Reviewerly/exHarmony huggingface dataset:
Performance analysis of different machine learning algorithms in breast cancer predictions

INTRODUCTION: There is a great percentage of failures in clinical trials of early detection of breast cancer.To do this, machine learning (ML) algorithms are useful to do diagnosis and prediction of cancer tumors with better accuracy.OBJECTIVE: In this study, we develop an ML model coupled with limited features to produce high classification accuracy in tumor classification.METHODS: We considered a dataset of 569 females diagnosed as 212 malignant and 357 benign types.For model development, three supervised ML algorithms namely support vector machines (SVM), logistic regression (LR), and Knearest neighbors (KNN) were employed.Each model was further validated by 10-fold cross-validation and performance measures were defined to evaluate the model outcomes.RESULTS: Both SVM and LR models generated 97.66% accuracy with total feature evaluation.With selective features, the SVM accuracy was improved by 98.25%.Whereas the LR model including limited features produced 100% of true positive predictions.CONCLUSION: The proposed models involved by selective features could improve the prediction accuracy of a breast cancer diagnosis.
Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees

It is becoming increasingly important to explain complex, black-box machine learning models. Although there is an expanding literature on this topic, Shapley values stand out as a sound method to explain predictions from any type of machine learning model. The original development of Shapley values for prediction explanation relied on the assumption that the features being described were independent. This methodology was then extended to explain dependent features with an underlying continuous distribution. In this paper, we propose a method to explain mixed (i.e. continuous, discrete, ordinal, and categorical) dependent features by modeling the dependence structure of the features using conditional inference trees. We demonstrate our proposed method against the current industry standards in various simulation studies and find that our method often outperforms the other approaches. Finally, we apply our method to a real financial data set used in the 2018 FICO Explainable Machine Learning Challenge and show how our explanations compare to the FICO challenge Recognition Award winning team.
Mel-Weighted Single Frequency Filtering Spectrogram for Dialect Identification

In this study, we propose Mel-weighted single frequency filtering (SFF) spectrograms for dialect identification. The spectrum derived using SFF has high spectral resolution for harmonics and resonances while simultaneously maintaining good time-resolution of some speech excitation features such as impulse-like events. The SFF spectrum can represent speech characteristics such as burst time and glottal closure instances better than the short-time Fourier transform (STFT) spectrum. Our hypothesis is that these intricate representations in the SFF spectrum should help in distinguishing dialects. Therefore, we built a dialect identification system which uses an unsupervised, bottleneck feature representation of the Mel-weighted SFF spectrogram (Mel-SFF spectrogram) with sequence-to-sequence deep autoencoders. The language invariance of the proposed system was evaluated using two datasets: the UT-Podcast database (English) and the STYRIALECT database (German). The proposed representations gave a relative improvement of 9.47% and 4.69% in unweighted average recall (UAR) compared to the best baseline method on the development and test datasets, respectively, of the UT-Podcast database. The proposed representations also gave a comparable performance to the best baseline method for the STYRIALECT database. In addition, the fusion of the autoencoder bottleneck features computed from the Mel-SFF and Mel-STFT spectrograms improved the overall performance indicating complementary information between these features. By further analyzing the performance of the proposed representation with different utterance lengths using the UT-Podcast database, we observed that the proposed representation performed better on short utterances. The improved performance given by the Mel-weighted SFF spectrogram for recognizing dialects in both databases supports our hypothesis.
Transmission estimation at the Cramér-Rao bound for squeezed states of light in the presence of loss and imperfect detection

Enhancing the precision of a measurement requires maximizing the information that can be gained about the quantity of interest from probing a system. For optical-based measurements, such an enhancement can be achieved through two approaches, increasing the number of photons used to interrogate the system and using quantum states of light to increase the amount of quantum Fisher information gained per photon. Here we consider the use of quantum states of light with a large number of photons, namely the bright single-mode squeezed state and the bright two-mode squeezed state, which take advantage of both of these approaches for the problem of transmission estimation. We show that, in the limit of large squeezing, these states approach the maximum possible quantum Fisher information per photon for transmission estimation that is achieved with the Fock state and the vacuum two-mode squeezed state. Since the bright states we consider can be generated at powers much higher than those of the quantum states that achieve the maximum quantum Fisher information per photon, they can achieve a much higher absolute precision as quantified by the quantum Cram\'er-Rao bound. We discuss the effects of losses external to the system on the precision of transmission estimation and identify simple measurement techniques that can saturate the quantum Cram\'er-Rao bound for the bright squeezed states even in the presence of such external losses.
Simulation Study on Coverage Path Planning of Autonomous Tasks in Hilly Farmland Based on Energy Consumption Model

