Reviewerly/exHarmony huggingface dataset:
Information Technology and Gender Economic Inclusion in Sub-Saharan Africa

This study investigates how ICT affects gender economic inclusion via gender parity education channels. We examine the issue using data from 49 countries in sub-Saharan Africa for the period 2004-2018 divided into: (i) 42 countries for the period 2004-2014; and (ii) 49 countries for the period 2008-2018. Given the overwhelming evidence of negative net effects in the first sample, an extended analysis is used to establish thresholds of ICT penetration that nullify the established net negative effects. We found that in order to enhance female labor force participation, the following ICT thresholds are worthwhile for the secondary education channel: 165 mobile phone penetration per 100 people, 21.471 internet penetration per 100 people and 3.475 fixed broadband subscriptions per 100 people. For the same outcome of inducing a positive effect on female labor force participation, a 31.966 internet penetration per 100 people threshold, is required for the mechanism of tertiary school education. These computed thresholds have economic meaning and policy relevance because they are within the established ICT policy ranges. In the second sample, a mobile phone penetration threshold of 122.20 per 100 people is needed for the tertiary education channel to positively affect female labor force participation.
High-precision Image Mosaic Algorithm Based on Adaptive Homography Transform

The Unmanned Aerial Vehicle (UAV) image mosaic is a research hotspot of digital image processing in recent years, which has extensively application prospect. The existing algorithms are sensitive to aerial images mosaic with large scale and perspective differences, and have the disadvantages of poor accuracy and image distortion, which can not obtain the global optimal mosaic effect. An aerial images mosaic method based on the adaptive homography matrix is proposed to realize high-precision mosaic of multi-scale and multi-view aerial images. The feature points of different UAVs aerial images are constructed by the Speeded Up Robust Features (SURF) algorithm. Fast Library for Approximate Nearest Neighbors (FLANN) is used to realize rough matching of feature points. The Random Sample Consensus (RANSAC) algorithm is used to eliminate the mismatched points to achieve the fine matching, and the position-dependent adaptive homography transform model is constructed to complete matching. Finally, the weighted average method is implemented to complete the fusion for the purpose of seamless image mosaic. The performance of the mosaicing algorithm based on adaptive homography transform is verified on a practical aerial dataset, and the results are compared with the related algorithms. Experimental results show that the proposed algorithm satisfies the requirements of high-precision mosaic, and has high efficiency.
Static Hand Sign Recognition Using Wavelet Transform and Convolutional Neural Network

Hand gestures play a crucial role in helping the deaf and hearing impairment community to communicate smoothly. Consequently, a robust hand sign recognition system will help the first responders to better communicate and understand those people, who cannot use the spoken languages. Therefore, in this research paper, we propose a novel approach for static hand sign recognition using convolutional neural network as the classifier and discrete wavelet transform as the feature extractor. We then implement our proposed approach using Python programming language. Moreover, we use the MNIST dataset, which is one of the publicly available datasets available at Kaggle website to further implement our research approach. Furthermore, we compared our approach with Hu moments, Zernike moments (Degree 8), and Krawtchouk moments feature extraction techniques along with SVM Linear, Radial, Random Forest, and K-Nearest Neighbor classifiers. As a result, our approach yields the 99.1% accuracy that outperforms the accuracies of above-discussed feature extraction methods and classifiers.
Real-Time Semantic Segmentation of Aerial Videos Based on Bilateral Segmentation Network

In recent years, deep learning algorithms have been widely used in semantic segmentation of aerial images. However, most of the current research in this field focus on images but not videos. In this paper, we address the problem of real-time aerial video semantic segmentation with BiSeNet[1]. Since BiSeNet is originally proposed for semantic segmentation of natural city scene images, we need a corresponding dataset to ensure the effect of transfer learning when applying it to aerial video segmentation. Therefore, we build a UAV streetscape sequence dataset (USSD) to fill the vacancy of dataset in this field and facilitate our research. Evaluation on USSD shows that BiSeNet outperforms other state-of-the-art methods. It achieves 79.26% mIoU and 93.37% OA with speed of 148.7 FPS on NVIDIA Tesla V100 for a 1920x1080 frame size input aerial video, which satisfies the demand of aerial video semantic segmentation with a competitive balance of accuracy and speed. The aerial video semantic segmentation results are provided at Our Repository.
Co-designing DSL quality assurance measures for and with non-programming experts

