Reviewerly/exHarmony huggingface dataset:
Offbeat Load Balancing Machine Learning based Algorithm for Job Scheduling

In cloud computing environments, parallel processing is required for large.scale computing tasks. Two different tasks are taken, and these tasks are independent of each other. These tasks are independently applied to Virtual Machines (VM). We proposed Offbeat Load Balancing (LB) Machine Learning algorithm using a task scheduling algorithm in Cloud Computing (CC) environments to reduce execution time. In this paper, the proposed algorithm is based on the concept of Random Forest Classifier and Genetic Algorithm and K-Means clustering algorithm for optimized load. The proposed algorithm shows that the average execution time of 3.5104 seconds (20 jobs, 5 Machines) and 15.85 seconds (20 jobs, 10 machines) is based on a study of load balancing algorithms that needs less execution time than other algorithms.
Independent assessment of a deep learning system for lymph node metastasis detection on the Augmented Reality Microscope

Several machine learning algorithms have demonstrated high predictive capability in the identification of cancer within digitized pathology slides. The Augmented Reality Microscope (ARM) has allowed these algorithms to be seamlessly integrated within the pathology workflow by overlaying their inferences onto its microscopic field of view in real time. We present an independent assessment of the LYmph Node Assistant (LYNA) models, state-of-the-art algorithms for the identification of breast cancer metastases in lymph node biopsies, optimized for usage on the ARM. We assessed the models on 40 whole slide images at the commonly used objective magnifications of 10x, 20x, and 40x. We analyzed their performance across clinically relevant subclasses of tissue, including breast cancer, lymphocytes, histiocytes, blood, and fat. Each model obtained overall AUC values of approximately 0.98, accuracy values of approximately 0.94, and sensitivity values above 0.88 at classifying small regions of a field of view as benign or cancerous. Across tissue subclasses, the models performed most accurately on fat and blood, and least accurately on histiocytes, germinal centers, and sinus. The models also struggled with the identification of isolated tumor cells, especially at lower magnifications. After testing, we reviewed the discrepancies between model predictions and ground truth to understand the causes of error. We introduce a distinction between proper and improper ground truth for analysis in cases of uncertain annotations. Taken together, these methods comprise a novel approach for exploratory model analysis over complex anatomic pathology data in which precise ground truth is difficult to establish.
OMNI-DRL: Learning to Fly in Forests with Omnidirectional Images

Perception is crucial for drone obstacle avoidance in complex, static, and unstructured outdoor environments. However, most navigation solutions based on Deep Reinforcement Learning (DRL) use limited Field-Of-View (FOV) images as input. In this paper, we demonstrate that omnidirectional images improve these methods. Thus, we provide a comparative benchmark of several visual modalities for navigation: ground truth depth, ground truth semantic segmentation, and RGB images. These exhaustive comparisons reveal that it is superior to use an omnidirectional camera to navigate with classical DRL methods. Finally, we show in two different virtual forest environments that adapting the convolution to take into account spherical distortions improves the results even more.
Prayatul Matrix: A Direct Comparison Approach to Evaluate Performance of Supervised Machine Learning Models

Performance comparison of supervised machine learning (ML) models are widely done in terms of different confusion matrix based scores obtained on test datasets. However, a dataset comprises several instances having different difficulty levels. Therefore, it is more logical to compare effectiveness of ML models on individual instances instead of comparing scores obtained for the entire dataset. In this paper, an alternative approach is proposed for direct comparison of supervised ML models in terms of individual instances within the dataset. A direct comparison matrix called \emph{Prayatul Matrix} is introduced, which accounts for comparative outcome of two ML algorithms on different instances of a dataset. Five different performance measures are designed based on prayatul matrix. Efficacy of the proposed approach as well as designed measures is analyzed with four classification techniques on three datasets. Also analyzed on four large-scale complex image datasets with four deep learning models namely ResNet50V2, MobileNetV2, EfficientNet, and XceptionNet. Results are evident that the newly designed measure are capable of giving more insight about the comparing ML algorithms, which were impossible with existing confusion matrix based scores like accuracy, precision and recall.
Results from a Mixed-Methods Survey and Focus Group to Assess Social Media and Technology Use By Older Adults in Northeast Tennessee to Inform Development of a Virtual Social Supper Club Nutrition Education Intervention

Upon completion, participant will be able to describe challenges and opportunities in designing virtual nutrition education programs for older adults.
An Analysis of Legal and Cybersecurity Issues in Internet of Things

