Reviewerly/exHarmony huggingface dataset:
Learning image representations for content-based image retrieval of radiotherapy treatment plans

Abstract Objective. In this work, we propose a content-based image retrieval (CBIR) method for retrieving dose distributions of previously planned patients based on anatomical similarity. Retrieved dose distributions from this method can be incorporated into automated treatment planning workflows in order to streamline the iterative planning process. As CBIR has not yet been applied to treatment planning, our work seeks to understand which current machine learning models are most viable in this context. Approach. Our proposed CBIR method trains a representation model that produces latent space embeddings of a patient’s anatomical information. The latent space embeddings of new patients are then compared against those of previous patients in a database for image retrieval of dose distributions. All source code for this project is available on github. Main results. The retrieval performance of various CBIR methods is evaluated on a dataset consisting of both publicly available image sets and clinical image sets from our institution. This study compares various encoding methods, ranging from simple autoencoders to more recent Siamese networks like SimSiam, and the best performance was observed for the multitask Siamese network. Significance. Our current results demonstrate that excellent image retrieval performance can be obtained through slight changes to previously developed Siamese networks. We hope to integrate CBIR into automated planning workflow in future works.
Noise Imitation Based Adversarial Training for Robust Multimodal Sentiment Analysis

As an inevitable phenomenon in real-world applications, data imperfection has emerged as one of the most critical challenges for multimodal sentiment analysis. However, existing approaches tend to overly focus on a specific type of imperfection, leading to performance degradation in real-world scenarios where multiple types of noise exist simultaneously. In this work, we formulate the imperfection with the modality feature missing at the training period and propose the noise intimation based adversarial training framework to improve the robustness against various potential imperfections at the inference period. Specifically, the proposed method first uses temporal feature erasing as the augmentation for noisy instances construction and exploits the modality interactions through the self-attention mechanism to learn multimodal representation for original-noisy instance pairs. Then, based on paired intermediate representation, a novel adversarial training strategy with semantic reconstruction supervision is proposed to learn unified joint representation between noisy and perfect data. For experiments, the proposed method is first verified with the modality feature missing, the same type of imperfection as the training period, and shows impressive performance. Moreover, we show that our approach is capable of achieving outstanding results for other types of imperfection, including modality missing, automation speech recognition error and attacks on text, highlighting the generalizability of our model. Finally, we conduct case studies on general additive distribution, which introduce background noise and blur into raw video clips, further revealing the capability of our proposed method for real-world applications.
Construction waste resource utilization and energy consumption calculation based on Internet of things

With the development of urbanization, the city has entered the stage of improving the quality of the stock, and the number of urban demolition has increased, resulting in the generation of construction waste and great pressure on the environment. The discharge of construction waste threatens the sustainable development of urban environment. And because large public buildings have high energy consumption and high emissions, it is urgent to establish an effective energy consumption measurement system. In the past, the calculation method for energy consumption was relatively single, which could not effectively manage energy consumption. This paper discusses how to use construction waste scientifically based on the Internet of things, and using advanced measurement technology to monitor and analyze building energy consumption is an effective way to solve this problem. Based on this, this paper constructs a building energy consumption measurement system through the Internet of things technology. By using BP linear neural network model to predict, the system effectively improves the prediction accuracy and makes it close to the actual value. The system is mainly based on the collection terminal, the centralized data terminal and the data management terminal and then builds a communication bridge between the terminals through the Internet of things and the Internet. The response time of energy consumption monitoring and management is relatively short, and the response speed of power control management and energy consumption management analysis is relatively fast, which can effectively measure the energy consumption of buildings. In addition, this paper also makes a brief analysis of the recycling of construction waste and puts forward the corresponding strategic analysis of resource utilization. This paper designs an effective energy consumption measurement system by introducing the Internet of things technology into the building field.
Ultrasound Image Synthetic Generating Using Deep Convolution Generative Adversarial Network For Breast Cancer Identification

Breast cancer is the leading cause of death in women worldwide; prevention of possible death from breast cancer can be decreased by early identification ultrasound image analysis by classifying ultrasound images into three classes (Normal, Benign, and Malignant), where the dataset used has imbalanced data. Imbalanced data cause the classification system only to recognize the majority class, so it is necessary to handle imbalanced data. In this study, imbalanced data can be handled by implementing the Deep Convolution Generative Adversarial Network (DCGAN) method as the addition of synthetic images to the training data. The DCGAN method generates synthetic images with feature learning on a Convolutional Neural Network (CNN), making DCGAN more stable than the basic generative adversarial network method. Synthetic and original images were further classified using the CNN GoogleNet method, which performs well in image classification and with reasonable computation cost. Synthetic ultrasound images were generated using a tuning hyperparameter in the DCGAN method to adjust the input size on GoogleNet for imbalanced data handling. From the experiment result, the implementation of DCGAN-GoogleNet has a higher accuracy in handling imbalanced data than conventional augmentation and other previous research, with an accuracy value reaching 91.61%, which is 1% to 4% higher than the accuracy value in the previous method.
Online Crossword Puzzle on Young Learners Vocabulary Knowledge

