Reviewerly/exHarmony huggingface dataset:
Cybersecurity as a response to combating cybercrime

Cybercrime is touted as any harmful behaviour that is in some way related to a computer but does not have a specific legal reference. Therefore, the objective of this study was to explore cybersecurity as a response to combating cybercrime, focusing on demystifying the prevailing threats, while offering recommendations to the African regions. Moreover, this qualitative study employed a non-empirical research design: Systematic review methodology to analyse grey literature and primary research studies peer-reviewed and published, restricted from 2010-2022, not following yearly sequential consideration. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Critical Appraisal Skill Programme (CASP) were employed to ensure the trustworthiness of the findings of this study based on reviewed conventional and seminal literature studies on this subject. The main findings of this study rest on a lack of a common universal definition of cybercrime, this has an impact on its prevention and ignores enormous economic value associated with the commission of this crime across the world, African regions included. It was also observed that this criminal act is presently committed with internet usage, consisting of copyright infringements, computer-related fraud, child or adult pornography, and network security violations, amongst others. Equally, addressing this scourge in African regions requires advanced skills and knowledge; exceeding the average computer and internet user. It is also critical to recognise the importance of implementing efficient cybersecurity methods, as policing this crime remains an important component of the Law Enforcement Agencies (LEA), and more innovative strategies are required and a globally coordinated response to this problem is urgently sought.
Integrating functional data analysis with case-based reasoning for hypertension prognosis and diagnosis based on real-world electronic health records

Abstract Background Hypertension is the fifth chronic disease causing death worldwide. The early prognosis and diagnosis are critical in the hypertension care process. Inspired by human philosophy, CBR is an empirical knowledge reasoning method for early detection and intervention of hypertension by only reusing electronic health records. However, the traditional similarity calculation method often ignores the internal characteristics and potential information of medical examination data. Methods In this paper, we first calculate the weights of input attributes by a random forest algorithm. Then, the risk value of hypertension from each medical examination can be evaluated according to the input data and the attribute weights. By fitting the risk values into a risk curve of hypertension, we calculate the similarity between different community residents, and obtain the most similar case according to the similarity. Finally, the diagnosis and treatment protocol of the new case can be given. Results The experiment data comes from the medical examination of Tianqiao Community (Tongling City, Anhui Province, China) from 2012 to 2021. It contains 4143 community residents and 43,676 medical examination records. We first discuss the effect of the influence factor and the decay factor on similarity calculation. Then we evaluate the performance of the proposed FDA-CBR algorithm against the GRA-CBR algorithm and the CS-CBR algorithm. The experimental results demonstrate that the proposed algorithm is highly efficient and accurate. Conclusions The experiment results show that the proposed FDA-CBR algorithm can effectively describe the variation tendency of the risk value and always find the most similar case. The accuracy of FDA-CBR algorithm is higher than GRA-CBR algorithm and CS-CBR algorithm, increasing by 9.94 and 16.41%, respectively.
Incels on Reddit: A study in social norms and decentralised moderation

This paper examines the challenges of combating misogyny and harmful social norms on the social networking and news site Reddit. Reddit has a long history of hosting racist, misogynistic, homophobic, and otherwise harmful communities. Incels — an Internet subculture that ascribes to deeply misogynistic beliefs — flourished on Reddit before several major subreddits were progressively banned. In this paper, we examine four incel subreddits to understand how the dynamic tensions between Reddit’s site-wide rules and the rules of particular incel subreddits play out in practice. We draw on archived Reddit data to provide a detailed analysis of how incel subreddits adapt to and resist pressure from Reddit and other users to comply with the site-wide rules and develop and maintain their rules and norms. Our study suggests that punitive measures, such as the threat of stricter rules and increased external moderation for explicitly prohibited content and behaviour is insufficient to foster prosocial subreddits. We show how stricter enforcement of the formal site-wide rules did not challenge or displace the underlying ideologies that foster toxic communities. Instead, we find that incels justified their toxic behaviour in the face of heavy external criticism. To cultivate safe and inclusive digital environments, we argue that Reddit must focus on improving the culture of subreddits. We conclude that driving real change when moderation is decentralized requires the committed and supported participation of moderators who can undertake the extensive work of tackling the underlying identity and ideology that brings hateful communities together.
User Evaluation and Metrics Analysis of a Prototype Web-Based Federated Search Engine for Art and Cultural Heritage

