Reviewerly/exHarmony huggingface dataset:
Roadmap to Artificial Intelligence and Intellectual PropertyAn Introduction

Abstract This introductory chapter provides an overview of the relationship between artificial intelligence (AI) and intellectual property (IP). While human beings have used various instruments and technologies to create and innovate, they themselves have been the main driving force of creativity and innovation. AI puts that into question, raising numerous challenges to the existing IP regime. Traditionally, the “intellectual” part of “intellectual property” refers to human intellect. However, since machines have become intelligent and are increasingly capable of making creative, innovative choices based on opaque algorithms, the “intellectual” in “intellectual property” turns out to be perplexing. Existing human-centric IP regimes based on promoting incentives and avoiding disincentives may no longer be relevant—or even positively detrimental—if AI comes into play. Moreover, AI has sparked new issues in IP law regarding legal subjects, scope, standards of protection, exceptions, and relationships between actors.
Scrutinising internet banking security solutions

Internet-banking is a crucial service offered by financial Institutions and has gained popularity at a high pace. Owing to the increasing usage of this service, it is being frequently targeted by adversaries. The login process by the user is one of the main points that are at risk of this assault. Hence, a robust security mechanism is essential for warding off those risks. Among other security solutions, a typical arrangement presently employed is the one-time password (OTP), i.e., passwords that remain valid for a single exchange or session. However, the majority of these password generation and processing mechanisms do not fulfil the requirement of usability and/or scalability and hence can be considered as less reliable/fragile. This paper reviews the security mechanisms in E-banking. The pros and con of OTP as well as other non-OTP security solutions have been presented. Finally, the prominence of open issues have been elucidated.
Modelling of volcanic ash with HYSPLIT and satellite observations: a case study of the 2018 Barren Island volcano eruption event, Andaman Territory, India

The present study aims to identify, characterize monitor and model the transport pathways of volcanic ashes and various features of the active phase of Barren Island volcano (BIV), Andaman and Nicobar Island, India during 2018 using the several Earth observation satellite technologies and field observations in the study area.Sentinel-2 satellite datasets have been used to identify volcanic eruption features such as lava flow, ash plume, cinder and vent and different directions of lava flow from the cinder cone during the 2018 eruptive phase of BIV.To visualize the major variations in thermal intensity and understand the behaviour of current volcanic activity, volcanic radiative power (VRP) and radiant fluxes of the recent eruptive phase were calculated using MIROVA.In addition, thermal anomaly was observed in the form of anomalous fire pixels for 44 days in FIRMS database.Also, NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS, VNP14IMGT) were used for validating the real-time activity of the 2018 volcanic eruption phase.The results obtained were closely related with the periods of high eruptions as observed in the Sentinel-2 datasets.The volcanic aerosol 'sulphur dioxide' (SO 2 ) data (time series-area averaged) were analysed as well as a five-day forward trajectory and volcanic ash model for each eruption event was developed using HYSPLIT model to identify the transport pathways and extent of volcanic ash cloud in the lower atmosphere during the eruptive phase of the volcano.
Sentiment Analysis of Afaan Oromoo Facebook Media Using Deep Learning Approach

The rapid development and popularity of social media and social networks provide people with unprecedented opportunities to express and share their thoughts, views, opinions and feelings about almost anything through their personal webpages and blogs or using social network sites like Facebook, Twitter, and Blogger.This study focuses on sentiment analysis of social media content because automatically identifying and classifying opinions from social media posts can provide significant economic values and social benefits.The major problem with sentiment analysis of social media posts is that it is extremely vast, fragmented, unorganized and unstructured.Nevertheless, many organizations and individuals are highly interested to know what other peoples are thinking or feeling about their services and products.Therefore, sentiment analysis has increasingly become a major area of research interest in the field of Natural Language Processing and Text Mining.In general, sentiment analysis is the process of automatically identifying and categorizing opinions in order to determine whether the writer's attitude towards a particular entity is positive or negative.To the best of the researcher's knowledge, there is no Deep learning approach done for Afaan Oromoo Sentiment analysis to identify the opinion of the people on social media content.Therefore, in this study, we focused on investigating Convolutional Neural Network and Long Short Term Memory deep learning approaches for the development of sentiment analysis of Afaan Oromoo social media content such as Facebook posts comments.To this end, a total of 1452 comments collected from the official site of the Facebook page of Oromo Democratic Party/ODP for the study.After collecting the data, manual annotation is undertaken.Preprocessing, normalization, tokenization, stop word removal of the sentence are performed.We used the Keras deep learning python library to implement both deep learning algorithms.Long Short Term Memory and Convolutional Neural Network, we used word embedding as a feature.We conducted our experiment on the selected classifiers.For classifiers, we used 80% training and 20% testing rule.According to the experiment, the result shows that Convolutional Neural Network achieves the accuracy of 89%.The Long Short Memory achieves accuracy of 87.6%.Even though the result is promising there are still challenges.
Floating Buoy Technology for Research Purposes

