Reviewerly/exHarmony huggingface dataset:
Bulletin WKU

Nowadays one of the aims set by teachers is to prepare active, competitive, versatile students at the school. One of the important things to getting this goal is to work with gifted children. Developing chemical industries in our country is the polymer industry. To solve chemical and environmental and other actual issues the using polymers and their compounds are important. For this reason, it is necessary to start teaching polymers and their preparation and properties in school courses. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; This article said "study of the physical-chemical properties of polymetallic complexes" with gifted children in a school course.
MECHANISMS AND TOOLS USED FOR RESOURCE ALLOCATION IN THE CLOUD

Pay-as-you-go access to computer resources is a major selling point of the cloud computing model. Cloud tenants demand complete networking of their dedicated resources to simply implement network functions and services, in addition to the conventional computer resources. The flexibility and convenience of on-demand resource provisioning make cloud computing a compelling computing platform. The key to meeting fluctuating needs and maximizing return on investment from Cloud-supporting infrastructure is dynamic resource allocation and reallocation. For traditional IaaS, we offer an energy-efficient resource allocation strategy based on bin packing. In this paper, we present an accurate energy-conscious method for initial resource allocation by casting the issue of energy-efficient resource allocation as a bin-packing model. The available VMs (virtual machines) employ a modified version of the max-min scheduling technique, which saves money and resources. The results of this study give a framework for comparing and contrasting the many different resource distribution approaches that have been proposed by other researchers. The importance of efficient data centers for the cloud is growing. Power consumption has been a major problem due to its expanding size and widespread usage. The overarching purpose of this effort is to create models and algorithms for resource allocation that are both energy-efficient and take into account a variety of relevant factors
The Commercial Side of Graph Analytics: Big Uses, Big Mistakes, Big Opportunities

Connectivity is the cornerstone of our contemporary world, permeating various sectors like retail, communications, biology, and finance. Although this inherent interconnectedness holds substantial meaning and predictive power, harnessing it for practical use in the business realm often proves challenging. In this presentation, we will delve into the commercial applications of graph analytics, highlighting both common pitfalls to avoid and promising opportunities to explore.
SEMG APPROACH FOR SPEECH RECOGNITION

Speech is the most familiar and habitual way of communication used by most of us. Due to speech disabilities, many people find it difficult to properly voice their views and thus are at a disadvantage. The research tackles the issue of lack of speech from a speech impaired user by recognizing it with the use of ML models such as Gaussian Mixture Model - GMM and Convolutional Neural Network - CNN. With properly recorded and cleaned muscle activity from the facial muscles it is possible to predict the words being uttered/whispered with a certain accuracy. The intended system will additionally also have a visual aid system which can provide better accuracy when used together with the facial muscle activity-based system. Neuromuscular signals from the speech articulating muscles are recorded using Surface Electro Myo Graphy (SEMG) sensors, which will be used to train the machine learning models. In this paper we have demonstrated various signals synthesized through the ElectroMyography system and how they can be classified using machine learning models such as Gaussian Mixture Model and Convolutional Neural Network for the visual-based lip-reading system.
E-HEALTH ARMBAND REMINDER WITH GPS TRACKER FOR STREET SWEEPER OF CENRO CALAMBA CITY, LAGUNA

This study presents the development of an E-Health Armband Reminder with a GPS tracker designed specifically for Street sweepers of CENRO Calamba City, Laguna. The objective of this project is to enhance the safety and efficiency of street sweeping operations by providing real-time monitoring and reminders to the sweepers. The E-health Armband Reminder incorporates a GPS tracker that allows supervisors to track the location of the sweepers in real-time. This feature enables efficient deployment of resources and facilitates prompt assistance in case of emergencies or accidents. Additionally, the armband serves as a reminder tool, providing alerts and notifications for hydration, Weather, and safety protocols. The system's development involved extensive research, analysis of user requirements, and collaboration with the street sweepers and CENRO Calamba City. The armband's design and functionality were optimized to suit the specific needs and constraints of the street sweeping operation. Preliminary testing of the E-Health Armband Reminder demonstrated promising results, showing improved task management as the Attendance monitoring and increased safety awareness among the street sweepers.
AN ENHANCED SOFTWARE AS A SERVICE (SAAS) ARCHITECTURAL MODEL FOR CLOUD BASED SECURITY USING HYBRID SYMMETRIC ALGORITHM

