Reviewerly/exHarmony huggingface dataset:
Transparency in Medicine: How eXplainable AI is Revolutionizing Patient Care

Deep Learning algorithms are considered “black-box” algorithms because it is not possible to analyse how they find the final result. This greatly limits their application in several domains, especially in fields like medicine, where errors can harm patients. To overcome this limitation, explainable AI techniques have been developed that allow us to understand the features of the input that have been relevant to the system to find the result. Most authors do not pay enough attention to explainable AI techniques, creating very basic and uninformative representations. For this reason, we analyse different heatmap-based eXplainable AI techniques for different medical problems related to chest x-rays classification, depending on the classification problem: binary and mutilabel. In our methodology, we divide the techniques into two groups to address the explainability in Artificial Intelligence applied to medicine, and show five representative examples of different visualisation techniques.
User Experience Evaluation on Nucleus Farma Website Using System Usability Scale

Technological developments require companies to continue to innovate in any way, one of which is using websites in terms of marketing and branding. The Nucleus Farma company has innovated using the website to increase the credibility of its products and as a means of branding. Currently, Nucleus Farma has marketed its products in more than 500 outlets in Indonesia. Product knowledge support is made to expand the market network to compete in the global market. However, the Nucleus Farma website needs to function correctly. From the research results that have been done, it shows 30% of users feel that the interface could be better, and 34% say that the information could be more informative. The reason for those problems is since it was published in 2018 until now, Nucleus Farma has never evaluated the quality of using the website or changed its appearance to improve the User Experience of the website. Testing and evaluation are part of increasing website user satisfaction and interaction. Therefore, this study aims to conduct a usability test on the Nucleus Farma website to determine how satisfied the users are. In conducting research, the authors use the System Usability Scale method. Researchers collected the data through a questionnaire to test its validity and reliability. The score obtained from a series of test and measurement was 65.3 (grade D), meaning users are not satisfied with the Nucleus website interface experience. This user experience evaluation results in a design plan focusing on user experience to increase user satisfaction in using the Nucleus Farma website.
Research on the Motion Characteristics of Trans-Media UAV Out of Water

In order to explore the motion characteristics of the trans-media unmanned aerial vehicle (UAV) dehydrated from the water surface into the air, this paper establishes a physical model and numerical simulation, and deeply analyzes the water movement process of the trans-media UAV from three aspects: dehydration characteristics, flight attitude control and motion stability, revealing its complex motion behavior characteristics. In the part of design and construction, the structural characteristics and basic design principles of trans-media UAV are discussed in detail. In the physical model part of the water outlet process, the complex phenomenon of trans-media UAV dehydration is emphatically discussed, and the trajectory of UAV is deduced by using advanced formulas. In the study of water outlet characteristics, the attitude control stability of UAV in the air is analyzed, and the restrictive effect of aerodynamic factors on UAV movement is revealed. The experiment simulation results show that the UAV is influenced by the coupling of hydrodynamic and aerodynamic forces on the water surface during the dehydration process, and its water outlet angle and speed have an important influence on the subsequent flight. In addition, the trans-media UAV can effectively control its attitude and enter the air stably after it leaves the water, which is of positive significance for promoting the development of UAV technology in the trans-media environment.
Analysis of Fraud Prediction and Detection Through Machine Learning

In today's world the rate of fraudulent activities has significantly elevated, because of which a need for a competent system is required. Among all the fraudulent activities insurance fraud has the most dominating rate of growth. Fraud studies have suggested, that upon identifying the similar characteristics of a fraudulent claim with the claimants, a system of forensic and data-mining technologies for fraud detection can be set up. In this, seek to define fraud and fraudster, and look at the types of fraud and followed by the consequences of fraud to financial systems. As fraud is getting widespread these days epically in the health care insurance system, dealing with this problem has become a necessity. Unsupervised machine learning algorithms such as K-Means clustering along with supervised algorithms used in machine learning, like support vector machines, logistic regression, design trees etc. can play a very vital role in binary class classifications, which would ultimately help in identifying and outreaching the desired goal of fraudulent detection. In the end, this paper specifies the best or the most appropriate model that could be used in the given dataset to produce the most accurate results, based on certain parameters of confusion metrics like accuracy, precision, and specificity.
Automated Test Case Generation from Activity Diagram using Depth-First Search

