Reviewerly/exHarmony huggingface dataset:
Enhancing Large Language Models’ Utility for Medical Question-Answering: A Patient Health Question Summarization Approach

Large language models (LLMs) offer tremendous potential for answering diverse questions and providing valuable insights. However, to maximize their utility, it is essential to formulate effective prompts or questions. This challenge is particularly pronounced when searching for health-related information inundates the web with questions from consumers about their health. In fact, patients with limited technical vocabulary and inadequate phrasing may struggle to submit questions that accurately convey their concerns, resulting in a significantly higher occurrence of false positives in answer generation. To address this pressing issue, we propose a solution for generating concise and understandable medical questions by summarizing consumer health questions (CHQs). Moreover, our method leverages state-of-the-art language models, has shown effective results. Specifically, we fine-tune a set of Transformer-based models, including Flan-T5, on low resources, using three different medical question summarization datasets. Furthermore, through ablation studies, we demonstrate how generative configuration choices and instruction fine-tuning can significantly impact the final results. Our best English model achieves scores of 54.32%, 38.08%, and 51.98% in terms of ROUGE-1, ROUGE-2, and ROUGE-L, respectively, outperforming exiting methods by 5.82 ROUGE-1 points and providing a potential means to craft good prompts.
Spatio&amp;#x2013;Temporal Retinex&amp;#x2013;Inspired Envelopes with Anisotropic Diffusion

Since the introduction of the Retinex theory by Land and McCann in 1971, a multitude of different families, versions, interpretations, implementations, and applications have been proposed. The applications for image enhancement mainly differ in (i) how they explore the locality of the images to determine the local context, and (ii) how they recompute the pixel values based on this context. STRESS (spatio-temporal Retinex-inspired envelopes with stochastic sampling) is one of many quite successful members of the family of Retinex-based image enhancement algorithms. It explores the locality using a stochastic sampling technique, resulting in two envelope images &#x2013; one maximum and one minimum envelope, completely enclosing the image signal and serving as a representation of the local image context. In this paper, we propose to exchange the stochastic sampling technique of STRESS, which causes significant chromatic noise, with an adapted version of constrained linear anisotropic diffusion for computing the envelopes, resulting in almost noise-free images. Using both subjective experiments and objective image metrics, we show that it improves the perceived and measured image quality and reduces noise artefacts.
An Intelligent Deep Learning Strategy for Breast Cancer prediction using feature ensemble learning

One in six women will be diagnosed with breast cancer (BC) by the time they reach their forties, making it the leading cause of death among this demographic. It's the leading killer in the world, yet it's hard to spot and diagnose in its early stages. Among women, BC is a foremost origin of death in third world countries. The prevention and treatment of these tumors can be greatly aided by screening and early diagnosis. At the moment, BC is the leading malignancy in females, even more so than ovarian cancer. When researchers get access to patient medical records, they can uncover hidden patterns in healthcare. The National-Cancer-Institute (NCI) reports that mortality rates from breast cancer can be reduced with early detection. In order to accurately identify patients with cancer, this work presents a voting classifier based on ensemble learning that integrates the logistic-regression(LR) & stochastic-gradient-descent(SGD) with deep-convoluted features. The ensemble voting classifier is fed deeply-convoluted features mined from the microscopic characteristics. This concept offers a refined framework for differentiating amid benign and malignant tumors. The results show that the highest level of classification accuracy, 100%, may be attained when utilizing voting classifiers with complex features. When compared to the traditional methods, the proposed method showed an improvement in accuracy. Thus, the research aids doctors in diagnosing and avoiding breast cancer.
An effective mechanism for secret sharing in Distributed Blockchain Systems

The main objective of the paper is for the implementation of a very secure data transmission way. It’s already known that blockchain technology is well-known for security. But still found some issues with storage cost due to data replication at each node, therefore distributed storage blockchain systems came into existence in order to overcome the limitations of traditional systems. Later on, this system was also enhanced to the Distributed Storage Blockchain with the Local Secret Sharing scheme in order to maintain or lower the communicational/ recovery cost-oriented issues. So, here the idea is to integrate the obtained distributed storage blockchain with the Local Secret Sharing schemes mechanism with image steganography to make the private messages much more secure from attackers. The scope of this project is just integrating the obtained encrypted text in Distributed Storage Blockchain (DSB) including the Local Secret Sharing (LSS) scheme with Image Steganography to implement our proposed model.
Automatic Violence Recognition System

