Reviewerly/exHarmony huggingface dataset:
Digital financial inclusion, informal economy and poverty reduction in Africa

Purpose This paper aims to examine the relationship between mobile phones, the internet, financial inclusion, the informal economy and poverty reduction. Design/methodology/approach The study examines the relationship between mobile phones, the internet, financial inclusion, the informal economy and poverty reduction using the system generalized method of moments approach and a panel data set of 42 African countries for the period 1995–2017. Findings The study shows that mobile penetration and internet usage have significant positive relationship with the informal sector. Financial inclusion has significant effects, meaning that increased financial inclusion is associated with a developed informal economy. Also, mobile penetration and internet usage play significant roles in the relationship between financial inclusion and the informal economy. Further, mobile penetration and internet usage have a significant positive relationship with poverty reduction. Similarly, financial inclusion has significant effects, meaning higher financial inclusion is associated with increased poverty reduction. The informal economy also has significant effects, suggesting that the development of the informal economy is associated with poverty reduction. Originality/value Most importantly, mobile penetration, internet usage and financial inclusion play significant roles in the link between the informal economy and poverty reduction.
Monitoring social distancing under various low light conditions with deep learning and a single motionless time of flight camera

The purpose of this work is to provide an effective social distance monitoring solution in low light environments in a pandemic situation. The raging coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has brought a global crisis with its deadly spread all over the world. In the absence of an effective treatment and vaccine the efforts to control this pandemic strictly rely on personal preventive actions, e.g., handwashing, face mask usage, environmental cleaning, and most importantly on social distancing which is the only expedient approach to cope with this situation. Low light environments can become a problem in the spread of disease because of people’s night gatherings. Especially, in summers when the global temperature is at its peak, the situation can become more critical. Mostly, in cities where people have congested homes and no proper air cross-system is available. So, they find ways to get out of their homes with their families during the night to take fresh air. In such a situation, it is necessary to take effective measures to monitor the safety distance criteria to avoid more positive cases and to control the death toll. In this paper, a deep learning-based solution is proposed for the above-stated problem. The proposed framework utilizes the you only look once v4 (YOLO v4) model for real-time object detection and the social distance measuring approach is introduced with a single motionless time of flight (ToF) camera. The risk factor is indicated based on the calculated distance and safety distance violations are highlighted. Experimental results show that the proposed model exhibits good performance with 97.84% mean average precision (mAP) score and the observed mean absolute error (MAE) between actual and measured social distance values is 1.01 cm.
Application of kernel extreme learning machine and Kriging model in prediction of heavy metals removal by biochar

Abstract Kernel extreme learning machine (KELM) and Kriging models are proposed to predict biochar adsorption efficiency of heavy metals. Both six popular ions (Pb2+, Cd2+, Zn2+, Cu2+, Ni2+, As3+) and single ion are considered to test the accuracy of KELM and Kriging models. Two ways (data selection and fix output value) are attempted to improve the model fitting accuracy and the best R2 can reach 0.919 (KELM) and 0.980 (Kriging). In addition, stepwise regression and local sensitivity analysis show that adsorption efficiency has strong relationship with pHsolute and T. Moreover, the most sensitive parameters are T, pHH2O, r, C and pHsolute. The accurate KELM and Kriging models identify the most important controlling factors on metal adsorption, and ultimately provide some sort of predictive framework that will be useful in selecting appropriate biochar for particular treatment scenarios. This, in turn, will reduce the number of metal-biochar adsorption experiments needed going forward.
Intelligent Autoscaling of Microservices in the Cloud for Real-Time Applications

Cloud applications are becoming more containerized in nature. Developing a cloud application based on a microservice architecture imposes different challenges including scalability at the container level. What adds to the challenge is that cloud applications impose quality of service (QoS) requirements and have various resource demands requiring a customized scaling approach. For example, real-time applications require near real time response time as a QoS. Existing autoscaling technologies such as Kubernetes offer some customization to a set of threshold values for autoscaling. The challenge is identifying the right values for the different autoscaling parameters that will guarantee QoS in a changing dynamic environment. Advancements in machine learning and reinforcement learning (RL) provides a means for autoscaling in cloud applications with no domain knowledge. In this article, we introduce an intelligent autonomous autoscaling system for microservices autoscaling in the cloud with QoS constraints. The system consists of two modules. The first module identifies the microservice resource demand via a generic autoscaling algorithm deployed on the Google Kubernetes Engine (GKE). Our algorithm adapts the Kubernetes autoscaling paradigm based on the application resource requirements. The second module uses reinforcement learning agents to learn and identify the autoscaling threshold values based on the resource demand and QoS. Experimental results show an enhancement in the microservice response time up to 20% compared to the default autoscaling paradigm. In addition, the RL agents can identify the autoscaling threshold values while maintaining a response time below the QoS constraint. Our proposed work provides a customized autoscaling solution for microservices in cloud applications while adhering to QoS constraints with minimum user interaction.
CNN Detection of GAN-Generated Face Images based on Cross-Band Co-occurrences Analysis