Content and metadata concerning a specialized field such as Art and Cultural Heritage are often scattered throughout the World Wide Web, making it hard for end-users to find, especially amid the vast and often commercialized general content of the Web. This paper presents the process of designing and developing a Federated Search Engine (FSE) that collects such content from multiple credible sources of the world of Art and Culture and presents it to the user in a unified user-oriented manner, enhancing it with added functionality. The study focuses on the challenges such an endeavor presents and the technological tools, design decisions and methodology that lead to a fully functional, Web-based platform. This implemented search engine was evaluated by a group of stakeholders from the wider fields of art, culture and media during a closed test and the insights and feedback gained by these tests are herein analyzed and presented. These insights contain both the quantitative metrics of user engagement during the testing period and the qualitative information presented by the stakeholders through interviews. The above findings are thoroughly discussed and lead to conclusions regarding the usefulness and viability of Web applications in the aggregation and diffusion of Art and Cultural Heritage related content.
Adding an Attention Layer Improves the Performance of a Neural Network Architecture for Synonymy Prediction in the UMLS Metathesaurus

Background: Terminology integration at the scale of the UMLS Metathesaurus (i.e., over 200 source vocabularies) remains challenging despite recent advances in ontology alignment techniques based on neural networks. Objectives: To improve the performance of the neural network architecture we developed for predicting synonymy between terms in the UMLS Metathesaurus, specifically through the addition of an attention layer. Methods: We modify our original Siamese neural network architecture with Long-Short Term Memory (LSTM) and create two variants by (1) adding an attention layer on top of the existing LSTM, and (2) replacing the existing LSTM layer by an attention layer. Results: Adding an attention layer to the LSTM layer resulted in increasing precision to 92.38% (+3.63%) and F1 score to 91,74% (+1.13%), with limited impact on recall at 91.12% (-1.42%). Conclusions: Although limited, this increase in precision substantially reduces the false positive rate and minimizes the need for manual curation.
HANDWRITTEN MATHEMATICAL EQUATION SOLVER

With recent developments in Artificial intelligence and deep learning every major field which is using computers for any type of work is trying to ease the work using deep learning methods. Deep learning is used in a wide range of fields due to its diverse range of applications like health, sports, robotics, education, etc. In deep learning, a Convolutional neural network (CNN) is being used in image classification, pattern recognition, Text classification, face recognition, live monitoring systems, handwriting recognition, Digit recognition, etc. In this paper, we propose a system for educational use where the recognition and solving process of mathematical equations will be done by machine. In this system for recognition of equations, we use a Convolutional neural network (CNN) model. The proposed system can recognize and solve mathematical equations with basic operations (-,+,/,*) of multiple digits as well as polynomial equations. The model is trained with Modified National Institute of Standards and Technology (MNIST) dataset as well as a manually prepared dataset of operator symbols (“-”,”+”, “/”, “*”, “(“, “)” ). Further, the system uses the RNN model to solve the recognized operations.
Perception of the quality of remote lessons in the time of COVID-19: A comparative study in Latin America quality of remote lessons in the context of COVID-19

This research examines the perception of undergraduate students of public and private universities in Latin America on the quality of the lessons that applied the emergency remote teaching (ERT) in the time of COVID-19. This study employs a mixed sequential approach, starting with six focal groups, and finishing with a quantitative validation exercise that uses exploratory and confirmatory factor analysis as well as regression models. Findings reveal that student perception is elicited along three dimensions: concerns related to academic quality, teaching strategies applied by professors, and access limitations. Moderation analysis shows that the relationship between teaching strategies and the concerns related to academic quality varies and that it even gets stronger when access limitations are reduced. Consequently, perception is limited by student access to maintain the teaching-learning process.
Born-Digital Universities

HEIs are nowadays subject to different disrupting events of a varying and transformative nature. Aside from the emergence of EdTechs, the COVID-19 pandemic has brought about the forced digitalization of most traditional higher education institutions (HEIs). In this context, a born-digital university is subject to the increasing dual pressure of traditional universities shifting their learning offers and competition from the new emerging EdTech players. This chapter provides an analysis of the competitive situation of HEIs and the envisaged business model changes required for a born-digital university to tackle this increasing competitive challenge in a context of ongoing digital transformation. It adopts a business approach for higher education, placing the student (as the main client) at the centre of the analysis. The starting point is the identification of the dimensions of student preferences in their choice of higher education provider with a view to analysing the position of the main types of competitors in higher education -traditional, face-to-face universities, and EdTechs- and to ultimately propose a matrix of required business model changes for a born-digital university.
I<scp>ntera</scp>C<scp>hem</scp>: Exploring Excited States in Virtual Reality with <i>Ab Initio</i> Interactive Molecular Dynamics

