Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Structural Deformation Test Based on Edge Detection and Digital Image Correlation

Digital image-correlation (DIC) algorithms rely heavily on the accuracy of the initial values provided by whole-pixel search algorithms for structural displacement monitoring. When the measured displacement is too large or exceeds the search domain, the calculation time and memory consumption of the DIC algorithm will increase greatly, and even fail to obtain the correct result. The paper introduced two edge-detection algorithms, Canny and Zernike moments in digital image-processing (DIP) technology, to perform geometric fitting and sub-pixel positioning on the specific pattern target pasted on the measurement position, and to obtain the structural displacement according to the change of the target position before and after deformation. This paper compared the difference between edge detection and DIC in accuracy and calculation speed through numerical simulation, laboratory, and field tests. The study demonstrated that the structural displacement test based on edge detection is slightly inferior to the DIC algorithm in terms of accuracy and stability. As the search domain of the DIC algorithm becomes larger, its calculation speed decreases sharply, and is obviously slower than the Canny and Zernike moment algorithms.
The Application of Interactive Visualization and Computer Vision in Intelligent Education Based on Big Data AI Technology

Considering the problem that teachers’ command gesture tracking error is due to their poor detection ability in the intelligent education of big data or technology, the application method of interactive visualization and computer vision in the intelligent education of big data or technology is designed. Three-dimensional coordinate transformations of teaching content information and visual processing of teaching information. Use computer vision to detect teachers’ actions and complete computer vision detection of interactive teaching. Using the rendering algorithm, the AI education environment is optimized without affecting computer vision detection. So far, the interactive visualization and computer vision application method design in intelligent big data technology education has been completed. The experimental results show that combining these two technologies can significantly improve the tracking effect of teachers’ instruction gestures, optimize the design effect of teaching content, and raise teaching levels.
A Small-Sample Text Classification Model Based on Pseudo-Label Fusion Clustering Algorithm

The problem of text classification has been a mainstream research branch in natural language processing, and how to improve the effect of classification under the scarcity of labeled samples is one of the hot issues in this direction. The current models supporting small-sample classification can learn knowledge and train models with a small number of labels, but the classification results are not satisfactory enough. In order to improve the classification accuracy, we propose a Small-sample Text Classification model based on the Pseudo-label fusion Clustering algorithm (STCPC). The algorithm includes two cores: (1) Mining the potential features of unlabeled data by using the training strategy of clustering assuming pseudo-labeling and then reducing the noise of the pseudo-labeled dataset by consistent training with its enhanced samples to improve the quality of the pseudo-labeled dataset. (2) The labeled data is augmented, and then the Easy Plug-in Data Augmentation (EPiDA) framework is used to balance the diversity and quality of the augmented samples to improve the richness of the labeled data reasonably. The results of comparison tests with other classical algorithms show that the STCPC model can effectively improve classification accuracy.
Usefulness and usability of heuristic walkthroughs for evaluating domain-specific developer tools in industry: Evidence from four field simulations

The usage of domain-specific languages (DSLs) is an approach to reduce complexity in software development by expert developers for selected application domains. To support expert developers, a DSL is often combined with a tailored, domain-specific developer tool, offering similar functionality as general-purpose programming environments (IDEs like Eclipse for Java) while integrating with a domain-specific toolchain (e.g., code or documentation generators, simulators). General-purpose development environments have been successfully evaluated for the programmer experience (PX) and for anomalies using heuristic walkthroughs as a mixed review technique combining cognitive walkthroughs and a heuristic evaluation. In this paper, we report on the usefulness and acceptance of heuristic walkthroughs as an PX evaluation technique applied to domain-specific languages and IDEs in an industry context. Heuristic walkthroughs are used in four interventions (field simulations) to assess the programming experience and usability of domain-specific, Eclipse-based IDEs in a concrete industry setting. Data on the usefulness and acceptance (perceived satisfaction) of the walkthroughs themselves are collected and analysed. Our studies show that, in practice, the instrument of walkthroughs is useful for revealing practically relevant PX anomalies, while maintaining acceptance by practitioners participating in the walkthroughs. The documented variant of heuristic walkthroughs is eligible to become adopted for future field studies in academic research and for evaluation projects in industry, in support of developing domain-specific developer tooling in an evidence-driven manner.
Predicting tax fraud using supervised machine learning approach

