Reviewerly/exHarmony huggingface dataset:
Enhancing Detection of Microcalcifications using FADHECAL for Early Stage Breast Cancer

Microcalcifications (MCCs) are reliable early signs of breast cancer. However, the small size of calcifications and low radiation factors used in digital mammograms cause low and poor quality mammogram images in detecting MCCs. This paper presents an image enhancement technique called Fuzzy Anisotropic Diffusion Histogram Equalization Contrast Adaptive Limited (FADHECAL) to enhance the details of MCCs in mammogram images by reducing the image noise while conserving contrast and brightness. A total of 23 mammogram images with MCCs were retrieved from the Mammographic Image Analysis Society’s database. The enhancement performance of FADHECAL was compared with Recursive Mean-Separate Histogram Equalization, Histogram Equalization and Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization. Image quality measurement tools of absolute mean brightness error (AMBE), structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) were used. The results showed that FADHECAL had the most superior results among other enhancement techniques, with 6.302 of AMBE, 20.453 of PSNR and 0.851 of SSIM. The proposed FADHECAL exhibited a high accuracy of 91.30% for the detection of MCCs. Hence, FADHECAL can be used as an ideal tool for identifying MCCs in early-stage breast cancer.
ANALISIS KEMAMPUAN MEMBACA TEKS KEPEWARAAN MAHASISWA TADRIS BAHASA INDONESIA

Reading is the main key to knowing various kinds of advances in science and technology. In the process of reading, schemata are involved in understanding the contents of the reading. The analysis in this study uses a quantitative description method with an experimental approach based on the results of reading comprehension of personality texts. The results of this study indicate that the ability of students to read personality texts in the Indonesian context based on student schemata is categorized as moderate.&#x0D; Keywords: reading, schemata, quantitative descriptive
Design and Implementation of Campus Maintenance and System Repair with Integration of Mobile Technology for E-commerce

The Android mobile application terminal operating system is constantly updated and developed, as well as the continuous optimization, promotion and application of Android smart phones and continuous updating of software technology, coupled with the continuous promotion of digital information engineering construction in various industries, the widespread use of smart phones, Android has gradually been hailed as a mobile application terminal and operating system with the largest number of users in the world. This system studies the design and implementation of the campus maintenance and system repair based on mobile technology. The purpose is to help the schools manage better and it allows students to participate more actively in the construction of the campus, and at the same time it provides teachers and students with a relaxed and happy lifestyle on campus. This article describes UI design and operations that can be used to maintain and repair Android phones on campus, including implementing the main interface, using controls, program operation, and resolving abnormal situations. The development of this system can bring more enthusiasm and good experience to users.
Research on Financial Risk Monitoring and Early Warning in China Based on Big Data Analysis

With the continuous development of China's economy, the financial industry plays a more and more important role in the national economy. At the same time, the financial risk(FR) are increasing and becoming increasingly complex. While sharing the huge benefits brought by financial globalization and financial deepening, China is also faced with greater FR from home and abroad, which greatly increase the possibility of financial crisis in China. In order to enhance the initiative of risk management and ensure the smooth operation of asset quality, we must rely on a powerful system tool, make use of the international advanced risk early warning management concept, and use Internet thinking and data mining technology to dig deep into hidden risk factors, discover hidden risks in advance and deal with them in time, so as to ensure the automatic monitoring ability of credit business. Based on the theoretical research on the generation, accumulation and diffusion of systematic FR, this paper explores the introduction of big data methods into the research on systematic FR in China, comprehensively uses traditional and emerging indicators and models in big data network analysis methods, analyzes the contagion effects of regional systematic FR from multiple perspectives and levels, improves research methods, and enriches research perspectives. The introduction of SIRS model on scale-free network provides a new powerful tool for the study of regional systemic FR.
Review of Target Detection Algorithms

Object detection is a popular direction of computer vision and digital image processing, which is widely used in robot navigation, intelligent video surveillance, industrial detection, aerospace and other fields, using computer vision to reduce human capital consumption has important practical significance. Because of the wide application of deep learning, the algorithm of target detection has been developed rapidly. This paper mainly introduces the traditional target detection algorithm and two kinds of target detection algorithm based on depth learning and the data set commonly used in target detection.
Junk Information Recognition Based on Naive Bayes

This work mainly aims to learn and improve the existing methods of identifying spam information. In this work, the fundamental central principle is applied to naive Bayes. The improvement part is to use naive Bayes to achieve automatic recognition and reporting of spam information in the system, as well as a refined classification of spam information. This paper aims to further deal with junk information (illegal information) through naive Bayes, so as to meet the needs of People's Daily life. In the simple junk information recognition, add life elements, so that junk information recognition more life, close to life, service human.
Analyzing the Evolution of Inter-package Dependencies in Operating Systems: A Case Study of Ubuntu

