Reviewerly/exHarmony huggingface dataset:
Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach

Public healthcare has a history of cautious adoption for artificial intelligence (AI) systems. The rapid growth of data collection and linking capabilities combined with the increasing diversity of the data-driven AI techniques, including machine learning (ML), has brought both ubiquitous opportunities for data analytics projects and increased demands for the regulation and accountability of the outcomes of these projects. As a result, the area of interpretability and explainability of ML is gaining significant research momentum. While there has been some progress in the development of ML methods, the methodological side has shown limited progress. This limits the practicality of using ML in the health domain: the issues with explaining the outcomes of ML algorithms to medical practitioners and policy makers in public health has been a recognized obstacle to the broader adoption of data science approaches in this domain. This study builds on the earlier work which introduced CRISP-ML, a methodology that determines the interpretability level required by stakeholders for a successful real-world solution and then helps in achieving it. CRISP-ML was built on the strengths of CRISP-DM, addressing the gaps in handling interpretability. Its application in the Public Healthcare sector follows its successful deployment in a number of recent real-world projects across several industries and fields, including credit risk, insurance, utilities, and sport. This study elaborates on the CRISP-ML methodology on the determination, measurement, and achievement of the necessary level of interpretability of ML solutions in the Public Healthcare sector. It demonstrates how CRISP-ML addressed the problems with data diversity, the unstructured nature of data, and relatively low linkage between diverse data sets in the healthcare domain. The characteristics of the case study, used in the study, are typical for healthcare data, and CRISP-ML managed to deliver on these issues, ensuring the required level of interpretability of the ML solutions discussed in the project. The approach used ensured that interpretability requirements were met, taking into account public healthcare specifics, regulatory requirements, project stakeholders, project objectives, and data characteristics. The study concludes with the three main directions for the development of the presented cross-industry standard process.
Automatic underwater polarization imaging without background region or any prior

Previous polarization underwater imaging methods based on the physical scattering model usually require background region included in the image and the prior knowledge, which hinders its practical application. In this paper, we analyze and optimize the physically feasible region and propose an improved method by degenerating intermediate variables, which can realize automatic underwater image recovery without background region or any prior. The proposed method does not need to estimate the intermediate variables in the traditional underwater imaging model and is adaptable to the underwater image with non-uniform illumination, which avoids the poor and unstable image recovery performance caused by inaccurate estimation of intermediate parameters due to the improper identification of the background region. Meanwhile, our method is effective for both images without background region and images in which the background region is hard to be identified. In addition, our method solves the significant variation in recovery results caused by the different selection of background regions and the inconsistency of parameter adjustment. The experimental results of different underwater scenes show that the proposed method can enhance image contrast while preserving image details without introducing considerable noise, and the proposed method is effective for the dense turbid medium.
Spatial Contrastive Learning for Few-Shot Classification

In this paper we explore contrastive learning for few-shot classification, in which we propose to use it as an additional auxiliary training objective acting as a data-dependent regularizer to promote more general and transferable features. In particular, we present a novel attention-based spatial contrastive objective to learn locally discriminative and class-agnostic features. As a result, our approach overcomes some of the limitations of the cross-entropy loss, such as its excessive discrimination towards seen classes, which reduces the transferability of features to unseen classes. With extensive experiments, we show that the proposed method outperforms state-of-the-art approaches, confirming the importance of learning good and transferable embeddings for few-shot learning. Code: https://github.com/yassouali/SCL.
Exploring the Performance of Ensemble Machine Learning Classifiers for Sentiment Analysis of COVID-19 Tweets

Since the beginning of the global COVID-19 pandemic, measuring public opinion has been considered as one of the most critical issues for decision-makers to fight against the pandemic, such as implementing a national lockdown, introducing quarantine procedure, providing health services, and the like. During the COVID-19 pandemic, decision-makers in several countries around the world made a number of critical decisions focused on public opinion to combat coronavirus. In the field of natural language processing, sentiment analysis has emerged for mining public opinion, while machine learning (ML) algorithms are very common for analyzing sentiment. In this research, approximately 12 thousand tweets from United Kingdom (UK) were rigorously annotated by three independent reviewers, and based on the labeled tweets, three different ensemble ML models were proposed to classify the tweet data into three sentiment labels: positive, negative, and neutral. The study found that stacking classifier (SC) showed the highest F1-score (83.5%), followed by the voting classifier (VC) (83.3%) and bagging classifier (BC) (83.2%).Rahman, Md. Mahbubar Islam, Muhammad Nazrul
Classically emulated digital quantum simulation for screening and confinement in the Schwinger model with a topological term

