Reviewerly/exHarmony huggingface dataset:
Website-Based Rice Harvest Data Collection Information System

The purpose of this research is to help agricultural institutions get accurate crop statistics and make it easier for farmers to submit crop data. That way farmers do not need to come directly to send their harvest data. The method used in this research is the descriptive method, by reading from various available journals. The results of this study are expected to increase the effectiveness in the delivery of crop data. With this website, the delivery of harvest data from farmers to agricultural institutions will run quickly to shorten the time. In addition, it is hoped that this website can produce precise and accurate data, to be able to answer the availability of rice and indications of a decrease or increase in the number of harvests.
Mixture of personality improved spiking actor network for efficient multi-agent cooperation

Adaptive multi-agent cooperation with especially unseen partners is becoming more challenging in multi-agent reinforcement learning (MARL) research, whereby conventional deep-learning-based algorithms suffer from the poor new-player-generalization problem, possibly caused by not considering theory-of-mind theory (ToM). Inspired by the ToM personality in cognitive psychology, where a human can easily resolve this problem by predicting others' intuitive personality first before complex actions, we propose a biologically-plausible algorithm named the mixture of personality (MoP) improved spiking actor network (SAN). The MoP module contains a determinantal point process to simulate the formation and integration of different personality types, and the SAN module contains spiking neurons for efficient reinforcement learning. The experimental results on the benchmark cooperative overcooked task showed that the proposed MoP-SAN algorithm could achieve higher performance for the paradigms with (learning) and without (generalization) unseen partners. Furthermore, ablation experiments highlighted the contribution of MoP in SAN learning, and some visualization analysis explained why the proposed algorithm is superior to some counterpart deep actor networks.
A Feature-based Transfer Learning to Improve the Image Classification with Support Vector Machine

In the big data era there are some issues regarding real-world classification problems.Some of the important challenges that still need to be overcome to produce an accurate classification model are the data imbalance, difficulties in labeling process, and differences on data distribution.Most classification problems are related to the differences in the data distribution and the lack of labels on some datasets while other datasets have abundant labels.To address the problem, this paper proposes a weighted-based feature-transfer learning (WbFTL) method to transfer knowledge between different but related domains, called cross-domain.The knowledge transfer is done through making a new feature representations in order to reduce the cross-domain's distribution differences while maintaining the local structure of the domain.To make the new feature representation we implement a feature selection and inter-cluster class distance.We propose two stages of the feature selection process to capture the knowledge of the feature and its relation to the label.The first stage uses a threshold to select the feature.The second stage uses ANOVA (Analysis of Variance) to select features that are significant to the label.To enhance the accuracy, the selected features are weighted before being used for the training process using SVM.The proposed WbFTL are compared to 1-NN and PCA as baseline 1 and baseline 2. Both baseline models represent the traditional machine learning and dimensionality reduction method, without implementing transfer learning.It is also compared with TCA, the first feature-transfer learning work on this same task, as baseline 3. The experiment results of 12 cross-domain tasks on Office and Caltech dataset show that the proposed WbFTL can increase the average accuracy by 15.25%, 6.83%, and 3.59% compared to baseline 1, baseline 2, and baseline 3, respectively.
Behavior Intention of Chronic Illness Patients in Malaysia to Use IoT-based Healthcare Services

The Internet of Things (IoT) has emerged as a trend in the healthcare industry to develop innovative solutions that enhance patient outcomes and operational efficiency.Healthcare has become more accessible, affordable, and efficient to sensors, wearables, and health monitors.The healthcare industry's adoption of the Internet of Things is lagging behind other sectors despite its many benefits.This study aims to investigate the extent to which chronic patients in Malaysia are using healthcare services made possible by the Internet of Things.To that end, this study proposes a unified framework to examine how these highlighted factors affect Behavioral Intention (BI) with regard to adopting IoT healthcare services.The innovation here is in bringing together three distinct theories: i) the Technology-Organization-Environment Framework (TOE), which is a framework for understanding how companies adopt new technologies; ii) the Unified Theory of Acceptance and Use of Technology (UTAUT); and iii) the Social Exchange Theory (SE).Patients in Malaysia who are coping with long-term health issues were surveyed online.This study also employs SPSS and Smart Partial Least Square (Smart PLS) for data analysis.Eleven hypothesized predictive components have been investigated.The results showed that chronic illness patients' BI towards adopting IoT solutions was considerably impacted by both individual and technological factors and related aspects.The impact of BI on Use Behaviour (UB) also showed similar outcomes.Moreover, trust somewhat mediates the impact of both individual and technological factors on BI.The findings of this investigation will be beneficial to policymakers and suppliers of healthcare in that country.Additionally, the patients and their family members would gain benefits from the study due to the fact that the delivery of comprehensive treatment, especially in the field of chronic disease management, will be improved through IoT-healthcare services.The Internet of Things will also let medical staff function remotely and professionally.
Students’ Readiness of Learning English Process During the New Normal at Junior High School

