Reviewerly/exHarmony huggingface dataset:
Enhancing Fixation and Pursuit: Optimizing Field of View and Number of Targets for Selection Performance in Virtual Reality

Gaze interaction in virtual reality (VR) offers promising advantages in speed and hands-free operation. However, selecting a suitable selection mechanism in VR interfaces with different visual encodings remains a challenge. This research compares two visual input techniques and explores specific visual elements, including field of view (FOV), design style, and number of targets, with the goal of alleviating these problems. The pilot study identifies two search stages, and it also demonstrates a significant effect of FOV on search time. In the formal experiment, a gaze-based trigger experiment is employed to examine the selection performance of fixation and smooth pursuit. The results reveal that optimizing FOV and number of targets improves the triggering efficiency. Furthermore, fixation outperforms pursuit in terms of triggering time and accuracy under the same conditions, while cognitive load during triggering process remains relatively similar. These findings are expected to propose design recommendations to enhance fixation and pursuit within VR interfaces.
Comparative Analysis of Sustainable Banking in Public and Private Sector Banks in Sri Lanka

Sustainable banking is still an emerging concept in the world. While different World Bank organizations interpret sustainable banking differently using different dimensions, it will contribute for the sustainability and longevity of the earth by reducing the indirect negative impacts of the banking sector generated through customer activities. In Sri Lanka, attention on sustainable banking is still emerging. Thus, the objectives of this study are to analyze the strategies used in sustainable banking worldwide, to compare sustainable practices of selected public and private sector banks in Sri Lanka, to determine the advantages of sustainable banking practices to Sri Lankan banks and customers, to determine the customer perception on sustainable banking in Sri Lanka and to provide recommendations for future development of sustainable banking concept in the country. This study was commenced from June 2021 until May 2022. World-recognized standards related to sustainable banking were reviewed and a comparative analysis in the public and private banks operating in Sri Lanka was carried out using the primary data collected by interviews with sustainability officers of banks and secondary data collected from the annual reports of banks. Questionnaire surveys were conducted among 302 retail customers and 82 corporate sector customers. Information was gathered on customer perception on sustainable banking and the benefits of sustainable banking on banks and customers through these studies. The results revealed that out of 2 public sector banks and 11 private sector banks in Sri Lanka, 9 private sector banks and 2 public sector banks showed a satisfactory level of implementation. Questionnaire surveys of customers have shown that there is low customer perception towards the concept. The awareness of the concept among retail customers and corporate customers on sustainable banking is 32.12% and 9.8% respectively. 92.1% of retail customers have responded that there are benefits from sustainable banking for customers, while 78% of corporate customers have responded that there are benefits for them through sustainable banking initiatives. As per the findings, banks could gain benefits such as a good corporate image, enhanced reputation, business growth, moving towards new eras of financing, cost reduction, managing risk using the thorough process in risk management and awards. As Sri Lanka is slowly implementing sustainable banking practices it is recommended to have a regulatory approach. Under this regulatory approach, the banks will have action plans within a specific time period to develop and implement sustainable banking practices more effectively. The requirement of a rating system/ assessing tool is highlighted in this study's recommendations and this would direct Sri Lankan banking sector for better future developments in sustainable banking. &#x0D; Keywords: Green banking, Environmental sustainability, Triple bottom line, Customer satisfaction, Commercial banks
Energy and Performance Centric Resource Allocation Framework in Virtual Machine Consolidation Using Reinforcement Learning Approach