Vocabulary is one of the essential language components in learning the English language. Therefore, the learners must learn vocabulary to be able to master four skills, which are listening, speaking, reading, and writing. Teaching vocabulary to young leaners is considered difficult. As a result, the teacher can use one of the media that can be applied in teaching vocabulary to young learners using an online crossword puzzle game. The aim of this research is to find out the effect whether teaching vocabulary using online crossword puzzle games have a significant effect on vocabulary knowledge for young learners. The research is conducted to the fourth grade students of SDIT-At takwin Sukabumi. Random sampling technique is used to get sample from the population and there are two classes that become the sample in this research. There are 25 students from class IV B and 25 students from class IV C. In conducting the research, quasi-experimental method and one group pre-test and post-test design are used. The data was taken from pre-test and post-test by giving vocabulary test. The data are calculated by using t-test formula. Based on the data calculation, the result of tcalculated is 5.0813 while the level significant of ttable 1.677 and the level significant of 0.05 with the degree of freedom (df) of 48. Therefore, it can be concluded the t-test value is higher that the t-table value (5.0813 1.677). It means that alternative hypothesis (Ha) is accepted, which is the result shows that the students in learning vocabulary with the experimental group has significant effect after giving the treatments using online crossword puzzle game. The students can memorize the word easier because they understand the meaning of the word by engaging in language learning experiences through visual aids.
Evaluation Human Resources Information System Using COBIT 5 Framework in Technology Insurance Company

Technology insurance companies experience problems with HRIS (Human Resources Information System), where there is no SOP in handling process problems and the risk of handling server problems and data operations that cause data leakage crimes in the HRIS system within the company. The problems are evaluated for governance using COBIT 5 as a framework. There are four selected domains, EDM04, APO07, APO12, and DSS03, based on the COBIT 5 Analysis process, which is adjusted to the problem. Data were collected through interviews with business, IT, management, and company directors. The results are at capability level 1. The EDM04 process gets an average of 73.44%, the APO07 process gets an average of 68.8%, the APO12 process gets an average of 57.36%, and the DSS03 process gets 59.7%, where the target company is level 2. The recommendations aim to increase the level of capability to the level targeted by the company.
Aspect-Based Sentiment Analysis in Bromo Tengger Semeru National Park Indonesia Based on Google Maps User Reviews

Technology can influence and shape a person's behavior patterns when planning tours, traveling, and after traveling. Visitors' reviews can be used as evaluation material to improve the quality of tourist destinations and become a determining factor for other tourists to visit or revisit the destinations. The process of utilizing these reviews can be done by assessing the aspects of tourist destinations based on reviews from visitors. This study aims to conduct an aspect-based sentiment analysis on one of the tourist destinations in Indonesia, namely Bromo Tengger Semeru National Park, based on reviews of Google Maps users. The aspects consist of attractions, facilities, access, and price. The sentiment classification model used is a machine learning model consisting of SVM, Complement Naïve Bayes, Logistic Regression, and transfer learning from pre-trained BERT, IndoBERT, and mBERT. Based on the experimental results, transfer learning from the IndoBERT model achieved the best performance with accuracy and F1-Score of 91.48% and 71.56%, respectively. In addition, among the machine learning models used, the SVM model gives the best results with an accuracy of 89.16% and an F1-Score of 62.23%.
Topic Modeling on Online News.Portal Using Latent Dirichlet Allocation (LDA)

The amount of News displayed on online news portals. Often does not indicate the topic being discussed, but the News can be read and analyzed. You can find the main issues and trends in the News being discussed. It would be best if you had a quick and efficient way to find trending topics in the News. One of the methods that can be used to solve this problem is topic modeling. Theme modeling is necessary to allow users to easily and quickly understand modern themes' development. One of the algorithms in topic modeling is the Latent Dirichlet Allocation (LDA). This research stage begins with data collection, preprocessing, n-gram formation, dictionary representation, weighting, topic model validation, topic model formation, and topic modeling results. Based on the results of the topic evaluation, the. The best value of topic modeling using coherence was related to the number of passes. The number of topics produced 20 keys, five cases with a 0.53 coherence value. It can be said to be relatively stable based on the standard coherence value.
Comparison of Forensic Tools on Social Media Services Using the Digital Forensic Research Workshop Method (DFRWS)