The hilly farmland in China is characterized by small farmland areas and dense farmland distribution, and the working environment is three-dimensional topographic farmland, so the working conditions in the field are relatively complex. In this working environment, the coverage path planning technique of a farmland autonomous task is harder than that of 2D farmland autonomous task. Generally, the path planning problem of 2D farmland is to construct the path cost model to realize the planning of agricultural machinery driving route, while for the path planning problem of three-dimensional terrain farmland in the hilly region, this paper proposes a covering path planning scheme that meets the requirements of autonomous work. Based on the energy consumption model, the scheme searches the optimal driving angle of agricultural machinery, prioritizes solutions to the problem of covering path planning within the scattered fields in the working area, and then searches through the genetic algorithm for the optimal order of traversing the paths of each field to complete the coverage path planning in the working area. On the one hand, the scheme optimizes the planning route in the fields from the angle of optimal energy consumption; on the other hand, through the genetic algorithm, the fields are connected in an orderly manner, which solves the comprehensive problems brought by the unique agricultural environment and farming system in China’s hilly areas to the agricultural machinery operation. The algorithm program is developed according to the research content, and a series of simulation experiments are carried out based on the program using actual farmland data and agricultural machinery parameters. The results show that the planned path obtained at the cost of energy consumption has a total energy consumption of 4771897.17J, which is 17.4% less energy consumption than the optimal path found by the path cost search; the optimization effect is evident.
A Closer Look Into the Characteristics of Fraudulent Card Transactions

Widespread use of Internet also had the substantial impact on the increase of the online card transactions especially with the beginning of the last decade. Along with the increase of online transactions, the worldwide banking sector was forced to deal with or to encounter an unforeseen number of fraudulent activities, yet. Hence, rule-based systems were designed to mark the high-risk transactions and let the experts to confirm the fraudulent nature of such transactions. As a countermeasure, static nature of rule-based systems were exploited by the latest attacks to go undetected. Thus, researchers aimed at designing adaptive fraud detection systems utilizing mainly machine learning techniques with the very recent application of deep learning. However, they were focused on detecting fraudulent activities but, to the best of our knowledge, none of them delved into the better understanding the characteristics of fraudulent card transactions in order to produce more resilient models. Therefore, in this study, we built the biggest data set ever used in a research, consisting of 4B non-fraud and 245K fraud transactions contributed to by the 35 banks in Turkey. Consequently, we introduce and examine the performance of profile-based fraud detection models, namely card-type based model, transaction characteristics based model, and amount-based model. Also, we made temporal and spatial analysis on our data set to show the robustness of the proposed models against aging and zero-day attacks.
Quality Assessment of 3D Printed Surfaces Using Combined Metrics Based on Mutual Structural Similarity Approach Correlated with Subjective Aesthetic Evaluation

Quality assessment of the 3D printed surfaces is one of the crucial issues related to fast prototyping and manufacturing of individual parts and objects using the fused deposition modeling, especially in small series production. As some corrections of minor defects may be conducted during the printing process or just after the manufacturing, an automatic quality assessment of object’s surfaces is highly demanded, preferably well correlated with subjective quality perception, considering aesthetic aspects. On the other hand, the presence of some greater and more dense distortions may indicate a reduced mechanical strength. In such cases, the manufacturing process should be interrupted to save time, energy, and the filament. This paper focuses on the possibility of using some general-purpose full-reference image quality assessment methods for the quality assessment of the 3D printed surfaces. As the direct application of an individual (elementary) metric does not provide high correlation with the subjective perception of surface quality, some modifications of similarity-based methods have been proposed utilizing the calculation of the average mutual similarity, making it possible to use full-reference metrics without the perfect quality reference images, as well as the combination of individual metrics, leading to a significant increase of correlation with subjective scores calculated for a specially prepared dataset.
Legal Aspects Regarding Online Learning Process for Students at University and Digital Education in European Union: Different Life, Challenges and the Ability to Continue Education during the Pandemic Caused by Coronavirus (COVID-19)

