Reviewerly/exHarmony huggingface dataset:
Automatic Pectoral Muscle Removal and Microcalcification Localization in Digital Mammograms

Breast cancer is the most common cancer diagnosed in women, and microcalcification (MCC) clusters act as an early indicator. Thus, the detection of MCCs plays an important role in diagnosing breast cancer.This paper presents a methodology for mammogram preprocessing and MCC detection. The preprocessing method employs automatic artefact deletion and pectoral muscle removal based on region-growing segmentation and polynomial contour fitting. The MCC detection method uses a convolutional neural network for region-of-interest (ROI) classification, along with morphological operations and wavelet reconstruction to reduce false positives (FPs).The methodology was evaluated using the mini-MIAS and UTP datasets in terms of segmentation accuracy in the preprocessing phase, as well as sensitivity and the mean FP rate per image in the MCC detection phase. With the mini-MIAS dataset, the proposed methods achieved accuracy scores of 99% for breast segmentation and 95% for pectoral segmentation, a sensitivity score of 82% for MCC detection, and an FP rate per image of 3.27. With the UTP dataset, the methods achieved accuracy scores of 97% for breast segmentation and 91% for pectoral segmentation, a sensitivity score of 78% for MCC detection, and an FP rate per image of 0.74.The proposed preprocessing method outperformed the state-of-the-art methods for breast segmentation and achieved relatively good results for pectoral muscle removal. Furthermore, the MCC detection module achieved the highest test accuracy in identifying potential ROIs with MCCs compared to other methods.
Computer Simulations of Scientific Peer Reviewing

A simple mathematical model of the scientific peer reviewing process is developed. Papers and reviewers are modeled as numerical vectors, respectively representing the paper’s value among multiple quality dimensions, and the importance given to these dimensions by a given reviewer. Computer simulations show that the model can reproduce various characteristics of a real-world paper decision process, and in particular its propensity to act as an “arbitrary” decision procedure for a range of submissions. A key finding of this study is that the appearance of randomness can be explained by a mismatch between high quality dimensions of a paper, and those valued by the reviewers it is assigned to. As a consequence, a program committee may exhibit arbitrariness even with a set of completely reliable reviewers. Various factors contributing to this arbitrariness are then examined, and alternate selection models are studied that could help reduce arbitrariness and reviewer effort.
Abstract Argumentation Frameworks with Domain Assignments

Argumentative discourse rarely consists of opinions whose claims apply universally. As with logical statements, an argument applies to specific objects in the universe or relations among them, and may have exceptions. In this paper, we propose an argumentation formalism that allows associating arguments with a domain of application. Appropriate semantics are given, which formalise the notion of partial argument acceptance, i.e. the set of objects or relations that an argument can be applied to. We show that our proposal is in fact equivalent to the standard Argumentation Frameworks of Dung, but allows a more intuitive and compact expression of some core concepts of commonsense and non-monotonic reasoning, such as the scope of an argument, exceptions, relevance and others.
LSTM and RBFNN Based Univariate and Multivariate Forecasting of Day-ahead Solar Irradiance for Atlantic Region in Canada and Mediterranean Region in Libya

For optimal functioning, grid-connected photovoltaic (GCPV) systems need day-ahead power forecasting, as this ensures overall enhanced management in areas such as reliability, scheduling, and efficiency in energy trading. Solar irradiation forecasts are especially important for obtaining photovoltaic (PV) power production predictions, given that that PV output represents a function of solar irradiation. Recently, the Long Short-Term Memory (LSTM) model is being increasingly applied in solar irradiance forecasting, but the performance of LSTM is still relatively unknown. The present paper explores how meteorological and geographical (i.e., exogenous) and past records of solar irradiance (i.e., endogenous) variables may be incorporated as input features in day-ahead solar irradiance forecasting models that use deep learning models. In this study, the results for the LSTM model are compared to those for the Radial Basis Function neural network (RBFNN) in relation to both multivariate time series forecasting (MTSF) and univariate time series forecasting (UTSF). The results of the comparisons show that the UTSF_LSTM model performs better than other models with regard to minimum forecasting errors. Our results have also been validated with data from a region that features different climatic conditions from those originally tested. Overall, the outcome of these investigations clearly indicate the superiority of the proposed UTSF_LSTM method when compared to the UTSF_RBFNN, MTSF_RBFNN, or MTSF_LSTM developed models with regard to the coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and the Root Mean Square Error (RMSE).
Epistemological Considerations of Text Mining: Implications for Systematic Literature Review

