Reviewerly/exHarmony huggingface dataset:
Automatic Speech Recognition and Pronunciation Error Detection of Dutch Non-native Speech: cumulating speech resources in a pluricentric language

The shortage of large-scale learners' speech corpora and precise manual annotations are two major challenges for automatic L2 speech recognition and error detection in L2 speech, especially for non-dominant varieties of pluricentric languages. In these cases, collecting and annotating large non-native (L2 learner) corpora for all language varieties is often unattainable. In this study, we investigated ways of addressing these problems through conventional and transfer learning Deep Neural Network (DNN) based Automatic Speech Recognition (ASR) and ASR-based pronunciation error detection (PED) by cumulating Netherlandic Dutch and Flemish Dutch speech resources. First, we show that for ASR the baseline system can be improved by combining the Netherlandic Dutch and Flemish Dutch datasets. Next, through the knowledge learned from models trained on the Netherlandic Dutch data, the Flemish Dutch learners' ASR model can be further improved. In order to evaluate the performance of the PED algorithms in the absence of learner speech data with pronunciation error annotations, we introduced plausible pronunciation errors in the native corpora based on knowledge from Flemish learner speech, in order to simulate non-native speech errors. For PED we found that the results are much better for a GOP classifier trained on Flemish Dutch data than for one trained on Netherlandic Dutch data. PED produced worse results when the Netherlandic Dutch data were merged with the Flemish Dutch data, while for ASR, lower WERs were attained. Whether adding Netherlandic Dutch data to Flemish Dutch data is beneficial, thus seems to depend on the specific task the data are used for. We discuss these results, compare them to those of related research and suggest avenues for future research.
Motion Magnified 3-D Residual-in-Dense Network for DeepFake Detection

Driven by the advances in deep learning, highly photo-realistic techniques capable of switching the identity and expression of faces have emerged. Cheap access to computing has brought such technology within the reach of anyone with a computer and Internet including people with sinister motives. To detect these forgeries, we present a novel compression resilient approach for deepfake detection in videos. The proposed approach employs motion magnification as a pre-processing step to amplify temporal inconsistencies common in forged videos. Utilizing these processed videos, we propose the 3D Residual-in-Dense ConvNet, which captures low level spatiotemporal features, which help classify a video as pristine or forged. The proposed method yields more than 93% average detection accuracy on the high compression variant of the FaceForensics++ dataset and achieves state-of-the-art performance on multiple benchmarks across the FaceForensics++ and CelebDF datasets. Further, we study the behavior of deepfake detection algorithms across ethnicities and demonstrate how the proposed method reduces the inherent bias against minority ethnicities prevalent in existing algorithms.
Wavelet-packets for deepfake image analysis and detection

As neural networks become able to generate realistic artificial images, they have the potential to improve movies, music, video games and make the internet an even more creative and inspiring place. Yet, the latest technology potentially enables new digital ways to lie. In response, the need for a diverse and reliable method toolbox arises to identify artificial images and other content. Previous work primarily relies on pixel-space CNNs or the Fourier transform. To the best of our knowledge, synthesized fake image analysis and detection methods based on a multi-scale wavelet representation, localized in both space and frequency, have been absent thus far. The wavelet transform conserves spatial information to a degree, which allows us to present a new analysis. Comparing the wavelet coefficients of real and fake images allows interpretation. Significant differences are identified. Additionally, this paper proposes to learn a model for the detection of synthetic images based on the wavelet-packet representation of natural and GAN-generated images. Our lightweight forensic classifiers exhibit competitive or improved performance at comparatively small network sizes, as we demonstrate on the FFHQ, CelebA and LSUN source identification problems. Furthermore, we study the binary FaceForensics++ fake-detection problem.
Ways to design a digital educational environment for K-12 education

Most educational institutions strive to create favourable conditions for students which meet educational needs of each student. It leads to high demand in the digital educational environment of K-12 education institutions. The article is devoted to the description of the concept, components and ways of designing the digital educational environment of a K-12 education institution through the transformation of educational activities. The importance of developing an educational policy of an educational institution in the field of digital technology is described. Authors present the model and the ways of designing the digital educational environment of the K-12 education institution. The necessity of self-assessment of digital technologies usage in the educational process by all its participants is substantiated; the ways of application of the European tool SELFIE for carrying out such self-analysis are described. Based on the adaptation of all components of the tool SELFIE for Ukrainian education, the results of its usage at one of the secondary schools in Kyiv are presented.
Use of physics to improve solar forecast: Part II, machine learning and model interpretability

