Reviewerly/exHarmony huggingface dataset:
MIGN: Multiscale Image Generation Network for Remote Sensing Image Semantic Segmentation

With the development of computer vision, the semantic segmentation of remote sensing images, which has become an important topic, has been utilized in various applications for image content analysis and understanding, such as urban planning, natural disaster monitoring, and land resource management. Many approaches have been proposed to address these problems. However, due to obvious differences in resolution, spatial structure, and semantics between remote sensing images and ordinary images, the semantic segmentation of remote sensing images is still challenging. In this paper, we propose a novel multiscale image generation network (MIGN) that can efficiently generate high-resolution segmentation results by considering both details and boundary information. In particular, a multi-attention mechanism method for semantic segmentation of remote sensing images is designed. The attention weight is calculated by capturing the interaction of cross dimensions in a two-branch structure, which can learn the underlying feature information and guarantee the performance of each pixel feature for final classification. We also propose an edge supervised module to ensure that the segmentation boundary has a more accurate performance. A multiscale image fusion algorithm based on the Bayes model is proposed to improve the accuracy of the segmentation module. The performance of our model is evaluated on the ISPRS Vaihingen and Potsdam datasets. The results show that our method is superior to the most advanced image segmentation methods in terms of MIoU and pixel accuracy.
Application of Big Data Analytics and Machine Learning to Large-Scale Synchrophasor Datasets: Evaluation of Dataset ‘Machine Learning-Readiness’

This manuscript presents a data quality analysis and holistic 'machine learning-readiness' evaluation of a representative set of large-scale, real-world phasor measurement unit (PMU) datasets provided under the United States Department of Energy-funded FOA 1861 research program [1].A major focus of this study is to understand the present-day suitability of large-scale, real-world synchrophasor datasets for application of commercially-available, off-the-shelf big data and supervised or semi-supervised machine learning (ML) tools and catalogue any major obstacles to their application.To this end, dataset quality is methodically examined through an interconnect-wide quantifications of basic bad data occurrences, a summary of several harder-to-detect data quality issues that can jeopardize successful application of machine learning, and an evaluation of the adequacy of event log labeling for supervised training of models used for online event classification.A global 'six-point' statistical analyses of several key dataset variables is demonstrated as a means by which to identify additional hard-todetect data quality issues, also providing an example successful application of big data technology to extract insights regarding reasonable operational bounds of the US power system.Obstacles for application of commercial ML technologies are summarized, with a particular focus on supervised and semi-supervised ML.Lessons-learned are provided regarding challenges associated with present-day event labeling practices, large spatial scope of the dataset, and dataset anonymization.Finally, insight into efficacy of employed mitigation strategies are discussed, and recommendations for future work are made.
Intelligent Interactive English Teaching System for Engineering Education

In order to improve the effect of English teaching, this paper combines with the intelligent algorithm to construct an intelligent interactive English teaching system for engineering education. In the process of interactive data processing, this paper specifies the time that a single pulse needs to be maintained and controls a counter to maintain a certain number of FPGA clocks. Moreover, data processing is completed in the digital domain, the modulation method is digital quadrature amplitude modulation, data transmission is generally in the form of a real signal, and the modulated IQ signal is converted back to a real signal. After constructing an intelligent interactive English teaching system for engineering education, this paper verifies that the intelligent interactive English teaching system for engineering education has certain effects through simulation teaching combined with the expert evaluation method.
Computing large 2D convolutions on GPU efficiently with the im2tensor algorithm

Attaining the best possible throughput when computing convolutions is a challenge for signal and image processing systems, be they HPC (High-Performance Computing) machines or embedded real-time targets. This importance is highlighted by the numerous methods and implementations available, often optimized for particular settings: small batched kernels or very large kernels, for example. In the meantime, GPUs (Graphics Processing Units) have become a first-class architecture for real-time and embedded processing. The power offered by those chips stems from their parallel nature, and this advantage has been exploited for convolutions in several libraries. Even more recently, the introduction of tensor cores on NVIDIA GPUs has opened up new limits in terms of attainable FLOPS (Floating-Point Operations per Second). For reaching that performance, GPU applications must use GEMMs (GEneral Matrix Multiplications), that tensor cores accelerate. We then developed an efficient GEMM-based 2D convolution algorithm in a general setting. On relatively large kernels (30–50-pixel wide), im2tensor is, to the best of our knowledge, the fastest method for computing 2D convolutions. We provide detailed performance analysis for different scenarios: small (1024 $$\times$$ 1024) and large (4096 $$\times$$ 4096) images, with convolutions kernels of sizes ranging 1 to 60-pixel wide, on two GPU cards: Jetson AGX Xavier (embedded) and Titan V (server-class). Moreover, the accuracy of im2tensor surpasses non-GEMM based methods, thanks to the larger-precision registers used by tensor cores for intermediate representations.
Application of Blockchain Technology in Water Rights Trading in the Irrigation Area under the Internet-of-Things Environment