Last-generation GAN models allow to generate synthetic images which are visually indistinguishable from natural ones, raising the need to develop tools to distinguish fake and natural images thus contributing to preserve the trustworthiness of digital images. While modern GAN models can generate very high-quality images with no visible spatial artifacts, reconstruction of consistent relationships among colour channels is expectedly more difficult. In this paper, we propose a method for distinguishing GAN-generated from natural images by exploiting inconsistencies among spectral bands, with specific focus on the generation of synthetic face images. Specifically, we use cross-band co-occurrence matrices, in addition to spatial co-occurrence matrices, as input to a CNN model, which is trained to distinguish between real and synthetic faces. The results of our experiments confirm the goodness of our approach which outperforms a similar detection technique based on intra-band spatial co-occurrences only. The performance gain is particularly significant with regard to robustness against post-processing, like geometric transformations, filtering and contrast manipulations.
SCALODEEP: A Highly Generalized Deep Learning Framework for Real‐Time Earthquake Detection

Abstract The detection of earthquake signals is a fundamental yet challenging task in observational seismology. A robust automatic earthquake detection algorithm is strongly demanded in view of the ever‐growing global seismic dataset. Here, we develop an automatic earthquake detection framework based on a deep learning approach (SCALODEEP). It extracts high‐order features embedded in three‐component seismograms by encoding a time‐frequency representation of the data (scalogram) into a deep network with skip connections. The SCALODEEP is trained and validated on an open‐source dataset from North California, and then employed to seismicity detection in four areas, including Arkansas, Japan, Texas, and Egypt. Despite vastly varying characteristics of regional earthquakes (e.g., focal mechanism, duration, and noise level), SCALODEEP successfully detects seismic signals over a broad range of local magnitudes (as low as −1.3 ) and outperforms conventional algorithms such as STA/LTA, FAST, and template matching. Compared to recently proposed deep learning based frameworks (e.g., CRED and Earthquake transformer), SCALODEEP achieves a superior generalization ability via a sophisticated network architecture. In summary, our study offers a promising new tool to improve existing earthquake detection systems and, as importantly, sheds light on designing an effective deep learning network for generalized earthquake detection.
Computational Prediction of Cervical Cancer Diagnosis Using Ensemble-Based Classification Algorithm

Abstract Cervical cancer is one of the most common cancers among women in the world. As at the earlier stage, cervical cancer has fewer symptoms. Cancer research is vital as the prognosis of cancer enables clinical applications for patients. In this study, we demonstrate a new approach that applies an ensemble approach to machine learning models for the automatic diagnosis of cervical cancer. The dataset used in the study is the cervical cancer dataset available at the University of California Irvine database repository. Initially, missing values are imputed (k-nearest neighbors) and then the data are balanced (oversampled). Two feature selection approaches are used to extract the most significant features. The proposed stacking architecture, applied for the first time on the cervical cancer dataset, used time elapse of 5.6 s and achieved an area under the curve score of 99.7% performing better than the methods used in previous works. The objective of the study is to propose a computational model that can predict the diagnosis of cervical cancer efficiently. Further, the proposed learning architecture is gauged with several ensemble approaches like random forest, gradient boosting, voting ensemble and weighted voting ensemble to perceive the enhancement.
Recommenders with a Mission

News recommenders help users to find relevant online content and have the potential to fulfill a crucial role in a democratic society, directing the scarce attention of citizens towards the information that is most important to them. Simultaneously, recent concerns about so-called filter bubbles, misinformation and selective exposure are symptomatic of the disruptive potential of these digital news recommenders. Recommender systems can make or break filter bubbles, and as such can be instrumental in creating either a more closed or a more open internet. Current approaches to evaluating recommender systems are often focused on measuring an increase in user clicks and short-term engagement, rather than measuring the user's longer term interest in diverse and important information.
Fairness Through Robustness