In the era of big data, the capacity to produce textual documents is increasing day by day. Our ability to generate large amounts of information has impacted our lives at both the individual and societal levels. Science has not escaped this evolution either, and it is often difficult to quickly and reliably “stand on the shoulders of giants”. Text mining is presented as a promising mathematical solution. However, it has not yet convinced qualitative analysts who are usually wary of mathematical calculation. For this reason, this article proposes to rethink the epistemological principles of text mining, by returning to the qualitative analysis of its meaning and structure. It presents alternatives, applicable to the process of constructing lexical matrices for the analysis of a complex textual corpus. At the same time, the need for new multivariate algorithms capable of integrating these principles is discussed. We take a practical example in the use of text mining, by means of Multivariate Analysis of Variance Biplot (MANOVA-Biplot) when carrying out a systematic review of the literature. The article will show the advantages and disadvantages of exploring and analyzing a large set of publications quickly and methodically.
Getting It Right and Getting It Wrong in Digital Archaeological Ethics

Though the ethics of archaeological practice have changed over the life of the discipline (and have arguably become more robust), full consideration has not yet been given to how digital methodologies and the emergence of digital technologies have created new areas requiring ethical introspection. The pace of adoption of digitally centred archaeological data and digitally facilitated archaeological practice has not been met by the adoption of discipline-wide standards related to archaeological ethics. The result of this mismatch in ethics and practice is the creation of archaeologists who utilize digital forms, but whose archaeology is ungrounded in frameworks that specifically consider the ethical burdens of digital tools, methodology, and theory. This chapter details views of digital archaeological ethics related to digital archaeology as tools, digital archaeology as methodology, and digital archaeological pedagogy.
Learning Remotely during a Pandemic: Are Students in a Developing Country Fully Equipped with Tools for Swift Changes?

Many higher educational institutes are forced to the online mode of lecture delivery without fully understanding students’ perspectives of remote study during COVID-19, and the resource limitations of students to engage successfully in remote learning. The present study aimed to understand this gap and the students’ perspective. We collected and analysed the devices, which used by the students to connect to remote learning in a developing country. It was found that the majority of students are dependent on the laptop-smartphone combination to engage in remote learning, particularly the students in rural areas. The results highlighted the importance of smartphones in the online learning experience, considering their affordability, relatively long battery life, inbuilt internet capabilities, and portability. Although students indicated their willingness for remote learning, they clearly recognised the need for face-to-face teaching return to avoid some of the challenges and disadvantages they face as a part of remote learning.
Distance Learning in Higher Education during the COVID-19 pandemic: A Systematic Literature Review

The COVID-19 pandemic led to the closing of universities and the forced shift from face-to-face to distance education showcasing the potential and limitations of this form of learning. We used several tools for bibliographic management like Rayyan, VOSViewer and NVIVO and followed the PRISMA protocol. The goal of this systematic literature review was to identify the main keywords, theoretical frameworks and research methodologies used. Our analysis of themes showed the existence of 3 nodes within the literature: students, teachers, and education. After searching in 5 databases, 1915 articles were found. After excluding the duplicates (63 articles), we screened and read the titles and abstracts of 1852 articles. Only 135 articles met the criteria for inclusion. Finally, 122 articles were selected for review and analysis. The most widely used methodology was case study analysis and TAM was the most used theoretical framework.
Design Pattern Based Distribution of Microservices in Cloud Computing Environment

Cloud computing is a paradigm that has already evolved. Cloud computing moved widely to microservices from monoliths. The modular cloud application has gained attention for Microservices. Intensive network communication is required to call the interdependent microservices operating inside the cloud nodes. This research focuses on container-based microservices pre-distribution techniques and proposes two distribution strategies i.e. design pattern distribution and random distribution. The microservices are arbitrarily distributed to the available data centers in the random allocation method. While the microservices are clustered in the pattern distribution based on behavioral design patterns, which identify common contact patterns between entities. A custom-built modeling environment has been used to evaluate the proposed method. The findings revealed that the pre-distribution of microservices in accordance with the application architecture trend led to substantial less response time for the calls made to services hosted at geographically dispersed data centers.
SrvfRegNet: Elastic Function Registration Using Deep Neural Networks

Registering functions (curves) using time warpings (re-parameterizations) is central to many computer vision and shape analysis solutions. While traditional registration methods minimize penalized-${\mathbb{L}^2}$ norm, the elastic Riemannian metric and square-root velocity functions (SRVFs) have resulted in significant improvements in terms of theory and practical performance. This solution uses the dynamic programming algorithm to minimize the ${\mathbb{L}^2}$ norm between SRVFs of given functions. However, the computational cost of this elastic dynamic programming framework – O(nT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> k) – where T is the number of time samples along curves, n is the number of curves, and k < T is a parameter – limits its use in applications involving big data. This paper introduces a deep-learning approach, named SRVF Registration Net or SrvfRegNet to overcome these limitations. SrvfRegNet architecture trains by optimizing the elastic metric-based objective function on the training data and then applies this trained network to the test data to perform fast registration. In case the training and the test data are from different classes, it generalizes to the test data using transfer learning, i.e., retraining of only the last few layers of the network. It achieves the state-of-the-art alignment performance albeit at much reduced computational cost. We demonstrate the efficiency and efficacy of this framework using several standard curve datasets.
A distributed algorithm for graph semi-supervised learning

