Reviewerly/exHarmony huggingface dataset:
Construction of commander's visual model under long-term voyage

Long-term voyage ships have the characteristics of isolation and airtightness, 24-hour continuous duty, and being far away from the coastline. The long-term voyage state will have an impact on the visual ability of the commander, which in turn will have an impact on the cognitive performance in the process of human-computer interaction. As a result, the human-computer interaction tasks that can be completed under normal circumstances may not be completed under long-term voyage state. The existing researches have insufficient description of visual model under long-term voyage status. Therefore, based on the original EMMA visual model, this paper designs a visual parameter collection experiment under the 90-day long-term voyage state, selects 8 subjects for data collection of eye movement preparation time and eye movement saccade speed, constructs a commander's visual model under 90-day long-term voyage and analyses the reasons for the changes in the commander's visual parameters.
Effects of crease features on crease visibility and goodness of smartphone foldable display

Recently, foldable smartphones have become popular due to their flexibility and portability. However, the line-shaped mark (crease) produced by folding and unfolding can negatively affect user experience. Since eliminating the mark is difficult with current technology, it is important to understand the relationship between crease features and human perception, and develop a grading system to establish crease design guidelines to improve user experience. In this study, 25 participants were recruited, and 17 prototypes with different crease features were compared in terms of crease visibility. The findings showed that deeper crease depth and larger folding radius increased crease visibility, with depth being the more influential factor. The grading system established that foldable display prototypes with a crease depth below 60 µm were considered "good" or better. Also, the study found that a single-line crease was less visible/noticeable than a multi-line crease. These findings may provide practical guidelines for designing creases for foldable displays.
Effect of Presentation of Advertisements during Visual Display Terminal Work on Concentration

This study aimed to examine the effects that advertisements viewed during visual display terminal (VDT) work have on workers as well as the appropriate conditions under which advertisements should be presented to both consumers and advertisers. We investigated the manner in which the position and interest in advertisements affected people’s ability to focus. Mock advertisements were displayed on a monitor while participants completed a specially created cognitive memory exercise. We evaluated the effects on concentration based on the results of the exercise, subjective evaluations of concentration, and eye-gaze information. This shows that the self-perception of concentration may have been affected by the advertisement, except when the commercial was placed on the right side of the screen. Additionally, it was hypothesized that differences in the level of interest in advertisements and the condition of the workers affected their concentration.
A continuation method for image registration based on dynamic adaptive kernel

Image registration is a fundamental problem in computer vision and robotics. Recently, learning-based image registration methods have made great progress. However, these methods are sensitive to abnormal transformation and have insufficient robustness, which leads to more mismatched points in the actual environment. In this paper, we propose a new registration framework based on ensemble learning and dynamic adaptive kernel. Specifically, we first use a dynamic adaptive kernel to extract deep features at the coarse level to guide fine-level registration. Then we added an adaptive feature pyramid network based on the integrated learning principle to realize the fine-level feature extraction. Through different scale, receptive fields, not only the local geometric information of each point is considered, but also its low texture information at the pixel level is considered. According to the actual registration environment, fine features are adaptively obtained to reduce the sensitivity of the model to abnormal transformation. We use the global receptive field provided in the transformer to obtain feature descriptors based on these two levels. In addition, we use the cosine loss directly defined on the corresponding relationship to train the network and balance the samples, to achieve feature point registration based on the corresponding relationship. Extensive experiments on object-level and scene-level datasets show that the proposed method outperforms existing state-of-the-art techniques by a large margin. More critically, it has the best generalization ability in unknown scenes with different sensor modes.
Research and Development of a Land Resource Management Information System Based on Multi-Dimensional Information Fusion

Research and development of a land resource management information system based on the multi-dimensional information fusion is studied in the manuscript.Information fusion as a multi-layered, multi-faceted, and multi-level process that involves the detection, correlation, combination, and estimation of data from multiple sources.In this study, the fusion model is integrated to make the land resource management information system efficient.Information resource management needs not only hard information technology as a theoretical support, but also soft management methods as a means.The application is defined and studied for improving the efficiency.
Research on Physical Education Teaching and Sports Training for College Students

After the country proposed the Sunshine Sports, physical education in colleges and universities started to promote the teaching reform work and built a sunshine sports teaching model in line with the national sports strategy, which achieved good results.In view of the problems existing in traditional physical education teaching and sports training, it is necessary to use the sunshine physical education teaching to change the current teaching status, and to fully exert the positive role in the process of physical education teaching and sports training by building the ecological environment of the sunshine physical education teaching and innovating the forms of activities in and out of the sunshine physical education, enhance students' physical fitness and cultivate students' sportsmanship.This article mainly provides an overview of the concepts, similarities and differences, and complementary relationships between physical education teaching and sports training in colleges and universities.
Cybersecurity – Security Operations Center