Deep neural networks (DNNs) are increasingly used in real-world applications (e.g. facial recognition). This has resulted in concerns about the fairness of decisions made by these models. Various notions and measures of fairness have been proposed to ensure that a decision-making system does not disproportionately harm (or benefit) particular subgroups of the population. In this paper, we argue that traditional notions of fairness that are only based on models' outputs are not sufficient when the model is vulnerable to adversarial attacks. We argue that in some cases, it may be easier for an attacker to target a particular subgroup, resulting in a form of robustness bias. We show that measuring robustness bias is a challenging task for DNNs and propose two methods to measure this form of bias. We then conduct an empirical study on state-of-the-art neural networks on commonly used real-world datasets such as CIFAR-10, CIFAR-100, Adience, and UTKFace and show that in almost all cases there are subgroups (in some cases based on sensitive attributes like race, gender, etc) which are less robust and are thus at a disadvantage. We argue that this kind of bias arises due to both the data distribution and the highly complex nature of the learned decision boundary in the case of DNNs, thus making mitigation of such biases a non-trivial task. Our results show that robustness bias is an important criterion to consider while auditing real-world systems that rely on DNNs for decision making. Code to reproduce all our results can be found here: https://github.com/nvedant07/Fairness-Through-Robustness
Flexible Body Partition-Based Adversarial Learning for Visible Infrared Person Re-Identification

Person re-identification (Re-ID) aims to retrieve images of the same person across disjoint camera views. Most Re-ID studies focus on pedestrian images captured by visible cameras, without considering the infrared images obtained in the dark scenarios. Person retrieval between visible and infrared modalities is of great significance to public security. Current methods usually train a model to extract global feature descriptors and obtain discriminative representations for visible infrared person Re-ID (VI-REID). Nevertheless, they ignore the detailed information of heterogeneous pedestrian images, which affects the performance of Re-ID. In this article, we propose a flexible body partition (FBP) model-based adversarial learning method (FBP-AL) for VI-REID. To learn more fine-grained information, FBP model is exploited to automatically distinguish part representations according to the feature maps of pedestrian images. Specially, we design a modality classifier and introduce adversarial learning which attempts to discriminate features between visible and infrared modality. Adaptive weighting-based representation learning and threefold triplet loss-based metric learning compete with modality classification to obtain more effective modality-sharable features, thus shrinking the cross-modality gap and enhancing the feature discriminability. Extensive experimental results on two cross-modality person Re-ID data sets, i.e., SYSU-MM01 and RegDB, exhibit the superiority of the proposed method compared with the state-of-the-art solutions.
Consensus on validation of forensic voice comparison

Since the 1960s, there have been calls for forensic voice comparison to be empirically validated under casework conditions. Since around 2000, there have been an increasing number of researchers and practitioners who conduct forensic-voice-comparison research and casework within the likelihood-ratio framework. In recent years, this community of researchers and practitioners has made substantial progress toward validation under casework conditions becoming a standard part of practice: Procedures for conducting validation have been developed, along with graphics and metrics for representing the results, and an increasing number of papers are being published that include empirical validation of forensic-voice-comparison systems under conditions reflecting casework conditions. An outstanding question, however, is: In the context of a case, given the results of an empirical validation of a forensic-voice-comparison system, how can one decide whether the system is good enough for its output to be used in court? This paper provides a statement of consensus developed in response to this question. Contributors included individuals who had knowledge and experience of validating forensic-voice-comparison systems in research and/or casework contexts, and individuals who had actually presented validation results to courts. They also included individuals who could bring a legal perspective on these matters, and individuals with knowledge and experience of validation in forensic science more broadly. We provide recommendations on what practitioners should do when conducting evaluations and validations, and what they should present to the court. Although our focus is explicitly on forensic voice comparison, we hope that this contribution will be of interest to an audience concerned with validation in forensic science more broadly. Although not written specifically for a legal audience, we hope that this contribution will still be of interest to lawyers.
Deep-seated features histogram: A novel image retrieval method