Nowadays, the technologies in the world developing a lot. Daily new technologies or new versions of existing technologies have been popping up with some benefits to the world. Just like artificial intelligence which has changed the life of people, similarly Internet of Things (IoT) helps the citizens to work more efficiently and to grow more. IoT linked with other technologies have changed the nature of work for the organizations and living style of human beings. Hence, the meaning of IoT in simple words is network of physical objects which means the physical devices/objects embedded with other technologies for the purpose of connecting and exchanging information/data with other devices and systems over the Internet. IoT plays a beneficial role in every sector such as education, agriculture, healthcare and it has been deeply interwoven in the lives of peoples and societies. Benefits aside, exchanging of data and connecting to the Internet, also connecting to potential cyber threats/crimes, where cyber security concept/law come into picture. In this paper the authors have discussed the evolution and meaning of Internet of Things, challenges involved, security in IoT and its legal implications with special references to Indian laws.
Cover figure

This paper summarizes the results of our investigations into estimating the shape of the glottal excitation source from speech signals. We employ the Liljencrants–Fant (LF) model describing the glottal flow and its derivative. The one-dimensional glottal source shape parameter Rd describes the transition in voice quality from a tense to a breathy voice. The parameter Rd has been derived from a statistical regression of the R waveshape parameters which parameterize the LF model. First, we introduce a variant of our recently proposed adaptation and range extension of the Rd parameter regression. Secondly, we discuss in detail the aspects of estimating the glottal source shape parameter Rd using the phase minimization paradigm. Based on the analysis of a large number of speech signals we describe the major conditions that are likely to result in erroneous Rd estimates. Based on these findings we investigate into means to increase the robustness of the Rd parameter estimation. We use Viterbi smoothing to suppress unnatural jumps of the estimated Rd parameter contours within short time segments. Additionally, we propose to steer the Viterbi algorithm by exploiting the covariation of other voice descriptors to improve Viterbi smoothing. The novel Viterbi steering is based on a Gaussian Mixture Model (GMM) that represents the joint density of the voice descriptors and the Open Quotient (OQ) estimated from corresponding electroglottographic (EGG) signals. A conversion function derived from the mixture model predicts OQ from the voice descriptors. Converted to Rd it defines an additional prior probability to adapt the partial probabilities of the Viterbi algorithm accordingly. Finally, we evaluate the performances of the phase minimization based methods using both variants to adapt and extent the Rd regression on one synthetic test set as well as in combination with Viterbi smoothing and each variant of the novel Viterbi steering on one test set of natural speech. The experimental findings exhibit improvements for both Viterbi approaches.
An interview with Miryung Kim - 2022 SIGSOFT Awardee

Miryung Kim received the 2022 SIGSOFT Influential Educator Award for outstanding achievements in undergraduate and graduate mentoring with emphasis on research excellence in software engineering, diversity, and inclusion. She received a Ph.D. in computer science from the University of Washington, Seattle (USA), and she is a Professor and a Vice Chair of graduate studies at UCLA Computer Science. Her research interests include the detection of code clones and code duplication, as well as solutions for their management and removal. She has helped to define the field of software engineering for data intensive computing.
Democratising Technological Innovation through Makerspaces

The Maker’s Space is an attempt to democratize technological innovation by providing space and tools to rural youth to experiment and learn. Lack of infrastructure and access to educational tools inhibits the youth in rural India from realizing their full potential and creating innovative context-appropriate solutions for their communities. // The Maker's Space initiative is based on the idea of ‘innovate, invent, peer-learn, co-create,’. In these spaces, the students and adolescents are exposed to a hands-on, STEM-based approach and creative ways of learning to encourage them to design, build, experiment and innovate while they engage in science, technology, art, engineering and mathematics. Therefore, it facilitates a shift from “learning to know” to “learning to do” and “learning to work together”. It also provides an unstructured learning space supported by the machine and digital tools of learning that allows children and youth to take ownership of their learning. The Maker’s Space initiative also designs the physical spaces in a manner that fosters self-reflection and immersive learning. To teach digital literacy, DEF will be employing a variety of formats such as online learning and bot-based learning. // This program has a special focus on girls and persons with disabilities. This special focus is aimed at addressing their disproportionately low representation in STEM education. It is envisaged that this STEM learning program will support them in accessing livelihood, education and quality day to day life. The program is also designed to help them to think critically and will enable them to make tools that are beneficial to persons with disabilities. // Maker’s Spaces consist of a digital centre equipped with STEM learning and digital skilling tools. Understanding the importance of confidence-building, these spaces also conduct mental health sessions and motivational sessions.
Distributed search and fusion for wine label image retrieval