Currently, most organizations are dependent on Information and Communication Technologies, in the sense of accomplishing their underly­ing business activities. In this scope, cybersecurity is considered the domain that has the strength to protect sensitive information, be it at the individu­al level or in an organizational context. The objective of this paper is to in­troduce the concept, relevance, and functions of a Security Operations Cen­tre. The methodology underlying the study was based on the use of the MI­TRE Adversarial Tactics, Techniques and Common Knowledge framework as a matrix of tactics and techniques based on real scenario observations. The main results emphasize the importance of incorporating the Security Oper­ations Center as a barrier against cybersecurity threats. Security Operations Center brings additional value to the organizational context, through peo­ple, processes and technologies while also using several frameworks to im­prove work management, incident response and incident control.
Create a Mobile App Product Roadmap

The previous chapter brought some light on different platforms for mobile apps and how to better understand the time necessary for a mobile app to be developed as well as short guidelines on how to choose a reliable mobile app development partner. In this chapter, we move to more concrete aspects like how to turn your idea into a roadmap and how to write one along with some specific examples of the best roadmap tools to use. Additionally, I bring you a list of mistakes you should be aware of when developing your business mobile app.
Why Does Your Business Need a Mobile App?

In our first chapter, we will explain why your business needs a mobile application—we will list the reasons and the benefits along with crucial characteristics of a good mobile app and examples of some off-the-shelf solutions, which can help you until you develop your own mobile app.
Named Entity Recognition Approaches Applied to Legal Document Segmentation

Document Segmentation is a method of dividing a document into smaller parts, known as segments, which share similarities that allow machines to distinguish between them. It might be useful to classify these segments, making it a problem with two steps: (I) the extraction of the segments; and (II) the annotation of these segments. The Named Entity Recognition problem's goal is to identify and classify entities within a text, having also to deal with those two questions: extraction and classification. In this study, we tackle the problem of Document Segmentation and the annotation of these segments through NER approaches, using CRF, CNN-CNN-LSTM and CNN-biLSTM-CRF models. The study is focused on Brazilian legal documents, proposing a data set of 127 annotated Portuguese texts from the Official Gazette of the Federal District, published between 2001 and 2015. The experiments were made using word-based and sentence-based models, with CRF sentence-based model showing the best results.
A Proposed Blockchain-Based Solution for a Data-Driven Vehicle Lifecycle Management

Vehicle lifecycle data and stakeholders’ data related to it have a non-open approach to data access. The lack of accurate, reliable, trans­parent, and accessible data consequently leads to a lack of trust, but also a risk analysis that is not always relevant. This paper consists of analyzing the factors to determine if a blockchain-based solution could increase trust and collaboration between different stakeholders in vehicle lifecycle man­agement and the insurance industry. The goal is to model a data-driven ap­proach on a blockchain-based solution that helps reduce barriers to finding data on all vehicle transactions. The methods used are based on the anal­ysis of the current systems and platforms of vehicle lifecycle management and insurance policies from the business model perspective and security as­pects. The proposed technical solution consists of a general architecture for a permissioned blockchain-based vehicle lifecycle management used by manufacturers, insurance companies, and other stakeholders.
A Framework for Meta-Learning in Dynamic Adaptive Streaming over HTTP

This work presents a framework with a taxonomy on meta-learning used in Dynamic Adaptive Streaming over HTTP (DASH). With the increasing complexity of network conditions and user preferences, there is a need for efficient adaptation mechanisms in DASH to provide optimal quality of experience (QoE) for users. Meta-learning, or learning to learn, has emerged as a promising approach to enhance adaptive streaming algorithms in DASH by leveraging prior knowledge and experiences. The proposed framework provides a systematic and structured approach for applying meta-learning techniques in the context of DASH. It encompasses essential components, including data collection and preprocessing, meta-model architecture, meta-training, meta-testing, fine-tuning, and continuous improvement. The taxonomy within the framework categorizes various aspects of meta-learning in DASH, such as meta-learning approaches, components, objectives, and applications
dRAIN: A Distributed Reliable Architecture for IoT Networks