Domain-specific languages seek to provide domain guarantees that eliminate many errors allowed by general-purpose languages. Still, a domain-specific language requires additional quality assurance measures to ensure that specifications behave as intended by the users. However, some domains may have specific quality assurance measures (e.g., proofs, experiments, or case studies) with little tradition of using quality assurance measures customary to software engineering. We investigate the possibility of accommodating such domains by conducting a workshop with 11 prospective users of a domain-specific language named MAL for the pension industry. The workshop emphasised the need for supporting actuaries with new analytical tools for quality assurance and resulted in three designs: quantity monitors let users identify outlier behaviour, fragment debugging lets users debug with limited evaluative power, and debugging spreadsheets let users visualise, analyse, and remodel concrete calculations with an established domain tool. Based on our experiences, we hypothesise that co-design workshops are a viable approach for DSLs in a similar situation.
A DSS-based Comparator for Facial Race Age Estimation

Facial age estimation is an essential feature in many applications satisfying the need to provide users with content that corresponds to their ages. However, providing an inclusive facial age estimation solution that is also high-performing is challenging due to the many different factors that influence the face. This article leverages DeepSets for Symmetric Elements (DSS) to propose an approach that aims to extract a reliable set of rich feature vectors for age estimation. It combines a DSS feature extractor, ternary classifier, and a race determiner. Precisely, the extractor consists of a siamese-like layer that applies a regular convolutional neural network to input images and an aggregation module that sums up all of the images and then adds them to the output from the siamese layer. To estimate the age, the ternary classifier obtains the feature vectors seeking to classify them into three possible outcomes that correspond to younger than, similar to, or older than. The correlation is achieved using identical pairs of input and reference images that belong to the same race. The result indicates the similarity between the images: the higher the score, the closer the similarity. With an accuracy of 94.8%, 95.2%, and 90.5% on the MORPH II, a race-inclusive dataset, and the FG-NET, we demonstrate that our proposal exemplifies facial age estimation particularly when the race factor is considered in the estimation.
Genetic Algorithm for Improved Transfer Learning Through Bagging Color-Adjusted Models

Computer vision has seen some breakthroughs in the last decade based on some methodological advances, but as well based on the availability of huge datasets like ImageNet for training. However, training data is generally scarce in remote sensing and even more in high-resolution or high-quality remote sensing of sensitive areas. Some efforts have been made to provide labeled public domain data, but aside lowresolution data, these activities are not sufficient yet. In this paper, we propose an alternative approach: we transform satellite images into a representation in which features learnt from Internet photography are more meaningful. We show how learnt colorspace transformations can enable significantly more stable transfer learning from ImageNet. As a consequence, small training datasets suffice allowing for significantly more diverse Earth observation applications. We present experiments on high-resolution remote sensing images of airplanes as featured in the 2020 Gaofen Challenge on Automated High-Resolution Earth Observation Image Interpretation.
Neoclassicism in I. F. Stravinsky’s Creative Heritage: Genres, Forms and Musical Language

The study aims to determine the musical and theoretical features of I. Stravinsky’s neoclassical creative work. The author identified causes and prerequisites for Stravinsky’s work in the neoclassical direction, analysed the conceptual features of neoclassicism in art. Scientific novelty lies in determining features of neoclassicism as a way of deforming the stylistic material of previous eras in Stravinsky’s creative work. As a result, it has been ascertained that the intrinsic features of neoclassicism in Stravinsky’s creative work are the use of forms and genres of music written by composers of the XVII-XIX centuries, rejection of homophonic texture in favour of polyphonic writing, orientation to melodism and linearity, rigid rhythmic organisation, plot mythologisation in theatrical genres.
Exploring Quantum Engineering from Resource Perspective

From the viewpoint of engineering cybernetics, research paradigm shift may be necessary for investigating quantum systems and quantum artificial intelligence. When a quantum system or constructing a quantum information processing system is manipulated, one should not only consider what kind of resource is available, but also consider whether or not there is enough resource to support the control of quantum systems. Our recent researches can be regarded as exploring quantum engineering from resource perspective. We intend to propose and answer the following questions: what will happen when quantum measurements, quantum computation, quantum entangled states or quantum network are available for the public. In our opinion, performing quantum measurement, completing quantum computation, preparing quantum entangled states and building quantum network can be regarded as cost of resources. Hopefully we should shape a new research paradigm from the resource perspective.
A Dynamic Declarative Composition Scheme for Stream Data Services

