Reviewerly/exHarmony huggingface dataset:
EDMSpell: Incorporating the error discriminator mechanism into chinese spelling correction for the overcorrection problem

Chinese spelling correction (CSC), which is designed to detect and correct typos in the text, is an important and challenging task that has attracted increasing attention. Current CSC models based on pre-trained language models (PLM) show powerful error correction capabilities, but cause the problem that many correct characters are incorrectly corrected. The problem of overcorrection is particularly serious in real-world application scenarios. To alleviate this situation, we propose a novel post-processing model: EDMSpell, which utilizes two discriminators to post-process the corrected results. Specifically, the final decision to adopt this correction is made by discriminating whether the original sentence and the corrected sentence are correct or not. To verify its effectiveness, we conduct comprehensive experiments and ablation tests. Experiments on the SIGHAN15 benchmarks show that EDMSpell can considerably lower the false-positive rate of the model, with an average reduction of 5.4 points, while also improving the error correction F1 metric by an average of 1.4 points on nine models.
Rethinking preparing pre-service teachers with 21st-century skills: What is the higher learning institution need to devise for this idea?

This study draws attention to the qualitative papers that concentrated on preparing pre-service teachers with 21st-century skills.A meta-ethnography method was used to trace, evaluate, and synthesize the data from twenty-two qualitative research investigations to acquire a better understanding of how to equip pre-service teachers with 21st-century skills.Based on the subject covered and the teaching methods used by higher learning institutions to provide preservice teachers with 21st-century abilities, the results of the first phase of the question identified nine themes.Conversely, core and contextual skills were covered.An extensive review of sample studies assessing skills to be communicated to pre-service teachers was looked at to better understand variations in the quality of these skills among pre-service teachers.The findings demonstrate the importance of preparing pre-service teachers with 21st-century skills and the types of skills that should be communicated to them, as these skills focus on developing pre-service teachers who are thoughtful, reflective thinkers and inquiring, problem solvers, decision-makers, and can use digital technology to overcome unprecedented challenges.Recommendations for more research are explored by integrating the review's findings.
Cloud Services User’s Recommendation System Using Random Iterative Fuzzy-Based Trust Computation and Support Vector Regression

Cloud computing is now a fundamental type of computing due to technological innovation and it is believed to be a benefit for mid-scale enterprises. The use of cloud computing is increasing daily, which improves service quality but also gives rise to security concerns. Finding trustworthy service can be very challenging, take a great deal of time, or produce subpar services. Due to these difficulties, the client needs a service that is dependable, suitable, time-saving, and trustworthy. As a result, from the end user’s perspective, adopting a cloud service’s trustworthiness becomes crucial. Trust is a measure of how well users’ expectations about a service’s capabilities are realized. In this research, a recommendation system for cloud service customers based on random iterative fuzzy computation (RIFTC) is proposed. RIFTC focuses on the assessment of trust using Quality of Service (QoS) characteristics. RIFTC calculates trust using the machine learning approach Support Vector Regression (SVR). RIFTC can helpfully recommend a cloud service to the end user and anticipate the trust values of cloud services.. Precision (97%), latency (51%), throughput (25.99 mbps), mean absolute error (54%), and re-call (97%) rates are used to assess how well this recommendation system performs. RIFTC’s average F-measure rate is calculated by adjusting the number of users from 200 to 300, and it is 93.46% more accurate on average with less time spent than the current methodologies.
Knowledge Classification turns into Book Classification: Some Milestones in the Journey

The concept book classification is based on the concept classification as found in philosophy or logic.The traditional classification concept was customized by the intellect of many stalwart of library science.Many eminent persons were involved in this process .Aristotle's traditional concepts; classical Indian philosophical thoughts of classification were customized for practical book classification.W C B Sayers, Melvil Dewey and S R Ranganathan are relevant in this regard.Sayers devised the grammar of library classification.Dewey opened the gateway of practical classification by devising the decimal system.S R Ranganathan has put the subject on a new dimension by introducing many concepts in theoretical and practical areas.The study aims to recollect the contributions library science stalwarts in library classification.
Digital Library: Processes, Services and Challenges

Digital libraries are the next stage in the convergence if technology push, falling costs, and worldwide connectivity are the omens for the upcoming decade revolution.Innovations like digital libraries are still in their infancy in emerging nations.They do, however, have the potential to develop into fundamental information creation and management technologies in the future.This article's goal is to introduce readers to the fundamental ideas, procedures, and features of digital libraries while also discussing the possibilities and problems they present.
Distance-Independent Background Light Estimation Method