Water rights trading is an important way to solve the shortage of water resources. Combined with the characteristics of water resources and the requirements of the market economy, the organic combination of the government and the market should be strengthened. Through the establishment of a water rights system and a water rights trading market, the implementation of water rights trading will be promoted. Due to the decentralized and distributed structure, blockchain technology greatly reduces its intermediary cost in the application process. The tamper-resistant timestamp feature can realize data tracking and information anticounterfeiting, and flexible programming attributes can promote the specification of market order. Firstly, the reasons for water rights trading are studied, and the water rights trading system is analyzed. It is found that the system has the problems of the cumbersome transaction process and high transaction costs. According to the characteristics of water rights trading, the alliance chain Hyperledger Fabric is selected as the underlying network system of the trading system. Secondly, combined with water rights trading, Hyperledger Fabric multichannel technology is used to design a three-channel accounting method, and the chain code for water rights trading is designed and implemented based on the Go language. CouchDB is adopted to store key-value data, and Ubuntu, Docker, and Docker Compose are used to construct an operating environment of the chain code. Finally, the proposed system is tested. The test findings reveal that the distributed water volume of experimental area 1 is 41.8368 million m3, the current water volume is 23.831 million m3, and the quota management water volume is 20.432 million m3. The distributed water volume in area 2 is 40.4605 million m3, the current water volume is 4317.64 m3, and the quota management water volume is 77.4795 million m3. The distributed water volume in area 3 is 65.6928 million m3, the current water volume is 77.472 million m3, and the quota management water volume is 64.412 million m3. The water rights trading system based on Hyperledger Fabric can record each transaction data, which can be stored in each node, and the data stored in each node are the same. The proposed scheme provides a certain reference for the application of blockchain technology in water rights trading in irrigation areas.
Privacy Preservation Learning with Deep Cooperative Method for Multimedia Data Analysis

In parallel with the rapid adoption of deep learning to multimedia data analysis, there has been growing awareness and concerns about data security and privacy. The recent advancement of federated learning enables many network clients to collaboratively train a model under the orchestration of a central server while preserving clients’ privacy. However, the standard assumption of independent and identical distribution (IID) may be broken under the federated learning because data label preferences may vary across clients. Recent efforts address this issue either by adapting a strong global model for each local model, respectively, or by training individual local models for similar clients together. However, both strategies degrade in highly non-IID scenarios. This work introduces a novel method, deep cooperative learning (DCL), to address this problem. It leverages the reciprocal structure between deep learning tasks in different clients to obtain effective feedback signals to enhance the learning process of personalized local models. To the best of our knowledge, this is the first time the non-IID is addressed under the principle of task interactions. We demonstrated the effectiveness of DCL on the two tasks of medical multimedia data analysis. The results show that our method presents a significant performance improvement compared with the standard federated learning method. In conclusion, this work developed a method for addressing non-IID problems in deep-learning-based privacy preservation learning. It allows the highly non-IID data to be used to improve the local model performance.
Modeling and Simulation of a Quantum Thermal Noise on the Qubit

Quantum noise or decoherence is a major factor impacting the performance of quantum technologies. On the qubit, an important quantum noise, often relevant in practice, is the thermal noise or generalized amplitude damping noise, describing the interaction with a thermal bath at an arbitrary temperature. A qubit thermal noise however cannot be modeled nor directly simulated with a few elementary Pauli operators, but instead requires specific operators. Our main goal here is to construct a circuit model for simulating the thermal noise from standard elementary qubit operators. Starting from a common quantum-operation model based on Kraus operators and an associated qubit-environment model, we derive a proper Stinespring dilated representation for the thermal noise. This dilated unitary model is then decomposed in terms of simple elementary qubit operators, and converted into a circuit based on elementary quantum gates. We arrive at our targeted simulator circuit for the thermal noise, coming with built-in easy control on the noise parameters. The noise simulator is then physically implemented and tested on an IBM-Q quantum processor. The simulator represents a useful addition to existing libraries of quantum circuits for quantum processors, and it offers a new tool for investigating quantum signal and information processing having to cope with thermal noise.
Integroly: Automatic Knowledge Graph Population from Social Big Data in the Political Marketing Domain

