Reviewerly/exHarmony huggingface dataset:
Handwritten Ancient Chinese Character Recognition Algorithm Based on Improved Inception-ResNet and Attention Mechanism

Chinese characters have distilled the Chinese nation's vast wisdom and values, but the general public's learning and enjoyment of ancient scripts are hampered by the fact that fonts from different dynasties have highly different styles, intricate structures, and diverse deformations. To solve the difficulty of ordinary people identifying ancient Chinese characters, an ancient font recognition system that is based on an improved Inception-ResNet network is proposed. ECA-Net is integrated into the Inception module, PReLU is utilized to activate the network, and the Nadam algorithm is used to enhance the model training effect. The experimental results demonstrate that the method outperforms the other five deep learning models with a recognition rate of 95.56% in the mixed font dataset consisting of six types of fonts: seal script, inscription, running script, cursive script, clerical script, and regular script.
An Automated Essay Scoring model Based on Stacking Method

In the latest two decades, thanks to AI technology, Automated Essay Scoring (AES) technology has also been rapidly developed. This technology to analyze and score essays automatically is a hot spot for the application of natural language processing in the field of education. Firstly, different encoding methods are used to obtain the lexical vectors of the text. Secondly, features of the seven aspects of the text are fully extracted. Then the comparative analysis of different ensemble learning models was studied on the English essay scoring competition dataset of Kaggle. Finally, a model based on the stacking method is proposed. The results show that the model based on the stacking method achieves the best results on all datasets. It improves the average QWK values on eight subsets by 1.0%~2.8% compared to the baseline models of neural networks and feature-engineered.
Zero-shot Entity Linking with Less Data

Entity Linking (EL) maps an entity mention in a natural language sentence to an entity in a knowledge base (KB).The Zero-shot Entity Linking (ZEL) extends the scope of EL to unseen entities at the test time without requiring new labeled data.BLINK (Wu et al., 2020) (BERT-based) is one of the SOTA models for ZEL.Interestingly, we discovered that BLINK exhibits diminishing returns, i.e., it reaches 98% of its performance with just 1% of the training data and the remaining 99% of the data yields only a marginal increase of 2% in the performance.While this extra 2% gain makes a huge difference for downstream tasks, training BLINK on large amounts of data is very resource-intensive and impractical.In this paper, we propose a neuro-symbolic, multi-task learning approach to bridge this gap.Our approach boosts the BLINK's performance with much less data by exploiting an auxiliary information about entity types.Specifically, we train our model on two tasks simultaneously -entity linking (primary task) and hierarchical entity type prediction (auxiliary task).The auxiliary task exploits the hierarchical structure of entity types.Our approach achieves superior performance on ZEL task with significantly less training data.On four different benchmark datasets, we show that our approach achieves significantly higher performance than SOTA models when they are trained with just 0.01%, 0.1%, or 1% of the original training data.
Prerequisite Graph Extraction from Lectures

The proliferation of video-sharing platforms and MOOCs has raised new challenges in the field of education. A challenging topic that is gaining an increased popularity is the identification of prerequisite relations between concepts in video lectures. In this paper, we propose unsupervised methods for prerequisite identification and the creation of a prerequisite graph. The contribution, compared to existing approaches, is the development of methods which (i) do not rely on external knowledge, (ii) do not require extensive training, and (iii) are intended to exploit both the lecture transcript and its visual features. Results from the preliminary evaluation are encouraging, and provide insights on the extraction of prerequisite relations from video transcripts compared to textbooks.
An Automatic Self-explanation Sample Answer Generation with Knowledge Components in a Math Quiz

Little research has addressed how systems can use the learning process of self-explanation to provide scaffolding or feedback. Here, we propose a model automatically generating sample self-explanations with knowledge components required to solve a math quiz. The proposed model contains three steps: vectorization, clustering, and extraction. In an experiment using 1434 self-explanation answers from 25 quizzes, we found 72% of the quizzes generated sample answers with all necessary knowledge components. The similarity between human-created and machine-generated sentences was 0.719, with a significant correlation of R = 0.48 for the best performing generation model by BERTScore. These results suggest that our model can generate sample answers with the necessary key knowledge components and be further improved by using the BERTScore.
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios

We introduce RESIN-11, a new schema-guided event extraction and prediction system that can be applied to a large variety of newsworthy scenarios.The framework consists of two parts: ( 1) an open-domain end-to-end multimedia multilingual information extraction system with weak-supervision and zero-shot learningbased techniques.(2) a schema matching and schema-guided event prediction system based on our curated schema library.We build a demo website 1 based on our dockerized system and schema library publicly available for installation 2 .We also include a video demonstrating the system. 3
Improving Image Super-Resolution Based on Multiscale Generative Adversarial Networks