We perform digital quantum simulation, using a classical simulator, to study screening and confinement in a gauge theory with a topological term, focusing on ($1+1$)-dimensional quantum electrodynamics (Schwinger model) with a theta term. We compute the ground state energy in the presence of probe charges to estimate the potential between them, via adiabatic state preparation. We compare our simulation results and analytical predictions for a finite volume, finding good agreements. In particular our result in the massive case shows a linear behavior for noninteger charges and a nonlinear behavior for integer charges, consistently with the expected confinement (screening) behavior for noninteger (integer) charges.
A secure framework for IoT-based smart climate agriculture system: Toward blockchain and edge computing

Abstract An intelligent climate and watering agriculture system is presented that is controlled with Android application for smart water consumption considering small and medium ruler agricultural fields. Data privacy and security as a big challenge in current Internet of Things (IoT) applications, as with the increase in number of connecting devices, these devices are now more vulnerable to security threats. An intelligent fuzzy logic and blockchain technology is implemented for timely analysis and securing the network. The proposed design consists of various sensors that collect real-time data from environment and field such as temperature, soil moisture, light intensity, and humidity. The sensed field information is stored in IoT cloud platform, and after the analysis of entries, watering is scheduled by implementing the intelligent fuzzy logic and blockchain. The intelligent fuzzy logic based on different set of rules for making smart decisions to meet the watering requirements of plant and blockchain technology provides necessary security to the IoT-enabled system. The implementation of blockchain technology allows access only to the trusted devices and manages the network. From the experimentation, it is observed that the proposed system is highly scalable and secure. Multiple users at the same time can monitor and interact with the system remotely by using the proposed intelligent agricultural system. The decisions are taken by applying intelligent fuzzy logic based on input variables, and an alert is transmitted about watering requirements of a field to the user. The proposed system is capable of notifying users for turning water motor on and off. The experimental outcomes of the proposed system also reveal that it is an efficient and highly secure application, which is capable of handling the process of watering the plants.
Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU

This paper describes the construction a short-text aspect-based sentiment analysis method based on Convolutional Neural Network (CNN) and Bidirectional Gating Recurrent Unit (BiGRU). The hybrid model can fully extract text features, solve the problem of long-distance dependence on the sequence, and improve the reliability of training. This article reports empirical research conducted on the basis of literature research. The first step was to obtain the dataset and perform preprocessing, after which scikit-learn was used to perform TF-IDF calculations to obtain the feature word vector weight, obtain the aspect-level feature ontology words of the evaluated text, and manually mark the ontology of the reviewed text and the corresponding sentiment analysis polarity. In the sentiment analysis section, a hybrid model based on CNN and BiGRU (CNN + BiGRU) was constructed, which uses corpus sentences and feature words as the vector input and predicts the emotional polarity. The experimental results prove that the classification accuracy of the improved CNN + BiGRU model was improved by 12.12%, 8.37%, and 4.46% compared with the Convolutional Neural Network model (CNN), Long-Short Term Memory model (LSTM), and Convolutional Neural Network (C-LSTM) model.
Machine Learning Based Comparative Analysis for Breast Cancer Prediction

One of the most prevalent and leading causes of cancer in women is breast cancer. It has now become a frequent health problem, and its prevalence has recently increased. The easiest approach to dealing with breast cancer findings is to recognize them early on. Early detection of breast cancer is facilitated by computer-aided detection and diagnosis (CAD) technologies, which can help people live longer lives. The major goal of this work is to take advantage of recent developments in CAD systems and related methodologies. In 2011, the United States reported that one out of every eight women was diagnosed with cancer. Breast cancer originates as a result of aberrant cell division in the breast, which leads to either benign or malignant cancer formation. As a result, early detection of breast cancer is critical, and with effective treatment, many lives can be saved. This research covers the findings and analyses of multiple machine learning models for identifying breast cancer. The Wisconsin Breast Cancer Diagnostic (WBCD) dataset was used to develop the method. Despite its small size, the dataset provides some interesting data. The information was analyzed and put to use in a number of machine learning models. For prediction, random forest, logistic regression, decision tree, and K-nearest neighbor were utilized. When the results are compared, the logistic regression model is found to offer the best results. Logistic regression achieves 98% accuracy, which is better than the previous method reported.
LAS-SG: An Elliptic Curve-Based Lightweight Authentication Scheme for Smart Grid Environments

