Reviewerly/exHarmony huggingface dataset:
Impact of a generalised SVG-based large-scale super-resolution algorithm on the design of light-weight medical image segmentation DNNs

ABSTRACTSetting up a complex CNN requires a powerful platform, several hours of run-time, and a lot of data for training. Here, we propose a generalised lightweight solution that exploits super-resolution and scalable vector graphics and uses a small-scale UNet as the baseline framework to segment different organs in MR and CT data. We selected the UNet since many researchers use it as the baseline, modify it in their proposal, and perform an ablation study to show the effectiveness of the proposed modification. First, we downsample the input 2D CT slices by bicubic interpolation. Using the architecture of the conventional UNet, we reduce the size of the network’s input, and the number of layers and filters to construct a lightweight UNet. The network segments the low-resolution images and prepares the mask of an organ. Then, we upscale the boundary of the output mask by the Scalable Vector Graphics technique to obtain the final border. This design reduces the number of parameters and the run-time by a factor of two. We segmented several tissues to prove the stability of our method to the type of organ. The experiments proved the feasibility of setting up complex deep neural networks with conventional platforms.KEYWORDS: light-weight deep neural networksscalable vector graphicsgeneralised segmentation frameworksmedical image segmentationView correction statement:Correction Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsMina EsfandiarkhaniMina Esfandiarkhani received a B.Sc. degree from the Azad University of Qazvin in 2013 and an M.Sc. degree in Biomedical Engineering from the Shahed University of Tehran in 2016. She is currently pursuing a Ph.D. degree in the Biomedical Engineering faculty of Shahed University. Her research interests include machine learning, computer vision, medical image processing, and artificial intelligence.Amir Hossein ForuzanAmir Hossein Foruzan received his B.S. from the Sharif University of Technology in Telecommunication Engineering. He received his M.S. and Ph.D. from Tehran University in Biomedical Engineering. Since 2011, he has been a faculty member of Shahed University. His research interest is medical image processing.
Retracted: Named Entity Recognition of Medical Text Based on the Deep Neural Network

[This retracts the article DOI: 10.1155/2022/3990563.].
Research on lateral-shear interferometry based on double checkerboard grating

The grating lateral-shear interferometry has a wide range of applications in wavefront sensing. The lateral-shear interferometer based on Ronchi grating can be applied for high-precision wavefront aberration detection of lithography projection lenses, which is one of the critical issues to overcome lithography challenges. The conventional Ronchi shearing interferometer object grating consists of two sets of one-dimensional Ronchi grating lines perpendicular to each other in order to obtain wavefront information in two directions, namely X and Y. During the experiment, one set is moved into the optical path for phase shifting and measurement, then the other set of grating lines is repeatedly operated. To reduce the complexity of experimental operations, this paper proposes a lateral-shear interference system based on double checkerboard gratings. The object grating and image grating are both set as checkerboard grating, and the rotation angle of the checkerboard grating varies according to the optical system's shear rate, allowing each phase shift to obtain wavefront information in X and Y directions. Multiple uniform phase shifts are performed to extract &plusmn; 1 order diffraction wavefronts of X and Y directions. The shear wavefront of the optical system to be measured is calculated from the four-wave interference light field, then the original wavefront can be restored by differential Zernike. Through theoretical analysis and simulation, this double checkerboard system can achieve wave aberration measurement with the same accuracy as traditional Ronchi interferometers, improving the real-time performance of projection lenses wave aberration measurement, and providing more possibilities for subsequent structural improvements of Ronchi lateral-shear interferometer.
Science, technology, engineering, and mathematics (STEM) liaison librarians: Perspectives on functions and frequencies for serving academic researchers

STEM liaison librarians' roles have broadened with new policies, data, and services stemming from the increase of data-driven research across sciences. The problem this study addresses is the lack of understanding of what tasks STEM liaison librarians do and how often. STEM liaison librarians from a sampling frame of 135 at 16 similarly Association of Research Libraries were surveyed with demographic and job analysis questions. A total of 41 participants completed the entirety of the survey. Findings indicate the majority of respondents serve 4 or more departments and continue to spend a majority of their time on traditional liaison tasks, but data-related duties are in greater demand. In these continually uncertain times with dwindling resources in academic libraries, a clearer picture of STEM liaison librarian tasks and the duration expended on each helps inform staffing of services. A better understanding of these duties and their frequency will add value to more accurate job descriptions, relevant curriculum, and training, as well as advance the understanding of what these information professionals do with their work time as well as tell us what they no longer do as much.
Model-free prediction of time series: a nonparametric approach