Convolutional neural networks have greatly improved the performance of image super-resolution. However, perceptual networks have problems such as blurred line structures and a lack of high-frequency information when reconstructing image textures. To mitigate these issues, a generative adversarial network based on multiscale asynchronous learning is proposed in this paper, whereby a pyramid structure is employed in the network model to integrate high-frequency information at different scales. Our scheme employs a U-net as a discriminator to focus on the consistency of adjacent pixels in the input image and uses the LPIPS loss for perceptual extreme super-resolution with stronger supervision. Experiments on benchmark datasets and independent datasets Set5, Set14, BSD100, and SunHays80 show that our approach is effective in restoring detailed texture information from low-resolution images.
Overcoming Data Availability Attacks in Blockchain Systems: Short Code-Length LDPC Code Design for Coded Merkle Tree

Light nodes in blockchains improve the scalability of the system by storing a small portion of the blockchain ledger. In certain blockchains, light nodes are vulnerable to a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>data availability</i></b> (DA) attack where a malicious node makes the light nodes accept an invalid block by hiding the invalid portion of the block from the nodes in the system. Recently, a technique based on LDPC codes called Coded Merkle Tree (CMT) was proposed by Yu <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>et al.</i></b> that enables light nodes to detect a DA attack by randomly requesting/sampling portions of the block from the malicious node. However, light nodes fail to detect a DA attack with high probability if a malicious node hides a small stopping set of the LDPC code. To mitigate this problem, Yu <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>et al.</i></b> used random LDPC codes that achieve large minimum stopping set size with high probability. Although effective, these codes are not necessarily optimal for this application, especially at short code lengths, which are relevant for low latency systems, IoT blockchains, etc.. In this paper, we focus on short code lengths and demonstrate that a suitable co-design of specialized LDPC codes and the light node sampling strategy can improve the probability of detection of DA attacks. We consider different adversary models based on their computational capabilities of finding stopping sets in LDPC codes. For a weak adversary model, we devise a new LDPC code construction termed as the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>entropy-constrained</i></b> PEG (EC-PEG) algorithm which <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>concentrates</i></b> stopping sets to a small group of variable nodes. We demonstrate that the EC-PEG algorithm coupled with a greedy sampling strategy improves the probability of detection of DA attacks. For stronger adversary models, we provide a co-design of a sampling strategy called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>linear-programming-sampling</i></b> (LP-sampling) and an LDPC code construction called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>linear-programming-constrained</i></b> PEG (LC-PEG) algorithm. The new co-design demonstrates a higher probability of detection of DA attacks compared to approaches in earlier literature.
Detection of Copy-Move Forgery Using Euclidean Distance and Texture Features

Given the pivotal role of digital images in our daily lives, it is important to detect copy-move forgery (CMF) of digital images.The detection of CMF is often based on feature detection and matching.For feature matching, the existing algorithms make use of the Euclidean distance.For feature detection, the Haar transform is one of the most popular techniques.This study retrieves image features through the Haar transform, and then simplifies the features by principal component analysis (PCA).After that, false boundaries were detected, localized, and removed.On this basis, the texture features of the input image were analyzed, using the gray-level co-occurrence matrix (GLCM).Finally, Euclidean distance was utilized to match features, and the mismatched features were labeled as forgeries.Then, the proposed approach was simulated in MATLAB, with accuracy as the performance metric.The simulation results show that our approach outperformed the PCA by 13.6% in accuracy.
Construction of an IoT customer operation analysis system based on big data analysis and human-centered artificial intelligence for web 4.0