Test cases are essential parts to perform software testing as they play very significant role defining the test steps to work. Test cases can be generated either manually or automatically. Creating test cases manually has several disadvantages, such as higher probability in producing inaccurate results. Furthermore, generating test cases manually is ineffective since manual methods will take longer and require high human efforts. Therefore, generating test cases automatically is more preferable to limit the previous problems. Automated testing can also provide more accurate results than manual testing because it is less prone to human errors encouraging software developers to generate test cases automatically. Test cases can be generated from various sources, including UML diagrams. In this paper, we propose a method to generate test cases automatically by utilising depth-first search algorithms. The UML diagram used in this study is an activity diagram. This paper also provides a method to parse activity diagram that is created using the visual paradigm application that has been converted into an XML format. Our method is able to parse and convert the XML format into graph or tree form. Using our proposed method, a test case search will be carried out using the depth-first search on the tree form to generate test cases. Lastly, we also evaluate our method by comparing the generated test cases with cyclomatic complexity, the method will be performed manually and automatically to validate the results of many scenarios. Our result showed that our method could generate the same scenario (100% coverage) as the manual validation method.
A Blockchain-Based Solution of Credit Investigation in Metaverse

With the development of the Metaverse, the credit of nodes in the Metaverse plays an important role. Firstly, it analyzes the current situation and challenges of the development of credit investigation in the Metaverse, and proposes a solution based on blockchain combining the real-name verification method of operators with the credit investigation results of the credit investigation system, which can provide services to nodes in Metaverse that need to conduct credit investigation, thus ensuring the reliability of transactions in the Metaverse, and providing a security guarantee for the development of the Metaverse.
Fast Image matting based on matting pyramids and surrogate models

Image matting is a foundation task in the field of computer vision. Pixel-pair-optimization-based image matting methods have attracted lots of attention due to their distinct advantages on parallelization and handling spatially disconnected foregrounds. However, these methods struggle when the computing time is limited. To address this issue, this study presents the image matting method based on matting pyramid and surrogate model (IMMPSM). IMMPSM speeds up the process of pixel pair optimization by replaces the evolutionary algorithm in the pyramid matting framework, which consumes a lot of computing time, with the efficient surrogate-model-based method. The constructed surrogate model is reused by the presented surrogate model share strategy to save computing time. Specifically, the constructed surrogate model is shared with the pixel pair optimization subproblem adjacent to current subproblem avoiding repeated computation on the surrogate model construction. Extensive experimental results show that IMMPSM can provide acceptable alpha mattes even under limited computing time.
The Meet-in-The-Middle Attack on The Matrix NTRU Cryptosystem

The Matrix NTRU cryptosystem, based on matrix rings and modular arithmetic, is a variant of the NTRU cryptosystem introduced in 1996. In the Matrix NTRU cryptosystem, messages are encrypted into ciphertext using a public key generated by the receiver, and decryption using the corresponding private key. The confidentiality of the private key is crucial for maintaining the cryptosystem’s security. This paper is dedicated to analyzing the protection of the private key in the Matrix NTRU cryptosystem using meet-in-the-middle attacks. It means the attackers found the private key by searching two matrices that, when combined, produce a result with specific characteristics. By scrutinizing the public key and employing a meet-in-the-middle strategy, the attacker can recover the private key. This paper comprehensively outlines the steps involved in the meet-in-the-middle attack and discusses the conditions the candidate’s private key must satisfy. The security analysis helps assess the vulnerability of the Matrix NTRU cryptosystem and highlights the importance of protecting the private key to ensure the cryptosystem’s security. Through this research, we contribute to the ongoing efforts in advancing cryptographic techniques and data protection measures.
A Brief Overview on Various Aspects of Recommendation System Based on Sentiment Analysis

Many industries, including e-commerce, media, finance, and utilities, have embraced recommender systems. To maximize customer happiness, this type of technology uses a vast quantity of data. These recommendations assist customers in selecting items, while companies can enhance product use. When it comes to analyzing social data, sentiment analysis may be used to acquire a better knowledge of users' thoughts & feelings, which is useful for enhancing the dependability of recommendation systems.However, this data may also be utilized to supplement user ratings of items. According to some, SA (Sentiment Analysis) of articles that may be found in online news sources and blogs or even in the recommender systems themselves can provide better recommendations to users. Research trends that connect sophisticated technological components of recommendation systems utilized in many service domains with the commercial aspects of these services are reviewed in this article. We must first conduct an accurate evaluation of recommendations models for RS (Recommendation Systems) using data mining & application service research.Deep learning architectures for breast cancer detection are the topic of this review. The following is a list of current machinelearning-based technologies that will be discussed in this survey.Research into recommendation systems is made possible by this study's examination of the numerous technologies and service trends to which recommendation systems may be applied, which gives a complete overview of the area.
Feature-Aligned Feature Pyramid Network and Center-Assisted Anchor Matching for Small Face Detection