An Operating System (OS) combines multiple interdependent software packages, which usually have their own independently developed architectures. When a multitude of independent packages are placed together in an OS, an implicit inter-package architecture is formed. For an evolutionary effort, designers/developers of OS can greatly benefit from fully understanding the system-wide dependency focused on individual files, specifically executable files, and dynamically loadable libraries. We propose a framework, DepEx, aimed at discovering the detailed package relations at the level of individual binary files and their associated evolutionary changes. We demonstrate the utility of DepEx by systematically investigating the evolution of a large-scale Open Source OS, Ubuntu. DepEx enabled us to systematically acquire and analyze the dependencies in different versions of Ubuntu released between 2005 (5.04) to 2023 (23.04). Our analysis revealed various evolutionary trends in package management and their implications based on the analysis of the 84 consecutive versions available for download (these include beta versions). This study has enabled us to assert that DepEx can provide researchers and practitioners with a better understanding of the implicit software dependencies in order to improve the stability, performance, and functionality of their software as well as to reduce the risk of issues arising during maintenance, updating, or migration.
An Experience Report on the Design and Implementation of an Ad-hoc Blockchain Platform for Tactical Edge Applications

The success of task groups operating in emergency response or military missions largely depends on their ability to access, process, and exchange information, which is enabled by tactical edge software applications. Designing such applications poses challenges for architects due to the need for resilience, trust, and decentralisation of information processing and exchange. Nevertheless, these challenges also present opportunities for exploring new architectural alternatives like blockchain-based decentralised systems. This paper reports the experience from an R &D partnership to architect and implement a decentralised ad-hoc platform for deploying and operating tactical edge software applications, which facilitate information processing and exchange among tactical edge task groups. The platform utilises a peer-to-peer architecture, aided by ad-hoc blockchain networks, to enable secure and decentralised information storage, update, and distribution. By leveraging a kernel-based, service-oriented architecture, the platform can be adapted to different mission contexts and integrated with various task groups’ systems and data sources. Additionally, we present a process and heuristics for architecting tactical edge software applications to operate on the platform. We further share our experience in deploying and operating the platform in a government-led field experiment and conclude the report with lessons learned from this case study.
PENERAPAN METODE HYBRID ARIMA-ANN UNTUK MEMPREDIKSI HARGA SAHAM PT. BNI (PERSERO) TBK

Stock price is the stock price that occurs in the stock market at a certain time determined by market participants. Stock prices fluctuate up and down from time to time, most investors only use instinct to predict stock prices. Therefore, it is necessary to analyze the time series to predict stock prices. One of the models in time series is ARIMA. However, the ARIMA model has the disadvantage of only being able to follow a linear time sequence pattern. to address these weaknesses, ANN models are used that can follow nonlinear patterns. This is the ARIMA-ANN hybrid. The purpose of this study is to identify the model and determine the accuracy of the Arima-ANN hybrid model and predict the stock price in the next period. The Data used is secondary data from the site investing.com. determination of ARIMA model is done by dividing the data into training data and testing data. The results obtained by the best ARIMA model is ARIMA (2,1,2) with MAPE 1.468% of the training data. With the best ARIMA model, obtained residuals for ANN input. The result obtained is the best network architecture 5-10-1 learning rate 0.3 with the smallest error value. From the test results of the hybrid model ARIMA-ANN to the testing data obtained MAPE value of 7.024%. Then the prediction for the next 25 days obtained an average of 7915 rupiah per day and MAPE 6.349%.Keywords: Hybrid ARIMA-ANN, Predictions, Stock
Evaluation on big data sharing model and key mechanism based on blockchain technology

Abstract Due to the reliability and wide application of blockchain technology, it has attracted wide attention worldwide. Blockchain technology is widely used in finance, logistics, medicine, system management, and Bitcoin. This paper aimed to study how to analyze and study the sharing model and key mechanism of Big Data (BD) based on blockchain technology. This paper analyzed the impact of BD information resource sharing, then built an Ethernet‐based BD sharing system, and tested the performance of the system. This paper also analyzed the system response time, node mining speed test, transmission speed, security and stability of Inter Planetary File System (IPFS). IPFS and blockchain Ethernet are installed on 20 hosts in Ali Cloud and Tencent Cloud servers and laboratories, respectively. The consensus mining main grid consists of 4 Windows 7 systems. In the stability test data of relevant systems, when the number of users was 20 and 100, the system stability was 99.77% and 96.93% respectively. To sum up, the system designed in this paper has certain stability and security, and is worthy of further promotion and application.
Bayesian inference and dynamic prediction for multivariate longitudinal and survival data