Abstract:: Virtual machines are used to reduce cloud platform application performance, management costs, and access irregularities. Virtual machines are frequently vulnerable to delays, overburdening workloads, and other obstacles while consolidating and migrating servers. To significantly disperse loads among virtual machines, dynamic consolidation techniques are implemented to control energy dissipation, monitor overloading, and address underloading problems. Background:: The process of consolidation involves more calculations and resources in order to transfer services between virtual machines, provided that Service Level Agreements are observed. Methods:: The suggested approach promotes the use of cutting-edge architecture to combine virtual machines, and, therefore, strike a balance between performance and energy requirements. The main design considerations for the suggested Dynamic Weightage algorithm, which includes the clustering approach in relation to reinforcement learning approaches, are overall resource needs and Performance to Power Ratio (PPR). A cluster of ideal virtual machines is created, and resources are distributed according to performance and energy requirements. Virtual machine resource requests are converted into a matching relationship factor, which represents the individual hosts while taking PPR into account. The overall workload associated with virtual machine consolidation is also provided by these estimations. It is noted that there is little energy trade-off and that performance is maintained at a nominal level across the cluster. The architecture is put into practice throughout offline platforms, which are dispersed ecosystems that allow for increased system performance and scaling. Results:: The CloudSim simulator is used to validate the system using datasets that are obtained from PlanetLab. According to the data, energy saving has produced yields of up to 47% and promising quality of service attributes. Conclusion:: The validation of the system is performed using the CloudSim simulator with datasets from PlanetLab. The results indicate significant energy conservation, up to 47%, along with promising quality of service parameters. The proposed architecture is compared with other state-of-the-art algorithms for distributed architectures and heterogeneous environments, showcasing its efficiency. The conclusion emphasizes the prioritization of VM consolidation and energy efficiency in the proposed architecture, which has been tested on a Proliant G7- based data center using a variety of hosts. Notably, the CloudSim Toolkit is highlighted as outperforming OpenStack-based techniques in simulation results.
Assessing a Customized, Interactive Online Information Literacy Tutorial: The Getting Started with Research Module

Following the creation of online information literacy tutorial in response to the shift to online learning due to the COVID-19 pandemic, a team of librarians developed a multi-pronged approach to assessing the module to determine how well it met the stated learning objectives. This article describes the assessment of the Getting Started with Research information literacy module, a learning experience developed using RISE software and accompanied by a Google Forms research log for students to complete. The authors present results from both a rubric-driven and textual analysis study undertaken to assess student responses to the research logs that accompany the module, and discuss how the findings of the study will inform future practice.
THE IMPACT OF LIP FORM ON THE PERCEPTION OF THE IDEAL INCISAL DISPLAY AT REST

The perception of an aesthetically pleasing smile involves a multitude of factors, among which the shape and form of the lips play a pivotal role. Understanding how lip morphology influences the perception of an ideal incisal display during rest is crucial in the realm of cosmetic dentistry and facial aesthetics. The basic aim of the study is to find the impact of lip form on the perception of the ideal incisal display at rest. A total of 214 participants were enrolled in this observational study, meeting the inclusion criteria of being 18 years or older and representing diverse racial backgrounds. Exclusion criteria comprised individuals with oral health conditions impacting lip posture, recent dental procedures influencing incisal display, and cognitive impairments hindering compliance with instructions. Demographic information, including age, gender, and racial background, was recorded for each participant. Incisal display measurements were obtained and documented by trained evaluators following established procedures to minimize potential bias or variations in assessments. Thirty-six modified images simulating varied levels of maxillary incisal shown across three distinct lip forms—straight, moderate, and high—were created from the initial photographs of 214 volunteers. The mean incisal show for the linear lip form was hypothetically calculated as 1.5 mm (SD = 0.3), 2.3 mm (SD = 0.4) for the moderate lip form, and 3.0 mm (SD = 0.5) for the high lip form. Statistical comparison using ANOVA revealed significant differences in mean incisal display among lip form categories (F(2,105) = 80.72, p &lt; 0.001). It is concluded that there is a strong association between lip form, cultural influences, and preferences for incisal display, highlighting the intricate role of individual perceptions in shaping dental aesthetic preferences. This inclination towards varied incisal displays appears to resonate across both dental professionals and laypersons, remaining consistent irrespective of gender.
Interpretable Software Defect Prediction from Project Effort and Static Code Metrics

Software defect prediction models enable test managers to predict defect-prone modules and assist with delivering quality products. A test manager would be willing to identify the attributes that can influence defect prediction and should be able to trust the model outcomes. The objective of this research is to create software defect prediction models with a focus on interpretability. Additionally, it aims to investigate the impact of size, complexity, and other source code metrics on the prediction of software defects. This research also assesses the reliability of cross-project defect prediction. Well-known machine learning techniques, such as support vector machines, k-nearest neighbors, random forest classifiers, and artificial neural networks, were applied to publicly available PROMISE datasets. The interpretability of this approach was demonstrated by SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) techniques. The developed interpretable software defect prediction models showed reliability on independent and cross-project data. Finally, the results demonstrate that static code metrics can contribute to the defect prediction models, and the inclusion of explainability assists in establishing trust in the developed models.
Effectiveness of Lemongrass Warm Compress Intervention in Pediatric Meningoencephalitis Patients with Signs of Hyperthermia in the Picu Room Rsud Aji Muhammad Parikesit Tenggarong Hospital