A distance-independent background light estimation method is proposed for underwater overhead images. The method addresses the challenge of the absence of the farthest point in underwater overhead images by adopting a global perspective to select the optimal solution and estimate the background light by minimizing the loss function. Moreover, to enhance the information retention in the images, a translation function is employed to adjust the transmission map values within the range of [0.1, 0.95]. Additionally, the method capitalizes on the redundancy of image information and the similarity of adjacent frames, resulting in higher computational efficiency. The comparative experimental results show that the proposed method has better restoration performance on underwater images in various scenarios, especially in handling color bias and preserving information.
Deep-Reinforcement-Learning-Based Object Transportation Using Task Space Decomposition

This paper presents a novel object transportation method using deep reinforcement learning (DRL) and the task space decomposition (TSD) method. Most previous studies on DRL-based object transportation worked well only in the specific environment where a robot learned how to transport an object. Another drawback was that DRL only converged in relatively small environments. This is because the existing DRL-based object transportation methods are highly dependent on learning conditions and training environments; they cannot be applied to large and complicated environments. Therefore, we propose a new DRL-based object transportation that decomposes a difficult task space to be transported into simple multiple sub-task spaces using the TSD method. First, a robot sufficiently learned how to transport an object in a standard learning environment (SLE) that has small and symmetric structures. Then, a whole-task space was decomposed into several sub-task spaces by considering the size of the SLE, and we created sub-goals for each sub-task space. Finally, the robot transported an object by sequentially occupying the sub-goals. The proposed method can be extended to a large and complicated new environment as well as the training environment without additional learning or re-learning. Simulations in different environments are presented to verify the proposed method, such as a long corridor, polygons, and a maze.
Unified platform for storing, retrieving, and analysing biomechanical applications data using graph database

Abstract Sensors and smart equipment are frequently used in biomechanical systems and applications in sports and rehabilitation to measure various physical quantities. Various sensors, measuring different parameters, can produce a large amount of data at high speeds and volumes that must be stored for real-time or post-processing and analysis. In addition to sensor data, metadata is an important component and can vary between biomechanical applications. Currently however, each application typically has its own unique data flow and storage solution. In this research, we present a universal data model solution that can be applied to any sensor-based biomechanical application in sport and physical rehabilitation. Our proposed cloud platform architecture allows for the manipulation of sensor data and metadata using a combination of Big Data and conventional techniques. The main idea of this research is to develop a platform that allows a universal way for any biomechanical application to handle its data regardless of the type of data and metadata. This is achieved by creating a universal data model, and implementing this data model in a generalized architecture using a graph database. We demonstrate the benefits of this approach using examples from existing biomechanical systems and describe the development of the cloud platform architecture and the underlying data model. We also provide an example of the use of this platform in a sport shooting application. This approach is unique in that it allows data from different sources and applications to be stored and processed using the same procedures and techniques, facilitating data analysis and application development. We envision this system will expand to multiple different biomechanical applications in the future. We expect that in time, the ability to compare various data and store different biomechanical datasets will become necessity. With the advantages of modern recommender systems and utilization of artificial intelligence, huge amounts of relevant and well-prepared data with useful metadata are required thus having such system is an important advantage for future biomechanical systems development. With the increase of people’s awareness and usage of devices that increase well-being and quality of life, presented platform and similar systems will play a pivotal role in shaping the future lifestyle.
Multi-Label Classification in Anime Illustrations Based on Hierarchical Attribute Relationships

In this paper, we propose a hierarchical multi-modal multi-label attribute classification model for anime illustrations using a graph convolutional network (GCN). Our focus is on the challenging task of multi-label attribute classification, which requires capturing subtle features intentionally highlighted by creators of anime illustrations. To address the hierarchical nature of these attributes, we leverage hierarchical clustering and hierarchical label assignments to organize the attribute information into a hierarchical feature. The proposed GCN-based model effectively utilizes this hierarchical feature to achieve high accuracy in multi-label attribute classification. The contributions of the proposed method are as follows. Firstly, we introduce GCN to the multi-label attribute classification task of anime illustrations, enabling the capturing of more comprehensive relationships between attributes from their co-occurrence. Secondly, we capture subordinate relationships among the attributes by adopting hierarchical clustering and hierarchical label assignment. Lastly, we construct a hierarchical structure of attributes that appear more frequently in anime illustrations based on certain rules derived from previous studies, which helps to reflect the relationships between different attributes. The experimental results on multiple datasets show that the proposed method is effective and extensible by comparing it with some existing methods, including the state-of-the-art method.
A Context-Aware, Computer-Vision-Based Approach for the Detection of Taxi Street-Hailing Scenes from Video Streams