InteraChem is an ab initio interactive molecular dynamics (AI-IMD) visualizer that leverages recent advances in virtual reality hardware and software, as well as the graphical processing unit (GPU)-accelerated TeraChem electronic structure package, in order to render quantum chemistry in real time. We introduce the exploration of electronically excited states via AI-IMD using the floating occupation molecular orbital-complete active space configuration interaction method. The optimization tools in InteraChem enable identification of excited state minima as well as minimum energy conical intersections for further characterization of excited state chemistry in small- to medium-sized systems. We demonstrate that finite-temperature Hartree-Fock theory is an efficient method to perform ground state AI-IMD. InteraChem allows users to track electronic properties such as molecular orbitals and bond order in real time, resulting in an interactive visualization tool that aids in the interpretation of excited state chemistry data and makes quantum chemistry more accessible for both research and educational purposes.
Learning-based low-rank denoising

Abstract The denoising of 2D images through low-rank methods is a relevant topic in digital image processing. This paper proposes a novel method that trains a learning network to predict the optimal thresholds of the singular value decomposition involved in the low-rank denoising of 2D images. To improve the denoising results, we apply the block-matching algorithm and classify each 3D block according to four parameters, which increase the specificity of the network for the prediction of the thresholds. Our method outperforms state-of-the-art methods for image denoising; furthermore, it is general with respect to the type of noise and provides an upper bound to the accuracy of the denoising of 2D images through the Singular Value Decomposition.
Losing Its Touch: Understanding User Perception of Multimodal Interaction and Smart Assistance

Intelligent Personal Assistants (IPA) are advertised as reliable companions in the everyday life to simplify household tasks. Due to speech-based usability issues, users struggle to deeply engage with current systems. The capabilities of newer generations of standalone devices are even extended by a display, also to address some weaknesses like memorizing auditive information. So far, it is unclear how the potential of a multimodal experience is realized by designers and appropriated by users. Therefore, we observed 20 participants in a controlled setting, planning a dinner with the help of an audio-visual-based IPA, namely Alexa Echo Show. Our study reveals ambiguous mental models of perceived and experienced device capabilities, leading to confusion. Meanwhile, the additional visual output channel could not counterbalance the weaknesses of voice interaction. Finally, we aim to illustrate users’ conceptual understandings of IPAs and provide implications to rethink audiovisual output for voice-first standalone devices.
A Hierarchical Representation Model Based on Longformer and Transformer for Extractive Summarization

Automatic text summarization is a method used to compress documents while preserving the main idea of the original text, including extractive summarization and abstractive summarization. Extractive text summarization extracts important sentences from the original document to serve as the summary. The document representation method is crucial for the quality of the generated summarization. To effectively represent the document, we propose a hierarchical document representation model Long-Trans-Extr for Extractive Summarization, which uses Longformer as the sentence encoder and Transformer as the document encoder. The advantage of Longformer as sentence encoder is that the model can input long document up to 4096 tokens with adding relative a little calculation. The proposed model Long-Trans-Extr is evaluated on three benchmark datasets: CNN (Cable News Network), DailyMail, and the combined CNN/DailyMail. It achieves 43.78 (Rouge-1) and 39.71 (Rouge-L) on CNN/DailyMail and 33.75 (Rouge-1), 13.11 (Rouge-2), and 30.44 (Rouge-L) on the CNN datasets. They are very competitive results, and furthermore, they show that our model has better performance on long documents, such as the CNN corpus.
Geospatial Blockchain: review of decentralized geospatial data sharing systems

Abstract. Blockchain technologies are driving the internet infrastructures into a transformation from Web 2.0 to Web 3.0. This remodels the internet foundations from a centralized approach, where data is hosted by a single actor, to a decentralized one in which data is distributed among peers in a network. Thanks to blockchain technologies, the evolution from centralized to decentralized applications (dApps) eliminate single points of failure, data censorship and data tampering. This transformation is not only important in the financial sector, where the technology is more evolved, but also in geospatial crowdsourcing activities. The objective of this work is to perform a literature review of current blockchain technologies used for sharing and crowdsourcing activities involving geospatial data. This study serves as a starting point for future works where the main purpose is to develop a geospatial sharing blockchain platform. At the present, two platforms have been developed for this purpose, FOAM and D-GIS. The former is a fully deployed implementation whose objective is to create a crowdsourced map. The latter is a platform designed to share geospatial studies publicly, however, it is only developed conceptually and not deployed. Additional to these works, other blockchain data sharing examples exist and are reviewed in this study as a baseline for future developments in the geospatial area. The output of this research indicates that it is feasible to use blockchain technology for the development of a crowdsourcing geospatial data-sharing platform.
Three Dimensional Denoising Filter For Effective Source Smartphone Video Identification and Verification