A buoy is a structure that is float on the sea surface used for the navigational, marine science, monitoring the meteorology and oceanography changes of the marine environment. This article presents a literature review on research and development of floating buoy technology for research purposes. The article starts with the historical background of various buoy technologies. Then, floating buoy working principles used for research are discussed. The reviewed according to the type of buoy technology for research purposes (tsunami buoy type, wave buoy type, weather buoy type, and profiling buoy type). This is followed by an overview of the buoy applications that have been undertaken by Malaysian research communities. The proposed concept design of buoy will be shown with the initial stability parameters analysis. To conclude, based on the research work undertaken thus far, what the researchers should expect as future work are suggested.
Applied Statistics with R

Abstract Whether at the undergraduate, graduate, or post-graduate level, Applied Statistics with R: A Practical Guide for the Life Sciences teaches readers to properly analyze data in an efficient, accessible, plainspoken, frank, and occasionally humorous manner. Readers will come away with the knowledge of which analyses they should use and when they should use them, an important skill in an age when the statistical analyses used in the life-sciences are becoming increasingly advanced. This book uses the statistical language R, which is the choice of ecologists worldwide and is rapidly becoming the ‘go-to’ stats program throughout the life-sciences. Written around a single real-world dataset, Applied Statistics with R which encourages readers to become deeply familiar with an imperfect but realistic set of data, much like they themselves might collect. Early chapters are designed to teach basic data manipulation skills and build good habits in preparation for learning more advanced analyses. This approach also demonstrates the importance of viewing data through different lenses, facilitating an easy and natural progression from linear and generalized linear models through to mixed effects versions of those same analyses. Readers will also learn advanced plotting and data-wrangling techniques, and gain an introduction to writing their own functions. Applied Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners throughout the life-sciences, whether in the fields of ecology, evolution, environmental studies, or computational biology.
Design of Financial Risk Control Model Based on Deep Learning Neural Network

In recent years, with the continuous increase of financial business, the risk of business is on the rise. Among them, major risk cases are frequent, the cases are increasingly complex, and the means of committing crimes are concealed. The main research contents of this paper include the preprocessing of internal and external financial data and the structure design of recurrent NNs. Its purpose is to design a financial risk control model based on a deep learning NNs, thereby reducing financial risk. The Borderline-SMOTE algorithm is used first to preprocess the sample data, and the oversampling method is used to eliminate the imbalance of the data, and then, the long short-term memory deep NNs algorithm is introduced to process the sample data with time series characteristics. The final experiment shows that LSTM has a better accuracy, reaching 0.9715, compared with traditional methods; the sample preprocessing method and risk control model proposed in this paper have better ability to identify fraudulent customers, and the model itself has faster iteration efficiency.
Efficient Burst Raw Denoising with Variance Stabilization and Multi-frequency Denoising Network

With the growing popularity of smartphones, capturing high-quality images is of vital importance to smartphones. The cameras of smartphones have small apertures and small sensor cells, which lead to the noisy images in low light environment. Denoising based on a burst of multiple frames generally outperforms single frame denoising but with the larger compututional cost. In this paper, we propose an efficient yet effective burst denoising system. We adopt a three-stage design: noise prior integration, multi-frame alignment and multi-frame denoising. First, we integrate noise prior by pre-processing raw signals into a variance-stabilization space, which allows using a small-scale network to achieve competitive performance. Second, we observe that it is essential to adopt an explicit alignment for burst denoising, but it is not necessary to integrate a learning-based method to perform multi-frame alignment. Instead, we resort to a conventional and efficient alignment method and combine it with our multi-frame denoising network. At last, we propose a denoising strategy that processes multiple frames sequentially. Sequential denoising avoids filtering a large number of frames by decomposing multiple frames denoising into several efficient sub-network denoising. As for each sub-network, we propose an efficient multi-frequency denoising network to remove noise of different frequencies. Our three-stage design is efficient and shows strong performance on burst denoising. Experiments on synthetic and real raw datasets demonstrate that our method outperforms state-of-the-art methods, with less computational cost. Furthermore, the low complexity and high-quality performance make deployment on smartphones possible.
Hero-Gang Neural Model For Named Entity Recognition