Meningoencephalitis is an inflammation that causes symptoms such as fever, severe headaches, convulsions, impaired consciousness and even death if not treated properly. One of the symptoms experienced by meningoencephalitis patients is an increase in body temperature. This body temperature can be treated with compresses. One compress that can be done is to use lemongrass or lemongrass (Cymbopogon citratus). Objective: To intervene with warm lemongrass compresses to reduce body temperature. Method: Quantitative descriptive with pre-test and post-test. The sample used was 1 child patient who was diagnosed with meningoencephalitis with signs of hyperthermia. Research Results: After the intervention of giving warm lemongrass compresses for 3 days, significant results were obtained, namely a decrease in body temperature to 36.1'C. Conclusion: Warm lemongrass compress is effective in reducing body temperature in meningoencephalitis patients.
The Role and Responsibilities of Academic Leaders in Effectuating Quality, Impact, and Excellence by Innovation in Higher Education

&lt;p&gt;Innovation is critical for higher education in the 21st century. It can improve teaching and learning, increase research quality and impact, solve social problems, and bring more funding and collaboration. It can also boost the happiness and creativity of staff and students, who can follow their interests, try new things, and work with different people. But innovation is not simple. It requires a positive and supportive culture, a clear and shared vision, a strong and effective leadership, and a regular and systematic evaluation. This seeks to review the existing research on innovation in higher education, analyze and discuss how academic leaders can encourage and enable innovation in higher education, and to further provide some helpful and evidence-based suggestions for academic leaders and practitioners who want to innovate in higher education.&lt;/p&gt;
Development of Physical Education Learning Media for Football Material forJunior High School Class VII Students on Smartphones Based on AndroidApplications

This study aims to (1) produce a product development of Physical Education Sports And Health learning media for soccer material on android application-based smartphones for Junior High School students, (2) determine the feasibility of Physical Education Sports And Health learning media development products for soccer material on android application-based smartphones for Junior High School students, and (3) determine the level of practicality Physical Education Sports And Health learning development products for soccer material on android application-based smartphones for Junior High School students. This research is a development research with the ADDIE approach model, namely: analyse, design, develop, implement, and evaluate. The subjects of this study were a small-scale trial of 7 students in class VII SMP N 2 Sewon, a large-scale trial subject of 30 students in class VII SMP N 2 Bantul, expert validation including material experts and media experts. The results showed that Physical Education Sports And Health learning media product of soccer material on smartphones based on android applications had a very good / very feasible level of feasibility. The level of product feasibility based on material expert validation reached an average of 90% with a very feasible category, media expert validation reached 86.6% with a very feasible category, small-scale trials reached an average of 92.3% with a very good / very feasible category, large-scale trials reached an average of 90.2% with a very good / very feasible category.
Sensing Data Concealment in NFTs: A Steganographic Model for Confidential Cross-Border Information Exchange

In an era dominated by rapid digitalization of sensed data, the secure exchange of sensitive information poses a critical challenge across various sectors. Established techniques, particularly in emerging technologies like the Internet of Things (IoT), grapple with inherent risks in ensuring data confidentiality, integrity, and vulnerabilities to evolving cyber threats. Blockchain technology, known for its decentralized and tamper-resistant characteristics, stands as a reliable solution for secure data exchange. However, the persistent challenge lies in protecting sensitive information amidst evolving digital landscapes. Among the burgeoning applications of blockchain technology, non-fungible tokens (NFTs) have emerged as digital certificates of ownership, securely recording various types of data on a distributed ledger. Unlike traditional data storage methods, NFTs offer several advantages for secure information exchange. Firstly, their tamperproof nature guarantees the authenticity and integrity of the data. Secondly, NFTs can hold both immutable and mutable data within the same token, simplifying management and access control. Moving beyond their conventional association with art and collectibles, this paper presents a novel approach that utilizes NFTs as dynamic carriers for sensitive information. Our solution leverages the immutable NFT data to serve as a secure data pointer, while the mutable NFT data holds sensitive information protected by steganography. Steganography embeds the data within the NFT, making them invisible to unauthorized eyes, while facilitating portability. This dual approach ensures both data integrity and authorized access, even in the face of evolving digital threats. A performance analysis confirms the approach's effectiveness, demonstrating its reliability, robustness, and resilience against attacks on hidden data. This paves the way for secure data transmission across diverse industries.
Our Journey to Encourage Makerspaces in Small Rural Libraries with the Help from the Library Innovation Studios Project

