Reviewerly/exHarmony huggingface dataset:
Customer Due Diligence in the FinTech Era: A Bibliometric Analysis

This study examined the current developments in customer due diligence (CDD) in the financial technology (FinTech) era. The study of anti-money laundering (AML) and combating financing of terrorism (CFT) gained prominence after the 2007–2009 global financial crisis (GFC), in which administrative penalties were issued to financial institutions. Faced with AML regulatory compliance issues, technological solutions were or are still being developed. Thus, several technological innovation developments have shaped the future direction of the CDD aspects in the AML/CFT sphere. A bibliometric review and meta-analysis was employed for the study. The Scopus database was utilised to generate the dataset for the study, while SciVal was applied for research metric analysis. The major findings revealed that the key research themes in this area include anti-money laundering, banks and crime, and cryptocurrency, as well as blockchain and corruption. It was also established that most of the research done in this area is focused on the United Kingdom, the United States, and China. The integration of CDD with FinTech is still an emerging area that requires interdisciplinary collaborations.
Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach

Introduction: Of all the cancers that afflict women, breast cancer (BC) has the second-highest mortality rate, and it is also believed to be the primary cause of the high death rate. Breast cancer is the most common cancer that affects women globally. There are two types of breast tumors: benign (less harmful and unlikely to become breast cancer) and malignant (which are very dangerous and might result in aberrant cells that could result in cancer). Methods: To find breast abnormalities like masses and micro-calcifications, competent and educated radiologists often examine mammographic images. This study focuses on computer-aided diagnosis to help radiologists make more precise diagnoses of breast cancer. This study aims to compare and examine the performance of the proposed shallow convolutional neural network architecture having different specifications against pre-trained deep convolutional neural network architectures trained on mammography images. Mammogram images are pre-processed in this study's initial attempt to carry out the automatic identification of BC. Thereafter, three different types of shallow convolutional neural networks with representational differences are then fed with the resulting data. In the second method, transfer learning via fine-tuning is used to feed the same collection of images into pre-trained convolutional neural networks VGG19, ResNet50, MobileNet-v2, Inception-v3, Xception, and Inception-ResNet-v2. Results: In our experiment with two datasets, the accuracy for the CBIS-DDSM and INbreast datasets are 80.4%, 89.2%, and 87.8%, 95.1% respectively. Discussion: It can be concluded from the experimental findings that the deep network-based approach with precise tuning outperforms all other state-of-the-art techniques in experiments on both datasets.
Unsupervised anomalous sound detection for industrial monitoring based on ArcFace classifier and gaussian mixture model

The operation state of machine can be monitored by performing anomalous sound detection (ASD). Unsupervised-ASD is a detection task in which the model detects unknown anomalous sounds without the use of anomalous sounds to train it. However, when detecting completely unknown anomalous samples, it is challenging to classify samples with high similarities and determine decision boundaries, leading to the poor detection performance. In light of the above deficiencies, we propose the ArcFace classifier and Gaussian mixture model (GMM) based unsupervised-ASD method. The Arcface loss-based classifier is proposed to aggregate the hidden features of different classes into the corresponding arc space, which increases the separability of samples. The GMM-based anomaly score calculation is presented to determine the more complex decision boundary. Experiments are carried out on the datasets provided by DCASE 2020 Task 2 and CRWU. As demonstrated by the areas under the receiver operating characteristic curve (AUC) and the partial AUC (pAUC), the proposed method has better performance compared with other methods in comparison.
Cyborg sociopolitical reconfigurations: Designing for speculative fabulation in learning

ABSTRACTABSTRACTBackground Everyday digital technologies play an important role in mediating human activity that is socio-political and humanizing. The everyday cyborg engages in speculative fabulation that is about fantastical new world-making in times of multiple crises. The work presented in this article builds on previous projects that have examined how everyday cultural practices mediate consequential learning that is transformative for communities of color.Methods Two social design-based studies draw from ethnographic analysis of two teacher education courses as well as two after-school programs focusing on digital fabrication and making and tinkering. Participants included 22 undergraduate pre-service teachers and 10 middle school students from schools in Latinx communities.Findings Collaborative cyborg activity, where expertise is distributed, emerged as pre-service teachers and youth collectively engaged with everyday socio-political issues. This article highlights cyborg sociopolitical technical reconfigurations, where learners assembled ideational and material tools to craft objects of learning activity that went beyond those established by schooling and imagined new possible futures.Contribution Designing learning ecologies for the everyday cyborg, in this case pre-service teachers and non-dominant youth, fosters an engagement with everyday dilemmas in ways that serve as catalysts for further learning and the new world-making of speculative fabulation. Disclosure statementNo potential conflict of interest was reported by the author(s).
Natural language processing for clinical notes in dentistry: A systematic review