With the advancement in technology, the tax base in Rwanda has become broader, and as a result, tax fraud is growing. Depending on the dataset used, fraud detection experts and researchers have used different methods to identify questionable cases. This paper aims to predict features of tax fraud using the most robust supervised machine-learning model. This research provides a context where a fraud expert can use a machine-learning model, and an implemented model offers instant feedback to the fraud expert. We evaluate supervised machine learning models such as Artificial Neural Network, Logistic Regression, Decision Tree, Random Forest, GaussianNB and XGBoost. Based on different evaluation metrics, Artificial Neural Network was the most robust model for predicting tax fraud. Findings reveal that the time of business that indicates the difference in time from when a business started and the time it was audited, the domestic businesses, taxpayers who import and export goods, those with no losses, those whose businesses are located in the eastern province, and those registered on withholding and Value Added Tax types are more susceptible to tax fraud. This study is among the few to evaluate the effectiveness of multiple supervised machine-learning models for identifying tax fraud factors on an accurate data set with numerous tax types. The evidence generated in the current study will serve as a valuable tool for both tax policymakers and auditors, as well as for enhancing awareness of more robust methods for predicting tax fraud.
Telecom quantum photonic interface for a 40Ca+ single-ion quantum memory

Abstract Entanglement-based quantum networks require quantum photonic interfaces between stationary quantum memories and photons, enabling entanglement distribution. Here we present such a photonic interface, designed for connecting a 40 Ca + single-ion quantum memory to the telecom C-band. The interface combines a memory-resonant, cavity-enhanced spontaneous parametric down-conversion photon pair source with bi-directional polarization-conserving quantum frequency conversion. We demonstrate preservation of high-fidelity entanglement during conversion, fiber transmission over up to 40 km and back-conversion to the memory wavelength. Even for the longest distance and bi-directional conversion the entanglement fidelity remains larger than 95% (98%) without (with) background correction.
A Model for Infrastructure Detection along Highways Based on Remote Sensing Images from UAVs

Infrastructure along the highway refers to various facilities and equipment: bridges, culverts, traffic signs, guardrails, etc. New technologies such as artificial intelligence, big data, and the Internet of Things are driving the digital transformation of highway infrastructure towards the future goal of intelligent roads. Drones have emerged as a promising application area of intelligent technology in this field. They can help achieve fast and precise detection, classification, and localization of infrastructure along highways, which can significantly enhance efficiency and ease the burden on road management staff. As the infrastructure along the road is exposed to the outdoors for a long time, it is easily damaged and obscured by objects such as sand and rocks; on the other hand, based on the high resolution of the images taken by Unmanned Aerial Vehicles (UAVs), the variable shooting angles, complex backgrounds, and high percentage of small targets mean the direct use of existing target detection models cannot meet the requirements of practical applications in industry. In addition, there is a lack of large and comprehensive image datasets of infrastructure along highways from UAVs. Based on this, a multi-classification infrastructure detection model combining multi-scale feature fusion and an attention mechanism is proposed. In this paper, the backbone network of the CenterNet model is replaced with ResNet50, and the improved feature fusion part enables the model to generate fine-grained features to improve the detection of small targets; furthermore, the attention mechanism is added to make the network focus more on valuable regions with higher attention weights. As there is no publicly available dataset of infrastructure along highways captured by UAVs, we filter and manually annotate the laboratory-captured highway dataset to generate a highway infrastructure dataset. The experimental results show that the model has a mean Average Precision (mAP) of 86.7%, an improvement of 3.1 percentage points over the baseline model, and the new model performs significantly better than other detection models overall.
Proposal of multidimensional quantum walks to explore Dirac and Schrödinger systems

We propose a multidimensional discrete-time quantum walk (DTQW), whose continuum limit is an extended multidimensional Dirac equation, which can be further mapped to the Schr\"odinger equation. We show in two ways that our DTQW is an excellent measure to investigate the two-dimensional (2D) extended Dirac Hamiltonian and higher-order topological materials. First, we show that the dynamics of our DTQW resembles that of a 2D Schr\"odinger harmonic oscillator. Second, we find in our DTQW topological features of the extended Dirac system. By manipulating the coin operators, we can generate not only standard edge states but also corner states.
A Trustworthy View on Explainable Artificial Intelligence Method Evaluation

In this article, we follow a process of explainable artificial intelligence (XAI) method development and define two metrics in terms of consistency and efficiency in guiding the evaluation of XAI explanations.
K-Means Clustering for Prophesy of Freshmen's Attainment with Euclidean Execution