Protecting cloud data from security breaches, preventing and ensuring safe cloud data is concealed from unauthorized access or infiltration by unwanted users are all part of cloud infrastructure security. This proposed technique is based on ensuring data confidentiality by adding extra-layer of security from the client side, developing a framework that implements the AES 256-bit key and Fernet 128-bits algorithms, which encrypt and decrypt files via random algorithm selection during uploads and downloads. When a file is uploaded to the cloud, the decryption key is recorded in the local server's file log. The software use randomized algorithm selection to provide security to ensure that an attacker having a prior link cannot reset the user account since the attacker cannot guess the token. Furthermore, invalidating this token ensures that the link cannot be used more than once if it was previously logged anywhere. To propose and verify the efficiency of the model the results generated(in quantitative values) are frequency (monobit) testing, with a minimum bit size of 64, given the P-values derived from the above study (i.e., 0.8838, 0.6187, 0.3768, 0.2817, 0.5843, 0.4167). The findings reveal throughput for encryption of 0.91531 and for decryption of 0.4854, space complexity of 0.4854, and entropy of 7.942616667.This research work studies the design of a robust, adaptable, and non-deterministic SaaS application that can be secure for enterprises and subsequent research studies will be conducted in the development.
Exploring Author, Article, and Venue Feature Sets for Rising Star Prediction in Academic Network

Rising stars are the researchers who are relatively new to the research area and have published fewer research articles, but their research work is of such standard that they have the potential to be top researchers in near future. Research work on the evaluation of researchers and prediction of rising stars is getting attention because it can be useful for selecting capable candidates for the jobs, hiring young faculty members for institutes, and seeking reviewers for journals and conferences and members for different committees. In this research study, the authors address the research problem of finding rising stars and propose novel features in diverse feature sets of three categories: article, author, and venue. The real-world data set has been extracted, preprocessed, and used from the Web of Science for empirical analysis. Several diverse supervised machine learning, ensemble learning algorithms, and deep learning are applied to the data set. The results, using classifiers, are compared based on standard performance evaluation measures to reveal the significance of the proposed as well as existing features. It also shows that the novel features play a significant role in finding rising stars. The ensemble-based machine learning classifier generalized linear model outperforms all other classifiers and gives the highest accuracy and F-measure compared to other models and the existing studies in the relevant literature.
Integration Design of Academic Information Systems And Learning Management Systems Using Web Services Rest-Based External Database

ICT products that are widely used in universities in Indonesia are E-Learning with a Learning Management System (LMS) and Academic Information System (SIA). The existence of these two products from ICT will greatly assist the running of academic business processes in a higher education institution. Of course, if the two are well integrated, especially for managing existing academic data. However, in many tertiary institutions, the two systems are still not integrated, such as in managing academic data. The huge amount of data and the complexity of these two systems will make the management and integration process difficult and inefficient if done conventionally. Seeing these problems, the researcher intends to conduct research on the integration of the two REST-based Web Service systems and use the External Database feature which will be used to synchronize data from SIA to E-Learning in real time.
A study oriented to significant learning: Learning styles

Education is a priority axis for the government of our country, Mexico. The teaching-learning process is a challenge in each classroom, its importance lies mainly in raising the productivity of society. In this context, the application of pedagogical approaches and environments that allow meaningful learning are sought. For its part, we identify learning styles as cognitive, physiological and affective traits, of how students perceive, interact and respond to different learning environments. The objective of this research is to analyze and evaluate learning styles as an indicator that serves as a starting point to distinguish the way in which students learn and guide the teaching act in the design of learning strategies according to a segmentation of students of according to their learning styles or their predominance to achieve meaningful learning. The study proposes a field investigation using the Honey Alonso Learning Styles Questionnaire (CHAEA) as a data collection instrument. The methodological process of the research was divided into two phases: the first covers the design and application of the CHAEA instrument. In the second, the results obtained by means of a frequency analysis are exhibited.
Implementation of Motion Detection Methods on Embedded Systems: A Performance Comparison

Recently, deploying machine learning methods and deep learning models to create an artificial intelligence system has gained huge interest.Several technologies, such as embedded GPU, ARM multicore processors, visual processor units VPUs, tensor processor units TPUs, and Field programmable arrays FPGAs, have been developed for this purpose.These processors and accelerators have been fitted on different edge boards and single computer boards SBCs.In this work, we present a performance comparison of background subtraction methods with many video resolutions on various technologies and boards.The tested boards are equipped with different versions of ARM multicore processors and embedded GPUs.The aim is to overcome the lack of such studies on embedded devices and compare the performance of these recent hardware configurations.The implementation was achieved on ARM CPUs configuration using OpenCV and on embedded GPU using CUDA OpenCV.Results show that for high computational methods and high-resolution videos, the GPU is four times faster than the CPU.For low-mid computational methods or low-mid video resolution, the GPU performance is reduced due to GPU-CPU bottleneck transfer.This performance comparison enables the reader to better choose the suitable hardware for his mobile application.
A Case of Project Risk Management Course: Students’ Feedback on Teaching and Learning

