method has desirable accuracy and efficiency.
A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination

This paper proposed and verified a new integrated approach based on the iterative super-resolution algorithm and expectation-maximization for face hallucination, which is a process of converting a low-resolution face image to a high-resolution image. The current sparse representation for super resolving generic image patches is not suitable for global face images due to its lower accuracy and time-consumption. To solve this, in the new method, training global face sparse representation was used to reconstruct images with misalignment variations after the local geometric co-occurrence matrix. In the testing phase, we proposed a hybrid method, which is a combination of the sparse global representation and the local linear regression using the Expectation Maximization (EM) algorithm. Therefore, this work recovered the high-resolution image of a corresponding low-resolution image. Experimental validation suggested improvement of the overall accuracy of the proposed method with fast identification of high-resolution face images without misalignment.
Diagnosis of Thyroid Nodules in Ultrasound Images Using Two Combined Classification Modules

Ultrasonography is a common modality for diagnosing thyroid nodules. Computer-aided classification of benign and malignant thyroid nodules in the ultrasound images is meaningful. A key challenge in this issue is that a lot of ultrasound images without obvious benign or malignant features are hindering deep learning model to extract effective features. Here, a novel method using two combined classification modules is proposed to separate and classify those images. Firstly, we adopt multi-fold cross-validation based a CNN model to pick out ultrasound images that hard to recognize. Then we add new labels to those images and put them back to the dataset that will be trained with the CNN model to form a tripartite classification module. Finally, a binary classification module will be trained exclusively to discriminate those obscure images. Experimental results on 4148 ultrasound images show that the proposed method lead to excellent performance, with an accuracy of 0.804, which demonstrate the value of this method.
Symmetric $k$-Means for Deep Neural Network Compression and Hardware Acceleration on FPGAs

Convolutional Neural Networks (CNNs) are popular models that have been successfully applied to diverse domains like vision, speech, and text. To reduce inference-time latency, it is common to employ hardware accelerators, which often require a model compression step. Contrary to most compression algorithms that are agnostic of the underlying hardware acceleration strategy, this paper introduces a novel Symmetric <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means based compression algorithm that is specifically designed to support a new FPGA-based hardware acceleration scheme by reducing the number of inference-time multiply-accumulate (MAC) operations by up to 98%. First, a simple <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means based training approach is presented and then as an extension, Symmetric <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means is proposed which yields twice the reduction in MAC operations for the same bit-depth as the simple <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means approach. A comparative analysis is conducted on popular CNN architectures for tasks including classification, object detection and end-to-end stereo matching on various datasets. For all tasks, the model compression down to 3 bits is presented, while no loss is observed in accuracy for the 5-bits quantization.
Multi-sequence Cardiac MR Segmentation with Adversarial Domain Adaptation Network

Automatic and accurate segmentation of the ventricles and myocardium from multi-sequence cardiac MRI (CMR) is crucial for the diagnosis and treatment management for patients suffering from myocardial infarction (MI). However, due to the existence of domain shift among different modalities of datasets, the performance of deep neural networks drops significantly when the training and testing datasets are distinct. In this paper, we propose an unsupervised domain alignment method to explicitly alleviate the domain shifts among different modalities of CMR sequences, e.g., bSSFP, LGE, and T2-weighted. Our segmentation network is attention U-Net with pyramid pooling module, where multi-level feature space and output space adversarial learning are proposed to transfer discriminative domain knowledge across different datasets. Moreover, we further introduce a group-wise feature recalibration module to enforce the fine-grained semantic-level feature alignment that matching features from different networks but with the same class label. We evaluate our method on the multi-sequence cardiac MR Segmentation Challenge 2019 datasets, which contain three different modalities of MRI sequences. Extensive experimental results show that the proposed methods can obtain significant segmentation improvements compared with the baseline models.