Social media applications currently play a role and become part of various human activities, on the other hand social media is also very vulnerable to various crimes. Some crimes on social media can be in the form of hate speech, defamation, fraud, gambling, pornography, and other harmful actions. This research applies the Digital Forensic Research Workshop (DFRWS) method to search for all data on twitter social media services running on the Android operating system using MOBILedit Forensic Express and Belkasoft Evidence Center tools. Twitter social media services in this research are used for activities by utilizing all the features in it. Activities carried out by twitter users become evidence that will be acquired using MOBILedit Forensic Express and Belkasoft Evidence Center tools. From the two tools used, a comparison was obtained that MOBILedit Forensic Express found more data on twitter social media than Belkasoft Evidence Center, the findings in these two tools made several contributions to social media investigations that run on the android operating system
Tuben: Re-introducing a computer program by Liljencrants and Fant (1975) for simulating vocal tract area transfer functions

Liljencrants and Fant (1975) designed a computer program for modelling vocal tract area transfer functions as sequences of rounded tubes. Here, we present Tuben, an updated version of the program, written in the Python programming language. We present a use case of the program, replicating vocal tract shapes for point vowels /a/, /i/, and /u/. The program has been made publicly available and presents researchers and students in speech-centric sciences with easily accessible vocal tract modelling and vowel synthesis.
An Assessment on Work Engagement and Organizational Learning Capability among Educators

This study aimed to assess the work engagement and organizational learning capability among educators of DMC College Foundation, Inc. during the second semester of 2022-2023. A quantitative descriptive-correlational research design was used in this study. Data were obtained from 47 educators of DMC College Foundation, Inc. Frequency count, Weighted Mean and Pearson Correlation was used to analyze the gathered data. The study found that when a teacher is tenured, he/she is more engaged in the organization. Among the indicators of work engagement, dedication has the highest mean which means that educators are very much dedicated on the job they are doing. In terms of organizational learning, educators are more immersed with the system of the school having dialogue as the indicator garnering the highest weighted mean. This shows that the school has a well-established protocol in relation to communicating members of the organization. Moreover, this study found out that there is a significant correlation between work engagement and organizational learning capability across age groups anyhow having a positive correlation, age is not a factor that will affect both work engagement and organizational learning capability. Also, the study concluded that there is no significant correlation between work engagement and organizational learning capability when grouped according to years in service. Additionally, work engagement and organizational capability shows a significant and strong positive correlation with each other.
Improved LSTM Method of Predicting Cryptocurrency Price Using Short-Term Data

As cryptocurrencies develop, it cannot be denied that crypto prices are volatile. One of the influencing factors is the increasing volume of transactions which attracts the interest of researchers to conduct research in developing coin price predictions from cryptocurrencies. The method, algorithm and amount of data affect the prediction results. In this study, prediction modelling will be carried out using the LSTM method and short-term data. This study will conduct two experiments using the simple LSTM method and utilising multivariate time series with LSTM. The smallest predicted value is obtained using an 80/20 data allocation distribution scenario, input layer LSTM = 360, Epoch = 500, a Solana coin with RMSE = 0.111, R2 = 0.9962. It can be interpreted that short-term data can be used in making predictive models. Still, special attention needs to be paid to the characteristics of the dataset used and the modelling methodology, and it is hoped that the results of this study can be used in further research.
A Graph Neural Network Recommendation Model with Knowledge Graph and Attention Mechanism

Abstract With the growth of e-commerce and social media platforms, recommendation systems have become crucial for businesses to increase user satisfaction and retention. Collaborative filtering has been the primary recommendation algorithm, but it faces challenges such as the cold start problem and data sparsity. Researchers have tried to introduce supplementary information to complement the data, and Graph Neural Networks (GNNs) have been useful in capturing the relevance of graphs in recommender systems. In addition, traditional GNNs only fuse the first-order neighborhood features of nodes and cannot capture the deeper structural relationships of nodes in the network. Therefore, when the data set is sparse and each node has only a very small number of neighbors, the recommendation quality of traditional GNN-based recommendation algorithms decreases significantly. In this paper, we propose a GNN recommendation model based on user-side information, which can effectively capture the potential interest of users through user relationship graph for recommendation, and fully exploit the large amount of association information among users. The traditional collaborative filtering idea is incorporated with the knowledge graph as the auxiliary information to fully utilize the potential information from both ends to improve the accuracy and diversity of the recommendation model. In addition, the interaction behaviors and opinions in the user-item graph are jointly captured. Different strengths of user-item interactions are considered, which indicate that various interactions contribute differently to user preferences (or item characteristics), and thus attention mechanisms are used to achieve this goal. The algorithm has been verified by public dataset, and it has better recommendation effect than several other classical recommendation algorithms.
Psychological Emotion Recognition of Students using Machine Learning based Chatbot