During the pandemic, distance learning was thought to be more effective and efficient. In light of this situation, online learning requires learning readiness because of changes in the way students learn. At SMPN 36 Sarolangun, Jambi, the goal of this study is to find out how students’ readiness of learning English processes during the new normal. In this study, researchers used a qualitative descriptive research method. The participants in this study were eighth-grade students at SMPN 36 Sarolangun Jambi. The purpose of this study was to describe students' readiness for a new normal of learning. The researcher chose the sample by looking for students who studied using blended learning. Purposive sampling was used in this research. The researcher found that the school had delivered subject matter online and offline, therefore, the researcher was interested in doing research at that school, which students still did not understand. In this case, the researcher found a problem in that students could not understand the material clearly when school became normal. Students have not been able to concentrate during the teaching and learning process, but there are still a small number of students who concentrate during the learning process. Readiness to learn can be seen in the reactions of most students because they have not been able to ask the teacher during lessons and have not been able to express ideas to the teacher.
Healthcare 4.0 implementation: opportunities and challenges in the healthcare sector

Healthcare 4.0 is the application of Industry 4.0 principles in healthcare, enabling real-time personalization of patient care. Although it has gained relevance in recent years due to the benefits of Industry 4.0, the concept is still little known by professionals in the Brazilian and international health sector. Using the literature review method and survey research with 26 experts in Healthcare 4.0, this study aims to identify opportunities and challenges in Healthcare 4.0 implementation projects in the health sector in Brazil. The relevance of this study is the identification of the main opportunities found: digital technology facilitates the collection of patient data; the strategic implementation of Healthcare 4.0 can bring competitive advantages to the organization; and the main challenges: the need for digital training of healthcare professionals for Healthcare 4.0, and the need to motivate everyone involved to adopt Healthcare 4.0, contributing to theory. The practical contribution is to help the healthcare professionals involved to understand the relevant points for successful Healthcare 4.0 implementation projects.
Development of an SVM-based Depression Detection Model using MFCC Feature Extraction

Early detection is crucial for effective therapy of depression, a mental disorder that impairs quality of life. With the accessibility of technology, writing articles about depression and identi fying those who could be depressed has become easier. In this paper, a machine learning-based speech-based depression detection system is built. The system uses natural language processing and voice analysis to extract information from speech patterns and determine if a person is depressed. It is difficult to create a model that matches all people bacause depression might seem different for each person and it can take a lot of time, money, and experienced specialists to properly collect and label data due to privacy and ethical issues, it might be challenging to obtain enough reliable data but our proposed system used DAIC-WOZ dataset as data source and used MFCC to extract relevant features and important characteristics from speech signals related to depression. The system is used to diagnose depression and has the potential for employing speech and language analytic methods to diagnose depression. Similar modes had achieved an accuracy between 75% to 80% but this paper achieved an accuracy of 89% which improves the functionality of the system compared to other systems.
Research on Named Entity Recognition Algorithm in Mechanical Design Domain Based on Logistic Regression

Named entity identification technology has advantages in natural language processing technology as the general technology does not solve the identification problem of mechanical design successfully. Based on the degree of tight connection and statistical characteristics of strings, the range of tight connections between each word was determined to identify domain words in mechanical design. By analyzing eigenvalues and using logistic regression, the closeness of adjacent word strings was determined with new terms. This method improved the accuracy and recall efficiency compared to general algorithms, effectively defining mechanical terminology.
Development and Application of University Financial Personnel Work Platform Based on Data Fusion and Blockchain

In order to alleviate the lack of information interaction between all financial work management systems within the school and solve the practical problems of information sharing among cross units, we formulated the construction implementation plan of the "Internet plus+financial management" system to establish a unified service system. Based on big data analysis and industry integration, the financial network center has been established by integrating the reimbursement management system, online approval management system, scientific quotation management system, search network, and other networks, using blockchain. Data analysis results showed that the successful implementation and application of the Financial Online Service Center has alleviated the difficulties of college students' reimbursement and tax processing, removing obstacles to big data analysis among government departments, and significantly improving office and management efficiency. Thus, diversified and comprehensive innovative financial services are enabled for the construction of financial informatization in universities nationwide.
TLEE: Temporal-wise and Layer-wise Early Exiting Network for Efficient Video Recognition on Edge Devices