AbstractWe propose a novel approach for model-free time series forecasting. Unlike most existing methods, the proposed method does not rely on parametric error distributions nor assume parametric forms of the mean function, leading to broad applicability. We achieve such generality by establishing a simple but powerful representation of a time series {Xt;t∈Z} with suptE|Xt|<∞, that is, Xt has a solution which is a linear combination of infinite past values. Then using the obtained solution a prediction algorithm is presented, with large sample theoretical guarantees. Simulation studies show favourable performance of the proposed method compared with popular parametric and neural networks methods, and suggest its superiority when the sample size is small. An application to practical time series is discussed.Keywords: Predictionnonparametric methodsneural networksα-stable distributionMSC2010 subject classifications:: Primary: 60G25Secondary: 62M20 Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 See https://www.sciencedirect.com/topics/engineering/left-inverse.
A Transmission Line Defect Detection Method Based on YOLOv7 and Multi-UAV Collaboration Platform

In order to prevent the economic losses caused by large-scale power outages and the life safety losses caused by circuit failures, the main purpose of this paper is to improve the efficiency, accuracy, and reliability of transmission line defect detection, and the main innovation is to propose a transmission line defect detection method based on YOLOv7 and the multi-UAV collaboration platform. First, a novel multi-UAV collaboration platform is proposed, which improved the search range and detection efficiency for defect detection. Second, YOLOv7 is used as a detector for multi-UAV collaboration platform, and several improvements improved the efficiency of defect detection under complex backgrounds. Finally, a complete transmission line defect images dataset is constructed, and the introduction of several defect images such as insulator self-blast and cracked insulators avoids the problem of low application value of single defect detection. The results indicate that the proposed method not only enhances the detection range and efficiency but also improves the detection accuracy. Compared with YOLOv5-S, which has good detection performance, YOLOv7 improves accuracy by 1.2%, recall by 4.3%, and mAP by 4.1%, and YOLOv7-Tiny achieves the fastest speed 1.2 ms and the smallest size 11.7 Mb. Even if the images contain complex backgrounds and noises, a mAP of 0.886 can still be obtained. Therefore, the proposed method provides effective support for transmission line defect detection and has broad application scenarios and development prospects.
Quantum Plexcitonic Sensing

While fundamental to quantum sensing, quantum state control has been traditionally limited to extreme conditions. This restricts the impact of the practical implementation of quantum sensing on a broad range of physical measurements. Plexcitons, however, provide a promising path under ambient conditions toward quantum state control and thus quantum sensing, owing to their origin from strong plasmon-exciton coupling. Herein, we harness plexcitons to demonstrate quantum plexcitonic sensing by strongly coupling excitonic particles to a plasmonic hyperbolic metasurface. As compared to classical sensing in the weak-coupling regime, our model of quantum plexcitonic sensing performs at a level that is ∼40 times more sensitive. Noise-modulated sensitivity studies reinforce the quantum advantage over classical sensing, featuring better sensitivity, smaller sensitivity uncertainty, and higher resilience against optical noise. The successful demonstration of quantum plexcitonic sensing opens the door for a variety of physical, chemical, and biological measurements by leveraging strongly coupled plasmon-exciton systems.
Research on a watermarking recognition method for PDF documents based on natural language processing

Digital watermarking, as a technology to protect copyright, integrity, copy prevention or direction tracking of digital products, is currently commonly used to protect confidential documents and files within enterprises. In view of the PDF document format which is widely used in enterprise documents, this paper presents a method of PDF document watermark recognition based on natural language processing technology. By collecting a large number of PDF documents, using the improved N-gram language model based on forward and reverse matching algorithms to segment text content, a KenLM language model based on language model probability and conditional probability calculation rules is established to identify PDF document watermarks, which effectively improves the accuracy of PDF document watermark recognition. The validity of this method is verified by selecting a PDF format document of an enterprise, training the language model and calculating the prediction accuracy.
A subjective logic-based reputation management scheme for highway Internet of Vehicles

With the widespread application of blockchain technology on the Internet of Vehicles (IoV), numerous Blockchain-based IoV (BIoV) solutions have emerged. As one of the important application scenarios of IoV, highway needs a highly reliable reputation management scheme to ensure secure data sharing. To solve the problems of privacy leakage and hostile attacks caused by the lack of trust between nodes in the highway BIoV scenario, a reputation management scheme for Highway Internet of Vehicles based on subjective logic (HISL) is proposed. Under the condition of low transmission rate of highway packets, real-time reputation update can effectively prevent malicious nodes. In addition, our system model ensures privacy security and data integrity in the highway IoV environment and effectively improves data sharing efficiency. The performance analysis results show that the HISL solution can detect the disguised malicious nodes in a shorter time, thus effectively solving the security problem in highway and even more IoV scenarios.
Efficient zero-knowledge proof for quadratic matrix relation over finite field with two witnesses