Automatic violence recognition system is an important tool for public safety. It can detect harsh behavior in public as well as non-crowded places like public parks, educational institutions, and other locations. The system uses a combination of convolutional Neural Network algorithm and a huge trained Dataset to recognize patterns associated with violence in order to accurately detect a violent situation and with the help of a transmitter, the detected signal can be intimated to the authority. With this technology, it is possible to reduce the risk of potential harm caused by violent incidents in crowded or public places. And also, it can be used to monitor jails and other areas where there is an increased risk of violence. By providing early detection services, automatic violence recognition systems can help ensure the safety of people in these locations.
Enhancing Cybersecurity: A Multilayered Approach to Phishing Website Detection Using Machine Learning

This paper employs machine learning techniques to combat the escalating threat of phishing attacks in the digital realm. The research builds a predictive model capable of differentiating between phishing and legitimate websites by examining a broad range of information retrieved from website URLs, including address bar-based properties, domain characteristics, and webpage content. To ascertain their effectiveness in this task, six well-known machine learning algorithms—Decision Tree, Random Forest, XGBoost, Deep Learning, Autoencoder Neural Network, and Support Vector Machines—are rigorously investigated. Notably, the Multilayer Perceptrons algorithm emerges as the standout performer, achieving an 86.4% accuracy in identifying phishing websites. This endeavor not only advances the field of cybersecurity but also empowers individuals to proactively safeguard themselves against the pervasive threat of phishing attacks in an increasingly interconnected digital landscape.
Comparative Assessment of deep learning methods for Prediction of Uterine Fibroid

Today, convolutional networks, a type of deep learning algorithm, are frequently used in medical science. Automatic disease detection, in particular, is of great importance in the medical field. For the detection and classification of tumors, it is necessary to segment the affected region and the tumor. In general, medical imaging techniques are used to diagnose the tumor, such as magnetic resonance imaging (MR), ultrasound (US) and computed tomography (CT). Our study surveysdeep learning's applications for image classification, object detection, segmentation. Accurate segmentation of the affected region, tumors, and spine from MR images remains challenging, however it is still challenging to achieve because of 1) Wide differences in the size and shape of tumors amongst individuals; 2) the lack of contrast between tissues and organs that are next to each other; and 3) the undetermined number of tumors. Neural networks are able to capture the region and tumors [2]. The survey concludes with current findings and an analysis of open research challenges and directions.
Machine Learning-Driven Detection and Prevention of Cryptocurrency Fraud

Numerous cutting-edge commercial possibilities have emerged as a result of the widespread use of cryptocurrencies, but it has also drawn a growing number of fraudulent individuals looking to commit fraud. This paper proposes an extensive strategy that makes use of machine learning(ML) techniques to meet the urgent demand for effective fraud detection tools inside the cryptocurrency industry. This paper presents a comprehensive investigation of several fraudulent practices that are common in the virtual currencies ecosystem. The next step has been to investigate several of ML approaches, including Adaptive Boosting(AdaBoost), Random Forest(RF) and Extreme Gradient Boosting(XGBoost), that are designed to spot unusual patterns suggestive of fraudulent behaviour. A crypto fraud detection dataset of fraud instances and real-world cryptocurrency transactions has been utilised in trials to gauge the effectiveness of the suggested approach. To gauge the accuracy and resilience of the models, performance metrics including precision, recall, and F1-score are used. In order to establish which algorithms are most suited for real-time fraud detection, multiple approaches have also been examined for efficiency and scalability. The results show how ML techniques can be used to improve the security of cryptocurrency networks. The XGBoost approach has the best accuracy, at 98%, followed by AdaBoost and RF, at 67% and 90% respectively. The suggested models show encouraging results in spotting fraudulent behaviour, with substantial successes in spotting previously unidentified attack patterns.
Seeing With Sound: Automatic Image Captioning with Auditory Output for the Visually Impaired

The human eye can discern imagery and add a mental annotation of it. However, the visually impaired community cannot visualize or label an image or environment. As an assistive technology for the visually impaired, the proposed system will automatically generate a highly relevant caption that accurately describes the events portrayed in an image. The generated caption will undergo further conversion and be presented as an auditory output for easy user accessibility. The captioning process is performed using Deep Learning algorithms – Convolutional Neural Network (CNN), InceptionV3 model, Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) – and Beam Search.
A Novel Fully-Automated Deep Learning Pipeline for the Effective Detection of Cervical Cancer

In the process of computer-assisted cervical cancer detection, one of the most critical challenges is the detection of aberrant cervical cells. In this research, we devise an artificial intelligence (AI) framework solution that enables to inevitably screen for undetermined significance cells in order to help expedite the successive medical assessment of the subject areas. This solution is founded on the premise that AI can be thought of as a form of learning. The system can be broken down into the following significant stages: In the initial step, cervical cell segmentation is carried out, which really accountable for effectively recovering cell images from the Entire Slide Image (ESI); Next there is a cell classification component known as Compact VGG that is built on a compacted visual geometric group (VGG) network. This component is the most important part of the framework and is what is utilised to construct the model. For evaluating the efficacy of the given developed framework, cervical cancer image dataset is used, which was extracted from public repository. Also, performance evaluation metrices is determined, which proves the proposed model produces the reliable outcome based on the results produced.
Enhancing Quadrotor UAV Efficiency Amidst Turbulent Winds in Mangrove Area: A Hybrid PID-Grey Wolf Optimizer Control Approach

