Reviewerly/exHarmony huggingface dataset:
Comparison of String Similarity Algorithm in post-processing OCR

The Optical Character Recognition (OCR) problem that often occurs is that the image used, has a lot of noise covering letters in a word partially. This can cause misspellings in the process of word recognition or detection in the image. After the OCR process, we must do some post-processing for correcting the word. The words will be corrected using a string similarity algorithm. So what is the best algorithm? We conducted a comparison algorithm including the Levenshtein distance, Hamming distance, Jaro-Winkler, and Sørensen – Dice coefficient. After testing, the most effective algorithm is the Sørensen-Dice coefficient with a value of 0.88 for the value of precision, recall, and F1 score
A Study on the Integrated Support System for University Students in Academic Probation

The purpose of this study is to provide implications for preparing university-level integrated support policies to prevent academic probation of university students from being warned and eliminated in the middle by presenting the role of related departments in the university. Therefore, the research method is to consider prior studies, interviews, case studies and expert consultation. The results of the study are as follows. First of all, through interviews with university students and case studies, it was confirmed that there was a need for management and support for understudied students, establishing a system to check academic achievement levels at any time, and integrated support for related departments. Second, through the review of academic warning programs, psychological support, support for classes and learning, individual and group program operations, development of inspection tools, and measures to overcome academic warnings were presented. Third, the university-level integrated support system and operation plan were presented by classifying the learning slump and academic warning experience process of university students into three stages during and after the semester. The significance of this study is as follows. Based on prior research related to bachelor's warning college students and researchers' long research and operation experience, the university-level role and integrated support measures were presented until the escape of bachelor's warning and mid-career.
Intelligent Person Monitoring system of the UHV Power Infrastructure Line Construction site based on Deep Neural Networks

Recently, many types of workers are required for the ultra-high voltage (UHV) power line construction sites in China. For safety, each worker's real-time status should be monitored. The key point of this monitoring system is identifying and tracking each worker accurately and quickly, which is the special multi-objects tracking (MOT) problem at the large open scene. In this paper, an intelligent person monitoring system with sensors composed of a camera and the ultra-wide band (UWB) radars is proposed. The proposed MOT method is composed of two parts. The one is the method of detecting and identifying persons, which is composed of an image person recognition method that is a Faster R-CNN combined with a re-ID branch, a UWB worker localization method, and a person association method. And the other is tracking these persons, which is composed of an person-track association algorithm by a similarity matrix, and a tracking method by the Kalman Filter. The experiments illustrate that our method can monitor multi-persons accurately and quickly in real time.
A Cloud-Edge Framework for Video Stream Analysis using Macroblock Partitioning

Deep neural networks (DNNs) have become the state-of-the-art solution for video stream analysis, but their high delay poses a challenge in meeting real-time requirements. Existing research focuses on compressing video while retaining enough information for server-side DNNs to perform high-precision inference. However, this approach can only reduce network transmission delay and may not be optimal for massive video analysis requests. This paper proposes a Cloud-Edge collaborative Video Analysis (CEVA) framework that uses depth estimation technology to divide the inference difficulty of different macroblocks in the video and offload them to edge and cloud servers for inference. Experimental results on the VisDrone dataset demonstrate that CEVA outperforms server-side feedback-driven methods in inference accuracy and reduces waiting delay by an average of 264 milliseconds. Compared to traditional offloading methods, CEVA doubles accuracy and increases the average delay by less than 100 milliseconds.
Face Recognition Algorithms: A Comparative Study

This research paper analyses Eigenfaces, Fisherfaces, KLT (Kanade-Lucas-Tomasi) and Viola-Jones face recognition algorithms.Examined under various conditions, including lighting changes, design changes, nonsolid deformations, occlusions, anomalies in the dataset, and image variations and resolution, the study examines its limitations, strengths and functions.The truth, speaking, is to give insight into their comparisons.Useful tips for choosing an algorithm based on the situation.Eigenfaces and Fisherfaces perform well in a controlled environment with limited variability.Viola-Jones has demonstrated high accuracy in face detection and object detection.KLT is mainly used for feature monitoring and optical measurement.This outcome facilitates a comprehensive understanding of the strengths and limitations of each algorithm, helping to make informed decisions about facial recognition in a variety of situations.
Decision Tree Based Decision Optimization Algorithm for Yangqin Performance