Machine learning (ML) models have been applied to forecast solar energy; however, they often lack clarity of interpretability and underlying physics. This work addresses such challenges by developing a hierarchy of ML models that gradually introduce predictors to improve the forecast accuracy based on a physics-based framework. Three ML models (ARIMA, LSTM, and XGBoost) are examined and compared with four physics-informed persistence models reported in Part I and the simple persistence model to assess the improvement of different models. The 7-year measurements at the U.S. Department of Energy's Atmospheric Radiation Measurement's Southern Great Plains Central Facility site are used for forecasts and evaluations. The results reveal that the step-by-step introduction of predictors leads to different improvements for models at different hierarchical levels. Comparison of the ML models with persistence models shows that LSTM and XGBoost outperform all the persistence models, with LSTM having the overall best performance; however, ARIMA underperforms the four physics-informed persistence models. This study demonstrates the importance and utility of incorporating physics into ML models in improving forecast accuracy by introducing a hierarchy of physics-based predictors, distinguishing predictor contributions, and enhancing the ML interpretability. The combined use of Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) significantly improves the forecast accuracy compared to using individual irradiances alone because the pair contains more information on cloud-radiation interactions.
Image Analysis in Digital Pathology Utilizing Machine Learning and Deep Neural Networks

Detection of regions of interest (ROIs) in whole slide images (WSIs) in a clinical setting is a highly subjective and a labor-intensive task. In this work, recent developments in machine learning and computer vision algorithms are presented to assess their possible usage and performance to enhance and accelerate clinical pathology procedures, such as ROI detection in WSIs. In this context, a state-of-the-art deep learning framework (Detectron2) was trained on two cases linked to the TUPAC16 dataset for object detection and on the JPATHOL dataset for instance segmentation. The predictions were evaluated against competing models and further possible improvements are discussed.
Blockchain-based cyber physical systems: Comprehensive model for challenge assessment

• Identified Blockchain implementation challenges are grouped into five main categories. • A two-phase fuzzy Delphi method is applied for challenges identification. • Fuzzy group-based BWM has been used to prioritize the considered challenges. • Various practical managerial insights are presented based on the results obtained. Blockchain as the trust‐enabling technology has supported and transformed cyber-physical systems in recent years. Therefore, many organizations have adopted blockchain-based technology to develop industrial software-product-service systems. However, the implementation of blockchain at the organizational levels can be related with several technical, organizational, legal, financial, and environmental challenges (intra-organizational and inter-organizational). Analysis of these challenges will play a significant role in organizational performance. This study has investigated an extensive list of challenges of software blockchain-based implementation in cyber-physical systems. The proposed comprehensive model is applied to the software development industry in Iran as a case study. Based on the experts’ opinions, challenge identification was conducted via the two-phase fuzzy Delphi method for medium and large organizations. Extensive quantitative and qualitative challenges are evaluated via the efficient best-worst fuzzy method. Obtained results indicate that smart contracts vulnerabilities and deterministic execution of the technical challenge group have the most critical importance and governance of the legal challenge group has the least vital importance. It is mentionable that most technical challenges have higher priority than other considered challenges.
Frequency-Tuned Universal Adversarial Attacks on Texture Recognition

Although deep neural networks (DNNs) have been shown to be susceptible to image-agnostic adversarial attacks on natural image classification problems, the effects of such attacks on DNN-based texture recognition have yet to be explored. As part of our work, we find that limiting the perturbation's lp norm in the spatial domain may not be a suitable way to restrict the perceptibility of universal adversarial perturbations for texture images. Based on the fact that human perception is affected by local visual frequency characteristics, we propose a frequency-tuned universal attack method to compute universal perturbations in the frequency domain. Our experiments indicate that our proposed method can produce less perceptible perturbations yet with a similar or higher white-box fooling rates on various DNN texture classifiers and texture datasets as compared to existing universal attack techniques. We also demonstrate that our approach can improve the attack robustness against defended models as well as the cross-dataset transferability for texture recognition problems.
Synergy effects of ICT diffusion and foreign direct investment on inclusive growth in Sub-Saharan Africa