Named entity recognition (NER) is a fundamental and important task in NLP, aiming at identifying named entities (NEs) from free text.Recently, since the multi-head attention mechanism applied in the Transformer model can effectively capture longer contextual information, Transformer-based models have become the mainstream methods and have achieved significant performance in this task.Unfortunately, although these models can capture effective global context information, they are still limited in the local feature and position information extraction, which is critical in NER.In this paper, to address this limitation, we propose a novel Hero-Gang Neural structure (HGN), including the Hero and Gang module, to leverage both global and local information to promote NER.Specifically, the Hero module is composed of a Transformer-based encoder to maintain the advantage of the self-attention mechanism, and the Gang module utilizes a multi-window recurrent module to extract local features and position information under the guidance of the Hero module.Afterward, the proposed multiwindow attention effectively combines global information and multiple local features for predicting entity labels.Experimental results on several benchmark datasets demonstrate the effectiveness of our proposed model.
Correction: Sibanda et al. Application of Drone Technologies in Surface Water Resources Monitoring and Assessment: A Systematic Review of Progress, Challenges, and Opportunities in the Global South. Drones 2021, 5, 84

In the original publication [...]
Cybersecurity Risk Assessment Approach for Malaysian Organizations: Malaysian Universities as Case Study

Malaysian organizations are following different methods for cybersecurity risk assessment such as Control Objectives for Information and Related Technologies (COBIT) and International Organization for Standardization (ISO) 27001. The higher education institutions in Malaysia are facing the same difficulties as different standards and approaches are used to evaluate the cybersecurity risk of their institutions. So, there is a lack of a cybersecurity risk assessment approach that takes Malaysian and international standards into consideration. This paper aims to develop a cybersecurity risk assessment approach for higher education institutions in Malaysia. The methodology of conducting this research is qualitative research using a case study. The framework has considered international cybersecurity standards such as the Holistic cybersecurity maturity assessment framework (HCYMAF) and local cybersecurity standards such as National Cyber Security Agency (NACSA) and the Malaysia Cyber Security Strategy (MCSS). In addition, a measurable instrument to assess cybersecurity risk has been proposed. The subject-matter expert can apply this approach to assess their organization's cybersecurity maturity and risk. The findings of this study shall be useful in overcoming the drawbacks of employing non-standard procedures in such evaluations, resulting in more accurate and reliable evaluation outcomes.
Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications

A reliable quantification of the worst-case influence of model uncertainty and external disturbances is crucial for the localization of vessels in marine applications. This is especially true if uncertain GPS-based position measurements are used to update predicted vessel locations that are obtained from the evaluation of a ship’s state equation. To reflect real-life working conditions, these state equations need to account for uncertainty in the system model, such as imperfect actuation and external disturbances due to effects such as wind and currents. As an application scenario, the GPS-based localization of autonomous DDboat robots is considered in this paper. Using experimental data, the efficiency of an ellipsoidal approach, which exploits a bounded-error representation of disturbances and uncertainties, is demonstrated.
Label Anchored Contrastive Learning for Language Understanding

Contrastive learning (CL) has achieved astonishing progress in computer vision, speech, and natural language processing fields recently with self-supervised learning.However, CL approach to the supervised setting is not fully explored, especially for the natural language understanding classification task.Intuitively, the class label itself has the intrinsic ability to perform hard positive/negative mining, which is crucial for CL.Motivated by this, we propose a novel label anchored contrastive learning approach (denoted as LaCon) for language understanding.Specifically, three contrastive objectives are devised, including a multi-head instance-centered contrastive loss (ICL), a label-centered contrastive loss (LCL), and a label embedding regularizer (LER).Our approach does not require any specialized network architecture or any extra data augmentation, thus it can be easily plugged into existing powerful pre-trained language models.Compared to the state-of-the-art baselines, LaCon obtains up to 4.1% improvement on the popular datasets of GLUE and CLUE benchmarks.Besides, LaCon also demonstrates significant advantages under the few-shot and data imbalance settings, which obtains up to 9.4% improvement on the FewGLUE and FewCLUE benchmarking tasks.
Towards data-centric what-if analysis for native machine learning pipelines

An important task of data scientists is to understand the sensitivity of their models to changes in the data that the models are trained and tested upon. Currently, conducting such data-centric what-if analyses requires significant and costly manual development and testing with the corresponding chance for the introduction of bugs. We discuss the problem of data-centric what-if analysis over whole ML pipelines (including data preparation and feature encoding), propose optimisations that reuse trained models and intermediate data to reduce the runtime of such analysis, and finally conduct preliminary experiments on three complex example pipelines, where our approach reduces the runtime by a factor of up to six.
Recent advances on effective and efficient deep learning-based solutions