As a traditional folk music, the yangqin has a large gap in the extraction of performance style and features compared with western instruments, and the existing decision making of the yangqin played by a robotic arm has relatively low utilization of the instrument surface. To address this research gap, this paper develops a decision tree based decision optimization algorithm for yangqin performance, which obtains the subsequent assignment results from the previous assignment results, the relative position of the central axis of the instrument surface, and other parameters. This paper illustrates the feasibility of this method from the principle, and designs several sets of controlled experiments to demonstrate that this method can effectively reduce the load of the robotic arm system, increase the utilization range of the instrument surface, and make the allocation results more similar to human habits.
Usability Attributes and Their Mapping in Various Phases of Software Development Life Cycle

It has been observed that development of interactive software has been increasing day by day. The success of this software depends upon the quality it provides to its end users. Therefore, it is a matter of concern for software development companies to produce high-quality software and provide a great user experience. Usability being the quality attribute has to be included and measured in every step of software development process in order to produce usable software. This research paper identifies a set of usability attributes that are mapped across various phases of the software development life cycle.
Human Part Semantic Segmentation Using Custom-CDGNet Network

Human body part segmentation is a semantic segmentation of human images task that entails labelling pixels in an image into their respective classes. The human body is composed of hierarchical structures in which each body part in the image has a particular individual location. Considering this knowledge, the sample class distribution technique was developed by collecting and applying the primary human parsing labels in vertical and horizontal dimensions. The proposed network exploits the underlying position distribution of the classes to make precise predictions with the help of these classes. We produce a distinct spatial guidance map by combining these guided features. This guidance map is then superimposed on our backbone network. Extensive experiments were executed on a large data set, i.e. LIP, and evaluation was done using the mean IOU and MSE-loss metrics. The proposed deep learning- based model surpasses the baseline model and adjacent state-of-the-art techniques with a 2.3% hike in pixel accuracy and a 1.4% increase in mean accuracy.
Asynchronous federated learning for tactical autonomy

Federated learning has been an active area of research for secure and privacy-aware learning where parameters and models are shared instead of actual data. However, there are some challenges to be addressed since federated learning relies on the aggregation process for the global model. If the learning server waits to receive all updates from all clients too long for the optimal global model, there might be a high delay. In this paper, we will design, develop and evaluate asynchronous federated learning by considering asynchronous client training and asynchronous aggregation that is applicable to tactical scenarios.
Corrections to “Linear Quantum State Observers”

Presents corrections to the article “Linear Quantum State Observers”.
Designing the 'new normal': Key insights from the pandemic for transforming online learning going forward

The deep disruption to education caused by the move to online learning during COVID-19 was unprecedented. While most educational stakeholders adapt to the transition back to a “new normal”, it seems an obvious time for constructivist reflection on the lessons learned. The aim of this longitudinal inquiry was to examine the experiences of higher education learners in Chile during the pandemic for potential insights to be gained. The focus was on students in multi-disciplinarian fields and the changes to their perspectives, practices, and identities. Working within the qualitative paradigm, data was gathered using interviews (&lt;i&gt;n=22&lt;/i&gt;), surveys (&lt;i&gt;n=1,054&lt;/i&gt;, n=1,137, n=205) and field notes. Learners’ experiences were mapped against contemporary e-learning theory and 21&lt;sup&gt;st&lt;/sup&gt; century learner identity goals. Through this lens, pedagogical practices, course designs, and uses of technology reflected in online spaces were found to influence learners and their learning in complex ways. Two key themes emerged in the analysis: (1) exposure to conventional pedagogy, information transfer and assessment-driven designs and technology challenges online, left students discouraged with their learning progress and disparaging of their future workplace preparedness and (2) students who experienced learner-centered practices online, supported by contemporary learning theory and community-building course designs and technologies, underwent transformative changes to their learning and identities and feeling confidently qualified for their post study careers. In view of the increasing role e-learning will play in this century, we believe further empirical research of this kind into a myriad of global, digital learning contexts, could help transform online learning and learners in our new post pandemic reality.
Matrix-Based Ant Colony Optimization for Large-Scale Balanced Multiple Traveling Salesmen Problem