Anxiety and depression can have a significant impact on students’ academic performance, however, these mental health impacts were increased during the Covid-19 pandemic, and accordingly students and parents need some people to share their feelings together; however, there are different types of social media apps and platforms such as Facebook, Twitter, Reddit, Instagram, and others. Twitter is one of the most popular social application that people prefer to share their emotional states. Interestingly, the psychologist and computer scientists are inspired to study these emotions. In this paper, we propose a chatbot for detecting the students feeling by using machine-learning algorithms. The authors used a dataset of tweets from Kaggle’s paltform, and it includes 41157 tweets that are all related to the COVID19. The tweets are classified into categories based on the feeling: Positive and negative. The authors applied Machine Learning algorithms, Support Vector Machines (SVM) and the Naïve Bayes (NB) and accordingly they compared the accuracy between them. In addition to that, the classifiers were evaluated and compared after changing the test split ratio. The result shows that the accuracy performance of SVM algorithm is better than Naïve Bayes algorithm, but the speed is extremely slow compared to Naive Bayes model. In future, other neural network algorithms such as the RNN, LSTM will be implemented, and Arabic tweets will be included in the future.
Automatic Multiple Choice Examination Questions Marking and Grade Generator Software

This paper discusses a feasible software solution that enables automatic marking and grading of scripts. Technology keeps expanding, and more advanced innovations are being implemented with time. The marking and allocation of grades for examina- tion scripts through human efforts are gradually becoming a thing of the past. Hence, machines and software applications are introduced to make the entire marking and grading of examination scripts more efficient, fast, and less tedious. Computer vision is an artificial intelligence (AI) knowledge domain that ensures devices obtain useful information from digital images, videos, and other visual inputs. Image processing and recognition, a unique part of computer vision alongside the python program- ming language and the OpenCV library was employed for this project. These are the most used in developing most recent applications that utilize, to some extent, arti- ficial intelligence to attain specific desired results. The result of the project seeks to develop a maintainable android software application that uses image processing technology to scan patterns or images and grades results of multiple-choice question scripts based on a set marking scheme. This ensures that desired results are obtained while increasing efficiency and productivity.
Diagnostic Performance of Deep Learning in Screened Mammogram: Systematic Review

The usage of artificial intelligence in medical image analysis has significantly surpassed that of earlier related technologies. This paper aimed to investigate the diagnostic accuracy of Artificial Intelligence (AI) based-deep learning models for breast cancer detection.We used the PICO (Patient/Population/Problem, Intervention, Comparison, Outcome) scheme to formulate the research question and construct our search terms. Studies were systematically examined from the available literature using the constructed search terms from PubMed, and ScienceDirect according to the PRISMA guidelines. The quality of the included studies was assessed using the QUADAS-2 checklist. The characteristics of each included study such as the study design, population, index test, and reference standard, were extracted. The sensitivity, specificity, and AUC for each study were also reported.In this systematic review, 14 studies were analyzed. Eight studies showed that AI was more accurate than radiologists in evaluating mammographic images, while one comprehensive study found AI to be less precise. Studies that reported sensitivity and specificity without radiologist intervention showed performance scores ranging from 16.0% to 89.71%. With radiologist intervention, sensitivity was between 62% to 86%. Only three studies reported a specificity of 73.5% to 79%. The AUC of the studies was between 0.79 and 0.95. Thirteen studies were retrospective, and one was prospective.There's inadequate evidence on the effectiveness of AI-based deep learning for breast cancer screening in clinical settings. More research is needed, including studies evaluating accuracy, RCTs, and large-scale cohort studies. This systematic review found that AI-based deep learning improves radiologists' accuracy, especially for novice radiologists. Younger, tech-savvy clinicians may be more accepting of AI. Although it can't replace radiologists, the encouraging results suggest it will play a significant role in identifying breast cancer in the future.