To identify and synthesise research on applications of natural language processing (NLP) for information extraction and retrieval from clinical notes in dentistry.A predefined search strategy was applied in EMBASE, CINAHL and Medline. Studies eligible for inclusion were those that that described, evaluated, or applied NLP to clinical notes containing either human or simulated patient information. Quality of the study design and reporting was independently assessed based on a set of questions derived from relevant tools including CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). A narrative synthesis was conducted to present the results.Of the 17 included studies, 10 developed and evaluated NLP methods and 7 described applications of NLP-based information retrieval methods in dental records. Studies were published between 2015 and 2021, most were missing key details needed for reproducibility, and there was no consistency in design or reporting. The 10 studies developing or evaluating NLP methods used document classification or entity extraction, and 4 compared NLP methods to non-NLP methods. The quality of reporting on NLP studies in dentistry has modestly improved over time.Study design heterogeneity and incomplete reporting of studies currently limits our ability to synthesise NLP applications in dental records. Standardisation of reporting and improved connections between NLP methods and applied NLP in dentistry may improve how we can make use of clinical notes from dentistry in population health or decision support systems.PROSPERO CRD42021227823.
Temporal Resolution of Input Weather Data Strongly Affects an Off-Grid PV System Layout and Reliability

Renewable energy systems design using average year weather data is a standard approach that works well for grid-tied systems, but for stand-alone ones, it leads to dramatic mistakes. We considered the effect of meteorological data temporal resolution (5, 10, 15, 20, 30 min; 1, 2, 3, 4 h) on a stand-alone hybrid system’s layout in terms of equipment cost, power supply reliability and maximum duration of interruption for monitoring equipment in the Alps. We have shown that lifecycle costs could be strongly (order of magnitude) underestimated for off-grid systems, as well as their reliability overestimated. Lower temporal resolution data lead to the underestimation of energy storage charge–discharge cycles (considering depth of discharge too)—real batteries are to be replaced more often, which matches our practical experience as well. Even a 5 to 10 min decrease in weather data temporal resolution leads to the estimated annual expenses being halved. In general, we recommend using 30 min. resolution.
A robust analysis of adversarial attacks on federated learning environments

Federated Learning is a growing branch of Artificial Intelligence with the wide usage of mobile computing and IoT technologies. Since this technology uses distributed computing paradigm to do the learning part, most of the participating components are mobile devices and come outside the range of protection offered by a centralized system. As a result, several security issues such as data leakage, communication issues, poisoning, system manipulation via the backdoor, and so on arise with the usage of such a methodology. These sorts of attacks are categorized into various categories concerning their modus operandi. In this study, we review such attacks, namely poisoning attacks, inferencing attacks, their types, and working in a Federated Learning environment in detail. This study will give a precise idea of security issues faced in Federated Machine Learning and possible solutions.
Ensemble Machine Learning for Predicting the Power Output from Different Solar Photovoltaic Systems

In this paper, ensemble-based machine learning models with gradient boosting machine and random forest are proposed for predicting the power production from six different solar PV systems. The models are based on three year’s performance of a 1.2 MW grid-integrated solar photo-voltaic (PV) power plant. After cleaning the data for errors and outliers, the model features were chosen on the basis of principal component analysis. Accuracies of the developed models were tested and compared with the performance of models based on other supervised learning algorithms, such as k-nearest neighbour and support vector machines. Though the accuracies of the models varied with the type of PV systems, in general, the machine learned models developed under the study could perform well in predicting the power output from different solar PV technologies under varying working environments. For example, the average root mean square error of the models based on the gradient boosting machines, random forest, k-nearest neighbour, and support vector machines are 17.59 kW, 17.14 kW, 18.74 kW, and 16.91 kW, respectively. Corresponding averages of mean absolute errors are 8.28 kW, 7.88 kW, 14.45 kW, and 6.89 kW. Comparing the different modelling methods, the decision-tree-based ensembled algorithms and support vector machine models outperformed the approach based on the k-nearest neighbour method. With these high accuracies and lower computational costs compared with the deep learning approaches, the proposed ensembled models could be good options for PV performance predictions used in real and near-real-time applications.
Latent Code Identification (LACOID): A Machine Learning-Based Integrative Framework [and Open-Source Software] to Classify Big Textual Data, Rebuild Contextualized/Unaltered Meanings, and Avoid Aggregation Bias