These days Academic Systems related to higher education learning facing serious issues for Educational Execution ability. We now observe for a system which has been used since a long time for the separation of the results of understudies which has been contingent on cluster of assignments, standard quantifiable estimations have also been used to sort out the data which is basically the scores which has been gained by understudies' students which has been presented. At this moment in time, K-mean gathering estimation for separating the result of data of under studies has been executed similarly. This model has been combined with the deterministic model to avoid the unwanted and delayed results of under studies, consequences of private Institutions in various states which are not an appalling benchmark for the development and growth of institutes that are related to academics. They are selecting a convincing decision by the educational institutes for execution of academics.
Blockchain Solution to Electronic Healthcare Records

Electronic Health Records (EHR) are very sensitive and private records that requires at most security and privacy. Because of lack of trustworthy and reliable health data sharing mechanisms, the majority of EHR data sharing is still done via mail. This impacts the proper treatment of patients and results in unwanted delays between participating organisations. The sharing sensitive health records via mail or physically results in various issues such as privacy, security and interoperability. As a decentralised framework, Blockchain provide cryptographic guarantees of information security, integrity, secrecy and smart contracts for access to data. Since EHR are a sensitive and private data, hence the paper proposed a system which uses permissioned blockchain solution for storing and sharing electronic healthcare records using the Hyperledger fabric framework. The proposed system allows the patients to have total control over their electronically generated medical records, and they are able to authorise doctors to examine and add new EHRs using grant and revoke access methods. The proposed system is inherently good in interoperability and secure due to nature of framework used and provided cryptoaraphic guarantee of non-tampering.
Quality Improvement of Image Datasets using Hashing Techniques

Image processing has become extremely important, with the consequences of real-time image processing failures being severe; thus, research and study in real-time image processing methods are extremely important. Some images contain incorrect information, requiring the use of techniques to improve the image and make it more understandable. Others require some pre-processing for the machine to understand and make important decisions about the image on its own, without any manual intervention. Part of pre-processing includes doing a clean-up of the dataset by removal of the bias that could appear. This paper presents the framework for detecting duplicates and near duplicates aiming towards making the dataset more efficient. This will in turn help in training the ML model to be better. This is achieved by implementing the Difference Hash (dHash), Average Hash (aHash), and Perceptual Hash (pHash). Hash functions are ideal to detect (near-)identical photos because of the robustness against minor changes, while also minimizing the number of false positive collisions. The proposed model is well-suited for various real-time image processing applications.
Sign Language Recognition System using CNN

The most common way of communication for a speech-impaired person is Sign Language. Generally, people do not learn Sign Language for communication with deaf and dumb people which causes their isolation. Sign language is an ancient language for communication that comes naturally, but since people are unaware of systematic sign language and the person cannot have a translator every time with him, there is a need of a mediator system which can translate sign language. For that purpose, a real-time method is presented in this paper where deep learning is used for ASL translation. The main aim of this project is to design a system that could identify the alphabets of American Sign Language that are being signed. The camera captures the frames of the hand being signed and then passed through a filter and later through a classifier for prediction of the class of the hand gestures. The proposed system is an initial step for creating a translator for sign language for making the communication easier. The result is an HCI system that enables people to communicate with D&M people without sign language being known.
Jetson Nano-Based Two-Way Communication System with Filipino Sign Language Recognition Using LSTM Deep Learning Model for Able and Deaf-Mute Persons

Communication is the foundation of what it is to be human. The majority of human communication is reliant on sounds. However, it is not the sole natural means of communication; other people employ alternative ways. One of which is the Deaf community’s language. Communication between the Deaf-mute community and hearing or able individuals is one of the various challenges the two parties encounter. In the Philippines, around 70% of the Filipino Deaf Community utilizes Filipino Sign Language (FSL) as their primary language, whereas some hearing persons may be illiterate in the Deaf community’s native language. With the given situation, this study created a twoway communication device using Jetson Nano, covering the proper translation of FSL to text and speech and the conversion of input speech into text. Ten (10) dynamic FSL gestures are considered in this study. The device used LSTM Deep Learning Model and MediaPipe to recognize the FSL gestures, then convert them into speech through Google Text-to-Speech (gTTS) API. The device also converts speech to text using Google Speech-to-Text (gSTT) API. Sixty (60) trials of a two-way conversation between a deaf-mute and a hearing person are performed. By conducting a Test of Proportion for a Two-Way Conversation, it has revealed that the prototype exceeded the standard value of 91.11%, which garnered an accuracy of 93.33%, rendering the device highly effective and reliable as a means of communication between a deaf-mute person and a fully able one.
Security Enhancement Based on Input-output Correlation Protection of Nonlinear Combinatorial Function in Quantum Noise Stream Cipher