The balanced multiple traveling salesmen problem (BMTSP) is a challenging combinatorial optimization problem with many practical applications. However, as the scale of the problem grows, it becomes computationally expensive to solve in a reasonable amount of time. To address this issue, we propose a matrix-based ant colony optimization (MACO). MACO redefines the ant colony optimization and uses matrix operations to accelerate the path construction, fitness evaluation, and pheromone update processes. We introduce a new encoding method to represent the population and use matrixed roulette wheel selection to speed up the path construction process. Experiments on the TSPLIB benchmark set show that MACO achieves comparable solution quality to traditional CPU-based ACO, while providing higher computational efficiency by utilizing GPU-accelerated matrix operations.
Data Science Applications

In Parts I & II, we have considered a communication problem wherein the goal is to deliver a sequence of information bits from a transmitter to a receiver over a channel. Two prominent channels were taken into consideration: (i) the additive white Gaussian channel (in Part I); and (ii) the wireline ISI channel (in Part II). What we did repeatedly throughout is: Given an encoding strategy (e.g., sequential coding, repetition coding), we strive to: Derive the optimal receiver for decoding the bit string; Analyze the decoding error probability when using the optimal receiver.
METHODS OF SOLVING OF GAS LAWS GRAPHIC PROBLEMS IN THE SCHOOL PHYSICS COURSE

The article deals with the theoretical and methodological aspects of the implementation of the author's methodical approach of solving graphic problems on gas laws as one of the key issues of the school physics course and an integral component of the students' subject competence. It is noted that despite the availability of various literary sources related to physics teaching methods, an important issue of the modern educational process in physics in general and problem solving in particular is the implementation of active methods, techniques and forms of learning to in school practice. They encourage students to active thinking, develop their cognitive interest, and form a culture of intellectual work and independent practical actions. It is noted that the effective assimilation of the main questions of the corresponding topic of the school course in physics, taking into account the principles of scientificity, clarity and generalization, will be facilitated by meaningful compactness, unity and a clear sequence of educational and cognitive actions of students during the solving of graphic problems at the first lessons. The author proposes the algorithm of problem solving that includes the following stages: 1) systematization of information about the main gas laws and their graphic representation; 2) students find out the nature of the change in one of the three parameters of the gas state; 3) construction of a closed process with an ideal gas on the other two diagrams for cases when individual sections of graphic represents loop process (that is, assume a change in all three gas parameters); 4) solving graphic problems for gas laws which are not loop processes; 5) solving graphical problems for loop processes in the form of circles on the initial diagrams. The authors' own pedagogical experience shows that the organization of the educational process based on the proposed methodical approach is effective and efficient, which is confirmed by the level of cognitive activity and educational success of both school students and students of higher education in recent years. Key words: graphic problems, gas laws, loop process, methods of solving physical problems, subject competence of students in physics, school physics course.
Transformational processes in modern foreign education: the curriculum of the discipline for higher education applicants of the degree of Doctor of Philosophy in the specialty " 014 Secondary education "

The work programme of the discipline “Transformational processes in modern foreign education” is designed to train the applicants for higher education of the third (educational and scientific) level, educational degree “Doctor of Philosophy”, field of knowledge 01 Education / Pedagogy, specialty: 014 “Secondary education”. The aim of the discipline “Transformational processes in modern foreign education” is the formation of comparative education competence of a researcher – a qualified specialist who has a high level of ability and readiness for professional activity in the field of home, European and world science and education. For teachers and applicants for higher education of the third (educational-scientific) level Educational degree “Doctor of Philosophy”, Field of knowledge 01 Education / Pedagogy, Specialty: 014 “Secondary education”.
An offer the journal couldn’t refuse

The article describes a two-year project (running from 2021 to 2022) that worked on getting Danish Open Access journals indexed in the Directory of Open Access Journals (DOAJ). The project was run in partnership by Copenhagen Business School (CBS Library), Royal Danish Library and Aalborg University Library, in close collaboration with DOAJ. All of the journals that participated are hosted on the libraries’ Open Journal Systems (OJS). In this article the authors demonstrate some of the challenges the journals and the project group faced in the inclusion process and in the assistance the project provided, as well as learning outcomes and perspectives.
A Weighted Knowledge Graph for Representing the Results of a Systematic Literature Review