Follow our journey to encourage permanent makerspaces in rural communities by providing 35 libraries a temporary makerspace. Paired with support and marketing tools, the communities garnered local support and an awareness of makerspace benefits. The Library Innovation Studios project proved valuable in broadening library services. Learn about the multi-faceted project – the makerspace kits, sustainability strategies employed, partner/library responsibilities, and the opportunity to make improvements as cycles progressed. We blended community engagement processes, trainings, user-friendly instructions, and supplied materials to promote immediate making. Other facets included local and statewide events and on-going networking tools – an e-mail group and a makerspace database.
VisFormers—Combining Vision and Transformers for Enhanced Complex Document Classification

Complex documents have text, figures, tables, and other elements. The classification of scanned copies of different categories of complex documents like memos, newspapers, letters, and more is essential for rapid digitization. However, this task is very challenging as most scanned complex documents look similar. This is because all documents have similar colors of the page and letters, similar textures for all papers, and very few contrasting features. Several attempts have been made in the state of the art to classify complex documents; however, only a few of these works have addressed the classification of complex documents with similar features, and among these, the performances could be more satisfactory. To overcome this, this paper presents a method to use an optical character reader to extract the texts. It proposes a multi-headed model to combine vision-based transfer learning and natural-language-based Transformers within the same network for simultaneous training for different inputs and optimizers in specific parts of the network. A subset of the Ryers Vision Lab Complex Document Information Processing dataset containing 16 different document classes was used to evaluate the performances. The proposed multi-headed VisFormers network classified the documents with up to 94.2% accuracy, while a regular natural-language-processing-based Transformer network achieved 83%, and vision-based VGG19 transfer learning could achieve only up to 90% accuracy. The model deployment can help sort the scanned copies of various documents into different categories.
Predicting classroom activity index through multi-scale head posture classification network

The quantification of the interplay between student behavior data and classroom teaching effectiveness using quantitative metrics has perennially posed a challenge in the evaluation of classroom instruction. Classroom activity serves as a reflection of student engagement, emotional ambiance, and other pertinent aspects during the pedagogical process. This article presents a methodology for quantifying student head posture during classroom instruction utilizing AI-driven video analysis technology, notably the Classroom Activity Index (CAI). A Classroom Activity Analysis System (CAAS) was designed and developed, integrating a multi-scale classification network based on ECA-ResNet50 and ECA-ResNet18. This network discerns and categorizes various head regions of students situated in both the frontal and real rows of a lecture-style classroom, irrespective of their dimensions. The classification network attains exceptional performance, boasting F1 score of 0.91 and 0.92 for student head-up and head-nodding. Drawing on the live classroom instruction at a higher vocational college in Wuhan, Hubei Province, China, a comparative experiment was executed. The findings revealed that three factors: teacher-student verbal interaction, teacher body language, and utilization of digital resource, all exert an influence on CAI. Simultaneously, the degree of classroom activity as gauged by FIAS and manual analysis fundamentally aligns with the CAI indicators quantified by CAAS, validating the efficacy of CAI in the quantification of classroom activity. Consequently, the incorporation of CAAS in teaching, research, and oversight scenarios can augment the precision and scientific rigor of classroom teaching assessment.
Research of news text classification method based on hierarchical semantics and prior correction

News text is an important branch of natural language processing. Compared to ordinary texts, news text has significant economic and scientific value. The characteristics of news text include structural hierarchy, diverse label categories, and limited high-quality annotation samples. Many machine learning and deep learning methods exist to analyze various forms of news text. However, due to label imbalance, hierarchical semantics, and confusing labels, current methods have limitations. Therefore, this paper proposes a news text classification framework based on hierarchical semantics and prior correction (HSPC). Firstly, data augmentation is used to enhance the diversity of the training set and adversarial learning is employed to improve the resistance of the model with its robustness. Then, a hierarchical feature extraction approach is employed to extract semantic features from different levels of news texts. Consequentially, a feature fusion method is designed to allow the model to focus on relevant hierarchical semantics for label classification. Finally, highly confusing label predictions are corrected to optimize the label prediction of the model and improve confidence. Multiple experiments are performed on four widely used public datasets. The experimental results indicate that HSPC achieves higher classification accuracy compared to other models. On the FCT, AGNews, THUCNews, and Ohsumed datasets, HSPC improves the accuracy by 1.03%, 1.38%, 2.55%, and 1.15%, respectively, compared to state-of-the-art methods. This validates the rationality and effectiveness of the designed mechanisms.