Alzheimer's disease (AD) is a complex neurological disorder impairing multiple domains such as cognition and daily functions. To better understand the disease and its progression, many AD research studies collect multiple longitudinal outcomes that are strongly predictive of the onset of AD dementia. We propose a joint model based on a multivariate functional mixed model framework (referred to as MFMM-JM) that simultaneously models the multiple longitudinal outcomes and the time to dementia onset. We develop six functional forms to fully investigate the complex association between longitudinal outcomes and dementia onset. Moreover, we use the Bayesian methods for statistical inference and develop a dynamic prediction framework that provides accurate personalized predictions of disease progressions based on new subject-specific data. We apply the proposed MFMM-JM to two large ongoing AD studies: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and National Alzheimer's Coordinating Center (NACC), and identify the functional forms with the best predictive performance. our method is also validated by extensive simulation studies with five settings.
The Problem of Sound Source Localization in the Theories of Intromissive Auditory Perception

In debates over perception （pratyakṣa-）, a touchstone doctrine of each tradition of classical India is laid bare in the determination of the relationship between the faculty of the senses （indriya-） and its respective object. Besides the well-known controversy over whether a direct contact between a sense and the object is required, there is another demarcation concerning the theories of extramission and intromission, in which the former proposes a kind of emanation from the faculty of the senses toward its object, while the latter postulates that something arrives at the sense from an external object. One of the observed facts which clearly exposes the contrast between the two theories is the so-called sound source localization, which is the listener’s capability to determine the location of a sound source to some extent. This is a drawback for the theory of intromissive auditory perception, for it implies that all sound is heard in the listener’s ear orifice in a way similar to what happens when listening to music with earphones.
A Functional Data Classification Framework Based on Functional Sufficient Dimension Reduction and Model Averaging

With the ever-evolving advancements in data collection and storage technologies, high-frequency data recorded in patterns that change over time have become increasingly common. In many application scenarios for this type of data, functional data classification has emerged as a prominent issue in the field of statistics. In light of this, this paper proposes a functional data classification model based on the functional sufficient dimensionality reduction method and the idea of model averaging. The proposed method utilizes techniques such as Functional Slice Inverse Regression (FSIR) and Functional Average Variance Estimation (FSAVE) to project an infinite-dimensional random function onto a function space spanned by a finite wiki function, ensuring that the original data's effective information for categorical variables is not lost. Additionally, the Bagging algorithm effectively addresses the overfitting and underfitting problems that arise in single predictive models, while employing model averaging instead of model selection to adaptively select the sufficient dimensionality reduction sub-directions. Notably, in the prediction stage, the number and types of base models are flexible. Empirical analysis demonstrates that the proposed method outperforms some comparative methods in terms of prediction accuracy and robustness.
Confidential Computing: Elevating Cloud Security and Privacy

Confidential Computing (CC) fundamentally improves our security posture by drastically reducing the attack surface of systems. While traditional systems encrypt data at rest and in transit, CC extends this protection to data in use. It provides a novel, clearly defined security boundary, isolating sensitive data within trusted execution environments during computation. This means services can be designed that segment data based on least-privilege access principles, while all other code in the system sees only encrypted data. Crucially, the isolation is rooted in novel hardware primitives, effectively rendering even the cloud-hosting infrastructure and its administrators incapable of accessing the data. This approach creates more resilient systems capable of withstanding increasingly sophisticated cyber threats, thereby reinforcing data protection and sovereignty in an unprecedented manner.
Game report data prediction model based on LSTM-GRU

With the increasing number of participants in wordles game, the number of reports changed, we firstly preprocessed the data by sliding window, and then analyzed the reasons for the change of the number of reports and predicted them by LSTM+GRU time series prediction model, and got that the change of the number of reports increased slowly in the early stage and decreased gradually in the later stage, then we predicted the change of the number of reports on March 1, and its change The interval is [22233, 22407], and it has a good fitting effect. Finally, through the comparison of different models, this model has the smallest error, which increases the credibility of the model. Finally, it is found that if this game does not innovate in time, the number of people will gradually decrease in the later.
Occupancy Map Abstraction for Higher Level Mission Planning of Autonomous Robotic Exploration in Hazardous Nuclear Environments