Abstract Internet of thing (IoT) building sensors can capture several types of building operations, performances, and conditions and send them to a central dashboard to analyze data to support decision-making. Traditionally, laptops and cell phones are the majority of Internet-connected devices. IoT tracking allows customers to close the distance between devices and enterprises by collecting and analyzing various IoT data through connected devices, customers, and applications on the network. There is a lack of requirements for IoT edge applications security and approval. There are no best practices regarding operations focused on IoT incidents. IoT elements are not covered by audit and logging requirements. In this article, a big data analytics-based customer operation (BDA-CO) system analyzes the operation. With the exponential rise in data usage, the explosive development in the IoT devices reflects the ideal overlap of big data growth with IoT. Big data analytics continuously evolving network raises trivial questions about the performance, distribution of data, analysis, and protection of data collection. IoT modifies almost all the construction industry characteristics. Human-centered artificial intelligence is described as systems that always improve because of human input while also delivering an effective experience between the human and the robotic. The IoT is the key factor that ensures greater building performance. It was the first evolution of technology in a long time to turn genuine inventions into an industry that depended heavily on paper and manual processes. The benefits of the IoT in construction are now quite obviously much heavier than those of current manual processes. As a result, more construction companies explore and incorporate IoT strategies to address their productivity challenges, increasing efficiencies and profits. The simulation analysis shows that the proposed BDA-CO model enhances the trust score of 98.5%, accuracy detection ratio of 93.4%, probability ratio of 97.6%, and security ratio of 98.7% and reduces the false negative ratio of 21.3%, response time of 10.5%, delay rate of 19.9%, and packet loss ratio of 15.4% when compared to other existing techniques.
A Survey on Image Segmentation for Handwriting Recognition

The machine learning community has helped solve many challenging computer vision-related tasks over the last decades. Many deep learning techniques have also evolved over the years concerning the segmentation of images for various computer vision-related tasks. Literature has witnessed a surge of numerous segmentation techniques in image processing applications. Segmentation has been lately proving as an efficient tool in affecting the recognition rate in any image processing task to a great extent. This is because only by employing the correctly segmented portions of the image can one further accurately process the image for various recognition tasks. We have thus surveyed the different segmentation methods in this brief literature, focusing on the segmentation methods for handwriting recognition. Researchers in this domain are also brought to notice the various challenges encountered.
YOLO-DSD: A YOLO-Based Detector Optimized for Better Balance between Accuracy, Deployability and Inference Time in Optical Remote Sensing Object Detection

Many deep learning (DL)-based detectors have been developed for optical remote sensing object detection in recent years. However, most of the recent detectors are developed toward the pursuit of a higher accuracy, but little toward a balance between accuracy, deployability and inference time, which hinders the practical application for these detectors, especially in embedded devices. In order to achieve a higher detection accuracy and reduce the computational consumption and inference time simultaneously, a novel convolutional network named YOLO-DSD was developed based on YOLOv4. Firstly, a new feature extraction module, a dense residual (DenseRes) block, was proposed in a backbone network by utilizing a series-connected residual structure with the same topology for improving feature extraction while reducing the computational consumption and inference time. Secondly, convolution layer–batch normalization layer–leaky ReLu (CBL) ×5 modules in the neck, named S-CBL×5, were improved with a short-cut connection in order to mitigate feature loss. Finally, a low-cost novel attention mechanism called a dual channel attention (DCA) block was introduced to each S-CBL×5 for a better representation of features. The experimental results in the DIOR dataset indicate that YOLO-DSD outperforms YOLOv4 by increasing mAP0.5 from 71.3% to 73.0%, with a 23.9% and 29.7% reduction in Params and Flops, respectively, but a 50.2% improvement in FPS. In the RSOD dataset, the mAP0.5 of YOLO-DSD is increased from 90.0~94.0% to 92.6~95.5% under different input sizes. Compared with the SOTA detectors, YOLO-DSD achieves a better balance between the accuracy, deployability and inference time.
Decentralized Voting System Based on Regions Using Facial Recognition

The proposed system for voting in this project provides a decentralized platform on which all relevant voting information will be available to the public; therefore, security and transparency are the primary objectives of this project. This system uses facial recognition which helps the Election Commission to avoid rigging, bogus results, disputes, and uncertain situations. It uses artificial intelligence for facial recognition of the voters by validating it with a dummy database in which complete detail of a person is available including name, father's name, id number, thumb impression, photo, etc. Then voters can cast their vote easily. Our project infrastructure includes the admin panel which requires login credentials. The system is restricted and cannot be accessed outside of the polling station. In the polling station, firstly, the voter has to input their legal details (NIC) for authentication which redirects the voter to the Facial Recognition window, where the voter's face is validated through this method then all the candidates’ lists will be displayed on the screen of the voter. The program keeps the vote count of every candidate on submission of each vote and displays the result of the election as soon as the voting process ends, which means no delay in results. As our system is a Decentralized system, it also sends the vote count to every node in an encrypted way.
How blockchain enables financial transactions in the banking sector