To equip students to be capable professionals and active citizens is the goal of university teaching and learning.Effective teaching and learning are influenced by a variety of factors, including the role of lecturers in creating and delivering high-quality courses, the relevance and organization of course materials, and the learning environment.Therefore, it is crucial to get student feedback on these concerns to ensure excellent teaching and learning.Students' opinions on the course material, the lecturers' professionalism, their teaching and learning activities, and the learning environment (infrastructure) will be gathered for this study.The study also aims to look at the relationships between these components.This study's input was acquired through a survey design and a questionnaire.132 students from project risk management classes at a public institution are participating in this study.Using IBM SPSS version 28, descriptive and correlation analysis of the feedback was performed.The results demonstrate that students' general opinions of the project risk management course's content were favorable and of high caliber.Additionally pleasant and exceptional are the lecturers' professionalism and the way they teach and study.The infrastructure is then suitable and conducive.Overall, there is a clear connection between the infrastructure, teaching and learning, professionalism of the lecturer, and the caliber of the course content.The results of this study can assist teachers in honing their teaching techniques and enhancing the caliber of their instruction.
Removal of occluding shadows in fringe-projection profilometry by co-phasing simultaneous fringes

We propose a spatio-temporal technique for demodulation of composed fringes produced by simultaneous operation of two projectors in fringe-projection profilometry (FPP). Using multiple projectors allows us to cope with casted shadows and specular reflections, which are fundamental limitations of the typical (1-projector 1-camera) FPP setup. In prior works, each additional projector increases the number of required phase-stepped images: if the typical setup utilizes N phase-stepped images, the 2-projector setup requires 2N images, and so on. Alternatively, in this work we propose a co-phasing technique that requires only N fringe patterns in the same fashion as the typical setup. The proposed technique fuses all available information before computing the partial phases, eliminating the need for multiple phase unwrapping and heuristics. A detailed mathematical description is provided and the viability of the proposal is confirmed with experimental results.
MANAJEMEN PERPUSTAKAAN SEKOLAH MENENGAH ATAS NEGERI 19 MERANGIN

This research is motivated by the importance of library management at SMA Negeri 19 Merangin to encourage and support the learning process in an educational institution which aims to describe library management in fulfilling and carrying out the roles and functions of library units in Indonesia. an educational institution. To reveal this, research has been carried out using a qualitative approach by conducting observations, interviews, and documentation to obtain data and information on library management, and analyzed by reducing, displaying, and verifying data, so that conclusions can be drawn. from the research process carried out. The results of the study can be concluded that library managers still lack good cooperation in procuring library collections, library managers do not have library science education qualifications and their understanding of library management is still low, library collections, in general, are not physically complete, and the management system is still manual.
Jazz Chants to Motivate Language Learning among Sarawakian Primary Pupils Towards Positive School Climate: An Inquiry on Teachers’ Perspectives

Since music and language are deeply related where musical training is able to stimulate the same part of the brain that is also associated with understanding language, using Jazz Chants in English language learning possess a remarkable potential at maximizing language learning, especially at creating fun and significant lessons.Nonetheless, its potential remains underresearched, especially on the implementation of Jazz Chants in Malaysian language classrooms towards creating a safe learning climate.Until language learning are given more alternatives, English language classrooms are viewed as flat, thus situating the purpose of this paper.Therefore, this research explored the possibility of using Jazz Chants to investigate teachers' perceptions on employing Jazz Chants in Malaysian English learning classrooms.To do so, five experienced teachers from Sarawak were selected for a semi-structured interview.The interview questions were validated by experts before the interviews were carried out.A thematic analysis was employed to analyse the interview data in three predetermined themes which are 'benefits', 'challenges' and 'suggestions'.In creating fun and meaningful learning that may harness towards (re)creating a positive school climate, as aspired by the MoE, this research provides a narrative on teachers' perspectives within the three discussed themes.
BiMuF: A Bi-directional Recommender System with Multi-semantic Filter for Online Recruitment

