Reviewerly/exHarmony huggingface dataset:
Digitizing and parsing semi-structured historical administrative documents from the G.I. Bill mortgage guarantee program

Purpose Many libraries and archives maintain collections of research documents, such as administrative records, with paper-based formats that limit the documents' access to in-person use. Digitization transforms paper-based collections into more accessible and analyzable formats. As collections are digitized, there is an opportunity to incorporate deep learning techniques, such as Document Image Analysis (DIA), into workflows to increase the usability of information extracted from archival documents. This paper describes the authors' approach using digital scanning, optical character recognition (OCR) and deep learning to create a digital archive of administrative records related to the mortgage guarantee program of the Servicemen's Readjustment Act of 1944, also known as the G.I. Bill. Design/methodology/approach The authors used a collection of 25,744 semi-structured paper-based records from the administration of G.I. Bill Mortgages from 1946 to 1954 to develop a digitization and processing workflow. These records include the name and city of the mortgagor, the amount of the mortgage, the location of the Reconstruction Finance Corporation agent, one or more identification numbers and the name and location of the bank handling the loan. The authors extracted structured information from these scanned historical records in order to create a tabular data file and link them to other authoritative individual-level data sources. Findings The authors compared the flexible character accuracy of five OCR methods. The authors then compared the character error rate (CER) of three text extraction approaches (regular expressions, DIA and named entity recognition (NER)). The authors were able to obtain the highest quality structured text output using DIA with the Layout Parser toolkit by post-processing with regular expressions. Through this project, the authors demonstrate how DIA can improve the digitization of administrative records to automatically produce a structured data resource for researchers and the public. Originality/value The authors' workflow is readily transferable to other archival digitization projects. Through the use of digital scanning, OCR and DIA processes, the authors created the first digital microdata file of administrative records related to the G.I. Bill mortgage guarantee program available to researchers and the general public. These records offer research insights into the lives of veterans who benefited from loans, the impacts on the communities built by the loans and the institutions that implemented them.
A new three-wavelength chromatic-corrected hybrid phase shifting method for single-camera digital photoelasticity

Digital photoelasticity is a non-contact inspection technique, that requires new strategies to unwrap the stresses map based on color images. Therefore, this paper presents a new three-wavelength chromatic-corrected hybrid phase-shift method for single-camera digital photoelasticity applications. For the experiments, the intensities of isochromatic images are simulated considering a birefringent sample compressed and a circular polariscope configured to produce a bright field image that avoids the effects caused by isoclinics. To increase the range of the fringe order, three LEDs with peaks of close wavelengths were used. Additionality, for each LED a RGB color image is simulated. The red channel of each image is used to generate a new synthetic chromatic-corrected image (CCI) thereby: the red channel of &lambda;3 is the red channel of the new CCI, and the green and blue channels of the CCI use the respective red channels of &lambda;2 and &lambda;1; additionally, the wavelengths must satisfy the following condition (&lambda;3 &gt; &lambda;2 &lt; &lambda;1). An inverted image of the CCI is computed. Thus, with the CCI and its inverted image, six images are stored, and with these images and some trigonometric relations proposed by Ekman and Nurse a wrapped phase map is extracted. Finally, an unwrapping algorithm is applied to reconstruct the stresses map. The results show that the method improves the detected maximum order and reduces stress map distortions compared to similar color phase shifting approaches. Furthermore, since the algorithm requires only a camera and a circular polariscope setup, it can be implemented in dynamic experimental applications.
Design of YOLOv2-tiny accelerator based on PYNQ-Z2 platform

Convolutional neural networks (CNNs) have been widely used in the field of image recognition. To meet the massive computational requirements of CNNs, GPUs or other intelligent computing hardware are typically used for data processing. FPGA supports parallel computing and is characterized by programmability, high performance, low energy consumption, and strong stability. In this paper, we improved and optimized the YOLOv2-Tiny algorithm by combining it with the hardware implementation based on FPGA's hardware structure. We divided the neural network tasks and preprocessed data using the 16-bit fixed-point method to reduce hardware resource consumption. By using the PYNQ-z2 development platform to accelerate the YOLOv2-Tiny CNN, we achieved target object detection and recognition. Compared with CPU (i7-10710U), the processing capacity was 2.94 times that of CPU, and the power consumption was 3.1% of CPU.
Reflect-AR: Insights into Mirror-Based Augmented Reality Instructions to Support Manual Assembly Tasks