We propose a method to protect the input-output correlation of the nonlinear combinatorial function in quantum noise stream cipher to prevent eavesdropper from attacking successfully. Simulation results show that this method can effectively resist attack.
Course recommendation model based on Knowledge Graph Embedding

The use of graphs as a method of storing data has begun to rise significantly in recent years, due to the new way of representing data in graphs. This is leveraged by the structure of graphs that facilitate modeling interactions between real-world entities. With the rapid development of technologies such as machine learning and deep learning, the digitalization of education has entered the era with artificial intelligence as the main feature. As an important part of artificial intelligence technology, knowledge graph as a format of data storing provides possibilities for smart education and promotes the innovation and development of smart education. In this context, we present in this communication a model that we built to facilitate reaching the appropriate lesson for each learner among a large group of lessons in an eLearning graph, this model is practically divided into two parts, the first in which we sort the learners by defining the profile of each of them, which will facilitate their classification into groups, and in the second part, we connect each learner with his appropriate path to achieve the desired lesson, by building a nodal path through sequential prediction until reaching the target lesson, relying mainly on embedding graph models.
Hierarchical DNN-Based Image Segmentation Algorithm Using Texton, Superpixels, and Layer-Adaptive Loss Functions

Image segmentation is critical to object-oriented image processing. Many conventional segmentation algorithms are based on the superpixel, since it integrates the pixels with similar colors and locations in prior and is beneficial for segmentation. Recently, several segmentation algorithms based on deep learning were developed. However, due to the irregular shape and size of superpixels, it is hard to apply the superpixel directly in a leaning-based segmentation algorithm. In this paper, we propose a novel segmentation method that well integrates the techniques of the deep neural network (DNN), the superpixel, adaptive loss functions, and multi-layer feature extraction. First, different from other learning-based algorithm, which applies an image or its bounding boxes as the input, we adopt the mean and the histogram differences of the features of two superpixels as the input of the DNN to determine whether they should be merged. Moreover, to well consider both large- scaled and small-scaled features, a hierarchical architecture is adopted. For different layers, the DNN models with different loss functions are applied. A larger penalty for over-merging is applied in the first layer and a larger penalty for over-segmentation is applied in the following layer. Moreover, according to human perception, the features of colors, areas, the gradient at the boundary, and the texton, which is highly related to the texture, are applied. Experiments show that the proposed method outperforms other state-of-the-art image segmentation methods and produces highly accurate segmentation results.
Model-Driven Approach to Design &amp; Architecture of Healthcare IoT Infrastructure

The Internet of Things (IoT) plays a major role in the development of novel approaches to tackle the challenges of a variety of domains and ecosystems. The medical field, for example, is a perfect use-case for the application of cyber-physical infrastructures. IoT enables healthcare providers to innovate their practices and provide better services to their patients. However, such a critical use case is subject to a multitude of challenges and concerns to ensure the efficiency of its processes and the safety of individuals. Moreover, the complexity of its domain and the gap separating its conceptual and technical aspects challenge development and healthcare teams in their tasks of maintaining such systems.Model-driven development is a popular methodology specifically aimed at such issues. This approach revolves around domain-specific concepts to create and develop the technical models.In this work, we propose a novel solution based on model-driven development to tackle the maintenance and complexity concerns mentioned. Through the implementation of a domain-specific language (DSL) and a code generator, we simplify the development of healthcare systems by translating conceptual entities into technical definitions of Kubernetes and KubeEdge resources composing the IoT infrastructure.
Lightweight Transformer with GRU Integrated Decoder for Image Captioning

Image captioning develops a relationship between visual and text information to generate a sequence of words as captions. Transformers perform machine translation and language comprehension together using encoder and decoder structure. With the aim of building a lightweight and production deployment friendly model, we present the Lightweight Transformer with a GRU integrated decoder for Image Captioning. In the presented model, the number of encoders and decoders in standard architecture are reduced to single encoder and GRU integrated decoder. Also, Multi-level rich visual features from incepptionV3 improves single unit encoder encoding performance. To validate the efficiency of the proposed Lightweight Transformer architecture extensive experiments are carried out on MSCOCO image captioning dataset. The model achieves appreciable performance in comparison to other state-of-the-art.