In information systems engineering, one of the most important success factors is understanding the latest scientific research on the use of IT solutions in the enterprise's field of activity. For this purpose, systemic literature reviews are often used to identify the most important concepts and their relationships in a respective domain. This paper illustrates how a knowledge graph can help to visualize and analyze the results of a systemic literature review. A simplified weighted knowledge graph, in which weights are assigned to its nodes, is constructed using the results of a systematic literature review on the application of the agile approach to the development of information systems in the public sector. The graph reflects and, also, helps to analyze the collected information on the state of the art of agile information systems engineering in public organizations.
A Linked Data Based Advanced Credit Rationale

A credit rationale forms an integral part of the credit granting and credit decision making process. Historically such credit rationales were written as a natural language document. This meant that automated handling of the meaning, i.e.: semantics, of such documents by machines was too complex to be done with sufficient accuracy. As a consequence, the creation and verification of a credit rationale is a time consuming and manual process. The solution that we propose is to create a Linked Data based credit rationale that we call the Advanced Credit Rationale (AdCR). Linked Data can ensure that the semantics in the credit rationale are both human and machine understandable. This enables the automated checking of the credit rationale for potential regulatory issues, as well as semantic based querying over a whole portfolio of such credit rationales. An issue that often hinders the use of a Linked Data based approach is that the creation of the knowledge graph can be challenging for domain experts. In this work we propose an approach that facilitates the creation of a credit rationale that is based on a Linked Data knowledge graph. We evaluate this approach with a prototype. We also demonstrate that with this approach the creation of a Linked Data based credit rationale should not take more time than a natural language based credit rationale. In addition we show that the structures that a Linked Data based credit rationale provides are generally seen as helpful.
Career Finder System using Rule-Based Filtering for University Student Candidates

As a current reality, students are frequently questioned about a suitable career path for the future, but they are unaware of the jobs offered by current industries. Moreover, students seeking university admission frequently encounter difficulties selecting courses and educational programs, and they are confronted with a variety of available courses. This research aims to make a mobile application for students to obtain employment career options appropriate to their educational qualifications because student is often asked about a suitable career for their future but have no idea about the available career path that appropriate. The methodology that implements in this research is Mobile Application Development Life Cycle (MADLC) that have four phases which is identification, design, development, and testing. The Visual Studio Code with Flutter plugin is used to develop the mobile application and its function. Firebase is used to get the database to store all the data and works as backend function of the application. The finished system was tested accordingly based on the functionality that listed all available function of the system. The system considers students' educational qualifications and academic achievements to provide personalized recommendations. This system can assist students in making career decisions and pursuing the right career path, saving them time, and reducing the risk of making wrong choices. This research indicates understanding the importance of career decision-making for students before continuing their university studies. In conclusion, this research seeks to enhance the ability of students to make decision of the available career path provided through recommendation system.
Detection of adversarial attacks on machine-learning systems

One of the major issues limiting the adoption of machine learning (ML) in applications where accuracy is critical is failure of an otherwise accurate system. Recent work has developed tools to independently measure the competency of machine learning models and the conditions that drive these competency differences. The purpose of this paper is to explore ways to detect and mitigate adversarial attacks by using the same tools to assess competency. We will introduce BAE System’s MindfuL software which assesses ML competency under varying environmental conditions. We then consider a few different types of adversarial attacks and describe detection experiments. We examine the predicted performance and strategy and use that information to detect adversarial attacks. We will present the results of these experiments and discuss the implications of this work and potential future directions for this research.
Synthetically generating human-like data for sequential decision-making tasks via reward-shaped imitation learning

We consider the problem of synthetically generating data that can closely resemble human decisions made in the context of an interactive human-AI system like a computer game. We propose a novel algorithm that can generate synthetic, human-like, decision making data while starting from a very small set of decision making data collected from humans. Our proposed algorithm integrates the concept of reward shaping with an imitation learning algorithm to generate the synthetic data. We have validated our synthetic data generation technique by using the synthetically generated data as a surrogate for human interaction data to solve three sequential decision making tasks of increasing complexity within a small computer game-like setup. Different empirical and statistical analyses of our results show that the synthetically generated data can substitute the human data and perform the game-playing tasks almost indistinguishably, with very low divergence, from a human performing the same tasks.