Reviewerly/exHarmony huggingface dataset:
Causal Fusion for Recommender System

Abstract Recommender system suffers from huge selection bias from users, which makes utilizing causal inference to solve this problem becomes a necessary problem. However, traditional rating model only utilizing biased dataset and unbiased estimator cannot remarkably be improved. Thus, we fused the unbiased dataset into training model and combine it with jointly learning method to better improve the original baselines. Extensive experiments verify that the proposed doubly robust joint leaning method after fusing unbiased, missing rating data can significantly outperform the state-of-the-art methods and can dramatically reduce the error from rating model with the increasing number of unbiased data.
Improved Causal Bayesian Optimization Algorithm with Counter-noise Acquisition Function and Supervised Prior Estimation

Abstract Causality is an important element in decision-making and interventions are required to optimize results of target values. In this paper, based on the model of Causal Bayesian Optimization, a counter-noise version of acquisition function is proposed and new prior estimation algorithms including Support Vector Regression, Ridge Regression and Random Forest are evaluated. This paper provides an improved framework to facilitate causal inference and optimization processes.
Predicting mortality index for ICU inpatients based on clinical data extracted from electronic health record

Abstract Predicting ICU inpatients mortality index needs to be improved to incorporate clinical data. It is also helpful to reflect the patient’s recovery and hospitals standards. In this research machine learning model LightGBM was trained and assessed. This study used a dataset for ICU admissions for adult patients from six countries. And a total of 130,000 patient records were included in the study. The final model achieved AUROC (95% CI) of 0.97, an accuracy of 0.95, and an F1 score of 0.81 on the dataset. Based on results, it is observed that machine learning models with the support of conventional mortality scoring indices can provide a successful and useful model for predicting the outcome of critical and severe cases in the ICU.
Accuracy Improvement Based on Classic Neural Network: Voting, Restarting and Quantization

Abstract Since AlphaGo beat the world Go champion in 2016, which attracted wide attention, the neural network has become more and more popular in recent years, and people’s research on it has gradually improved and been applied in different fields. Today, artificial intelligence and machine learning have become an essential part of modern society and intelligent systems. We do image recognition, speech recognition, and visual learning, closely related to machine learning. However, in machine learning, unsatisfactory training results or even training failure is always encountered. Therefore, in machine learning, it is imperative to improve the accuracy of neural network training results. In this paper, speech recognition, image processing, MNIST, and other classical neural network models will be used to set the training parameters of the neural network better and improve the accuracy of training through voting, quantization, restart, and other methods. The part of research is aiming to find the relationship between restart numbers on the training process and the total extent of learning improvement. At the same time, several algorithms on utilizing these restart numbers are to be compared and selected. Finally, the conclusion is drawn that the more restart made in the training process with a convolutional neural network, the less profit on accuracy improvement we gain from restarting the process.
Gated Multi-task Learning for QoS-aware Service Recommendation

Abstract In the ever-increasing diversity of cloud environment, the recommendation of web service-related applications based on service quality is a basic tasks that both service providers and web developers are very interested in. A key challenge is how to get personalized and accurate QoS values of web services from the multi-source information of users/services. In addition, complex contexts and multi-tasking attributes also make the collaborative services quality prediction difficult to meet. Inspired by the progress of multi-task deep learning, this paper proposes a new framework of service quality prediction called multi-task gated expert network (MGEN), which is based on gating mechanism and multi-task learning. The MGEN consists of three parts: expert-based context-source feature extraction, gated interaction learning and information fusion. First, multiple expert networks are used to capture the latent representation in raw data of web services, including both implicit features in the QoS matrix and explicit features of service descriptions. In the process, several independent experts extract features from different dimensions. Second, output embedding of different expert networks is transmitted to each task layer through different weights, learning both global and local high-order feature interactions. Third, the interactive information of different tasks is fused by embedding splicing, and then fed back to the prediction module for final service recommendation. Results of the comparative experiment demonstrate that the proposed MGEN significantly outperforms the state-of-the-art models of service recommendation, where both the implicit and explicit information of various multi-source features and interaction sequences are effectively extracted. In addition, the gating mechanism performs well in the QoS prediction by independently modeling each task.
Two Applications of Manifold Regularization in Deep Learning Architectures