Social media sites have become platforms for conversation and channels to share experiences and opinions, promoting public discourse. In particular, their use has increased in political topics, such as citizen participation, proselytism, or political discussions. Political marketing involves collecting, monitoring, processing, and analyzing large amounts of voters’ data. However, the extraction, integration, processing, and storage of these torrents of relevant data in the political domain is a very challenging endeavor. In the recent years, the semantic technologies as ontologies and knowledge graphs (KGs) have proven effective in supporting knowledge extraction and management, providing solutions in heterogeneous data sources integration and the complexity of finding meaningful relationships. This work focuses on providing an automated solution for the population of a political marketing-related KG from Spanish texts through Natural Language Processing (NLP) techniques. The aim of the proposed framework is to gather significant data from semi-structured and unstructured digital media sources to feed a KG previously defined sustained by an ontological model in the political marketing domain. Twitter and political news sites were used to test the usefulness of the automatic KG population approach. The resulting KG was evaluated through 18 quality requirements, which ensure the optimal integration of political knowledge.
Combining a Population-Based Approach with Multiple Linear Models for Continuous and Discrete Optimization Problems

Population-based approaches have given us new search strategies and ideas in order to solve optimization problems. Usually, these methods are based on the performance carried out by a finite number of agents, which by the interaction between them they evolve and work all over the search space. Also, it is well-known that the correct employment of parameter values in this kind of method can positively impact their performance and behavior. In this context, the present work focuses on the design of a hybrid architecture which smartly balances the population size on run-time. In order to smartly balance and control the population size, a modular approach, named Linear Modular Population Balancer (LMPB), is proposed. The main ideas behind the designed architecture include the solving strategy behind a population-based metaheuristic, the influence of learning components based on multiple statistical modeling methods which transform the dynamic data generated into knowledge, and the possibilities to tackle both discrete and continuous optimization problems. In this regard, three modules are proposed for LMPB, which concern tasks such as the management of the population-based algorithm, parameter setting, probabilities, learning methods, and selection mechanism for the population size to employ. In order to test the viability and effectiveness of our proposed approach, we solve a set of well-known benchmark functions and the multidimensional knapsack problem (MKP). Additionally, we illustrate promising solving results, compare them against state-of-the-art methods which have proved to be good options for solving optimization problems, and give solid arguments for future work in the necessity to keep evolving this type of proposed architecture.
Generalized uncertainty relations for multiple measurements

Abstract The uncertainty relation is regarded as a remarkable feature of quantum mechanics differing from the classical counterpart, and it plays a backbone role in the region of quantum information theory. In principle, the uncertainty relation offers a nontrivial limit to predict the outcome of arbitrarily incompatible observed variables. Therefore, to pursue a more general uncertainty relations ought to be considerably important for obtaining accurate predictions of multi-observable measurement results in genuine multipartite systems. In this article, we derive a generalized entropic uncertainty relation (EUR) for multi-measurement in a multipartite framework. It is proved that the bound we proposed is stronger than the one derived from Renes et al. in [Phys. Rev. Lett. 103,020402(2009) ] for the arbitrary multipartite case. As an illustration, we take several typical scenarios that confirm that our proposed bound outperforms that presented by Renes et al. Hence, we believe our findings provide generalized uncertainty relations with regard to multi-measurement setting, and facilitate the EUR’s applications on quantum precision measurement regarding genuine multipartite systems.
Image Encryption Using Block Chain and Chaos for Secure Communication

In the present decade, images have become a valu-able source of information. They play an essential role in medical, the defence, engineering, and other fields. As a result, image information security is a pressing concern. Over the past few years, blockchain has received a lot of attention in the context of trust difficulties and the removal of third parties. The article that follows offers a blockchain-based image encryption approach. The image is encrypted and the values are stored on the blockchain using the suggested technique. In transmission, the blockchain assures confidentiality and anonymity. On the basis of NPCR, UACI, and information entropy evaluation, the robustness of the proposed approach has been assessed against differential assault. Entropy analysis is used to illustrate the strength of the system against a brute force assault. The result came close to the optimal value of 8. The suggested method is also demonstrated to be effective in preventing data loss during transmission.
Deep Learning in Neural Networks: An Overview