Graph semi-supervised learning (GSSL) plays an important role in data classification by leveraging the similarity across the graph topology and convex optimization with Laplacian-based regularization. However, the current algorithm to solve the problem is centralized approach calling for heavy computational cost, particularly when the data is of large volume. In this paper, an innovate distributed algorithm is proposed to solve the problem, which is based on the decomposition of the similar graph. Contrary to the centralized approach, the distributed algorithm only requires the neighboring information for solving the optimization. It is proved that difference between the solutions of the distributed algorithm and centralized counterpart is upper bounded. We apply the proposed algorithm to both the synthetic and real-world datasets. The numerical results verify the effectiveness of the proposed distributed algorithm.
Superresolution reconstruction method for ancient murals based on the stable enhanced generative adversarial network

Abstract A stable enhanced superresolution generative adversarial network (SESRGAN) algorithm was proposed in this study to address the low-resolution and blurred texture details in ancient murals. This algorithm makes improvements on the basis of GANs, which use dense residual blocks to extract image features. After two upsampling steps, the feature information of the image is input into the high-resolution (HR) image space to realize an improvement in resolution, and the reconstructed HR image is finally generated. The discriminator network uses VGG as its basic framework to judge the authenticity of the input image. This study further optimized the details of the network model. In addition, three loss optimization models, i.e., the perceptual loss, content loss, and adversarial loss models, were integrated into the proposed algorithm. The Wasserstein GAN-gradient penalty (WGAN-GP) theory was used to optimize the adversarial loss of the model when calculating the perceptual loss and when using the preactivation feature information for calculation purposes. In addition, public data sets were used to pretrain the generative network model to achieve a high-quality initialization. The simulation experiment results showed that the proposed algorithm outperforms other related superresolution algorithms in terms of both objective and subjective evaluation indicators. A subjective perception evaluation was also conducted, and the reconstructed images produced by our algorithm were more in line with the general public’s visual perception than those produced by the other compared algorithms.
Developing Question Generation System for Bahasa Indonesia Using Indonesian Standard Language Regulation

In education, teacher will frequently ask his student to measure the level of understanding for each student. Meanwhile, the need for self-evaluation for students is requiring the system that able to automatically generate question whenever they want. In order to tackle that problem, there is an area of research, called as question generation (QG). Question generation is a task to make a system that able to automatically generate questions as good as human. In English, there are already many researches in this area, that is from traditionally using pattern matching, or statistically with machine learning. Meanwhile, there is still not many works in Bahasa Indonesia. This research tries to drive the research in question generation, especially in Bahasa Indonesia. Tata Bahasa Baku Bahasa Indonesia (TBBI) is a set of standard language rules in Bahasa Indonesia, is used in this work to convert statement sentence into interrogative sentence. This method allows the more directed pattern matching for converting statement into question. By using this method, the system is able to generate up to 5,000 different questions, with just one book.
Binary Differential Evolution based Feature Selection Method with Mutual Information for Imbalanced Classification Problems

The feature selection process aims to eliminate redundant attributes in the data set, thus it leads to improved classification accuracy. The solution to a feature selection problem is challenging due to the ever-increasing data volume. This problem gets more complicated in the case of imbalanced data sets. Most of the traditional feature selection methods weigh on the majority class when selecting the informative feature subset, thus the selected features often get a bias towards the majority class and neglect the significance of the minority class in the whole process, which results in poor classification performance in the case of minority class objects. Multiple evolutionary algorithms based feature selection methods have been introduced in the past but most of them ignore the class imbalance problem while selecting the most informative feature subset. In this article, we propose a binary differential evolution algorithm with Manhattan distance-based mutation, which employs a joint mutual information maximization based feature selection criteria along with a novel class distribution-based weight assignment scheme to tackle the class imbalance problem. In the experimental studies, we have tested the performance of the proposed method on well-known data sets using three widely-used performance metrics (Average Classification Accuracy, F-measure, G-Means). According to the empirical results, the proposed method performs better than its contenders in most of the data sets.
Classification of Inundation Level using Tweets in Indonesian Language