Abstract Most existing recommendation research has been concentrated on unidirectional recommendation, i.e. only recommending items to users. However, the platform needs to achieve bi-directional recommendation in many real-world scenarios. For example, in an online recruitment scenario, the recommender system not only needs to recommend positions to candidates, but also recommend candidates to enterprises. In this paper, we develop a new bi-directional recommendation model for online recruitment termed BiMuF (Bi-directional recommendation with Multi-semantic Filter). In BiMuF, an encoder component is utilized to learn the text embeddings, a multi-semantic filter component is designed to capture important graph representation, and a graph learning component is designed to learn the graph embeddings. In addition, a multi-task learning framework is designed to achieve bi-directional recommendations. In the multi-task learning framework, we share text embeddings and graph embeddings to alleviate the problems of data sparsity, data asymmetry, and feature generalization in online recruitment. Extensive experiments in a real-world task show that BiMuF outperforms the state-of-the-art methods, verifying the effectiveness of the designs of our model.
SiamSMN: Similarity Map Fusion Siamese Network for Visual Object Tracking

Abstract The existing Siamese trackers have achieved increasingly successful results in visual object tracking. However, the interactive fusion among multi-layer similarity maps after cross-correlation has not been fully studied in previous Siamese network-based methods. To address this issue, we propose a novel SiamSMN for visual object tracking, which consists of feature extraction network, multi-scale fusion module and prediction head. First, the feature extraction network is used to extract the features of the template image and the search image, which is calculated by a depth-wise cross-correlation operation to produce multiple similarity feature maps. Second, we propose an effective multi-scale fusion module that can extract global context information for object search and learn the interdependencies between multi-level similarity maps. In addition, to further improve tracking accuracy, we design a learnable prediction head module to generate a boundary point for each side based on the coarse bounding box, which can solve the problem of inconsistent classification and regression during the tracking. Extensive experiments on four public benchmarks demonstrate that the proposed tracker has a competitive performance among other state-of-the-art trackers.
Unpaired Document Image Denoising for OCR using BiLSTM enhanced CycleGAN

Abstract The recognition performance of optical character recognition (OCR) models can be sub-optimal when document images suffer from various degradations. Supervised deep learning methods for image enhancement can generate high-quality enhanced images. However, these methods demand the availability of corresponding clean images or ground truth text. Sometimes this requirement is difficult to fulfill for real-world noisy documents. For instance, it can be challenging to create paired noisy/clean training datasets or obtain ground truth text for noisy point-of-sale receipts and invoices. Unsupervised methods have been explored in recent years to enhance images in the absence of ground truth images or text. However, these methods focus on enhancing natural scene images. In the case of document images, preserving the readability of text in the enhanced images is of utmost importance for improved OCR performance. In this work, we propose a modified architecture to the CycleGAN model to improve its performance in enhancing document images with better text preservation. Inspired by the success of CNN-BiLSTM combination networks in text recognition models, we propose modifying the discriminator network in the CycleGAN model to a combined CNN-BiLSTM network for better feature extraction from document images during classification by the discriminator network. Results indicate that our proposed model not only leads to better preservation of text and improved OCR performance over the CycleGAN model but also achieves better performance than the classical unsupervised image pre-processing techniques like Sauvola and Otsu.
A SAT Solver + Computer Algebra Attack on the Minimum Kochen-Specker Problem

Abstract One of the most fundamental results in the foundations of quantum mechanics is the Kochen--Specker (KS) theorem, a `no-go' theorem which states that contextuality is an essential feature of any hidden-variable theory. The theorem hinges on the existence of a mathematical object called a KS vector system. Although the existence of a KS vector system was first established by Kochen and Specker, the problem of the minimum size of such a system has stubbornly remained open for over 50 years. In this paper, we present a new method based on a combination of a SAT solver and a computer algebra system (CAS) to address this problem. We improve the lower bound on the minimum number of vectors in a KS system from 22 to 23 and improve the efficiency of the search by a factor of over 1000 when compared to the most recent computational methods. Finding a minimum KS system would simplify experimental tests of the KS theorem and have direct applications in quantum information processing, specifically in the security of quantum cryptographic protocols based on complementarity, zero-error classical communication, and dimension witnessing.
Predicting word learning from feedback-locked event-related potentials during learning with Feed-forward neural networks - the role of data preprocessing

We analyze feedback-locked electroencephalography data from a wordlearning experiment with an Feed-forward Neural Network (FNN) in orderto model the result of learning. Our research question is: Does modelingin FNN rely on data that are preprocessed in the same way as they are forconventional statistical analysis, or is it beneficial for the FNN to use datathat are less preporocessed and, therefore, contain more variation?Our modeling shows that an FNN does indeed benefit more from data thatis less preprocessed and contains more variation.