Worldwide, the medical challenge brought by the pandemic caused by SARS-Cov-2 coronavirus (COVID-19/COronaVIrusDisease-2019) is overlapping with the challenge of continuing the educational process in schools and universities and this article highlights the situation regarding the studies at university. In Romania, the courses in school education and higher education have been suspended since March 2020, as a preventive measure against the transmission of coronavirus disease (COVID-19). After the pandemic caused by the SARS-CoV-2 coronavirus was declared, in Romania the state of emergency was established in order to limit the infection with coronavirus disease and, for this purpose, measures restricting the free movement off all persons were regulated. In this uncertain and unpredictable context regarding the evolution of the COVID-19 pandemic and its impact on education worldwide, with schools and universities affected by a level of disruption, without precedent in the past, it is essential and imperative for education to continue. This article discusses the need and the possibility of continuing the learning process even if schools and universities are closed and face to face courses are suspended, using online resources, forcing an online education, and also the juridical implications regarding the choice of this path. This article analyzes the online learning process for studies at university during COVID-19 pandemic through correlations with the Digital Education Action Plan adopted by the European Commission.
GSSA: Pay attention to graph feature importance for GCN via statistical self-attention

Graph convolutional network (GCN) has been proved to be an effective framework for graph-based semi-supervised learning applications. The core operation block of GCN is the convolutional layer, which enables the network to construct node embeddings by fusing both attributes of nodes and relationships between nodes. Different features or feature interactions inherently have various influences on the convolutional layers. However, there are very limited studies about the impact of feature importance in GCN-related communities. In this work, we attempt to augment convolutional layers in GCNs with statistical attention-based feature importance by modeling the latent interactions of features, which is complementary to the standard GCNs and only needs simple calculations with statistics rather than heavy trainings. To this end, we treat the feature input of each convolutional layer as a separate multi-layer heterogeneous graph, and propose Graph Statistical Self-Attention (GSSA) method to automatically learn the hierarchical structure of feature importance. More specifically, we propose two modules in GSSA, Channel-wise Self-Attention (CSA) to capture the dependencies between feature channels, and Mean-based Self-Attention (MSA) to reweight similarities among features. Aiming at each graph convolutional layer, GSSA can be applied in a "plug and play" way for a wide range of GCN variants. To the best of our knowledge, this is the first implementation that optimizes GCNs from the feature importance perspective. Extensive experiments demonstrate that GSSA can promote existing popular baselines remarkably in semi-supervised node classification tasks. We further employ multiple qualitative evaluations to get deep insights into our method.
Deep Learning and Higher Degree F-Transforms: Interpretable Kernels Before and After Learning

One of the current trends in the deep neural network technology consists in allowing a man-machine interaction and providing an explanation of network design and learning principles.In this direction, an experience with fuzzy systems is of great support.We propose our insight that is based on the particular theory of fuzzy (F)-transforms.Besides a theoretical explanation, we develop a new architecture of a deep neural network where the F-transform convolution kernels are used in the first two layers.Based on a series of experiments, we demonstrate the suitability of the F-transform-based deep neural network in the domain of image processing with the focus on recognition.Moreover, we support our insight by revealing the similarity between the F-transform and first-layer kernels in the most used deep neural networks.
Tackling the SHL challenge 2020 with person-specific classifiers and semi-supervised learning

The SHL recognition challenge 2020 was an open competition in activity recognition where the participants were tasked with recognizing eight different modes of locomotion and transportation with smartphone sensors. The main challenges were that the training data was recorded by a different person than the validation and test data, and that the smartphone location in the test data was unknown to the participants. We, team "Third time's a charm", tackled the first challenge by attempting to identify the persons with clustering, and then performed cluster/person-specific feature selection to build a separate classifier for each person. The smartphone location appears not to make much difference. We also used semi-supervised learning to classify the test data. Internal tests using this methodology yielded an accuracy of 81.01%.
A powerful comparison of deep learning frameworks for Arabic sentiment analysis

Deep learning (DL) is a machine learning (ML) subdomain that involves algorithms taken from the brain function named artificial neural networks (ANNs). Recently, DL approaches have gained major accomplishments across various Arabic natural language processing (ANLP) tasks, especially in the domain of Arabic sentiment analysis (ASA). For working on Arabic SA, researchers can use various DL libraries in their projects, but without justifying their choice or they choose a group of libraries relying on their particular programming language familiarity. We are basing in this work on Java and Python programming languages because they have a large set of deep learning libraries that are very useful in the ASA domain. This paper focuses on a comparative analysis of different valuable Python and Java libraries to conclude the most relevant and robust DL libraries for ASA. Throw this comparative analysis, and we find that: TensorFlow, Theano, and Keras Python frameworks are very popular and very used in this research domain.
Nonlocal spatial attention module for image classification