With the explosive growth in video streaming comes a rising demand for efficient and scalable video understanding. State-of-the-art video recognition approaches based on Convolutional Neural Network (CNN) have shown promising performance by adopting 2D or 3D CNN architectures. However, the large data volumes, high resource demands, and strict latency requirements have hindered the wide application of these solutions on resource-constrained Internet-of-Things (IoT) and edge devices. To address this issue, we propose a novel framework called TLEE that enables the input samples the abilities of both Temporal-wise and Layer-wise Early Exiting on 2D CNN backbones for efficient video recognition. TLEE consists of three types of modules: gating module, branch module, and feature reuse module. The gating module determines for an input video from which frame of this video to exit the per-frame computation, while the branch module determines for an input frame from which layer of the CNN backbone to exit the per-layer computation. Besides, based on the accumulated features of frame sequences from exit branches, the feature reuse module generates effective video representations to enable more efficient predictions. Extensive experiments on benchmark datasets demonstrate that the proposed TLEE can significantly outperform the state-of-the-art approaches in terms of computational cost and inference latency, while maintaining competitive recognition accuracy. In addition, we verify the superiority of TLEE on the typical edge device NVIDIA Jetson Nano.
Soundboard Scholar no. 7 (Complete)

This PDF is a compilation of all articles from Soundboard Scholar no. 7, provided for convenience. Please visit https://digitalcommons.du.edu/sbs/vol7/iss1/ for individual PDFs, which should be used for citation. This PDF was updated on March 4, 2022 to include Robert Ferguson's obituary of Thomas Heck.
Project-Based Learning Interactive Canva Media: Improving High School Students' English Learning Outcomes

The purpose of this research is to produce interactive media based on the Canva application PjBL that is feasible and effective in improving students' English learning outcomes at SMA Negeri 1 Padang Bolak.The type of research used is the development model from Borg and Gall and the instructional design step from Dick and Carey which is divided into 4 stages including the needs analysis stage, the product design stage, the validation and evaluation stage, and the final product stage.The results showed that: product validation showed a score percentage of 89.16% for material expert validation, 80% for media expert validation, 93.75% for design expert validation, and 98.56% for student responses.The results of the normality and homogeneity tests show that the research data has been declared normal and homogeneous.The results of the hypothesis test show that the value of t count is 3.03 and the value of t table is 1.66, where t count > t table.The results of this study indicate that interactive media based on the project-based learning model assisted by the Canva application is effective in improving student learning outcomes.
Fast CNN enhancement using channel attention and residual networks for image super-resolution

Single image super-resolution (SISR) refers to the process of reconstructing a high-resolution (HR) image from a low-resolution (LR) input image. Deep learning super-resolution algorithms have widely been used to solve SISR tasks. However, the demanding computation cost and memory occupation incurred through training the deep learning models has been hindering its real-world application. In this paper, we rebuild FSRCNN and apply it to solve SISR tasks. Firstly, we change the original training dataset to RealSR, a larger dataset consisting of real-world images. Secondly, channel attention and residual blocks have been applied to the mapping layers and important parameters including learning rate and optimizer have been reset. Thirdly, we change the cost function from loss to loss and replace the activation function from parametric rectified linear unit (PReLU) to exponential linear unit (ELU), to verify the discrepancies between different loss functions and activation functions. Finally, we compare the rebuilt models with the official FSRCNN based on the Peak signal-to-noise ratio(PSNR) and thestructural similarity index measure(SSIM) on three common test datasets. The original model achieves better performance on all the test datasets across different scale factors while the rebuilt models show better generalization capability. Our analyses illustrate that residual blocks can slightly promote model performance while different loss functions and activation functions do not generate an evident impact on the rebuilt model.
Monte-Carlo tree search with Epsilon-Greedy for game of amazons

The Game of the Amazons is an abstract strategy board game. It has a high computational complexity similar to the game of Go. Due to its NP-complete nature and large branching factor of game tree, finding the optimal move given a specific game state is infeasible and it is not trivial to design a computer algorithm that is competitive to an expert in the game of amazons. One way to tackle this problem is to leverage the Monte-Carlo Tree Search by using random simulations. In this article, a computationally cheap heuristic function is proposed and use together with Monte-Carlo Tree Search algorithm with Epsilon-Greedy policy aiming to design a competitive AI for the Game of the Amazon. The effectiveness of the -greedy based Monte-Carlo algorithm is compared to the widely used MCTS with Upper Confidence Bound and other classical tree search method such as breadth-first search, depth-first search, minmax search and alpha-beta pruning.
Hyperautomation in Super Shop Using Machine Learning