Manual assembly tasks can be difficult and tedious without assistance. Here, augmented reality (AR) can help to ease the task load and lower the time and error rates by interactive in-situ instructions. Such approaches are often implemented by video or optical see-through AR. In order to test a system that is as low-threshold as possible and easy to implement, we developed a prototype with a mirror display supplemented by a RGB-D camera and evaluated it against an well-established AR HMD (HoloLens 2). For this purpose, in our study 10 participants placed 3D printed bricks via both technologies. The quantitative and qualitative analysis revealed that the fulfillment of this task with a professional, established product AR HMD yet remains unmatched in terms of the time required, error rate, usability and task load due to the prototype status and the remaining technical shortcomings. However, the mirror display setup prototype met with interest from the participants as a novel, but unfamiliar and thus more difficult to use way of interaction. Furthermore, we report implementation challenges and advises. The empirical insights of our prototype and the first-time comparison to an established AR HMD aims to foster refining future work with half-silvered AR mirrors as an little researched field and many different fields of application.
TOUCH: A Multi-sensory Communication System that Communicates Emotions

During the pandemic, many people have found themselves physically isolated from each other for long periods. As a consequence, online communication tools have become the primary mean for social interaction among people in quarantine. However, texts and other chatting tools do not adequately transmit the complex emotions hidden behind them. Without the stimulation of sensations from face-to-face communications, people hinder their ability to observe others’ emotions and feelings effectively. In this work, we propose a system that aims to improve emotional communication. We present Touch—a combination of a social media app and a wearable device that can receive and display multi-sensory feedback, including light, digital display, vibrations, and tactile interaction to transfer emotions. While the app encourages interaction between users through virtual spaces, the wearable device further enriches people's communication by offering another means to express emotions not afforded by virtual interactions. Together, these two elements of the Touch project offer a sounding solution to the problem of misunderstanding in communication.
CoSy - AI enhanced assistance system for face to face communication trainings in higher healthcare education

This paper presents the current state of the Communication Support System (CoSy) which is a web-based system meant for assisting communication training in the healthcare domain. To improve feedback quality and thus learning outcomes of future healthcare professionals, CoSy generates feedback regarding a case-based student-actor conversation. The feedback contains verbal and para verbal aspects, i. e. speech rate, used word, and conversation share. Therefore, the system implements three components: (1) Audio recording of the conversation, (2) AI-based analysis of that conversation, and (3) visualization of corresponding feedback. In line with the large-scale project LABORATORIUM, a human-centered design approach is used to design the system, while a machine learning algorithm is built and trained with domain experts to guarantee reliable speech analysis. The result is a flexible and assistive system that can be used to complement communication training in which various healthcare situations can be practiced and discussed in class. Future work will also face a real-time analysis as well as more complex analysis aspects of communication like emotions or paraphrasing.
A Novel Classification Layer Technique for ASL Analysis Using CNN

American sign language (ASL) is the most well-known sign language in deaf communities and has expanded to be a sign language in other countries, including Thai sign language (TSL). Currently, computer vision and artificial intelligence (AI) technologies are fast stepping and expanding into many fields of human living. There is much knowledge in ASL applications, which can also be exploited to develop TSL applications in the future. This study will analyze the Convolution Neural Network classifier (CNN) type for ASL, ranging from ordinary CNN classifiers, CNN with Support Vector Machine (SVM) classifiers, and Ensemble CNN classifiers. We set the augmented validation dataset to mimic the natural world environment. The new CNN model is better than the previous state of art ASL CNN model in the augmented validation dataset. Furthermore, to improve the proposed CNN model, we tested 2 experiments, first was to replace the dense classifier in the CNN model with an SVM classifier, and the second was to ensemble two pieces of knowledge from 2 learned CNN models with averaging aggregation of SoftMax's probability. Finally, the best validation accuracy can be achieved by a novel model from the CNNs' ensemble techniques with averaging aggregation of SoftMax's probability.
Sentiment Analysis of Consumer Reviews Using Machine Learning Approach

Sentiment analysis is a beneficial method in natural language processing to understand the sentiments of end consumers. This research is focused on sentiment analysis on the IMDb dataset based on a standard Recurrent Neural Network (RNN) with two, three, and four layers of Gated Recurrent Unit (GRU) and Bidirectional Gated Recurrent Unit (BiGRU). In addition, this research uses the GloVe word embedding model to transform raw sentiment texts into meaningful vectors for creating a seen dataset. The presented method has shown more accuracy in identifying sentiments with multiple negations than existing algorithms. Apart from the RNN-GRU and RNN-BiGRU models, this paper has also tested various algorithms such as standard RNN, SVM, LSTM, CNN, Random Forest, Naïve Bayes, Logistic Regression, and Neural Networks with the same dataset (IMDb). It is noticed that the presented RNN-GRU and RNN-BiGRU models outperform other models in polarizing unlabeled sentiments.
Classifying Activities of Electrical Line Workers Based on Deep Learning Approaches Using Wrist-Worn Sensor