The present study critically examines the synergy effect of information communication technology (ICT) diffusion and foreign direct investment (FDI) on inclusive growth in Sub-Saharan African (SSA) countries using a modified system-generalized method of moments (GMM) model based on panel data covering the period 2005-2020. This study differs significantly from the previous studies in four ways: (i) this study uses a more comprehensive measure of ICT by computing a composite ICT index, which takes into account several ICT indicators; (ii) some existing study uses a narrow proxy of inclusive growth using the Gini index as a proxy, while others consider three patterns of economic growth dynamics (GDP growth, real, and per capita GDP). For robust analysis, we computed a composite inclusive growth index that takes into account several shared growth indicators; (iii) our model captures the heterogeneity effect of the interaction term of FDI and ICT diffusions on two groups of SSA countries, unlike the previous studies that estimated the joint impact of FDI and ICT on the whole group of countries; (iv) we contribute to the extant studies by determining the threshold level at which ICT diffusion may determine the effect of FDI on inclusive growth. Overall, empirical results show that the positive effect of FDI on inclusive growth could intensify when ICT diffusion is beyond a given threshold level, while inflation and vulnerable unemployment deteriorate inclusive growth, among others. We are of the opinion that ICTs should engage to cushion present and future environmental threats/natural catastrophes through improving geographical monitoring and concerted reaction coupled with other policy recommendations paying special attention to Sustainable Development Goals (SDGs) fifteen (15)-inclusive growth.
Incorporating blockchain technology in information systems research

In this Editorial Note for the special issue on "Incorporating Blockchain Technology in Information Systems Research," we first focus on the need on conducting blockchain research in the information systems field. We briefly describe the nine outstanding articles that were selected for publication in this special issue. The editorial note ends with a research agenda for future scholars interesting in studying the blockchain phenomenon.
Addressing Hate Crime in the 21st Century: Trends, Threats, and Opportunities for Intervention

Hate crimes, often referred to as bias-motivated crimes, have garnered greater public attention and concern as political rhetoric in the United States and internationally has promoted the exclusion of people based on their group identity. This review examines what we know about the trends in hate crime behavior and the legal responses to this problem across four main domains. First, we describe the legal framework and recent attempts to expand hate crime protections beyond historically disenfranchised groups. Second, we examine recent trends and patterns of hate crime victimization. Third, we review what is known about those who perpetrate hate crimes and those who experience hate crime victimization. Finally, we examine the efficacy of efforts to respond to and prevent hate crime. This review examines a wide range of bias-motivated harms and suggests how future research and policy can be more inclusive of victimization extending beyond traditionally understood hate crimes.
Cloud technologies as a tool of creating Earth Remote Sensing educational resources

This article is dedicated to the Earth Remote Sensing (ERS), which the authors believe is a great way to teach geography and allows forming an idea of the actual geographic features and phenomena. One of the major problems that now constrains the active introduction of remote sensing data in the educational process is the low availability of training aerospace pictures, which meet didactic requirements. The article analyzes the main sources of ERS as a basis for educational resources formation with aerospace images: paper, various individual sources (personal stations receiving satellite information, drones, balloons, kites and balls) and Internet sources (mainstream sites, sites of scientific-technical organizations and distributors, interactive Internet geoservices, cloud platforms of geospatial analysis). The authors point out that their geospatial analysis platforms (Google Earth Engine, Land Viewer, EOS Platform, etc.), due to their unique features, are the basis for the creation of information thematic databases of ERS. The article presents an example of such a database, covering more than 800 aerospace images and dynamic models, which are combined according to such didactic principles as high information load and clarity.
DAFS: a domain aware few shot generative model for event detection

More and more, large-scale pre-trained models show apparent advantages in solving the event detection (ED), i.e., a task to solve the problem of event classification by identifying trigger words. However, this kind of model depends heavily on labeled training data. Unfortunately, there is not enough such data for some particular areas, such as finance, due to the high cost of the data annotation process. Besides, the manually labeled training data has many problems like uneven sampling distribution, poor diversity, and massive long-tail data. Recently, some researchers have used the generative model to label data. However, training the generative models needs rich domain knowledge, which cannot be obtained from a Few-Shot resource. Therefore, we propose a Domain-Aware Few-Shot (DAFS) generative model that can generate domain based training data through a relatively small amount of labeled data. First, DAFS utilizes self-supervised information from various categories of sentences to calculate words’ transition probability under different domain and retain key triggers in each sentence. Then, we apply our joint algorithm to generate labeled training data that considers both diversity and effectiveness. Experimental results demonstrate that the training data generated by DAFS significantly improves the performance of ED in actual financial data. Especially when there are no more than 20 training data, DAFS can still ensure the generative quality to a certain extent. It also obtains new state-of-the-art results on ACE2005 multilingual corpora.
Semantic coherence markers: The contribution of perplexity metrics