The purpose of this research was to determine how we can optimize both customer and seller experiences in a super shop using hyperautomation technology. Here, a smart bot was employed to speed up responses of simple consumer queries by utilizing natural language processing in real time. We also used machine learning frameworks, such as XGBoost, linear regression, random forest, and hybrid models together, to predict future product demand. In addition, data mining methods, such as the Apriori algorithm, FP growth algorithm, and GSP algorithm, were used to find out which algorithm can be used to determine the right way to place a product to increase the super shop sale.
Penerapan Media Loose Part dalam Pembelajaran Keaksaraan Awal pada Masa Pandemi Covid-19

The purpose of this research is to: 1) describe the lesson plan. 2) describe the implementation of learning. 3) describe learning barriers. 4) describe early literacy learning solutions using Loose part media during the pandemic at Mojoreno Kindergarten, Sidoharjo District, Wonogiri Regency. This type of research is a type of descriptive qualitative research. The research approach is a fixed case study type. The location of the research was carried out at Mojoreno Kindergarten, Sidoharjo District, Wonogiri Regency. The time of research was in semester II of the 2021/2022 school year, with the object of research being group B students at Mojoreno Kindergarten, a total of 15 children. Sources of data in this study come from documents, events and activities as well as places and locations of research. Data collection techniques in the form of in-depth interviews, observation and document analysis. The data validation technique in this study was triangulation, namely source triangulation and method triangulation. Data analysis was performed using an interactive model. The research carried out shows that: 1) learning plans that contain learning steps, 2) implementation of learning that is in accordance with the prepared lesson plans, 3) the emergence of obstacles in learning that Loose part media 4) solutions to overcome obstacles encountered, namely working with parents of students in preparing media and documenting learning outcomes or by using small notes to record children's progress.
Review of automatic image cropping in practical application

There is now a greater demand for photo Aesthetics and a desire for higher quality images. As a result there is also a greater demand for image cropping. Although there is a lot of work currently addressing this need, there has not been a comprehensive paper on the current state of development in this area until now. So this paper summarise the classical approach and some general datasets and some evaluation metrics. More importantly, this paper analyse the current challenges in the field and hope that future research will address these issues and lead to a better development of the field.
Research advanced in deep learning-based image restoration

In the computer vision community, which tries to address issues with image data change, loss, and missing that may arise in the process of image collecting, transmission, and preprocessing, image restoration has always been a hot study topic.Learning the contextual information of the visible regions in an image is the basic idea of image restoration algorithms, which perform noise removal, missing region repair and transformation on the regions to be restored to reproduce the original image content. Most of the early image restoration algorithms rely on the design of manual features, whose accuracy and generalization ability can not meet the practical application requirements. Due to the rapid advancement of CNN, in recent years, image repair accuracy and effect have made breakthroughs, and have been widely used in old photos, movies, videos and other repair scenes. In this paper, three restoration algorithms (sequence-based restoration, CNN-based restoration and GAN-based restoration) are comprehensively introduced while the research progress of image restoration based on depth learning is reviewed. Secondly, we quantitatively compare the performance of different repair algorithms and analyze their advantages and disadvantages. Finally, we summarize the challenges facing the area of image restoration research and evaluate its potential future directions.
Current state and future development of artificial intelligence in marketing

Artificial intelligence (AI) has emerged as a trend in various industries. In the marketing field, artificial intelligence has been the main driver of its growth and an important tool for companies, organisations, and marketing specialists. However, there is a gap between the existing AI research and the actual application of the technologies in marketing. To bridge the gap, this study focuses on the current state of research on artificial intelligence in marketing and AI technologies used in marketing. Research articles on this topic are searched from the ScienceDirect database and the IEEE Xplore database and the non-empirical studies are filtered out by selection criteria. Then the selected articles are analysed using the method of bibliometrics and content analysis. This study describes the landscape of research in artificial intelligence in marketing, summarises AI technologies applied in the marketing field, provides advice on applying AI applications to marketing for marketing specialists, and presents future research directions for AI specialists. Results show that the application of AI in marketing will increase fast in the coming years. In particular, companies and organisations may greatly benefit from the use of AI technology in marketing to increase sales, cut costs, improve firm productivity, boost innovation, and achieve organisational goals.