Extreme flood events are expected to occur more frequently as climate change has yet to show signs of improvement. This has the potential to lead to higher rainfall and floods that would come more quickly. Early warning systems may sometimes fail to provide quick information when conditions in the field may not match to what is known in the information center, such as a malfunctioning water pump or a water level that has increased relatively quickly. Therefore, this study aims to provide an alternative source of information that may provide inundation level during flood condition based on tweets from Twitter. The proposed model is expected to provide output in the form of inundation level categories, namely “high”, “medium”, “low”, and “unknown”. 10-fold stratified cross validation with seven variations of classifiers were used to evaluate the model. The best relevance classification resulted in 90.6% accuracy (SVM Linear SVC), 89.05% average precision (SVM RBF), and 82.03% average F1-score (SVM Linear SVC) and average recall of 84.10% (Logistic Regression). The best classification results of inundation level resulted in accuracy (82.74%), average precision (85.44%) average recall (68.07%) and average F1-score (71.43%). All of them were obtained by using the SVM Linear SVC.
Intelligent Assistant Decision-Making Method for Power Enterprise Customer Service Based on IoT Data Acquisition

The prevailing era of the Internet of Things (IoT) has renewed all fields of life in general, but, especially with the advent of artificial intelligence (AI), has drawn the attention of researchers into a new paradigm of life standards. This revolution has been accepted around the world for making life comfortable with the use of intelligent devices. AI-enabled machines are more intelligent and capable of completing a specific task which saves a lot of time and resources. Currently, diverse methods are available in the existing literature to handle different issues of real life based on AI and IoT systems. The role of decision-making has its prominence in the AI-enabled and IoT systems. In this article, an AI- and IoT-based intelligent assistant decision-making method is presented for power enterprise customer service. An intelligent model of the customer service data network is designed, and the method of collecting data from IoT to assist decision-making is presented. Then, the semantic relationship of customer service data is defined, and the sharing scope of data transmission and resources are determined to realize intelligent assistant decision-making of customer service in power enterprises. Simulation results show that the proposed method improves the decision data transmission speed and shortens the transmission delay, and the network performance of data interaction is better than that of the existing methods.
Towards Faster Polynomial-Time Lattice Reduction

The lll algorithm is a polynomial-time algorithm for reducing d-dimensional lattice with exponential approximation factor. Currently, the most efficient variant of lll, by Neumaier and Stehlé, has a theoretical running time in $$d^4\cdot B^{1+o\left( 1\right) }$$ where B is the bitlength of the entries, but has never been implemented. This work introduces new asymptotically fast, parallel, yet heuristic, reduction algorithms with their optimized implementations. Our algorithms are recursive and fully exploit fast matrix multiplication. We experimentally demonstrate that by carefully controlling the floating-point precision during the recursion steps, we can reduce euclidean lattices of rank d in time $$\tilde{O}(d^\omega \cdot C)$$ , i.e., almost a constant number of matrix multiplications, where $$\omega $$ is the exponent of matrix multiplication and C is the log of the condition number of the matrix. For cryptographic applications, C is close to B, while it can be up to d times larger in the worst case. It improves the running-time of the state-of-the-art implementation fplll by a multiplicative factor of order $$d^2\cdot B$$ . Further, we show that we can reduce structured lattices, the so-called knapsack lattices, in time $$\tilde{O}(d^{\omega -1}\cdot C)$$ with a progressive reduction strategy. Besides allowing reducing huge lattices, our implementation can break several instances of Fully Homomorphic Encryption schemes based on large integers in dimension 2,230 with 4 millions of bits.
The Relationship between Language Proficiency Level and L2 Digital Literacy Self-efficacy: A Study of EFL University Students

The evolving digitization of teaching and learning in higher education institutions requires students to be digitally literate (Miller 2015). Despite the echoes of being “digital natives” (Prensky 2001), many EFL students experience difficulties when locating, retrieving, evaluating, and synthesizing digital information at their disposal, especially when the information is in English. To this end, this study is conducted to scrutinize the relationship between EFL students’ second language (L2) digital literacy skills and strategies (DLSs) self-efficacy and their English proficiency level. A total of 93 Saudi students majoring in English at Prince Sattam bin Abdulaziz University were surveyed for their English proficiency level and their abilities to use three major domains of digital literacy skills. The data were analyzed statistically using descriptive measures and ANOVA. The results revealed a significant positive correlation between English proficiency and L2 DLSs. Students with intermediate and upper-intermediate English levels displayed low efficacy in their abilities to navigate, evaluate, and synthesize online information compared to advanced English users. The study concluded that students with higher English proficiency are more responsive to digital literacy skills and can perform well in digitally enhanced environments than basic English users. Pedagogical implications and areas for future research are discussed..