The performance of face detection has made great progress with the rapid development of deep learning. However, small faces only contain an extremely limited number of pixels, which makes them suffer from poor feature representation and low-quality positive anchors. These problems make small face detection still a highly challenging task In this paper, we conduct an in-depth study from the perspective of feature learning and anchor matching and present a small-scale aware face detector (SFDet), which can accurately detect small faces. Specifically, we first propose a feature alignment module (FAM), which learns the semantic transformation offset and adaptively upsamples features using it as a guide. FAM can construct a powerful feature pyramid to learn more discriminative and robust feature representation of small faces. Then, we design an IoU-and-center based anchor matching strategy (ICAMS), which introduces center point information as a new matching metric to complement the traditional intersection over union (IoU) metric and simultaneously uses these two metrics in the anchor matching stage. ICAMS can ensure the generation of high-quality positive anchors of small faces. Comprehensive experiments are conducted on two challenging face detection datasets, and the results demonstrate the effectiveness of our method.
Credit Risk Prediction System For MSME Loan Process

BPR XYZ is one of the BPR (Rural Bank) located in West Java - Indonesia, that has two primary businesses, which are: (1) collecting funds from customers in the forms of savings and deposits and (2) providing loans to customers. BPR XYZ successfully disbursed approximately more than IDR 300 billion micro-loans to the Micro, Small and Medium Enterprise (MSME) segment and will keep increasing gradually in 2022. Their Non-Performing Loan (NPL) rate in 2021 was above 5% (five percent), more significant than the national standard, and most likely to increase in 2022. This happens because many of their MSME customers cannot pay the debt. This study will conduct predictive analysis using Naïve Bayes and K-Nearest Neighbors algorithm (K-NN) to predict the NPL. From our experiments using historical data and five classes of NPL, we found that naïve bayes do not perform well, with average accuracy just only 19%; meanwhile, K-NN obtains a performance of 59%. Random Forest gained 52%, and SVM with 48%. We tried to restructure the NPL classification label to only two classes with these results. We got better results where K-NN algorithm gained the best performance with an accuracy level of 74%.
HAMCap: A Weak-Supervised Hybrid Attention-Based Capsule Neural Network for Fine-Grained Climate Change Debate Analysis

Climate change (CC) has become a central global topic within the multiple branches of social disciplines. Natural Language Processing (NLP) plays a superior role since it has achieved marvelous accomplishments in various application scenarios. However, CC debates are ambiguous and complicated to interpret even for humans, especially when it comes to the aspect-oriented fine-grained level. Furthermore, the lack of large-scale effective labeled datasets is always a plight encountered in NLP. In this work, we propose a novel weak-supervised Hybrid Attention Masking Capsule Neural Network (HAMCap) for fine-grained CC debate analysis. Specifically, we use vectors with allocated different weights instead of scalars, and a hybrid attention mechanism is designed in order to better capture and represent information. By randomly masking with a Partial Context Mask (PCM) mechanism, we can better construct the internal relationship between the aspects and entities and easily obtain a large-scale generated dataset. Considering the uniqueness of linguistics, we propose a Reinforcement Learning-based Generator-Selector mechanism to automatically update and select data that are beneficial to model training. Empirical results indicate that our proposed ensemble model outperforms baselines on downstream tasks with a maximum of 50.08% on accuracy and 49.48% on F1 scores. Finally, we draw interpretable conclusions about the climate change debate, which is a widespread global concern.
Detection of Hate Speech Using Improved Deep Learning Techniques