Blockchain technology is the most important technological revolution of the second decade of the 21st century. The banking sector is one of the major sectors where blockchain has played a significant role in recording and processing various financial transactions, inter-bank transfers, and digital format agreements through a distributed ledger system. It harms the transactional costs, which influence the financial markets. The global financial system being the most popular sector is prone to many errors and frauds. Blockchain technology can help prevent these problems by enabling a decentralised network that permits all parties to review. The present study attempted to analyse the problems in existing banking financial transactions, understand the importance of transparency and study the usage of blockchain in the banking sector. It suggests a research model for solving financial transaction problems by applying blockchain technology. The study uplifts the security and transparency of blockchain technology throughout the paper.
Analysis of intelligent solar ICU water ambulance to optimize energy

To enhance health and medical facilities, some authorities have recently introduced an emergency special water vehicle known as a 'water ambulance'. This vehicle is distinct for remote areas. To operate the ambulance service, environmental issues and energy resources have become major concerns. This is a scope of research to address the issues. First, the possibility of using solar energy as the sole power source for a vessel is a perfect solution to these issues. Second, solar vessels operate everywhere without polluting the environment. In addition, another contribution of the research is to integrate a control unit in a water ambulance which is called an 'intelligent unit'. This unit will prioritize the source of energy to ensure uninterrupted power to emergency medical equipment and ambulances. This paper proposes a dynamic design of a solar water ambulance that is subject to replacing small conventional fuel-run watercraft vehicles.
Prioritizing the People: Developing a Method for Evaluating a Collection’s Description of Diverse Populations

Following the long overdue cancellation of the Library of Congress Subject Heading “Illegal alien” many libraries are assessing options for remediating problematic descriptive language in their catalogs. This paper presents a method of evaluating LCSH corresponding to historically marginalized populations—women, African Americans, Indigenous peoples, and LGBTQIA + people—by measuring cooccurrences of LCSH and Library of Congress Classifications (LCC) across a library’s collection. Starting with a sample of 468,100 MARC records, data was extracted with Python then organized and graphed with R. The results offer a holistic perspective of descriptive language assigned to these populations and produced data that can help guide collection development and local subject description policies.
How Image Exchange Breaks Down: the Image Library Perspective

While in the last decade there has been significant technical infrastructure development to support standards-based image exchange through organizations like Integrating the Healthcare Enterprise, Carequality, DICOM, and HL7 FHIR, the human operationalization of such infrastructure using centralized, intuitive, standards-based applications remains the cornerstone of effective and reliable electronic image exchange. Image libraries managing the highly transactional and often uncertain inflows and outflows of images have a unique perspective on the challenges of image exchange. This manuscript will summarize frequent collaboration and communication, release of information, staffing, technology, information localization, and analytics difficulties for image exchange from the perspective of the image library staff managing the transactions.
Research on reading recognition of pointer meter based on improved U-net network

Aiming at the automatic reading task of pointer meter in industrial applications, in order to solve the problems of indistinct meter features, complex background environment and slow recognition speed involved in the reading process of pointer meter, a new type of U-net network is proposed to segment Instrument hands and scales. The network replaces the backbone feature extraction part in the traditional U-net network with the bneck module in MobileNet, and fuses it with the enhanced feature extraction part. First, use web crawlers and data enhancement to expand the dataset; second, use the YOLO network to filter out the complex background information of the collected images and extract the dashboard area. Input the dial area information into the improved version of the U-net network to segment the meter scale and pointer; finally, convert the annular image into a rectangular image, and obtain a one-dimensional array of scale and pointer values through the rectangular image, and then calculate Pointer meter readings. Theoretical analysis and experimental results show that the new U-net can segment the pointer and scale of the meter more accurately, and the segmentation accuracy is increased from 90.79% of the traditional U-net network to 93.63%. The proposed automatic reading method of the pointer meter has good performance. The accuracy and anti-interference ability of the system, the average basic error is 0.48%, which meets the requirements of the application.
Does a general structure exist for adaptation/learning algorithms?

There are many parameter adaptation/learning algorithms (PALA) used in adaptive control, system identification and neural networks (Nesterov, Conjugate gradients, Momentum back propagation, Averaged gradient, Integral+proportional+derivative, …). For most of these algorithms unfortunately there are no results available for the choice of the various coefficients (weights) allowing to guarantee the stability of the parameter estimator for any value of the learning rate and for any initial conditions. All these algorithms are in fact particular cases of a general structure for the PALA which is introduced in this paper. This structure is characterized by the presence of an embedded ARMA (Auto Regressive Moving Average) filter. Taking into account the inherent feedback structure of these adaptation/learning algorithms, passivity approach is used for addressing the stability issue. Conditions which will assure the stability of this general structure will be provided and then particularized for the specific algorithms described in the paper. The impact of the MA and AR terms of the embedded filter upon the performance of the algorithms will be emphasized through simulation.