Quadrotor Unmanned Aerial Vehicles (UAVs) offer versatile platforms for various applications including disaster response and environmental monitoring. However, their effective utilization in wind-disturbed environments such as mangroves poses unique challenges due to complex wind turbulence, impacting flight stability and navigation accuracy. Traditional control systems often fall short of ensuring robust and precise control in such conditions. This study presents a hybrid control approach combining a Proportional-Integral-Derivative (PID) control system with the Grey Wolf Optimizer (GWO) for enhanced UAV performance in challenging conditions. The PID controller, known for its effectiveness in industrial control systems, provides a control loop feedback mechanism to minimize flight errors, while the GWO, a bio-inspired optimization algorithm, automates the process of tuning PID parameters. Preliminary results show that this hybrid PID-GWO system significantly improves the UAV's robustness and adaptability under varying wind conditions, outperforming the standalone PID controller. This research illuminates a new direction for optimizing UAV performance in wind-disturbed environments and suggests further exploration of bio-inspired optimization techniques in UAV control systems.
Unraveling the Gestures: Indonesian Sign Language Classification via Faster Region-CNN

Communication activities are one of the needs for human being. However, differences in speech styles between non-disabled people and people with disabilities like the mute and the deaf where they use sign language to communicate will be easier if there’s tools that can help them to communicate. Using Faster R-CNN (Region Convolutional Neural Network) and utilizing technology from camera assistance which is processed using computer vision and machine learning in the sign language reading process, that can become bridge for communication activities between one another. Faster R-CNN method used for processing sign language classification, in this case SIBI (Sistem Isyarat Bahasa Indonesia) is used for dataset training. The results of this study produce a model with mAP = 0.50 IoU amounting to 100.00% and mAP = 0.75 IoU amounting to 98.07% under predetermined conditions.
Disentangling Parsing and Grammar in Subject Pronouns Interpretation in Italian

In the present study, we investigate if and how null and overt subject pronouns interpretation preferences in Italian can be influenced by two factors: (i) the presence of c-commanding antecedents (Rizzi 2018) and (ii) the ‘impatient parser’ (Sorace and Filiaci 2006; Fedele and Kaiser 2014). To disentangle the effects of c-commanding antecedents and of the ‘impatient parser’, we compare experimental conditions differing for only one of the two factors. The comparison demonstrates that c-commanding antecedents influence the interpretation of null pronouns but not the interpretation of overt pronouns, while no effect of the impatient parser is found for either null or overt pronouns. In addition, we found that external referent interpretation preferences are modulated by Principle C effects for null and overt pronouns, albeit to different degrees. External referent choices also increase when an
Analisis dan Perancangan Sistem Informasi Seleksi Karyawan Baru pada PT.Erafone Artha Retailindo Cabang Pekanbaru

Efficient and organized employee recruitment management is very important for the success of a company. In this digital era, web-based technology offers a great opportunity to improve the recruitment process by reducing time, costs and increasing the quality of prospective employees. This study aims to analyze and design an innovative and easy-to-use web-based recruitment system for the company PT ERAFONE ARTHA RETAILINDO to increase efficiency and accuracy in the recruitment process. The research method we use is waterfall and interviews with HR managers and the company's recruitment team to understand the process. current recruitment. We also carry out document analysis, such as historical data on previous recruitment and the recruitment platform used. The results of the system implementation show a significant increase in the response time of the recruitment process and the quality of candidates invited to further selection stages. The HR team also reported reduced workload and increased productivity due to the efficiency of the new recruitment system&#x0D; Keywords: Information systems, recruiting employees, system design, HR, waterfall
Pengaruh Sistem Informasi Akuntansi, Sistem Pengendalian Internal Terhadap Kinerja Karyawan Dengan Insentif Karyawan Sebagai Variabel Moderasi

This study aims to analyze the Effect of Accounting Information Systems 1; and Internal Control System 2; Against Employee Performance with Employee Incentives 3; As a Moderating Variable at BMT Satria Nuban Muamalah. The type of research used in this study is quantitative with a descriptive approach, in this study researchers used primary data through questionnaires and the population in this study amounted to 25 employees, then the sampling technique in this research used purposive sampling. The research data used SPSS version 26 software. The results of this study indicate that the Accounting Information System has a positive and significant effect on employee performance, the Internal Control System has no effect on employee performance, and employee incentives moderate the accounting information system on employee performance, and employee incentives moderate the internal control system on employee performance at BMT Satria Nuban Muamalah.
The Development of Android Application Media Based on PQ4R Learning Strategy on Cognitive Learning Result of Class X Students on Ecosystem Materials