The field o f human activity recognition (HAR) is a priority for cutting-edge study because of its potential to revolutionize the way we understand and improve our everyday lives. A large variety of ordinary, everyday tasks has been classified using H AR. Nevertheless, inc ontrast to basic human actions, the increasing demands of numerous real-world applications have attracted the interest of the HAR area of study. Electrical line workers (ELWs) face a variety of challenges, including long hours, working in isolated locations, and performing particularly hazardous tasks. Wearable sensor-based HAR allows for unobtrusive tracking of ELW efficiency and security. This study explores deep learning strategies for automatically categorizing ELWs' complicated actions through sensor data collected through a wrist-worn device. We propose ResNeXt, a deep residual neural network, and evaluate it with other deep learning networks for their ability to categorize ELW activities effectively. We employ a publicly available benchmark dataset that includes 10 ELW tasks. The results of the experiment demonstrate that the proposed ResNeXt achieved the highest accuracy (98.74%) and F1-score (98.81%) compared to other deep learning networks studied.
Machine Learning Algorithms for Signal Demodulation and Determination of HF Communication Channel Parameters

High-frequency radio waves tend to be reflected from the ionosphere, which is why data transmission in this way can be carried out over long distances without additional repeaters. With an increase in the transmission rate, distortions of the transmitted signal occur, associated with changes in the reflection of the signal from the ionosphere over time. To study these processes in the Matlab environment, a HF communication channel was modeled (based on the Watterson model) and data was collected during data transmission at various channel parameters. On these data, several machine learning models were built to demodulate the signal, as well as determine the parameters of the communication channel from the received signal.
Privacy-Aware Blockchain-Based AV Parking System Registration Scheme

Innovation and automation remain at the forefront of every evolving technological trend, and Autonomous Vehicles (AV) are an acute instance of this. As research continues on ways to improve the efficiency and cost-effectiveness of AVs, the aspect of parking systems is being brought to light. A crucial element of an effective AV parking scheme is having strong user privacy and cybersecurity capabilities to defend against numerous online attackers and protect users’ sensitive information. In this paper, we present a privacy-preserving blockchain-based registration scheme for AV parking systems capable of achieving the aforementioned imperative. Our proposed scheme focuses on a modified kNN encryption technique to encrypt and match the interested parking spots of AV users with the available parking slots of participating parking lots in the form of vector matrices. Additionally, the blockchain provides secure payment fairness and transparency to ensure transactional satisfaction from both the AV user and the parking lots without the need for a financial third party. Our security and privacy analysis further indicate that our proposed scheme is robust and efficient.
Towards PHP Vulnerability Detection at an Intermediate Language Level

Web applications are a prime target for malicious actors to obtain private user information, such as credit card numbers and other sensitive details. Over the years, the number of vulnerabilities and attacks has increased, demonstrating that current solutions have shortcomings. For example, they can be prone to error or require too much resources/time from developers (or security analysts) to deliver results. This paper presents a new approach to detect vulnerabilities in web applications written in PHP by analyzing their representation in an Intermediate Language (IL) and simulating the execution through a new data structure.
Research on Water Rights Trading System based on Blockchain Technology

For the large amount of data volume, fast update, high timeliness, cumbersome transaction processes, high data sharing requirements, high transaction costs, etc., decentralization, high security and reliability, scalability, high sharing, intelligentization, blockchain technology has a strong superiority in the construction of smart water affairs. According to the characteristics of water rights transactions, the Hyperledger Fabric of the Alliance Chain Project is selected as the underlying network system of the water rights trading system to build a water right trading prototype system. Layer architecture, build a three -channel bookkeeping method for designing the alliance chain multi -channel technology, designing confirmation, transactions, and registration, designing the chain code of water rights transaction, and achieving multi -chain operations through chain code calls, which can not only meet different data between institutions, Sharing demand can also increase the throughput of the entire Fabric network. The water right trading system based on the blockchain can simplify the water rights transaction process and reduce the cost of water rights transaction. The system has the characteristics of high scalability, efficient and controllable dynamic management, safe and reliable, low cost, and convenient management operation and maintenance. Transactions provide new ideas more efficiently and safely.
Research on Railway Transportation Data Grading Based on Collaborative Context Metamodel