It was only up till recent times that computer science and its were sufficient for the application in basic principles. With the in the field of artificial intelligence, the subset Deep learning is towards substantial research and advances, creating diverse We cannot consider deep learning to be an individual approach; it is a collective term which comprises fields from contrasting to be associated with the common spine—Deep learning. Basis for strong approach in deep learning lies in cognizance of the of deep learning. The implementations can be performed vastly in fields through implication of not just one but numerous algorithms achieving our goal. The architecture of deep learning has enhanced in previous years exponentially, and as per demand, the refinement of learning implying that the architecture is dynamic. A few of the most improvised architectures are mentioned below: 30Recurrent neural networks (RNNs) Long short-term memory (LSTM)/gated recurrent unit (GRU) Convolutional neural networks (CNNs) Deep belief networks (DBN) and deep stacking networks (DSNs) Open source software options for deep learning. The area of implementation for deep learning in problem solving is vast. Feed forward networks are very effective as well as recurrent networks can be a good source for the solution of the deep learning problems. The Framework for deep learning can be implemented in software packages for the useful creation of neural network. The framework needs an implementation on a standardized scale and hence needs industrial experts for the framework to be implemented. The entire framework is in simple terms based on the Diagnosis of the problem and further, evaluating the problem. It is evident that the architecture and framework of deep learning is vast and expanding its horizons to every field possible for implementation. Therefore deep learning architecture and framework would be vitalized, with step by step conception. The architecture would be simplified as well as illustrated. All the aforesaid architecture like Recurrent neural network, Long short term memory/gated recurrent unit, convolutional, Deep belief—deep stack as well as open source would be simplified as well as illustrated.
Xlnet Parallel Hybrid Network Sentiment Analysis Based on Sentiment Word Augmentation

Aiming at the problem that some sentiment words in sentiment analysis tasks are not highly concerned and it is difficult to capture the long-distance dependence between sentences, a parallel mixed sentiment analysis network EXLNnet-BG-Att-CNN that integrates the characteristics of sentiment words is proposed. First of all, the basic emotion dictionary used is HowNet, Dalian University of Technology Chinese Emotion Dictionary Database, and National Taiwan University's Emotion Dictionary, combined with the additional expansion of related emotional polarity words, to obtain a more targeted emotional dictionary and design An emotional word selection segmentation algorithm (DicSentencePieceSelect) is proposed. Secondly, use XLNnet to encode the sentence and the vectors processed by the dictionary word selection algorithm respectively to obtain the deep semantic features of the text and merge them. Then, the feature vectors are input into the parallel network of BiGRU-ATT and TextCNN respectively, and the dual attention mechanism is used, which can not only take into account the advantages of the global features of the text sequence, but also further extract local features to achieve semantic enhancement. Finally, the output vectors of the various networks are fused, and the activation-pooling layer is used to avoid the occurrence of over-fitting. Compared with multiple existing models, the accuracy rate of Acc is higher, and the accuracy rate of the model reaches 96.05%.
Adversarial Detection based on Local Cosine Similarity

Deep neural networks (DNNs) have attracted extensive attention because of its powerful performance in many areas, however, DNNs are obviously vulnerable to adversarial examples. By crafting the adversarial examples with human imperceptible perturbations, adversary can confuse the judgement of DNNs. In our work, we propose a simple yet effective adversarial detection method based on Local Cosine Similarity (LCS). Motivated by the idea that the direction of adversarial perturbation matters most, we propose to utilize cosine similarity which is sensitive to direction changes, therefore, is able to effectively discriminate subtle direction differences between adversarial example and normal example. Meanwhile we introduce the local operation, which limits the cosine similarity calculation within k-nearest neighbors so that better to characterize the difference between adversarial example and normal example. Extensive experimental results show that the extracted LCS features can well distinguish adversarial examples from normal examples and achieve better performances.
Multi-Camera Multi-Person Tracking and Re-Identification in an Operating Room

Multi-camera multi-person (MCMP) tracking and re-identification (ReID) are essential tasks in safety, pedestrian analysis, and so on; however, most research focuses on outdoor scenarios because they are much more complicated to deal with occlusions and misidentification in a crowded room with obstacles. Moreover, it is challenging to complete the two tasks in one framework. We present a trajectory-based method, integrating tracking and ReID tasks. First, the poses of all surgical members captured by each camera are detected frame-by-frame; then, the detected poses are exploited to track the trajectories of all members for each camera; finally, these trajectories of different cameras are clustered to re-identify the members in the operating room across all cameras. Compared to other MCMP tracking and ReID methods, the proposed one mainly exploits trajectories, taking texture features that are less distinguishable in the operating room scenario as auxiliary cues. We also integrate temporal information during ReID, which is more reliable than the state-of-the-art framework where ReID is conducted frame-by-frame. In addition, our framework requires no training before deployment in new scenarios. We also created an annotated MCMP dataset with actual operating room videos. Our experiments prove the effectiveness of the proposed trajectory-based ReID algorithm. The proposed framework achieves 85.44% accuracy in the ReID task, outperforming the state-of-the-art framework in our operating room dataset.