In large-scale private computing applications, various arithmetic relations appear as or can be reduced to matrix relations. In this paper, we establish the efficient zero-knowledge proof (ZKP) for the quadratic matrix relation over finite field Fp with two witness matrices. In private computing tasks, lots of typical relations are instances or special cases of this form, e.g., matrix multiplicative relation, inverse relation, isometric relation, etc. Different from the widely-applied vectorspecific method, our method is matrix-specific. The matrix equation is treated as a tensor equality and probabilisticequivalent reduction techniques are applied to reduce the non-linear matrix relation to simple vector relation. To the authors’ best knowledge, currently, there are no matrix-specific methods to ZKP for nonlinear matrix relations. Compared against the current (vector-specific) method, our method substantially outperforms it in all critical aspects, e.g., for n-raw t-column matrix witnesses, the required size of common reference string (c.r.s.) can be compressed by a factor of 2nt and the number of messages, group and field elements are all reduced by a factor of ≈2 for large-size witnesses. Computational complexities in both methods are almost the same.
Gated axial transformer network for mass segmentation in mammographic

Mammography is main tests used for breast cancer risk assessment. However, due to the high similarity of the mass to breast tissue, blurring of the mass edges causes the task of segmenting the breast mass on mammograms to be challenging. To reduce computational costs and the workloads of radiologists, deep learning techniques based on computer vision have become a common implementation in the field of medical image segmentation. However, owing to the locality of the convolutional operation, the neural networks cannot effectively learn global and remote semantic information. We propose a novel gated axial transformer network (GATNet) framework for the mass segmentation of mammograms. GATNet uses an encoder-decoder structure. First, we use axial attention to decompose 2D self-attention into two one-dimensional self attentions. Second, we construct an efficient location-sensitive gated transformer module for image features to establish remote contextual dependencies. We conducted evaluations the proposed GATNet on two publicly available breast mass segmentation datasets. The average Dice similarity coefficients between the GATNet results and INBreast and CBISDDSM data were 80.98% and 83.63%, respectively. The experimental results indicate that GATNet effectively reduces both the computational and spatial complexity of the medical image segmentation network, demonstrating its remarkable performance.
Research on cloud native batch scheduler technology

This paper reviews the evolution of IT system architecture and highlights that container technology based on cloud computing is an inevitable trend in the future of IT technology. And big data and artificial intelligence are also closely related to container technology. For instance, the architecture for computing-storage separation and the distributed training of large models are uninterruptedly dependent upon container technology. Numerous container-based applications require robust management tool suites. Although Kubernetes is a well-known tool for container orchestration, its native scheduling is not tailored towards offline workloads, like big data and artificial intelligence. In this context, this paper first examines the principles of Kubernetes native scheduler and identifies the challenges encountered when dealing with batch tasks, then presents the concept, the workflow, and integration methods of batch schedulers, and finally summarizes its significant features and several open-source frameworks. Ultimately, enterprises’ IT infrastructure is continuously moving towards cloud and containerization. Comprehensive scheduling architectures that support both online and offline tasks are crucial, as they significantly impact the enterprise’s overall IT scalability, efficiency, and business outcomes. Therefore, this area has garnered the industry’s attention.
A simple baseline for signal detection and extraction

In the context of modern information warfare, wireless communication technology has been widely used. The transmission mode of wireless communication is to use electromagnetic waves in the air to carry out information exchange. Compared with the traditional way of transmitting information through cables, wireless communication has better adaptability, expansibility, and easy maintenance. With the rapid development of wireless communication technology, the number of various electromagnetic equipment is increasing, and the communication signal system and modulation modes are becoming more and more complex and diversified. The overlapping of various electromagnetic signals makes the electromagnetic environment increasingly complicated. It has become a key problem that needs to be solved in the field of communication countermeasures to quickly discover signals from the broadband spectrum and locate important enemy signals through individual recognition, protocol analysis, and other technologies. In this paper, we propose a simple baseline approach for signal detection and extraction. First, we store the received IQ signal in a buffer pool. In order to realize real-time processing for broadband signals, we first perform blind estimation of signal to noise ratio (SNR), and filter out the signals based on the estimated SNR, and then only process the signals whose SNR is greater than a certain threshold. Then we detect the filtered signal by fast Fourier transform and threshold comparison from the frequency domain, and locate the time domain and frequency domain position of the signal. Our experiments demonstrate that our proposed approach can effectively detect the signals in real-time. When the SNR is greater than 3db, the signal detection error is not more than 0.01.
Efficient zero-knowledge proof for quadratic matrix relation over finite field with three or four witnesses