In the nuclear industry, the need for improved reliability in current and future technology hinders the deployment of autonomous robotic systems. The following research aims to develop a method of reliably mapping a large environment and abstracting the map into a sparse node graph to create a more efficient data form. The proposed data form allows for efficient storage whilst maintaining important map features and coverage. The method utilises an expanding node algorithm to convert standard occupancy maps to a sparse node graph representation. The algorithm’s effectiveness has been tested on simulated maps and real-world maps to test the compression factor for a wide range of scenarios. The algorithm is expanded to function on a semi-unknown map abstracting during exploration.
A horseshoe mixture model for Bayesian screening with an application to light sheet fluorescence microscopy in brain imaging

In this paper we focus on identifying differentially activated brain regions using a light sheet fluorescence microscopy—a recently developed technique for whole-brain imaging. Most existing statistical methods solve this problem by partitioning the brain regions into two classes: significantly and nonsignificantly activated. However, for the brain imaging problem at the center of our study, such binary grouping may provide overly simplistic discoveries by filtering out weak but important signals that are typically adulterated by the noise present in the data. To overcome this limitation, we introduce a new Bayesian approach that allows classifying the brain regions into several tiers with varying degrees of relevance. Our approach is based on a combination of shrinkage priors, widely used in regression and multiple hypothesis testing problems, and mixture models, commonly used in model-based clustering. In contrast to the existing regularizing prior distributions, which use either the spike-and-slab prior or continuous scale mixtures, our class of priors is based on a discrete mixture of continuous scale mixtures and devises a cluster shrinkage version of the horseshoe prior. As a result, our approach provides a more general setting for Bayesian sparse estimation, drastically reduces the number of shrinkage parameters needed, and creates a framework for sharing information across units of interest. We show that this approach leads to more biologically meaningful and interpretable results in our brain imaging problem, since it allows the discrimination between active and inactive regions, while at the same time ranking the discoveries into clusters representing tiers of similar importance.
Comparisons of Automated Machine Learning (AutoML) in Predicting Whistleblowing of Academic Dishonesty with Demographic and Theory of Planned Behavior

Machine learning has been very promising in solving real problems, but the implementation involved difficulties mainly for the inexpert data scientists. Therefore, this paper presents an automated machine learning (AutoML) to simplify and accelerate the modeling tasks. Focused on Python and RapidMiner rapid modeling tools, Tree-based Pipeline Optimization Tool (TPOT) and AutoModel were used. This paper presents a comprehensive comparison between these tools with regard to the prediction accuracy and Area Under Curve (AUC) in classifying real cases of whistleblowing academic dishonesty among undergraduate students of two universities in Indonesia. Additionally, the correlations weight from demographic and Theory of Planned Behavior (TOB) attributes in the different machine learning models are also discussed. All the machine learning algorithms from TPOT and AutoModel are considerable powerful to generate good accuracy level (between 70-93% of AUC) in classifying both cases of whistleblowing and non-whistleblowing on the hold-out samples from the testing process. Generally, based on the validation results of the prediction models, demographic attributes presented more importance than the TBP attributes. The findings of this study will be a great interest of many research scholars to conduct a more in-depth analysis on AutoML for many domains mainly in education and academic misconduct fields.•AutoML is the first of its kind to be empirically compared between TPOT and AutoModel in an application to predict academic dishonesty whistleblowing.•Besides accuracy performances of the AutoML, the proportion of the variance of each attribute from demographic and Theory of Planned Behavior (TPB) is also presented in the prediction models of academic dishonesty whistleblowing.•AutoML is a convenient and reproducible rapid modeling method of machine learning to be used in many kinds of prediction problem.
Integrating blockchain technology and cloud services in healthcare: a security and privacy perspective

Throughout recent years, smart city development has gained considerable prominence because of its potential to enhance the living conditions of urban residents. This concept has a broad scope, covering a wide range of areas, including smart communities, smart transportation, and smart healthcare. Most smart city services, particularly healthcare-related, rely on the assessment, processing, and real-time sharing of big healthcare data to make intelligent decisions. Given the current state of the healthcare industry, it necessitates an ongoing supply of healthcare products and services, thereby enhancing its viability. Cloud-based services have made it increasingly important to develop new methods for discovering and selecting these services. Blockchain technology eliminates the need for centralized authorities in certifying and maintaining the integrity of information, facilitating transactions and exchanges of digital assets, and allowing for secure and pseudo-anonymous transactions and direct agreements between parties interacting. The technology possesses an array of key properties, including immutability, decentralization, and transparency, which may solve pressing healthcare issues, such as incomplete patient records at the point of care and difficulty accessing the information of individuals. This paper focuses on integrating blockchain technology and cloud computing in the healthcare industry to improve the security and privacy of medical data, reduce costs, and improve the efficiency of healthcare processes. Additionally, it can enable secure and direct communication between patients and healthcare providers.