The field of digital image and video forensics has recently seen significant advances and has attracted attention from a growing number of researchers given the availability of imaging functionalities in most current multimedia devices at no cost including smartphones and tablets. Photo response non-uniformity (PRNU) noise is a sensor pattern noise characterizing the imaging device. However, estimating the PRNU from smartphone videos can be a challenging process because of the lossy compression that digital videos normally undergo for various reasons in addition to other non-unique noise components that interfere with the video data. This paper presents a new filtering technique for PRNU estimation based on the three-dimensional discrete wavelet transform followed by a 3D wiener filter. The rationale is that the 3D filter can filter out the compression artifacts along the temporal dimension in a more effective way than simple averaging. Experimental results on a public video dataset captured by various smartphone devices have shown a significant gain obtained with the proposed approach over the well-known two-dimensional wavelet-based Wiener approach.
EnviroCoin: A Holistic, Blockchain Empowered, Consensus-Based Carbon Saving Unit Ecosystem

The capacity of the greenhouse gas recycling mechanism in nature has long reached its limit, resulting in a sharply increasing trend in the marginal cost of recycling one unit of human-produced carbon and other greenhouse gas emissions. Externalities associated with the marginal cost of greenhouse gas emissions affect health, climate, and the economy, which have urged global authorities and governments to request urgent actions to slow down the production of such pollutants. Nonetheless, without public awareness and a holistic mechanism to monetise the impact of pollutants, a universal strategy to reduce greenhouse gas emissions is condemned to failure. This paper presents an overview of emerging technologies that can come together to offer an innovative solution for monetising, incentivising and realising the reduction of greenhouse gas emissions. Through a brief review of the literature, an innovative ecosystem is proposed for the first time, accounting for a unified platform for carbon verification, validation and monetisation, which can be adopted as a business model to validate and monetise the amount of incentivising businesses and people for carbon saving on a decentralised blockchain platform.
Intelligent Deep Learning Enabled Human Activity Recognition for Improved Medical Services

Human Activity Recognition (HAR) has been made simple in recent years, thanks to recent advancements made in Artificial Intelligence (AI) techniques.These techniques are applied in several areas like security, surveillance, healthcare, human-robot interaction, and entertainment.Since wearable sensorbased HAR system includes in-built sensors, human activities can be categorized based on sensor values.Further, it can also be employed in other applications such as gait diagnosis, observation of children/adult's cognitive nature, stroke-patient hospital direction, Epilepsy and Parkinson's disease examination, etc. Recentlydeveloped Artificial Intelligence (AI) techniques, especially Deep Learning (DL) models can be deployed to accomplish effective outcomes on HAR process.With this motivation, the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR (IHPTDL-HAR) technique in healthcare environment.The proposed IHPTDL-HAR technique aims at recognizing the human actions in healthcare environment and helps the patients in managing their healthcare service.In addition, the presented model makes use of Hierarchical Clustering (HC)-based outlier detection technique to remove the outliers.IHPTDL-HAR technique incorporates DL-based Deep Belief Network (DBN) model to recognize the activities of users.Moreover, Harris Hawks Optimization (HHO) algorithm is used for hyperparameter tuning of DBN model.Finally, a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different aspects.The experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior performer compared to other recent techniques under different measures.
An Exploration of Post-Editing Effectiveness in Text Summarization

Automatic summarization methods are efficient but can suffer from low quality.In comparison, manual summarization is expensive but produces higher quality.Can humans and AI collaborate to improve summarization performance?In similar text generation tasks (e.g., machine translation), human-AI collaboration in the form of "post-editing" AIgenerated text reduces human workload and improves the quality of AI output.Therefore, we explored whether post-editing offers advantages in text summarization.Specifically, we conducted an experiment with 72 participants, comparing post-editing provided summaries with manual summarization for summary quality, human efficiency, and user experience on formal (XSum news) and informal (Reddit posts) text.This study sheds valuable insights on when post-editing is useful for text summarization: it helped in some cases (e.g., when participants lacked domain knowledge) but not in others (e.g., when provided summaries include inaccurate information).Participants' different editing strategies and needs for assistance offer implications for future human-AI summarization systems.