In large-scale private computing applications, various arithmetic relations appear as or can be reduced to matrix relations. In this paper, we establish the efficient zero-knowledge proof (ZKP) for the quadratic matrix relation over finite field Fp with three or four witness matrices. In private computing tasks, lots of arithmetic relations are instances or special cases of such form, particularly some matrix structural decomposition relations. Different from the widely applied vectorspecific method, our method is matrix-specific. The matrix equation is treated as a tensor equality and probabilisticequivalent reduction techniques are applied to reduce the non-linear matrix relation to simple vector relation. To the authors’ best knowledge, currently, there are no matrix-specific methods to ZKP for nonlinear matrix relations. Compared against the current general linearization (vector-specific) method, our method substantially outperforms it in all critical aspects, e.g., for n-by-t matrix witnesses the required size of common reference string (c.r.s.) can be compressed by a factor of 2nt and the number of rounds, group and field elements in messages are all decreased by a factor of ≈2 for large-size witnesses. Computational complexities are almost the same in both methods.
Robot intent recognition method based on state grid business office

Artificial intelligence is currently in an era of change, not only changing the artificial intelligence technology itself but also changing human society. It has become more and more common to use artificial intelligence as the core human-computer interaction technology to replace manpower. Intention recognition is an important part of the human-machine dialogue system, and deep learning technology is gradually being applied to the task of intent recognition. However, intent recognition based on deep learning often has problems such as low recognition accuracy and slow recognition speed. In response to these problems, this paper designs a BERT fine-tuning to improve the network structure based on the pre-training model and proposes new continuous pre-training goals. To improve the accuracy of intent recognition, a method based on multi-teacher model compression is proposed to compress the pre-training model, which reduces the time consumption of model inference.
Research on autonomous driving image recognition based on a new real-time object detection model YOLOv5st

Considering the complexity of the driving environment and the high uncertainty of road conditions in autonomous driving, this paper proposes a real-time object detection model YOLOv5st, which is the integration of the advantages of YOLOv5s and Swin Transformer to achieve image recognition and object detection tasks in autonomous driving environment perception. The proposed model YOLOv5st is tested on the Transportation of Wuhan China (TOWC) traffic dataset, and the results show that the mean Average Precision (mAP) of the model is 89.8% in the validation set, which is a 2.0% improvement compared to YOLOv5s. And the average inference time of a single image of the new model is 20.6 ms, which can meet the real-time requirements. The proposed improved model has the characteristics of high accuracy, fast speed, and strong robustness, and is suitable for object detection tasks in autonomous driving vehicles. This study provides a new fusion algorithm for the perception of autonomous driving environments and provides new ideas and methods for the practical application of the algorithm through testing on the Wuhan traffic environment dataset.
Quick area picture division with the minimum square method

Picture division has a special meaning for computer visualization and schema identification. Fast target extraction from deterministic images is an important problem facing real-time picture manipulation. Traditional areal models rely on globally converged messages to achieve fault-minimized segmentation. Its image segmentation is ineffective and takes up a lot of time. To address this problem, this paper proposes a model that Fast Region Image Segmentation of the Least Squares (FRISLS). Specifically, the target as well as the backdrop of the primary picture is approximated by just a pair of constants in order to establish the minimum error function. The weight matrix is used to reduce the influence of the background on image segmentation, and least squares are introduced to achieve fast convergence of the model. Through comparison with other area model-based approaches, it is validated the effectiveness of the study. The results indicate that this method ensures high precision of picture division, and enhances the performance of picture splitting efficiency.
A study on privacy protection technology for cross-border transactions based on blockchain technology

With the acceleration of global economic integration, cross-border transactions are becoming more and more frequent. However, traditional cross-border transaction systems generally suffer from inefficiency, high cost and privacy leakage. In this paper, we study the privacy protection problem in cross-border transactions and propose a privacy protection scheme for cross-border transactions based on block-chain technology. First, this paper briefly introduces the basic principles of block-chain technology and its application scenarios in cross-border transactions. Second, to address the shortcomings of existing privacy protection technologies, this paper proposes a privacy protection scheme that combines data encryption technology, zero-knowledge proof and hybrid network. The scheme achieves automatic execution and verification through smart contracts, which effectively protects the privacy of cross-border transaction participants. Finally, the effectiveness of the privacy protection scheme proposed in this paper in improving the level of privacy protection for cross- border transactions is verified through experimental evaluation and result analysis. This study is important for promoting the development of cross- border transactions, improving transaction efficiency and protecting user privacy.