With the popularity of wine culture and the development of artificial intelligence (AI) technology, wine label image retrieval becomes more and more important. Taking an wine label image as an input, the goal of this task is to return the wine information that the user hopes to know, such as the main brand and sub-brand of the wine. The main challenge in wine label image retrieval task is that there are a large number of wine brands with the imbalance of their sample images which strongly affects the training of the retrieval system based on deep learning. To solve this problem, this article adopts a distribted strategy and proposes two distributed retrieval frameworks. It is demonstrated by the experimental results on the large scale wine label dataset and the Oxford flowers dataset that both our proposed distributed retrieval frameworks are effective and even greatly outperform the previous state-of-the-art retrieval models.
Online Application in Blended Learning; EFL Students’ Perspective

Pandemic Covid-19 has changed many features over the world, particularly for educational aspect. The significance roles of teachers in pandemic situation is should be able to intensely transform the learning styles that used in the classroom; face to face into blended learning. Information Communication Technology (ICT) is one of alternative ways for teachers in teaching English as Foreign Language (EFL) using online application. Thus, this research is aimed to observed the implication of online application in State Islamic University Prof. K. H. Saifuddin Zuhri Purwokerto, especially in English major at 2nd and 4th semesters. This research uses descriptive qualitative with a sampling approach which gathered with an online survey followed by in-dept interview. The result present that the most used application in the classroom is Google Meet. Most of students are claimed that Google Meet is helpful for learning activity in many aspects besides the difficulties. Thus, it can be concluded that the students’ perspective on blended learning had a positive impact of the blended learning.
The Effectiveness of Kahoot as a Medium for Grammar Exercises in English Education Department

The development of information and technology in the modern era of globalization cannot be avoided in the world of education. Technology is also needed to support learning assessment. Learning technologies that support assessment are available on the internet. Teachers must be familiar with technology that supports assessment in order to take advantage of it. The technology in question makes assessment more fun, efficient and effective. Kahoot is a platform that teachers can use to assess student learning outcomes. This study is aimed to examine the effectiveness of kahoot as a Medium for Grammar Exercises in English Education Department. The research population includes TBI students, consisting of 20 students at UIN Prof. K.H. Saifuddin Zuhri Purwokerto in the academic year of 2019/2020. This study uses a pre-experiment that uses one class. the average value between the pre-test and post-test experienced a difference. the average value of the pretest is 39,50 and the mean post-test score of 67,00. The significant value of the research was 0.000, significant level 0.05., it means that the mean before being taught using Kahoot application was lower than after being taught using Kahoot application. Thus, it can be concluded that as a medium, Kahoot has effectiveness application for teaching grammar Exercises in English Education Department.
Engaging Humanities &amp;amp; Humanistic Social Sciences Faculty, Staff, and Students Through XR

As a Karp Library Fellow for the University of Rochester’s hub for extended reality technology, Studio X, I conducted research on the applications and potential that XR (extended reality) technology offers to the humanities and humanistic social sciences during the spring 2021 semester. My research involved three phases: finding pre-existing use cases, holding a focus group with Learning Initiatives Librarians, and conducting a student survey. From these three phases of research, I curated a range of XR use cases across humanities and humanistic social science fields and developed targeted engagement strategies for these users, which I implemented in the fall of 2021. From this, I began to plan an Intro to XR workshop that could be tailored for any group who wanted to learn about these amazing technologies.
Pedagogical Approaches to Graduate Education in Learning Experience Design Using Immersive Technologies Online

This Practitioner Stream oral presentation chronicles the effort to develop a 100% online immersive technology course within a college of education at a Carnegie R1 Doctoral University in the Mid-Atlantic region of the United States. Immersive technologies hold great promise as technological tools to improve teaching and learning in higher education. In industrial applications, immersive technologies such as augmented reality (AR), virtual reality (VR), and mixed reality (MR) already serve critical training needs in diverse fields such as commercial aviation, the military, and healthcare. While these industries, many well-financed, are pushing the boundaries of what is possible in training and development, the teaching and learning space could benefit from new or enhanced instructional approaches and opportunities in the field of higher education, which was significantly disrupted by the COVID-19 pandemic.
Implementing VR Across the Curriculum

I will share my experience evangelizing for the adoption of virtual reality technology at a small liberal arts college. I will then share concrete examples of how virtual reality is being used in different areas of the curriculum including anatomy, kinesthesiology, interior architecture, education, nursing, and communications.
Light &amp;amp; Shadow App: A photo lighting workshop in Augmented Reality

The App Light and Shadow is a Portrait Lighting Workshop in Augmented Reality. The App allows students to virtually step into a full-size photo lighting studio and study the placement of studio lights for portrait photography in three dimensions. The App was developed during the covid-19 restrictions of 2020 and 2021 when in-person learning was not possible. The goal of the app is to recreate the photo studio learning environment and allow students to study the placement of lights in an interactive and immersive format. Students can use the App with a smartphone or tablet to view lighting setups from different points of view and visualize in three dimensions the effect of lighting on the portrait.
Autonomy Illusion in Immersive Virtual Learning Environments