The purpose of this research is to analyze the feasibility of the android application media based on PQ4R learning strategy according to experts and users and to find out the effectiveness of the media on the cognitive learning result of class X MIPA SMA Negeri 1 Batang on ecosystem material. This type of research is Research and Development (R &amp; D) with research procedures using the ADDIE model, namely Analysis, Design, Development, Implementation, and Evaluation. Data collection techniques using observation, interview, questionnaire, and test. The results showed that the percentage obtained from material experts, media experts, and users, namely teachers and students with very decent criteria. The results of the study showed that there was a significant increase in cognitive learning result after using the android application media based on the PQ4R learning strategy on ecosystem material in class X MIPA SMA Negeri 1 Batang which was proven by the results of hypothesis test with Fcount (16,094) &gt; Ftable (3,93). It is also proven in the results of the average posttest score of 87.81 compared to the average pretest value of 47.20 with an N-Gain of 0.77 in the high criteria. This study concludes that the android application media based on the PQ4R learning strategy on ecosystem material was declared very feasible by material experts, media experts, and users, namely teachers and students to use in learning and effectively improve student cognitive learning result.
OSD Witnesses for Bipartite States

In this chapter we introduce the new type of witness, which is based on the Schmidt decomposition of operators. In the first part we will give the main idea and show how to construct the entanglement witness. Further, we will show that it is strictly stronger than fidelity-based witnesses. In the second part some approaches to numerically find those witnesses are discussed.
Integrating Predictive Big Data Analytics with Behavioral Machine Learning Models for Proactive Threat Intelligence in Industrial IoT Cybersecurity

This paper introduces a comprehensive framework for industrial Internet of Things (IoT) cybersecurity, integrating multiple algorithms to enhance threat intelligence. The proposed framework encompasses five key algorithms, each addressing specific aspects of data preprocessing, time series analysis, predictive analytics, and behavioral machine learning. The Data Preprocessing and Integration algorithm refines raw IoT data through a meticulous 20-step process, ensuring high-quality input for subsequent analyses. The Time Series Analysis algorithm delves into temporal patterns, while the Random Forest algorithm focuses on predictive analytics for proactive threat detection. The LSTM Ensemble algorithm extends the analysis into behavioral machine learning, capturing temporal dependencies and detecting anomalies. The Weighted Average Ensemble combines outputs from predictive analytics and behavioral models, leveraging their correlation for enhanced threat intelligence. An ablation study dissects the individual contributions of each algorithmic component, shedding light on their specific impacts. The results highlight the significance of each step, guiding optimizations for improved performance. The proposed framework outperforms existing methods in various performance metrics, showcasing its potential as a robust solution for proactive threat intelligence in complex industrial environments. This framework stands at the forefront of industrial IoT cybersecurity, offering a holistic and adaptive approach to address evolving threats. The ablation study enhances the transparency and understanding of the framework, contributing to its continuous refinement and effectiveness in safeguarding critical industrial systems.
Pengaruh Tingkat Pemahaman Akuntansi, Persepsi Karir, Dan Prestasi Akademik Terhadap Niat Untuk Mengambil Sertifikasi Profesi Chartered Accountant (CA) Motivasi Sebagai Variabel Moderasi (Studi Kasus Pada Mahasiswa S1 Akuntansi Di Universitas Muhammadiyah

This type of quantitative research uses a comparative causal study, namely&#x0D; analysis, that is directed to investigate cause-and-effect relationships. Data was collected&#x0D; using a questionnaire method and processed using SPSS Statistics 25 with a sample of 55. This study aimed to determine the effect of the level of accounting understanding, career perception, and academic achievement on the intention to take Chartered Accountant (CA) professional certification motivation as a moderating variable. On accounting students at the University of Muhammadiyah Metro. The results showed that (1) the relationship between the level of accounting understanding and the intention to take chartered accountant (CA) professional certification did not have a significant effect (2) career perception significantly influenced the intention to take chartered accountant (CA) professional certification (3) achievement relationship. There is no significant effect on the academic intention to take chartered accountant (CA) professional certification (4) motivation can moderate the relationship between the level of accounting understanding and intention to take chartered accountant (CA) professional certification (5) motivation can moderate the relationship between career perception and intention to take chartered accountant (CA) professional certification (6) motivation can moderate the relationship between academic achievement and intention to take chartered accountant (CA) professional certification.