With the increasing deployment of autonomous taxis in different cities around the world, recent studies have stressed the importance of developing new methods, models and tools for intuitive human-autonomous taxis interactions (HATIs). Street hailing is one example, where passengers would hail an autonomous taxi by simply waving a hand, exactly like they do for manned taxis. However, automated taxi street-hailing recognition has been explored to a very limited extent. In order to address this gap, in this paper, we propose a new method for the detection of taxi street hailing based on computer vision techniques. Our method is inspired by a quantitative study that we conducted with 50 experienced taxi drivers in the city of Tunis (Tunisia) in order to understand how they recognize street-hailing cases. Based on the interviews with taxi drivers, we distinguish between explicit and implicit street-hailing cases. Given a traffic scene, explicit street hailing is detected using three elements of visual information: the hailing gesture, the person's relative position to the road and the person's head orientation. Any person who is standing close to the road, looking towards the taxi and making a hailing gesture is automatically recognized as a taxi-hailing passenger. If some elements of the visual information are not detected, we use contextual information (such as space, time and weather) in order to evaluate the existence of implicit street-hailing cases. For example, a person who is standing on the roadside in the heat, looking towards the taxi but not waving his hand is still considered a potential passenger. Hence, the new method that we propose integrates both visual and contextual information in a computer-vision pipeline that we designed to detect taxi street-hailing cases from video streams collected by capturing devices mounted on moving taxis. We tested our pipeline using a dataset that we collected with a taxi on the roads of Tunis. Considering both explicit and implicit hailing scenarios, our method yields satisfactory results in relatively realistic settings, with an accuracy of 80%, a precision of 84% and a recall of 84%.
Cybersecurity in a Large-Scale Research Facility—One Institution’s Approach

A cybersecurity approach for a large-scale user facility is presented—utilizing the National High Magnetic Field Laboratory (NHMFL) at Florida State University (FSU) as an example. The NHMFL provides access to the highest magnetic fields for scientific research teams from a range of disciplines. The unique challenges of cybersecurity at a widely accessible user facility are showcased, and relevant cybersecurity frameworks for the complex needs of a user facility with industrial-style equipment and hazards are discussed, along with the approach for risk identification and management, which determine cybersecurity requirements and priorities. Essential differences between information technology and research technology are identified, along with unique requirements and constraints. The need to plan for the introduction of new technology and manage legacy technologies with long usage lifecycles is identified in the context of implementing cybersecurity controls rooted in pragmatic decisions to avoid hindering research activities while enabling secure practices, which includes FAIR (findable, accessible, interoperable, and reusable) and open data management principles. The NHMFL’s approach to FAIR data management is presented. Critical success factors include obtaining resources to implement and maintain necessary security protocols, interdisciplinary and diverse skill sets, phased implementation, and shared allocation of NHMFL and FSU responsibilities.
Toward a Real-Time Analysis of Column Height by Visible Cameras: An Example from Mt. Etna, in Italy

Volcanic plume height is one the most important features of explosive activity; thus, it is a parameter of interest for volcanic monitoring that can be retrieved using different remote sensing techniques. Among them, calibrated visible cameras have demonstrated to be a promising alternative during daylight hours, mainly due to their low cost and low uncertainty in the results. However, currently these measurements are generally not fully automatic. In this paper, we present a new, interactive, open-source MATLAB tool, named ‘Plume Height Analyzer’ (PHA), which is able to analyze images and videos of explosive eruptions derived from visible cameras, with the objective of automatically identifying the temporal evolution of eruption columns. PHA is a self-customizing tool, i.e., before operational use, the user must perform an iterative calibration procedure based on the analysis of images of previous eruptions of the volcanic system of interest, under different eruptive, atmospheric and illumination conditions. The images used for the calibration step allow the computation of ad hoc expressions to set the model parameters used to recognize the volcanic plume in new images, which are controlled by their individual characteristics. Thereby, the number of frames used in the calibration procedure will control the goodness of the model to analyze new videos/images and the range of eruption, atmospheric, and illumination conditions for which the program will return reliable results. This also allows improvement of the performance of the program as new data become available for the calibration, for which PHA includes ad hoc routines. PHA has been tested on a wide set of videos from recent explosive activity at Mt. Etna, in Italy, and may represent a first approximation toward a real-time analysis of column height using visible cameras on erupting volcanoes.
A Flexible Code Review Framework for Combining Defect Detection and Review Comments