The communication among smart meters (SMs) and neighborhood area network (NAN) gateways is a fundamental requisite for managing the energy consumption at the consumer site. The bidirectional communication among SMs and NANs over the insecure public channel is vulnerable to impersonation, SM traceability, and SM physical capturing attacks. Many existing schemes’ insecurities and/or inefficiencies call for an efficient and secure authentication scheme for smart grid infrastructure. In this article, we present a privacy preserving and lightweight authentication scheme for smart grid (LAS-SG) using elliptic curve cryptography. The proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LAS-SG</i> is proved as secure under the standard model. Moreover, the efficiency of the LAS-SG is extracted through a real-time experiment, which attests that proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LAS-SG</i> completes a round of authentication in 20.331 ms by exchanging only two messages and 192 B. Due to the adequate efficiency and ample security, the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LAS-SG</i> is more appropriate for SG environments.
Efficient Dropout-Resilient Aggregation for Privacy-Preserving Machine Learning

With the increasing adoption of data-hungry machine learning algorithms, personal data privacy has emerged as one of the key concerns that could hinder the success of digital transformation. As such, Privacy-Preserving Machine Learning (PPML) has received much attention from both academia and industry. However, organizations are faced with the dilemma that, on the one hand, they are encouraged to share data to enhance ML performance, but on the other hand, they could potentially be breaching the relevant data privacy regulations. Practical PPML typically allows multiple participants to individually train their ML models, which are then aggregated to construct a global model in a privacy-preserving manner, e.g., based on multi-party computation or homomorphic encryption. Nevertheless, in most important applications of large-scale PPML, e.g., by aggregating clients' gradients to update a global model for federated learning, such as consumer behavior modeling of mobile application services, some participants are inevitably resource-constrained mobile devices, which may drop out of the PPML system due to their mobility nature. Therefore, the resilience of privacy-preserving aggregation has become an important problem to be tackled. In this paper, we propose a scalable privacy-preserving aggregation scheme that can tolerate dropout by participants at any time, and is secure against both semi-honest and active malicious adversaries by setting proper system parameters. By replacing communication-intensive building blocks with a seed homomorphic pseudo-random generator, and relying on the additive homomorphic property of Shamir secret sharing scheme, our scheme outperforms state-of-the-art schemes by up to 6.37$\times$ in runtime and provides a stronger dropout-resilience. The simplicity of our scheme makes it attractive both for implementation and for further improvements.
Activation Functions

Activation functionsActivation functions lie at the core of deep neural networksLearning deep neural networks allowing them to learn arbitrarily complex mappings. Without any activation, a neural network learn will only be able to learn a linear relation between input and the desired output. The chapter introduces the reader to why activation functionsActivation functions are useful and their immense importance in making deep learning successful. A detailed survey of several existing activation functionsActivation functions is provided in this chapter covering their functional forms, original motivations, merits as well as demerits. The chapter also discusses the domain of learnable activation functionsActivation functions and proposes a novel activation ‘SLAF’ whose shape is learned during the training of a neural network. A working model for SLAFSLAF is provided and its performance is experimentally shown on XOR and MNIST classificationClassification tasks.
IV-PSNR—The Objective Quality Metric for Immersive Video Applications

This paper presents a new objective quality metric that was adapted to the complex characteristics of immersive video (IV) which is prone to errors caused by processing and compression of multiple input views and virtual view synthesis. The proposed metric, IV-PSNR, contains two techniques that allow for the evaluation of quality loss for typical immersive video distortions: corresponding pixel shift and global component difference. The performed experiments compared the proposal with 31 state-of-the-art quality metrics, showing their performance in the assessment of quality in immersive video coding and processing, and in other applications, using commonly used image quality assessment databases– TID2013 and CVIQ. As presented, IV-PSNR outperforms other metrics in immersive video applications and still can be efficiently used in the evaluation of different images and videos. Moreover, basing the metric on the calculation of PSNR allowed the computational complexity to remain low. Publicly available, efficient implementation of IV-PSNR software was provided by the authors of this paper and is used by ISO/IEC MPEG for evaluation and research on the upcoming MPEG Immersive video (MIV) coding standard.
Imbalanced data preprocessing techniques for machine learning: a systematic mapping study