Labeling or classifying textual data and qualitative evidence is an expensive and consequential challenge. The rigor and consistency behind the construction of these labels ultimately shape research findings and conclusions. A multifaceted methodological conundrum to address this challenge is the need for human reasoning for classification that leads to deeper and more nuanced understandings; however, this same manual human classification comes with the well-documented increase in classification inconsistencies and errors, particularly when dealing with vast amounts of documents and teams of coders. An alternative to human coding consists of machine learning-assisted techniques. These data science and visualization techniques offer tools for data classification that are cost-effective and consistent but are prone to losing participants’ meanings or voices for two main reasons: (a) these classifications typically aggregate all texts configuring each input file (i.e., each interview transcript) into a single topic or code and (b) these words configuring texts are analyzed outside of their original contexts. To address this challenge and analytic conundrum, we present an analytic framework and software tool, that addresses the following question: How to classify vast amounts of qualitative evidence effectively and efficiently without losing context or the original voices of our research participants and while leveraging the nuances that human reasoning bring to the qualitative and mixed methods analytic tables? This framework mirrors the line-by-line coding employed in human/manual code identification but relying on machine learning to classify texts in minutes rather than months. The resulting outputs provide complete transparency of the classification process and aid to recreate the contextualized, original, and unaltered meanings embedded in the input documents, as provided by our participants. We offer access to the database ( González Canché, 2022e ) and software required ( González Canché, 2022a , Mac https://cutt.ly/jc7n3OT , and Windows https://cutt.ly/wc7nNKF ) to replicate the analyses. We hope this opportunity to become familiar with the analytic framework and software, may result in expanded access of data science tools to analyze qualitative evidence (see also González Canché 2022b , 2022c , 2022d , for related no-code data science applications to classify and analyze qualitative and textual data dynamically).
Selection of pedagogical conditions for training STEM teachers to use augmented reality technologies in their work

The focus of the study is on choosing the pedagogical conditions that will best prepare STEM teachers to utilize augmented reality tools in their classrooms. 20 conditions were initially proposed during a brainstorming session and were then sorted into five groups: (1) conditions relating to the material support of the educational process, (2) methods and forms of training, (3) best practices for utilizing augmented reality in education, (4) unique circumstances made in order to achieve the goal of training STEM teachers; and (5) conditions relating to the psychological and pedagogical support of students. A survey was conducted with 94 participants to determine the importance of these conditions. The processing of the survey data allowed for the selection of the following conditions: (a) accessibility of immersive digital educational resources for STEM teachers and mobile hardware for augmented reality (laptops, tablets, smartphones, augmented reality glasses, etc.), (b) inclusion of augmented reality-related topics in STEM teachers' curricula, (c) use of research methodologies and interactive technologies in the STEM classroom, (d) having hands-on experience with the use of augmented reality technologies in STEM instruction.
A local spline regression-based framework for semi-supervised sparse feature selection

Feature selection (FS) is extensively applied in many machine learning applications for the selection of relevant features from data sets. A lot of unlabeled data are available in a variety of applications that can be exploited for semi-supervised FS to address the lack of labeled data and improve learning performance. Recently, semi-supervised sparse FS based on graph Laplacian has obtained considerable research interest, which uses the correlation between features in the process of FS. However, the Laplacian regularization has a weak extrapolating power and a bias towards the constant geodesic function, and cannot retain the local topology well. In this paper, a spline regression-based framework for semi-supervised sparse FS (SRS3FS) is proposed, which uses the mixed convex and non-convex ℓ2,p-norm (0 < p ≤1) regularization to select the relevant features and consider the correlation between features. The framework exploits local spline regression to retain the geometry structure of labeled and unlabeled data and encodes the data distribution. A unified iterative algorithm is presented to solve the proposed framework for the convex and non-convex cases, and its convergence is theoretically and experimentally proved. Experiments on several data sets illustrate the effectiveness of our framework in the selection of the most relevant and discriminative features.
Effects of light, heat and relative humidity on the accelerated testing of photovoltaic degradation using Arrhenius model