To enhance the capability of neural networks, research on attention mechanism have been deepened. In this area, attention modules make forward inference along channel dimension and spatial dimension sequentially, parallelly, or simultaneously. However, we have found that spatial attention modules mainly apply convolution layers to generate attention maps, which aggregate feature responses only based on local receptive fields. In this article, we take advantage of this finding to create a nonlocal spatial attention module (NL-SAM), which collects context information from all pixels to adaptively recalibrate spatial responses in a convolutional feature map. NL-SAM overcomes the limitations of repeating local operations and exports a 2D spatial attention map to emphasize or suppress responses in different locations. Experiments on three benchmark datasets show at least 0.58% improvements on variant ResNets. Furthermore, this module is simple and can be easily integrated with existing channel attention modules, such as squeeze-and-excitation and gather-excite, to exceed these significant models at a minimal additional computational cost (0.196%).
Truth or Square: Aspect Ratio Biases Recall of Position Encodings

Bar charts are among the most frequently used visualizations, in part because their position encoding leads them to convey data values precisely. Yet reproductions of single bars or groups of bars within a graph can be biased. Curiously, some previous work found that this bias resulted in an overestimation of reproduced data values, while other work found an underestimation. Across three empirical studies, we offer an explanation for these conflicting findings: this discrepancy is a consequence of the differing aspect ratios of the tested bar marks. Viewers are biased to remember a bar mark as being more similar to a prototypical square, leading to an overestimation of bars with a wide aspect ratio, and an underestimation of bars with a tall aspect ratio. Experiments 1 and 2 showed that the aspect ratio of the bar marks indeed influenced the direction of this bias. Experiment 3 confirmed that this pattern of misestimation bias was present for reproductions from memory, suggesting that this bias may arise when comparing values across sequential displays or views. We describe additional visualization designs that might be prone to this bias beyond bar charts (e.g., Mekko charts and treemaps), and speculate that other visual channels might hold similar biases toward prototypical values.
Rural-based Science, Technology, Engineering and Mathematics teachers’ and learners’ acceptance of mobile learning

Background: Science, Technology, Engineering and Mathematics (STEM) is faced with many challenges resulting in learners' poor performance at matriculation level in South Africa. However, prior research has shown that mobile learning (m-learning) can be used to alleviate the challenges of STEM education. Prior research focused on tertiary institutions' students and lecturers, in developed countries. However, very little is known about rural school STEM teachers' and learners' acceptance of m-learning. Objectives: The article investigates factors that rural-based STEM teachers and learners consider important when adopting mobile learning. Furthermore, the study also seeks to examine if there is a statistically significant difference between teachers' and learners' acceptance of mobile learning. Method: The research employed a quantitative approach. Stratified random sampling was used to select 350 teachers and learners to participate in the survey. Valid questionnaires received were 288 (82%), and data were analysed using partial least squares structural equation modelling. Results: The proposed model explained 64% of the variance in rural-based STEM teachers' and learners' behavioural intention to use m-learning. Perceived attitude towards use was found to be the best predictor of teachers' and learners' behavioural intention. The results also showed no significant difference between teachers' and learners' path coefficients. Conclusion: The research recommends that awareness campaigns, infrastructure, mobile devices and data need to be made available for m-learning to be successfully adopted in rural areas.
A Systematic Literature Review of Practical Virtual and Augmented Reality Solutions in Surgery

From a strong practical point of view, we offer an overview of virtual and augmented reality solutions in medicine. We thus analyzed practical and industrial work included in peer-reviewed articles and conference proceedings.
Quantifying the Significance and Relevance of Cyber-Security Text Through Textual Similarity and Cyber-Security Knowledge Graph

In order to proactively mitigate cyber-security risks, security analysts have to continuously monitor sources of threat information. However, the sheer amount of textual information that needs to be processed is overwhelming, and it requires a great deal of mundane labor to separate the threats from the noise. We propose a novel approach to represent the relevance and significance of the cyber-security text in quantitative numbers. We trained custom Named Entity Recognition (NER) model and constructed a Cyber-security Knowledge Graph (CKG) to infer the subjective relevance of the cyber-security text to the user and to generate correlation features. In addition, the significance of the given text was analyzed in terms of its textual similarity with different repositories of pre-defined “significant” text and the maximum similarities were computed. These analysis results then act as features of the classifier to generate the significance score. The experimental result showed that the overall system could determine the significance and relevance of the text within a controlled environment with 88% accuracy.