The rapid increase in the number and variety of smart devices connected to the Internet has increased the need to ensure resilience, reliability, and traceability when transferring data within the current Internet of Things (IoT) network. The adoption of Distributed Ledger Technologies (DLT) can provide data with the above-mentioned features, but the low scalability and high cost related to the adoption of classical DLTs, like the blockchains, results in ineffective integration with most of IoT systems. Conversely, other DLTs, DAGs (Directed Acyclic Graph), possess benefits comparable to those of blockchains without presenting most of the limitations that prevent their application in the IoT domain. Therefore we present dRAIN: a distributed Reliable Architecture for IoT Networks. The adoption of this architecture can grant the communication, management, supervision, and updating of distributed IoT devices, guaranteeing the resilience of the system and the reliability and traceability of exchanged data. In order to test the scalability potential and to assess the actual limitation of the proposed architecture, we developed both a physical and virtual (simulated) Proof of Concept. The results of our analysis show adequate execution times for the operations, guaranteeing high levels of security with acceptable performance, and prove the architecture suitable for most IoT applications that do not require to process external data in real-time.
The relationship between sociocultural adaptation and L2 pragmatic development during online study abroad

Although several linguists have examined the studies on study abroad (SA), limited studies have focused on online study-abroad contexts. The current study investigates the production of pragmatic routine and its influence on online sociocultural adaptation during online study abroad. This study involved Indonesian students in English and Chinese-taught programs at Beihang University, China. As a mixed method, this study uses Vocabulary Knowledge Scale (VKS) and Sociocultural Adaptation Scale (SCAS) to measure the students’ pragmatic routines production and online sociocultural adaptation. VKS scale is used to investigate the expression categories, including expressions of thanking, requesting, complementing, and greeting. In addition, SCAS is also used to explore the students’ sociocultural aspects, cognitive aspects, and behavioral situations. As a result, the study shows that most Indonesian students enrolling in Chinese and English-taught programs tend to produce pragmatic routines even though the Chinese aspect is produced more than the English aspect. Furthermore, there is also a positive contribution to the production of pragmatic routine and its development influenced by sociocultural adaptation during online study abroad.
The Effects of Blockchain Technology on Corporate Governance: Evidence from Emerging Economy

Abstract Modern developments in digitization have completely changed business structures and operating procedures. The core structure of a database can be built using the distributed technology known as blockchain, which combines data blocks and hash chains. Blockchain has been recommended by academics as one of the foundational elements of corporate governance. Though previous studies examined the impact of blockchain on firm numerous extents, few study has investigated the impact of blockchain technology on corporate governance. We examined the effects of blockchain technology on corporate governance of financial institutions in Nigeria. We use multiple regression over 121 responses. Samples were collected using a random sampling method. Results indicates that blockchain technology has positive impact on corporate governance suggesting the removal of agents as intermediaries in corporate governance through code, peers’ connectivity, and collaboration. Our results help managers transform the regulatory, financial, and entire governance structure of financial institutions.
DESIGN OF LIBRARY MANAGEMENT INFORMATION SYSTEM (SIMPUSTAKA) BASED ON LARAVEL FRAMEWORK

The objective of this study is to develop a design for SimpusTaka as a website-based library using the Laravel Framework so that a conducive, systematic and computerized system can be created at the Ksatrya Lima Satu college foundation. SimPusTaka’s design was developed from the foundation's website, library guidebooks, and the existing studies. The primary emphasis of this research centers around the three phases of the Borg &amp; Gall model’s development. The study began with data collection through interviews and literature reviews, followed by the planning and initial development stages of SimPusTaka. The findings suggest that the researchers utilized the Borg &amp; Gall development stages to design SimPusTaka as a foundation for creating a system for managing library information that is based on the web using the Laravel framework. During the data collection phase, the researchers conducted a thorough literature review using previous studies, guidelines for library services, and the Ksatrya Lima Satu Higher Education Foundation website. Interviews were conducted with the YPKLS school librarian. At the SimPusTaka design stage, the researcher made a list of SimPusTaka features according to the suggestions given from the data collection stage, namely homepage features, search for books, about us, librarian, survey satisfaction, contact, registration form, login.
An Analysis of Student's Difficulties in English Speaking, a Descriptive Study