Machine Learning (ML) algorithms have been increasingly replacing people in several application domains-in which the majority suffer from data imbalance. In order to solve this problem, published studies implement data preprocessing techniques, cost-sensitive and ensemble learning. These solutions reduce the naturally occurring bias towards the majority sample through ML. This study uses a systematic mapping methodology to assess 9927 papers related to sampling techniques for ML in imbalanced data applications from 7 digital libraries. A filtering process selected 35 representative papers from various domains, such as health, finance, and engineering. As a result of a thorough quantitative analysis of these papers, this study proposes two taxonomies-illustrating sampling techniques and ML models. The results indicate that oversampling and classical ML are the most common preprocessing techniques and models, respectively. However, solutions with neural networks and ensemble ML models have the best performance-with potentially better results through hybrid sampling techniques. Finally, none of the 35 works apply simulation-based synthetic oversampling, indicating a path for future preprocessing solutions.
Knowledge management and performance: a bibliometric analysis based on Scopus and WOS data (1988–2021)

Purpose This study aims to analyze the trends manifested in literature from the area of knowledge management and performance, with emphasis on bibliometric analysis. Design/methodology/approach To explore the studies focused on the area under investigation, the authors performed a search in ISI Web of Science and Scopus using the combination of keywords such as “Knowledge management” AND “Performance.” Generally, this study covered a period of 33 years, from 1988 to 2021 because the first study was published in 1970 and the databases have not covered all the journals and studies which date back to the early 1970s or 1980s. The final data set comprised 1,583 publications with 40 articles removed during the screening and eligibility process. Findings The results of the bibliometric analysis indicate that the interest in the area of knowledge management and performance has significantly increased, especially from 2000 to 2021. The application of bibliometric analysis on the relationship between knowledge management and performance uncovered various themes, productive authors and widely cited documents. The study highlighted how the knowledge management–performance relationship has evolved over the years and how the interplay between knowledge management and performance may help the firms in gaining the sustainable competitive advantage. Originality/value To the best of the author’s knowledge, this study is the first of its kind to conduct the bibliometric analysis on knowledge management and performance. This study can be a starting point for scholars interested in understanding how knowledge management is related to performance.
Visual Servoing With Photometric Gaussian Mixtures as Dense Features

The direct use of the entire photometric image information as dense features for visual servoing brings several advantages. First, it does not require any feature detection, matching, or tracking process. Thanks to the redundancy of visual information, the precision at convergence is highly accurate. However, the corresponding highly nonlinear cost function reduces the convergence domain. In this paper, we propose visual servoing based on the analytical formulation of Gaussian mixtures to enlarge the convergence domain. Pixels are represented by two-dimensional Gaussian functions that denote a “power of attraction.” In addition to the control of the camera velocities during the servoing, we also optimize the Gaussian spreads allowing the camera to precisely converge to a desired pose even from a far initial one. Simulations show that our approach outperforms the state of the art and real experiments show the effectiveness, robustness, and accuracy of our approach.
Robust Discriminative Metric Learning for Image Representation

Metric learning has attracted significant attentions in the past decades, for the appealing advances in various realworld applications such as person re-identification and face recognition.Traditional supervised metric learning attempts to seek a discriminative metric, which could minimize the pairwise distance of within-class data samples, while maximizing the pairwise distance of data samples from various classes.However, it is still a challenge to build a robust and discriminative metric, especially for corrupted data in the real-world application.In this paper, we propose a Robust Discriminative Metric Learning algorithm (RDML) via fast low-rank representation and denoising strategy.To be specific, the metric learning problem is guided by a discriminative regularization by incorporating the pair-wise or class-wise information.Moreover, low-rank basis learning is jointly optimized with the metric to better uncover the global data structure and remove noise.Furthermore, fast low-rank representation is implemented to mitigate the computational burden and make sure the scalability on large-scale datasets.Finally, we evaluate our learned metric on several challenging tasks, e.g., face recognition/verification, object recognition, and image clustering.The experimental results verify the effectiveness of the proposed algorithm by comparing to many metric learning algorithms, even deep learning ones.
Atomic Bell measurement via two-photon interactions

We introduce a complete Bell measurement on atomic qubits based on two-photon interactions with optical cavities and discrimination of coherent states of light. The dynamical system is described by the Dicke model for two three-level atoms interacting in two-photon resonance with a single mode of the radiation field, which is known to effectively generate a nonlinear two-photon interaction between the field and two states of each atom. For initial coherent states with a large mean photon number, the field state is well represented by two coherent states at half-revival time. For certain product states of the atoms, we prove the coherent generation of Greenberger-Horne-Zeilinger states with two atomic qubits and two orthogonal Schr\"odinger cat states as a third qubit. For arbitrary atomic states, we show that discriminating the two states of the field corresponds to different operations in the Bell basis of the atoms. By repeating this process with a second cavity prepared in a phase-shifted coherent state, we demonstrate the implementation of a complete Bell measurement. The experimental feasibility of our protocols is discussed for cavity-QED, circuit-QED, and trapped-ion setups.