Low-level features and deep features each have their own advantages and disadvantages in image representation. However, combining their advantages within a CBIR framework remains challenging. To address this problem, we propose a novel image-retrieval method: the deep-seated features histogram (DSFH). Its main highlights are: 1) Low-level features are extracted by simulating the human orientation selection and color perception mechanisms. This follows the human habit of looking at conspicuous regions and then less-conspicuous ones. 2) A novel method, ranking whitening, is proposed for extracting deep features via low-level features and combining them to obtain deep-seated features. 3) The proposed method is straightforward and reduces the vector dimensionality of the FC7 layer of a pre-trained VGG-16 network, and significantly improves image-retrieval precision. Comparative experiments demonstrate that the proposed method outperforms several state-of-the-art methods, including low-level feature-based, deep feature-based, and fused feature-based methods, in terms of precision/recall, area under the precision/recall curve metrics, and mean average precision. The proposed method provides efficient CBIR performance and not only has the power to discriminate low-level features, including color, texture, and shape, but can also match scenes of similar style.
Blockchain-Based Privacy-Preserving Driver Monitoring for MaaS in the Vehicular IoT

Driving behaviors are highly relevant to automotive statuses and on-board safety, which offer compelling shreds of evidence for mobility as a service (MaaS) providers to develop personalized rental prices and insurance products. However, the direct dissemination of driving behaviors may lead to violations of identity and location privacy. In this paper, our proposed mechanism first achieves the verifiable aggregation and immutable dissemination of performance records by exploiting a blockchain with the proof-of-stake (PoS) consensus. Moreover, to acquire a driver's aggregated performance record from the blockchain, the proposed scheme first realizes quick identification with a Bloom filter and further approaches the target performance record through an oblivious transfer (OT) protocol. A performance evaluation shows that during the acquisition of the records, the computational complexity of our scheme is only related to the scale of the records contained in one transaction. However, the computational complexity of one traditional scheme without a Bloom filter depends on the scale of the records generated during each time slot. Furthermore, the computational complexity of another traditional scheme without aggregation relies on the scale of the records contained in one transaction, as well as the length of a driver's performance history. We also investigate the trade-off between the privacy level and computational complexity, and we determine the optimal number of data records in each transaction.
Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we introduce a novel context-aware model based on deep neural networks to address the challenges of recognizing offline handwritten Arabic text, including isolated digits, characters, and words. Specifically, we propose a supervised Convolutional Neural Network (CNN) model that contextually extracts optimal features and employs batch normalization and dropout regularization parameters. This aims to prevent overfitting and further enhance generalization performance when compared to conventional deep learning models. We employ a number of deep stacked-convolutional layers to design the proposed Deep CNN (DCNN) architecture. The model is extensively evaluated and shown to demonstrate excellent classification accuracy when compared to conventional OAHR approaches on a diverse set of six benchmark databases, including MADBase (Digits), CMATERDB (Digits), HACDB (Characters), SUST-ALT (Digits), SUST-ALT (Characters), and SUST-ALT (Names). A further experimental study is conducted on the benchmark Arabic databases by exploiting transfer learning (TL)-based feature extraction which demonstrates the superiority of our proposed model in relation to state-of-the-art VGGNet-19 and MobileNet pre-trained models. Finally, experiments are conducted to assess comparative generalization capabilities of the models using another language database , specifically the benchmark MNIST English isolated Digits database, which further confirm the superiority of our proposed DCNN model.
Multi-disease prediction using LSTM recurrent neural networks

• We propose a deep learning approach to perform multi-disease prediction. • The approach uses a long short-term memory network to address temporal challenges. • The performance of the approach is validated on a real-world healthcare dataset. • The proposed approach can be used for intelligent clinical decision support. Prediction of future clinical events (e.g., disease diagnoses) is an important machine learning task in healthcare informatics research. In this work, we propose a deep learning approach to perform multi-disease prediction for intelligent clinical decision support. The proposed approach utilizes a long short-term memory network and extends it with two mechanisms (i.e., time-aware and attention-based) to conduct multi-label classification based on patients’ clinical visit records. The former mechanism (time-aware) is used to handle the temporal irregularity across clinical visits whereas the latter mechanism (attention-based) assists in determining the importance of each visit for the prediction task. Using a large clinical record data set (over 5 million records) collected from a hospital in Southeast China, we show that our proposed approach outperforms a variety of traditional and deep learning methods in predicting future disease diagnoses. We further study the impacts of different time interval choices for the time-aware mechanism and compare the performances of existing attention-based mechanisms with the one proposed in our study. Our work has implications for supporting physician diagnoses via the use of intelligent systems and more broadly for improving the quality of healthcare service.