Abstract With the rapid development of machine learning today, deep learning has shown great advantages in many fields. Due to the high cost of datasets with labels, its branch semi-supervised learning has become an important research area. However, existing semi-supervised learning methods perform poorly on extremely small datasets, for instance, only one labeled example for each class. Hence, we introduce methods that apply manifold approaches to DL(Deep Learning) and frameworks that implement the idea. Potential application scenarios were analyzed based on experiments on synthetic and real-world experiments.
Indigenous Research and Data Management in Electronic Archives

This chapter introduces a framework for indigenous research and data management in electronic archives that aligns with indigenous worldviews and practices. It discusses indigenous communities' challenges in owning and controlling their data and the need for a culturally relevant framework for managing indigenous data in electronic archives. The proposed eight-step framework emphasises community control, data sovereignty, and ethical data management practices; and includes key components such as community engagement, informed consent, and culturally relevant metadata standards. Best practices for data sharing and partnership building with non-indigenous institutions are also discussed, as well as the steps for implementing the framework and the role of stakeholders in the process. Evaluation metrics for measuring the framework's success are proposed. The chapter concludes by emphasising the importance of community control and ethical data management practices in preserving and protecting indigenous cultural heritage and identity in electronic archives.
Effect of Benign Biopsy Findings on an Artificial Intelligence–Based Cancer Detector in Screening Mammography: Retrospective Case-Control Study

Background Artificial intelligence (AI)–based cancer detectors (CAD) for mammography are starting to be used for breast cancer screening in radiology departments. It is important to understand how AI CAD systems react to benign lesions, especially those that have been subjected to biopsy. Objective Our goal was to corroborate the hypothesis that women with previous benign biopsy and cytology assessments would subsequently present increased AI CAD abnormality scores even though they remained healthy. Methods This is a retrospective study applying a commercial AI CAD system (Insight MMG, version 1.1.4.3; Lunit Inc) to a cancer-enriched mammography screening data set of 10,889 women (median age 56, range 40-74 years). The AI CAD generated a continuous prediction score for tumor suspicion between 0.00 and 1.00, where 1.00 represented the highest level of suspicion. A binary read (flagged or not flagged) was defined on the basis of a predetermined cutoff threshold (0.40). The flagged median and proportion of AI scores were calculated for women who were healthy, those who had a benign biopsy finding, and those who were diagnosed with breast cancer. For women with a benign biopsy finding, the interval between mammography and the biopsy was used for stratification of AI scores. The effect of increasing age was examined using subgroup analysis and regression modeling. Results Of a total of 10,889 women, 234 had a benign biopsy finding before or after screening. The proportions of flagged healthy women were 3.5%, 11%, and 84% for healthy women without a benign biopsy finding, those with a benign biopsy finding, and women with breast cancer, respectively (P&lt;.001). For the 8307 women with complete information, radiologist 1, radiologist 2, and the AI CAD system flagged 8.5%, 6.8%, and 8.5% of examinations of women who had a prior benign biopsy finding. The AI score correlated only with increasing age of the women in the cancer group (P=.01). Conclusions Compared to healthy women without a biopsy, the examined AI CAD system flagged a much larger proportion of women who had or would have a benign biopsy finding based on a radiologist’s decision. However, the flagging rate was not higher than that for radiologists. Further research should be focused on training the AI CAD system taking prior biopsy information into account.
Blockchain-Based Volunteers Management System

Blockchain technology is attracting huge attention across multiple industries. It sparks a revolution unlike anything else since the start of the Internet, since it goes against old ideas, changing the way that both individuals and organizations will function in the future. In this paper, we are presenting the design and implementation of a blockchain-based volunteers management system. This system will assist all volunteers and managers, even those who plan to join the volunteering community, in keeping track of new activities and be rewarded in a secure, yet transparent way.
Neural Extended Kalman Filter Tracker Using the ReLu Function

The Neural Extended Kalman Filter (NEKF) is a proven method to provide on-line adaptability for estimation. To approximate the actual system behavior more closely than other adaptive Kalman filter based methods, the NEKF estimates both the states of the system and the weights of a neural network that adapts the system model. The NEKF can be used for off-line system identification but has been primarily employed for on-line adaptive modeling for control problems and maneuver target tracking. In past implementations, the NEKF has been implemented using a standard three-layer (input, single hidden, and output) neural network. A variety of squashing functions have been utilized and compared to one another, including the sigmoid function, the hyperbolic tangent, piece-wise linear approximations to the sigmoid, the radial basis function, and the elliptical basis function (EBF). Recently, a newer neural activation function, the Rectified Linear Unit (ReLU), has been employed for deep-learning applications. With claims of improved computational efficiencies over techniques such as the sigmoid function, implementation with the NEKF for an on-line process, such as target tracking, could provide benefits in timing. In this paper, the NEKF is implemented the currently prevalent ReLU function. The NEKF will be applied to a target tracking problem with a maneuvering target using a two-hidden layer neural network with the ReLU as the activation function.
Analog Frequency Sampling Filters