LSTM was introduced to solve the problem that RNNs faced, such as suffering from the vanishing gradient problem. Researchers have improved the LSTM model using various techniques. However, most improved models are still based on LSTM’s network structure, which needs to address the weight parameters falling into local extrema. Moreover, they can suffer from poor learning efficiency and gradient disappearance. The paper presented an advanced deep learning methodology aimed at significantly improving the detection of hate speech using LSTM supported by feature extraction, feature selection, and RNN. Our model exhibits outstanding performance, achieving high precision, recall, F1-score, and accuracy at 97%. In comparison, the other models, such as RNN, achieved 56%, LSTM+RNN achieved 80%, By employing RNN with feature extraction and feature selection, an accuracy of 96% was achieved. Likewise, LSTM with feature extraction and feature selection demonstrated comparable results, reaching 96% accuracy across all performance metrics. This shows that the proposed model surpassed the Plain LSTM model employing deep learning techniques.
Meteorological data analysis using extreme learning machines

A practical study of statistical modelling language packages R has been carried out using regularization algorithms, more precisely one of the algorithms called the Extreme Learning Machine (ELM). Due to its simple implementation, ELM requires less researcher intervention in setting its parameters. At the same time, the generalization performance of ELM is not sensitive to the dimensionality of the feature space (the number of hidden nodes). Even on a medium-power personal computer, this class of neural networks has made it possible to perform numerous experiments on model building, forecasting and identifying cause-effect relationships in meteorological time series, downloaded from the climate monitoring system of IMCES SB RAS in a reasonable amount of time.
Application of UAV technology in aquaculture operation communication system

Traditional communication systems are unable to control the data generated by aquaculture operations due to the signal data receiving interval continuously. Therefore, it is impossible for all nodes to directly conduct closed-loop communication with the terminal. As a result, the control effect of the system on aquaculture water quality gradually weakens, thus ultimately affecting the results of aquaculture operations. On this basis, the application of UAV technology in aquaculture operation communication systems is explored. In the aspect of hardware design, the transmission framework of UAV communication data is constructed; the decoding of various types of communication data is performed; the circuit unit of the data receiving serial port is designed; and the control and management of communication data signal are implemented. In the aspect of software design, based on UAV technology, an aquaculture communication model is constructed; the constraints of aquaculture operations are intensively processed; the communication of aquaculture operations with full coverage of paths and positions is planned; and an improved communication system is designed. Taking the control efficiency of the oxygen-increasing machine as the test object, through real-time monitoring of the water quality parameters in the aquaculture area, the test results are shown that the communication system proposed in this paper can control the oxygen-increasing machines in the aquaculture pond with water quality problems, and timely regulate the ecological environment of the aquaculture pond, which has practical application significance.
Searchable Symmetric Encryption with Efficient Updates and Small Leakage

Searchable encryption needs to be efficient, secure and dynamic. However existing dynamic schemes have many drawbacks, some of them are not efficient or leaking too much information to adversaries, some can not support dynamic operation perfectly. In this paper we propose an efficient dynamic searchable symmetric encryption scheme with small leakage and good updates. Our scheme supports efficient single-keyword searches, leaks very little information during searching and updating(add or delete), and it can realize dynamic operation perfectly. We prove it to be secure. Furthermore, we show that it is efficient both in searching time and index size. And it can be paralleled running while searching.
Energy Consumption Prediction Module in the Linoleum Production

The software module for predicting energy consumption in the linoleum production is specifically created to anticipate the amount of energy needed for the manufacturing process of this eco-friendly flooring material, which is made from linseed oil, wood flour, and cork dust. Its purpose is to assist manufacturers in enhancing their energy efficiency and decreasing production expenses.
Detection Method of the Secondary Protective Rope for Electric Power Workers Based on UAV Image and YOLO Algorithm

Outdoor electric power workers usually work at heights. In addition to wearing safety belts, operators wear a secondary protective rope to prevent electric power construction workers from falling. The secondary protective rope plays an essential role in protecting the personal safety of operators and reducing personal injury. Therefore, the secondary protective rope is significant for safety inspection in the electric power industry. The detection of the secondary protective rope based on UAV images and the YOLO algorithm is an effective detection method. Yolov5 adopts a unique data enhancement method to integrate the CSP structure into the backbone network while using an improved SPPF structure in the Neck section and combining FPN and PAN. Based on the image set of the secondary protective rope collected by crewless aerial vehicles, the experimental results show that the Yolov5 algorithm has a higher average accuracy than the previous Yolo algorithm, enhances the generalization ability in small target scenarios, meets the performance requirements for the wear detection of the secondary protective rope at the power line construction site, and has a certain promoting effect on the construction safety of the power industry.