Devising automatic tools to assist specialists in the early detection of mental disturbances and psychotic disorders is to date a challenging scientific problem and a practically relevant activity. In this work we explore how language models (that are probability distributions over text sequences) can be employed to analyze language and discriminate between mentally impaired and healthy subjects. We have preliminarily explored whether perplexity can be considered a reliable metrics to characterize an individual's language. Perplexity was originally conceived as an information-theoretic measure to assess how much a given language model is suited to predict a text sequence or, equivalently, how much a word sequence fits into a specific language model. We carried out an extensive experimentation with healthy subjects, and employed language models as diverse as N-grams - from 2-grams to 5-grams - and GPT-2, a transformer-based language model. Our experiments show that irrespective of the complexity of the employed language model, perplexity scores are stable and sufficiently consistent for analyzing the language of individual subjects, and at the same time sensitive enough to capture differences due to linguistic registers adopted by the same speaker, e.g., in interviews and political rallies. A second array of experiments was designed to investigate whether perplexity scores may be used to discriminate between the transcripts of healthy subjects and subjects suffering from Alzheimer Disease (AD). Our best performing models achieved full accuracy and F-score (1.00 in both precision/specificity and recall/sensitivity) in categorizing subjects from both the AD class, and control subjects. These results suggest that perplexity can be a valuable analytical metrics with potential application to supporting early diagnosis of symptoms of mental disorders.
Farmland Aerial Images Fast-Stitching Method and Application Based on Improved SIFT Algorithm

The main black land conservation measure in China is the straw return to the fields.The processing of high-resolution images collected by aerial photography of UAVs through image stitching technology can provide image information for achieving fast and accurate detection of straw cover over large areas.The classical SIFT algorithm has many drawbacks, such as high dimensionality of feature descriptors, high computational effort, and low matching efficiency.To solve the problems above, this study proposes an improved algorithm.First, the method down sampled the high-resolution images before detecting the features to reduce the number of feature points and improve the efficiency of feature detection.Then, matching among feature points is achieved by gradient normalization-based feature descriptors to improve the matching accuracy.Next, the Progressive Sample Consistency algorithm eliminates the mismatch points and optimizes the transformation model.Finally, the images are fused with optimal stitching combined with fade-in and fade-out to achieve high-quality stitching.The comparative experimental results show that compared with the traditional SIFT and the speed-up robust feature algorithms, the algorithm has the advantage of the speed and good robustness to angle rotation, and makes full use of the texture information and the detail information, so it has higher accuracy.Compared with the traditional methods, the panoramic stitching image quality herein is excellent and can be applied to subsequent straw cover detection, the straw cover error is ≤3%, meeting the demand for large-area straw cover detection.Overall, the method proposed herein achieves an ideal balance between accuracy and efficiency; and outperforms other widely used and superior methods.
NL-SHADE-LBC algorithm with linear parameter adaptation bias change for CEC 2022 Numerical Optimization

In this paper the adaptive differential evolution algorithm is presented, which includes a set of concepts, such as linear bias change in parameter adaptation, repetitive generation of points for bound constraint handling, as well as non-linear population size reduction and selective pressure. The proposed algorithm is used to solve the problems of the CEC 2022 Bound Constrained Single Objective Numerical Optimization bench-mark problems. The computational experiments and analysis of the results demonstrate that the NL-SHADE-LBC algorithm presented in this study is able to demonstrate high efficiency in solving complex optimization problems compared to the winners of the previous years' competitions.
Unfolding the blockchain era: Timeline, evolution, types and real-world applications

Blockchain technology is based on a series of time-stamped and decentralized immutable records without third-party involvement. Since the infancy of the term, it has attained attention from academic researchers and industrial contributors, globally. The extensive characteristics of blockchain have created new investigation domains in multiple industries including finance, medical, education, governance etc. In this study, we have conducted the systematic literature mapping to collect all relevant research in the field of blockchain since the inception of this exclusive paradigm and presented a comprehensive assessment and analysis on the extracted research papers in a systematic manner. We have rigorously reviewed the relevant research papers found in the literature for understanding the state of the art techniques, methodologies and, applications of using blockchain in different industries. Moreover, we also have presented the evolution and future insights on the use of blockchain in several real-world applications and also consolidated the blockchain into different high level technical and applied perspectives.