This paper develops two types of analog frequency sampling filters. Both types of frequency sampling filters interpolate a frequency response through the filter’s Fourier series coefficients, or frequency samples from the filter’s frequency response, and use these frequency samples as coefficients in the filter’s implementation. These filters have the potential to be programmable and have exact linear phase; however, practical limitations prohibit the filter from achieving exact linear phase.
Securing Electronic Health Records (EHR) from Tampering Using Blockchain

Electronic health records EHRs are digital representations of a patient’s medical history, and authorized healthcare professionals can access and share health-related data from them. EHRs provide a number of advantages over conventional paper-based medical records, including increased data accuracy, speed, and accessibility. EHRs also make it simple for healthcare professionals to exchange patient data with other professionals, improving results and care coordination. In general, EHRs are a crucial part of contemporary healthcare systems and significantly enhance patient care and results. To protect patient privacy and stop unwanted access, alteration, or disclosure of personal health information, EHRs must be securely stored EHRs security issues arise from the possibility for unauthorized access to and modification of private patient data. To protect EHRs and stop tampering, it is crucial to install strong security features including securing, access limits, and audit trails. Overall, sustaining patient trust and delivering high-quality healthcare services depend on the security of EHRs. This paper proposes a solution for securing EHRs from tampering using Blockchain technology (BCT). Blockchain is a decentralized, secure, and transparent ledger system that generates a permanent and tamper-proof record of all transactions, offering the solution for protecting electronic health information from hacking.
Environmental sensor placement with convolutional Gaussian neural processes

Abstract Environmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However, it is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. Gaussian process (GP) models are widely used for this purpose, but they struggle with capturing complex non-stationary behaviour and scaling to large datasets. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues. A ConvGNP uses neural networks to parameterise a joint Gaussian distribution at arbitrary target locations, enabling flexibility and scalability. Using simulated surface air temperature anomaly over Antarctica as training data, the ConvGNP learns spatial and seasonal non-stationarities, outperforming a non-stationary GP baseline. In a simulated sensor placement experiment, the ConvGNP better predicts the performance boost obtained from new observations than GP baselines, leading to more informative sensor placements. We contrast our approach with physics-based sensor placement methods and propose future steps towards an operational sensor placement recommendation system. Our work could help to realise environmental digital twins that actively direct measurement sampling to improve the digital representation of reality.
SMART Education Framework to Assess the Knowledge of Engineering Students

This article explores the application of the SMART Education Framework in assessing the knowledge of engineering students. The SMART Education Framework is an approach to education that emphasizes the integration of technology into the learning process. It consists of four key components: Strategy, Methodology, Assessment, and Resources. In this study, engineering students were exposed to the SMART Education Framework, and their knowledge was assessed using formative and summative assessments. The results showed that the SMART Education Framework can enhance the assessment of engineering students’ knowledge, enabling personalized learning and providing educators with the tools and resources they need to effectively integrate technology into the learning process. The findings of this study have important implications for the use of the SMART Education Framework in engineering education and suggest that it has the potential to revolutionize the way engineering students are assessed.
COMPUTER VISION-BASED TILE COUNTING MODEL FOR AUTOMATED PROGRESS MONITORING

Construction industry in Korea has grown based on apartments.Tiles are installed in most of bathrooms of apartment houses so the numbers of tiles are huge to manage.Therefore, this paper proposes a computer vision-based model for automated tile counting.This model uses line detection and rectangle packing algorithm.The results of proposed model appeared that the average of accuracy in line detection and intersection points extraction stage is 98.8% with standard deviation of 1.2%.In calculation stage, the proposed model shows 90.1% of accuracy with standard deviation of 9.4% This implies model can solve the overcounting problem by quickly and accurately calculating the number of tiles with images acquired by the inspector's mobile phone in the field.Furthermore, it can contribute to creating a transparent construction industry.
Fast and Accurate K-means Clustering Based on Density Peaks