Energy demands in Nigeria has increased tremendously over the past decade due to the increasing levels of industrialization and population size. This has led to a higher reliance on solar photovoltaic (PV) modules for power generation since Nigeria receives abundant solar energy. However, no information exists on the lifetime and reliability of PV modules operating in the Nigerian climate. The information on how long PV modules will last when operated in the Nigerian weather becomes necessary since many private and Government parastatals are heavily investing in installing and relying on PV generated electricity. The objective of this study is to compute the acceleration factor and effective temperatures in different strategic locations in Nigeria by considering the combined effects of light, heat and irradiation. The analysis is conducted for different PV module configurations (glass-cell-polymer) and (glass-cell-glass) using the Arrhenius model. A 13-year (2005–2017) climatic data of seven strategic locations that encompass the six climatic zones in Nigeria including the National Capital was used. Results are that when relative humidity was neglected, the acceleration factors for the seven states ranged from 1 to 4. However, when relative humidity was considered, Northern states with relative humidity of about 50% had the highest acceleration factors of up to 50, while other regions with relative humidity above 80% had peak acceleration factors of about 14. The results obtained from this study should provide useful guidelines on the lifespan testing of PV modules operating in Nigeria as a case study and other geographical regions as well.
Hybrid Project Management between Traditional Software Development Lifecycle and Agile Based Product Development for Future Sustainability

The purpose of this review is to look at the future sustainability of project management by combining agile methodology and product management. Digital transformation is evolving rapidly, and the question is whether traditional project management is sustainable. To ensure sustainability, a hybrid approach is taken by combining agile methodology to ensure the latest trend in software development approaches are maintained. Agile methodologies were crafted in 2000, but it has only been widely used since 2011 for software development. This paper will focus on the types of agile requirements engineering methods used in the agile framework, used and described in various articles, journals, and studies over the last six years, and how this can be applied together with traditional project management.
Residual attention based uncertainty-guided mean teacher model for semi-supervised breast masses segmentation in 2D ultrasonography

Breast tumor is the second deadliest disease among women around the world. Earlier tumor diagnosis is extremely important for improving the survival rate. Recent deep-learning techniques proved helpful in the timely diagnosis of various tumors. However, in the case of breast tumors, the characteristics of the tumors, i.e., low visual contrast, unclear boundary, and diversity in shape and size of breast lesions, make it more challenging to design a highly efficient detection system. Additionally, the scarcity of publicly available labeled data is also a major hurdle in the development of highly accurate and robust deep-learning models for breast tumor detection. To overcome these issues, we propose residual-attention-based uncertainty-guided mean teacher framework which incorporates the residual and attention blocks. The residual for optimizing the deep network by enabling the flow of high-level features and attention modules improves the focus of the model by optimizing its weights during the learning process. We further explore the potential of utilizing unlabeled data during the training process by employing the semi-supervised learning (SSL) method. Particularly, the uncertainty-guided mean-teacher student architecture is exploited to demonstrate the potential of incorporating the unlabeled samples during the training of residual attention U-Net model. The proposed SSL framework has been rigorously evaluated on two publicly available labeled datasets, i.e., BUSI and UDIAT datasets. The quantitative as well as qualitative results demonstrate that the proposed framework achieved competitive performance with respect to the previous state-of-the-art techniques and outperform the existing breast ultrasound masses segmentation techniques. Most importantly, the study demonstrates the potential of incorporating the additional unlabeled data for improving the performance of breast tumor segmentation.
Granularity-aware distillation and structure modeling region proposal network for fine-grained image classification

Fine-grained visual classification (FGVC) aims to identify objects belonging to multiple sub-categories of the same super-category. The key to solving fine-grained classification problems is to learn discriminative visual feature representation with only subtle differences. Although previous work based on refined feature learning has made great progress, however, high-level semantic features often lack key information for fine-grained visual object nuances. How to efficiently integrate semantic information of different granularities from classification networks is a critical. In this paper, we propose Granularity-aware Distillation and Structure Modeling region Proposal Network(GDSMP-Net). Our solution integrates multi-granularity hierarchical information through a multi-granularity fusion learning strategy to enhance feature representation. In view of the inherent challenges of large intra-class differences in FGVC, a cross-layer self-distillation regularization is proposed to to strengthen the connection between high-level semantics and low-level semantics for robust multi-granularity feature learning. On this basis, we use a weakly supervised method to generate local branches, and the collaborative learning of discriminative semantics and structural semantics based on local regions, facilitating model to perceive contextual information to capture structural interactions between local semantics. Comprehensive experiments show that our method achieves state-of-the-art performance on four widely-used challenging datasets.(CUB-200-2011, Stanford Cars, FGVC-Aircraft and NA-birds).