With the fast development of Sensor Network, Internet of Things, mobile devices, and pervasive computing, enormous amounts of sensor devices are deployed in physical world. Data streams produced by these sensor devices, deployed broadly, can be used to create various value-added applications. Facing continuous, real-time, high-frequency, low-valued data streams, how to flexibly and efficiently cooperate them for creating valuable application is very crucial. In this study, we propose a service-oriented manner to realize flexible streams integration. It considers data stream produced by one sensor data as a stream data service and utilizes composing multiple services to realize the cooperation among sensor devices. Firstly, we propose a stream data service model based on Event-Condition-Action rules, which can encapsulate steam data as services and continuously and timely process stream data into value-added events. Then, we propose a declarative method which can dynamically compose stream data services. Based on two kinds of declarative rules, that is, sink-rules and connect-rules, multiple data streams can be dynamically integrated through flexible service composition. To ensure the performance of service composition, we also employ a sensor partition strategy and process multiple service compositions in parallel. Through comprehensive evaluations by experiments, our service composition method shows both good efficiency and effectiveness.
PYNQ-Based Rapid FPGA Implementation of Quantum Key Distribution

In this paper, we present a real-time Quantum Key Distribution (QKD) implementation on Field Programmable Gate Arrays (FPGAs) for secure communication. We propose a novel methodology with a Python-based programming interface for rapid development on FPGA. Our methodology revolves three phases of development. In the first phase, a reference model of an entangled photon source and the proposed QKD system are developed in Python. Next, the reference model is passed through a thorough verification phase. In the second phase, the reference model is implemented on the Processing System (PS) part of the FPGA. Finally in the third phase, the computationally intensive part of the QKD architecture is off-loaded on to the Programmable Logic (PL) part of the FPGA for acceleration. We employ PYNQ framework in our QKD development and successfully combine the convenience of Python productivity with FPGA based acceleration.
Leveraging Machine Learning in Financial Fraud Forensics in the Age of Cybersecurity

Financial sectors are lucrative cyber-attack targets because of their immediate financial gain. As a result, financial institutions face challenges in developing systems that can automatically identify security breaches and separate fraudulent transactions from legitimate transactions. Today, organizations widely use machine learning techniques to identify any fraudulent behavior in customers' transactions. However, machine learning techniques are often challenging because of financial institutions' confidentiality policy, leading to not sharing the customer transaction data. This chapter discusses some crucial challenges of handling cybersecurity and fraud in the financial industry and building machine learning-based models to address those challenges. The authors utilize an open-source e-commerce transaction dataset to illustrate the forensic processes by creating a machine learning model to classify fraudulent transactions. Overall, the chapter focuses on how the machine learning models can help detect and prevent fraudulent activities in the financial sector in the age of cybersecurity.
Super-resolution reconstruction method of transmission line key components image based on SRGAN

Aiming at the problem of unmanned aerial vehicle inspection images being susceptible to environmental interference during shooting, resulting in blurry image capture and inability to accurately identify defects in key components of transmission lines, this paper uses SRGAN to super-resolution reconstruction of low-resolution inspection images to improve image quality to meet the needs of deep learning algorithms or manual accurate recognition of line defects. First, a high-resolution image data set of key components of the transmission line is produced, and the data set is obscured as a low-resolution image data set. Then the PaddlePaddle framework is used to build the SRGAN super-resolution network model to perform super-resolution reconstruction on the low-resolution data. In model training, the model parameters are optimized according to the training situation, the optimal model is obtained, and the reconstruction experiment on low-resolution images is performed. The experimental results show that the image generated by SRGAN is similar to the high-resolution image in sharpness, and has achieved good results.
그림책을 활용한 놀이중심 생태환경수업이 초등학교 저학년 학생들의 생태적 감수성에 미치는 효과

The purpose of this study is to investigate the effect of play-centered ecological environment classes using picture books on the ecological sensibility of elementary school students. The subjects of this study consisted of 25 students from one class of Y Elementary School in B Metropolitan City as an experimental group and 23 students from another class as a comparison group. The experimental group conducted a play-centered ecological environment class using picture books, and the comparison group conducted general textbooks and tutorials. For the implementation of this study, a pre- and post-tests on ecological sensibility were conducted and the results were analyzed in a quantitative and qualitative ways. The results of this study are as follows. First, the experimental group applying play-centered ecological environment classes using picture books showed a statistically significant improvement in ecological sensitivity. Second, the play-centered ecological environment class using picture books changed the behavioral and emotional ecological sensibility of elementary school students positively. Taking these results together, in this study, when elementary school students receive a play-centered ecological environment classes using picture books, they can take a multisensory approach with interest in learning, thereby improving their ecological sensitivity to nature and living things.
Document Classification and Information Extraction framework for Insurance Applications