K-means is a widespread clustering algorithm characterized by its simplicity and efficiency. K-means behavior, though, strongly depends on the initialization of the cluster centers (centroids) and tends to be stuck in a local suboptimal solution. Many techniques have been devised to overcome these problems, e.g., by a global strategy to reduce locality of centroid adjustments or by using density peaks for centroids initialization. This paper proposes an improved version of the K-means—DPKM-based on the concepts of density peaks. Density peaks have been proved to be a key for solving clustering problems where not-spherical regions with complex point distributions are involved. Centroids are actually selected from density peaks by using a technique borrowed from the DK-means++ initialization method, which ensures centroids are not only points with higher density, but also far-away from each other. DPKM is implemented in Java using parallel streams and lambda expressions which are capable of delivering good execution times on large datasets on multicore machines with shared memory. The efficiency and reliability of DPKM are demonstrated by applying it to challenging synthetic datasets often used as benchmarks for clustering methods.
Predicting Word Importance Using a Support Vector Regression Model for Multi-document Text Summarization

The extractive text summarization method assigns a score to a sentence based on the weights of the words in the sentence. The traditional method of measuring the sentence importance is the TF * IDF-based method which uses frequency statistics for a word to measure its importance. In this paper, we present a multi-document summarization (MDS) approach that computes the score of each sentence based on word importance predicted by a support vector regression (SVR) model and produces a multi-document summary by ranking sentences according to sentence scores. The proposed MDS system has been evaluated using a benchmark dataset named the DUC2004 dataset. The results in terms of ROUGE scores show the effectiveness of the proposed method in producing better multi-document summaries.
Burrows–Wheeler Transform for Enhancement of Lossless Document Image Compression Algorithms

Images compression has always been important topic, and nowadays, it becomes essential due to the huge requirement for storage and transfer images over internet in abundance. Several diverse image compression methods have been proposed. In this research, a method using Burrows–Wheeler transform algorithm is proposed that restructures the compression data to enhance the performance of subsequent image compression algorithms. A two-dimensional grayscale image is reshaped to one-dimensional data array. Here, the most suitable linearization procedure is used during this conversion. Then the Burrows–Wheeler transform is carried out over this data that restructure the data in such a way that the resultant data became further compressible. Then the enhanced run length encoding method is applied, and its result is stored in two variables. The first variable consists of various gray levels encountered in the image, and the other variable stores their consecutive repetitions called as runs. Then over the second variable consisting of run counts, the Huffman encoding algorithm is applied. Similarly, a dictionary-based method is used in the second approach considering these run counts as the input. Finally, these achieved compression outcomes are evaluated and compared. The performance of the proposed method is compared with the different existing methods like discrete cosine transform block code truncation, singular vector sparse reconstruction, discrete cosine transforms, and Gaussian pyramid methods. The performance metrics used are mean square error, compression ratio, signal-to-noise ratio, and peak signal-to-noise ratio. Compared to these existing methods, the proposed method performs better.
Mathematical Modeling of 2D Discontinuous Objects by New Information Operators

Paper investigates methods for restoring discontinuous functions from different information about it using different information operators. The information operators used are the operators of interpolation and approximation, when information about an object is represented in the form of one-sided values in a given system of points, and interlination, when information is represented in the form of one-sided traces along a given system of lines. The article tests the hypothesis that discontinuous functions should also be approximated by discontinuous structures. Discontinuous interpolation, approximation, and interlinear splines are constructed to approximate the discontinuous function of two variables; theorems on the approximation errors of these splines and their estimation are given. The case when the domain of definition is triangulated by right triangles is considered. A comparative analysis of the developed approximation methods is carried out. The interlination of functions can be widely used in automating the design of aircraft and automobile bodies; in obtaining and processing the results of hydroacoustic and radar studies, in solving problems of computed tomography, in digital signal processing, and in many other fields.
Calibration of Optimal Trigonometric Probability for Asynchronous Differential Evolution

Parallel optimization and strong exploration are the main features of asynchronous differential evolution (ADE). The population is updated instantly in ADE by replacing the target vector if a better vector is found during the selection operation. This feature of ADE makes it different from differential evolution (DE). With this feature, ADE works asynchronously. In this work, ADE and trigonometric mutation are embedded together to raise the performance of an algorithm. The work finds out the best trigonometric probability value for asynchronous differential evolution. Two values of trigonometric mutation probability (PTMO) are tested to obtain the optimum setting of PTMO. The work presented in this paper is tested over a number of benchmark functions. The benchmark functions’ results are compared for two values of PTMO and discussed in detail. The proposed work outperforms the competitive algorithms. A nonparametric statistical analysis is also performed to validate the results.