This editorial briefly analyses, describes, and provides a short summary of a set of selected papers published in a special issue focused on deep learning methods and architectures and their application to several domains and research areas. The set of selected and published articles covers several aspects related to two basic aspects in deep learning (DL) methods, efficiency of the models and effectiveness of the architectures These papers revolve around different interesting application domains such as health (e.g. cancer, polyps, melanoma, mental health), wearable technologies solar irradiance, social networks, cloud computing, wind turbines, object detection, music, and electricity, among others. This editorial provides a short description of each published article and a brief analysis of their main contributions.
EndoNuke: Nuclei Detection Dataset for Estrogen and Progesterone Stained IHC Endometrium Scans

We present EndoNuke, an open dataset consisting of tiles from endometrium immunohistochemistry slides with the nuclei annotated as keypoints. Several experts with various experience have annotated the dataset. Apart from gathering the data and creating the annotation, we have performed an agreement study and analyzed the distribution of nuclei staining intensity.
An efficient single image dehazing algorithm based on transmission map estimation with image fusion

Single image dehazing is always the focus of attention in the field of image processing. The major improvements in most optical scattering model based dehazing algorithms focused on the transmission map estimation since that the improper of transmission map estimation may cause over-high contrast or color distortion in the restored images. In this paper, we proposed an efficient single image dehazing algorithm in which the image fusion strategy is employed to adaptively improve the accuracy of transmission map estimation. Specifically, the transmission map obtained by the dark channel is corrected with Gamma transformation firstly. Then the original transmission map and its corrected version are fused with the weighting average image fusion algorithm to preserve the details in original transmission map. Moreover, the fused transmission map is filtered by guided filter to avoid the halo and block artifacts in the dehazed images. Finally, the dehazed images are reconstructed with the revised transmission map by optical scattering model. We test the proposed method on both natural and artifact haze images. The proposed method is compared with 8 state-of-the-art dehazing methods on three indexes include natural image quality evaluator (NIQE) index, structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) indexes. Both subjective and objective evaluations demonstrate that the proposed method achieves competitive results.
Study on the Application of Digitized Resources in English Language Teaching under a Blended Teaching Model

In the post-pandemic era, with the constant updating and development of information technology, both at home and abroad, the close connection between the modern education system and information technology has been demonstrated. Along with the continuous innovation of modern educational technology tools, the practical application about blended teaching has been developed significantly, and the value of this model, which integrates the advantages of lecture-based teaching and independent learning, for effective teaching of English education has received wide attention. Therefore, in the current educational environment, exploring the application of digital teaching resources in colleges and universities, based on the blended teaching mode, under the conditions of information technology has become a comprehensive need to adapt to the new educational environment, as well as improve the teaching effectiveness. The research background and content of this paper has been summarized by sorting out the current teaching situation in the context of the current pandemic and the background of the development of the blended teaching model, while combining the definition of the core concept, to discusses the strategies for developing and applying digitized teaching resources to teaching practice under this model, thereby to outline the teaching effects produced by the application of this model. Meanwhile, it is further argued that the teaching and practice process of foreign language education needs to continuously absorb advanced educational concepts, timely update and improve teaching methods and develop and utilize multimodal teaching resources, in order to provide a guarantee for the overall improvement of teaching quality, enhance students’ fully comprehensive cognition and understanding of English language learning, and exert sustainable teaching effectiveness.
Testing the Effects of the Digital Linguistic Landscape on Engineering Education for Smart Construction

This study investigates the mechanism of digital linguistic landscapes in enabling engineering education for smart construction according to the educational dimensions of A (ability), S (skill), and K (knowledge). A questionnaire survey was conducted based on the core concepts of the informative dimension and symbolic dimension in digital language landscape as well as the ability dimension, knowledge dimension, and skill dimension in engineering education. Structural equation modeling (SEM) was used as the test method. The results of the research demonstrate that the informative dimension and symbolic dimension are two main aspects of DLL in education of engineering students for smart construction. Additionally, DLL has a significant positive impact on the ability, knowledge, and skill education of engineering students for smart construction. The research has theoretical and practical significance, as it not only enriches research on the relationship between DLL and engineering education for smart construction but also expands the theoretical understanding of engineering education from the perspective of linguistics. Furthermore, the study explores the path of the practical application of digital language landscape to engineering education for smart construction.