Document Intelligence is an essential subclass in the field of machine learning. It plays a vital role in insurance applications and other sectors. In this work, we showcase a business application that uses two different but Complimentary techniques: document classification and entity extraction. We also provide an overview of an end-to-end production level system that incorporates deep learning models deployed at scale. The system’s backbone relies on trained models carefully analyzed and designed to generalize well on existing and future usecases. Through empirical evidence, we provide insights into several models trained on our insurance-related datasets and highlight models that have shown good performance across multiple datasets in our real-world insurance setting.
Conceptual Modeling of Gender-Inclusive Requirements

As technologies revolutionize the way we live, overlooking gender perspectives in the development of digital solutions results in gender-biased technology that, instead of advancing gender inclusion, creates new barriers in achieving it. This paper proposes a conceptual model for gender inclusion in software development. We started by performing a systematic mapping study to gather the relevant concepts from the existing body of knowledge. This served as groundwork for the definition of a conceptual model of gender-inclusive requirements.
Reproducibility Companion Paper

In this companion paper, we provide the details of the reproducibility artifacts of the paper "Visual Relation of Interest Detection" presented at MM'20. Visual Relation of Interest Detection (VROID) aims to detect visual relations that are important for conveying the main content of an image. In this paper, we explain the file structure of the source code and publish the details of our ViROI dataset, which can be used to retrain the model with custom parameters. We also detail the scripts for component analysis and comparison with other methods and list the parameters that can be modified for custom training and inference.
The Reciprocal Algebraic Integers Having Small House

Let $\alpha$ be an algebraic integer of degree $d$, which is reciprocal. The house of $\alpha$ is the largest modulus of its conjugates. We proved that $d$-th power of the house of reciprocal $\alpha$ has a limit point. We presented a property of antireciprocal hexanomials. We compute the minimum of the houses of all reciprocal algebraic integers of degree $d$ having the minimal polynomial which is a factor of a $D$-th degree reciprocal or antireciprocal polynomial with at most eight monomials, say $\mathrm{mr}(d)$, for $d$ at most 180, $D\le 1.5d$ and $D\le 210$. We show that it is not necessary to take into account unprimitive polynomials. The computations suggest several conjectures.
A Comparative Evaluation of Different Keyword Extraction Techniques

Retrieving keywords in a text is attracting researchers for a long time as it forms a base for many natural language applications like information retrieval, text summarization, document categorization etc. A text is a collection of words that represent the theme of the text naturally and to bring the naturalism under certain rules is itself a challenging task. In the present paper, the authors evaluate different spatial distribution based keyword extraction methods available in the literature on three standard scientific texts. The authors choose the first few high-frequency words for evaluation to reduce the complexity as all the methods are somehow based on frequency. The authors find that the methods are not providing good results particularly in the case of the first few retrieved words. Thus, the authors propose a new measure based on frequency, inverse document frequency, variance, and Tsallis entropy. Evaluation of different methods is done on the basis of precision, recall, and F-measure. Results show that the proposed method provides improved results.
COMPREHENSIVE EXPLANATIONS FRAMEWORK FOR MACHINE LEARNING MODELS TRAINED ON TABULAR DATA

In the light of technological and scientific advances of the past decade machine learning models are becoming an inseparable part of many businesses. One of the shortcomings of ML models is the lack of transparency, which may result in a number of problems: hidden biases in the model, customer distrust, low adoption and usage etc. To increase the trust among the customers, the explainable techniques should find their way to the end customers in a digestible format. In this work, we will explore some ML explainability methods and provide a framework for presenting them in a comprehensive manner.
Planning-oriented optimization strategy for visualizing power distribution networks: from global to local

The spatial distribution of power facilities is uneven, thereby making the topology of geographical wiring diagrams (GWDs) based on the actual coordinates unclear. A single-line diagram has the advantage of a clear topology but it lacks spatial locations. A GWD has the advantage of accurate spatial locations but it lacks a clear topology. Visualizing distribution networks for planning requires both features. We proposed a new planning-oriented method for optimizing the visualization of distribution networks. From the global perspective, we proposed an improved force-directed (FD) algorithm by introducing a space restriction strategy and node–edge repulsion strategy to promote the expansion of the distance between distribution facilities within a limited buffer. We then constructed the constrained Delaunay triangulation to identify the compact districts (CDs) and used a genetic algorithm to optimize the parameters for the improved FD algorithm. A novel visualization evaluation indicator was also proposed for quantitatively assessing the visualizations. From a local perspective, the fisheye algorithm was used to optimize the CDs to further improve the visualization of the distribution network. We verified the proposed methods with real-world data. We used limited spatial displacement in exchange for maximum topology clarity to balance the accurate spatial location and topology clarity.