Data grading plays a key role of big data governance and data sharing. Normally data grading method defining data levels depending on scope of influence which caused by leakage and tampering. But Data grading relies on business context and business processes, specially in railway domain. To solve this problem, we propose a method for grading railway data, which allows us to grade context-heavy, multiple types of data. We have sorted out the business processes of railway transportation and organized information about the related business. The information thus extracted from the data enables the construction of context metamodels and the determination of security levels for railway transport data based on inference rules. We selected a typical context of railway transport and used the method to grade the data of the context to verify the effectiveness of our method for grading data with strong Sceneability.
Secure Medical Image Storage Using Cross-Chain Technology

The storage and management of medical images present significant challenges due to their unique characteristics that require careful consideration of security and privacy. Traditional centralized server-based approaches to medical image storage and management are vulnerable to server crashes, which can result in data exposure risks. Blockchain technology can enhance the security of medical image data, but it may negatively affect data throughput. To address this issue, this paper proposes a blockchain data storage system that leverages image classification and deep learning-based pre-processing to improve the efficiency of blockchain storage. By pre-screening the medical data to be stored, the proposed approach can mitigate the negative impact of blockchain technology on data throughput. The verification results show that the proposed method can increase the storage capacity of blockchain and improve data throughput by distributing medical image data across multiple blockchains.
BERT-BiLSTM-CRF Based Named Entity Recognition Method for Controlled Speech

As research on automatic speech recognition technology in the controlled speech field continues to deepen, a variety of controlled speech recognition systems have emerged. However, due to varying levels of quality, it is essential to evaluate the effectiveness of these systems. A crucial factor in assessing controlled speech recognition systems is their accuracy in recognizing keywords. As such, it is of significant value to investigate techniques to extract essential keywords from recognized controlled speech texts. To this end, this paper proposes a BERT-BiLSTM-CRF based model for named entity recognition research. Furthermore, this model’s performance was tested on the CLUENER2020 dataset, providing feasible and promising results. Given the limitations of available data and research for entity recognition in the controlled speech field, this paper also designed a named entity recognition dataset for this field using the BIO annotation method. This achievement not only expands the research scope of named entity recognition but also provides critical groundwork for future advancements in the field of controlled speech recognition.
Exploration on Machine Learning Course Reform in Application-Oriented Universities under the Background of Artificial Intelligence

In view of the current teaching situation of machine learning in application-oriented universities, based on the teaching concept of "student-centered, output oriented", the contents of machine learning in application-oriented universities have been reconstructed, and a four stage teaching mode is proposed. What’s more, taking written digit recognition as a typical teaching case, the teaching methods of machine learning course are discussed, and puts forward suggestions on three aspects: fostering scientific spirit, strengthening group cooperation, and promoting learning through competition, so as to improve the teaching effect of machine learning course in application-oriented universities.
Research on Semantic Verification Method of Control Instruction Based on Ensemble Learning

For flight safety, the semantic verification of radiotelephony communication is of great significance, which can avoid aviation accidents caused by communication errors between pilots and air traffic controllers. To achieve this goal, we used an ensemble learning strategy in machine learning and applied it to deep learning to build an ensemble learning model for semantic verification. Firstly, we used three different pre-trained language models based on the Transformer architecture for fine-tuning on the control instruction semantic verification dataset. Then, we used the Bagging ensemble classification method to integrate the trained base models, and finally used a voting method based on accuracy weights to output the ensemble model. The experimental results show that our ensemble model can significantly improve the discriminative performance of the model without increasing its complexity. Compared with the best-performing base model, the discriminative accuracy has increased by 1.4%.
Video Logging Casing Fault Detection Model Based on Improved-YOLOv5s

Video logging technology is an important means to detect casing fault. At present, most of the fault detection models are constructed by R-CNN (Region Convolution Neural Network) or YOLO (You Only Look Once) according to the image data obtained from video logging. However, these models may have problems such as few sample data, uneven object distribution, and difficulty in focusing on object information. This paper proposes a casing fault detection model based on Improved- YOLOv5s, the baseline model uses YOLOv5s, and Mosaic-9 image enhancement method is used in the data preprocessing stage, which not only expands the sample and object, but also increases Batch-size of the input network in a disguised way; in the model building stage, attention mechanism module is integrated to make the model focus on specific objects; finally, the enhanced results are input into the model for training, and the casing fault detection model is obtained. The comparative experiment shows that: mAP@0.5 (mean Average Precision IoU=0.5) index is 0.982; mAP@0.5:0.95 (IoU=[0.5:0.95]) index is 0.823, which is nearly 20 percentage points higher than YOLOv5s, indicating that this model has better fault detection effect under the condition of video logging few sample data and uneven object distribution.