The essential language ability for communication is speaking, particularly in English, where it is necessary for two-way communication that both parties can understand. Despite this, many students need to utilize their opportunities to practice speaking English more. As a result, this study aims to investigate the problems and difficulties that students face when speaking English. The descriptive methodology used in this study is qualitative. Data from interviews and direct observation were used to learn more about students' challenges as they tried to improve their English. The three steps of analysis that come after data collection are data reduction, data presentation, and data conclusion. The findings indicated that students found it challenging to practice English conversation because they lacked confidence and courage and were afraid to make mistakes. Also, they needed an intense environment and a suitable vocabulary. As a result, encouraging students to practice speaking English requires an effective teaching method.
Optimizing Orthognathic and Maxillofacial Surgery Outcomes: Integration of Surgery First Approach (SFA), Early Recovery after Surgery (ERAS), CAD/ CAM and Extended Reality (XR) in the Metaverse to Minimize Overnight Hospitalization

To meet the increasing demand for orthognathic and maxillofacial surgery, our clinic has focused on ensuring patient safety, promoting recovery, achieving aesthetic outcomes, improving functionality, and enhancing overall quality of life.We have implemented the Surgery First Approach (SFA) and Early Recovery after Surgery (ERAS) protocols within a crossdisciplinary team framework.Through a retrospective analysis of 42 patients over a one-year period, we have demonstrated successful postoperative management, resulting in all patients being discharged within 24 hours without any complications.These improvements in practice efficiency and reduction in patient burden have had a significant positive impact on patients' quality of life.Furthermore, the integration of Holoeyes Extended Reality (XR), Computer-Aided Design (CAD)/Computer-Aided Manufacturing (CAM), and Ultrasonic Bone Aspirator (UBA) has led to substantial enhancements in surgical outcomes and reduced recovery times.Our findings unequivocally support the notion that our integrative approach not only optimizes aesthetic results but also significantly enhances patients' quality of life, thereby paving the way for the future of orthognathic and maxillofacial surgery.
Developing a basic neural network to classify images from the MNIST dataset

My thesis focuses on an important and currently popular topic: "Developing a basic neural network to classify images from the MNIST dataset" This topic is very important for current and future research.The main goal of this project is to create a basic neural network capable of efficiently classifying images from the MNIST dataset, a critical benchmark for image classification tasks.The MNIST dataset is widely known and widely used in computer vision and machine learning.Designing an accurate neural network to classify images from this dataset is a crucial milestone in the development of more advanced computer vision systems.By exploring the basic architecture of a neural network, this study aims to provide an overview of the basic principles of image classification tasks.The PhD thesis deals with the complex process of training a neural network using the MNIST dataset, evaluating its performance and fine-tuning its classification accuracy.Through this research, researchers and professionals gain a deeper understanding of the basic concepts and techniques involved in developing neural networks for image classification.In addition, the results of this study contribute to the wider body of knowledge in computer vision and machine learning, enabling further advances and applications in areas such as object recognition, pattern analysis and visual perception.Finally, the development of a basic neural network to classify images from the MNIST dataset is a crucial step towards building more advanced and sophisticated computer vision systems.This doctoral thesis lays the foundation for further research and innovation in the field of image classification, providing valuable knowledge and methods for further research.
Improving Irony Detection by Balancing Methods and Feature Selection

Irony is a linguistic phenomenon that can be seen as a funny or strange aspect of a situation that is very different from what is expected, using words that say the opposite of what they really mean, often as a joke, and with a voice that shows that. When it is just text, detecting irony becomes quite challenging. In this paper, we adopt an approach organized into three stages: feature extraction, sampling techniques, and feature selection to detect ironic texts written in the Portuguese language. We evaluate our strategy on the IDPT corpus and achieve 0.55 balanced accuracy, outperforming state-of-the-art results. Moreover, we found out that both sampling techniques and feature selection may improve the results.
Goal Recognition as a Deep Learning Task: The GRNet Approach

Recognising the goal of an agent from a trace of observations is an important task with many applications. The state-of-the-art approach to goal recognition (GR) relies on the application of automated planning techniques. We study an alternative approach, called GRNet, where GR is formulated as a classification task addressed by machine learning. GRNet is primarily aimed at solving GR instances more accurately and more quickly by learning how to solve them in a given domain, which is specified by a set of propositions and a set of action names. The goal classification instances in the domain are solved by a Recurrent Neural Network (RNN). The only information required as input of the trained RNN is a trace of action labels, each one indicating just the name of an observed action. A run of the RNN processes a trace of observed actions to compute how likely it is that each domain proposition is part of the agent's goal, for the problem instance under consideration. These predictions are then aggregated to choose one of the candidate goals. An experimental analysis confirms that GRNet achieves good performance in terms of both goal classification accuracy and runtime, obtaining better results w.r.t. a state-of-the-art GR system over the considered benchmarks. Moreover, such a state-of-the-art system and GRNet can be combined achieving higher performance than with each of the two integrated systems alone.