With the prevalence of virtual reality (VR), new research has focused on its applications in various fields, including education. The notion of presence has been touted as advantageous for learning in the virtual environment; however, there has been little research on the subject concerning learning. This paper examines one of the themes from the interpretive phenomenological analysis (IPA) of a pedagogical informed virtual learning experience simulating the local solar system through the lens of five grade nine students. Data for this study was gathered through semi-structured interviews and video observations of the experience to understand the relationship presence has on learning in virtual environments. This paper highlights the sense of autonomy participants felt that led to a phenomenon identified as autonomy illusion. Autonomy illusion is the feeling of control over learning brought on by the removal of conventional classroom delimiting factors in place of environmentally designed delimitations, creating the illusion of freedom described as a symbiotic dichotomy of two opposing features, pedagogical freedom and environmental imposed delimitations, which help define the illusion.
Augmented Reality Affording Immersive Learning Experiences in Museum Education

The educational possibilities of augmented reality (AR) are attracting increased attention among museum educators and curators as novel, yet largely unexplored, technologies to enrich museum experiences and learning. AR is an emerging technology that has potential to create immersive learning experiences by pairing digital and physical content. Drawing on Gibson’s concept of affordances–actions [5], see also [6], in this presentation, we consider how AR can be used in museum education to enrich visitors’ immersive learning experiences. We share what we have learned from our design-based AR-research project in a Canadian science museum to further discuss the theoretical and practical insights for designing AR-mediated museum exhibits for immersive science, technology, engineering, and mathematics (STEM) learning. Participants will also be given hands-on opportunitiesto test and play with the designed AR app during the presentation. In addition to identifying potential affordances of AR for immersive museum learning, the presentation will illustrate design strategies for integrating AR- enriched museum exhibits and app development. Importantly, this presentation will invite participants to re-envision museum education and museum exhibits through the potentials of AR. The presentation will offer museum educators and curators, researchers, and immersive technology designers and developers’ insights into AR in museum education with consideration to its potentials and challenges.
Games and Graphics in Unreal Engine 5 - teaching from within the Game Engine.

This presentation will discuss the use of Unreal Engine in the revamped Graphics and Games degree. This will show the use of VR mode for first-person editing in VR and the combination with multi-user editing which allows the lecturer to talk to the students from within the game they are developing inside the editor. The ability to edit and discuss the visuals of a game from within the game while others are editing and controlling the game world is a new style of interaction. We will also discuss the use of GitHub co-pilot in Visual Studio while working in the Unreal Engine. What are the benefits and challenges of AI-supported game development?
Fraud web page detection based on bidirectional LSTM and attention mechanism

With the increasing popularity of the Internet, network security issues are also increasing and there are more and more fraudulent web pages, which also brings great obstacles to the governance of network security. However, for the detection of fraudulent web pages, most of the previous detection methods are based on web page characteristics. Multiple information on web page content, such as web page text, HTML, images, and other content is also an important feature for detecting and discovering fraudulent web pages. In this paper, the text dataset of fraudulent web pages obtained by the crawler technique is used to extract the features of the text content sentences using the bidirectional LSTM model, and the feature weights of the text dataset of fraudulent web pages are enhanced by introducing an attention mechanism to obtain a feature vector representation of the text data sentences and then the classification detection is carried out. The experiments show that the accuracy and F1 values of the bidirectional LSTM model are higher than those of other models. The bidirectional LSTM model on the addition of the attention mechanism performed better, yielding an accuracy result of 90.85%, which is a 2.7% increase in accuracy and 3.09% increase in F1 value compared to the bidirectional LSTM model. Therefore, the method is effective in detecting fraudulent web pages and has some practicality in cyber security governance.
An improved method to D-S evidence theory based on the reward and punishment factors

Aiming at the problem that the classical evidence combination formula will fail in case of serious conflict between evidences, an improved method of Dempster-Shafer (D-S) evidence theory based on reward and punishment factors is proposed. Firstly, a conflict allocation formula based on the basic probability assignments is constructed, and then a calculation method of reward and punishment factors based on credibility is proposed. The reward and punishment factors are substituted into the conflict allocation formula to correct the proportion of conflict allocation. Finally, the modified conflict allocation formula is used for evidence fusion. Simulation results show that the proposed algorithm can effectively solve the conflict between pieces of evidence and has better convergence and stability compared to the classical combination formula and other improved methods when multiple pieces of evidence are fused. In the process of practical application, the performance of the algorithm can be changed by adjusting the size of specific parameters, which improves the flexibility of the application of the algorithm.