Defects and errors in code are different in that they are not detected by editors or compilers but pose a potential risk to software operation. For safety-critical software such as airborne software, the code review process is necessary to ensure the proper operation of software applications and even an aircraft. The traditional manual review method can no longer meet the current needs with the dramatic increase in code sizes and variety. To this end, we propose Deep Reviewer, a general and flexible code review framework that automatically detects code defects and correlates the review comments of the defects. The framework first preprocesses the data using several methods, including the proposed D2U flow. Then, features are extracted and matched by the detector, which contains a pair of twin LSTM models, one for code defect type detection and the other for review comment retrieval. Finally, the review comment output function is implemented based on the masks generated by the code defect types. The method is validated using a large public dataset, SARD. For the binary-classification task, the test results of the proposed are 98.68% and 98.67% in terms of precision and F1 score, respectively. For the multi-classification task, the proposed framework shows a significant advantage over other methods.
Federated Learning Hyper-Parameter Tuning for Edge Computing

Edge computing is widely recognized as a crucial technology for the upcoming generation of communication networks and has garnered significant interest from both industry and academia. Compared to other offloading models like cloud computing, it provides faster data processing capabilities, enhanced security measures, and lower costs by leveraging the proximity of the edge servers to the end devices. This helps mitigate the privacy concerns associated with data transfer in edge computing, by reducing the distance between the data source and the server. Raw data in typical edge computing scenarios still need to be sent to the edge server, leading to data leakage and privacy breaches. Federated Learning (FL) is a distributed model training paradigm that preserves end devices’ data privacy. Therefore, it is crucial to incorporate FL into edge computing to protect data privacy. However, the high training overhead of FL makes it impractical for edge computing. In this study, we propose to facilitate the integration of FL and edge computing by optimizing FL hyper-parameters, which can significantly reduce FL’s training overhead and make it more affordable for edge computing.
Spatiotemporal bias of the human gaze toward hierarchical visual features during natural scene viewing

The human gaze is directed at various locations from moment to moment in acquiring information necessary to recognize the external environment at the fine resolution of foveal vision. Previous studies showed that the human gaze is attracted to particular locations in the visual field at a particular time, but it remains unclear what visual features produce such spatiotemporal bias. In this study, we used a deep convolutional neural network model to extract hierarchical visual features from natural scene images and evaluated how much the human gaze is attracted to the visual features in space and time. Eye movement measurement and visual feature analysis using the deep convolutional neural network model showed that the gaze was more strongly attracted to spatial locations containing higher-order visual features than to locations containing lower-order visual features or to locations predicted by conventional saliency. Analysis of the time course of gaze attraction revealed that the bias to higher-order visual features was prominent within a short period after the beginning of observation of the natural scene images. These results demonstrate that higher-order visual features are a strong gaze attractor in both space and time, suggesting that the human visual system uses foveal vision resources to extract information from higher-order visual features with higher spatiotemporal priority.
Error Bounds for Approximations Using Multichannel Deep Convolutional Neural Networks with Downsampling

Deep learning with specific network topologies has been successfully applied in many fields. However, what is primarily called into question by people is its lack of theoretical foundation investigations, especially for structured neural networks. This paper theoretically studies the multichannel deep convolutional neural networks equipped with the downsampling operator, which is frequently used in applications. The results show that the proposed networks have outstanding approximation and generalization ability of functions from ridge class and Sobolev space. Not only does it answer an open and crucial question of why multichannel deep convolutional neural networks are universal in learning theory, but it also reveals the convergence rates.
Software Transparency for Design of Legal Machines

This chapter further investigates compliance. Software transparency is in the focus. The context is the changeover from a text culture to a machine culture in law. The target is the explainable legal machine. We remark that equal access to e-procedures does not guarantee justice. We formulate two requirements for legal machines: (1) the software architecture must be accessible, and (2) the software must provide legal protection. In the engineering phase for these requirements, the legal requirements must flow down to lower-level specifications.