Reviewerly/exHarmony huggingface dataset:
Cyber Security Strategies While Safeguarding Information Systems in Public/Private Sectors

Many private and public organizations in the UAE and around the world are facing challenges in protecting their information and systems from external cyber-attacks due to the increase in the usage of computer networks within worldwide businesses. The objective of this research study is to explore the strategies that are implemented by the public and private sectors in the UAE to safeguard their data and information systems from cyber-attacks. The findings of the study indicated that public organizations in the UAE do have effective strategies in place to safeguard their information and systems against any cyber-attacks. These strategies include providing adequate training to their employees to create awareness among them and developing robust cyber security strategies in line with the UAE National Cyber Security strategy framework. Public and some private organizations are vigilant in assessing, identifying, and mitigating cyber security risks and threats through well-designed organizational strategies. The research also concludes that protecting the information system can reduce cyber threats and can lead to improved business practices. The findings of this study will lay the foundations for other private and public sectors to use them in their organizational practices, which will help them to decrease the data breaches, and protect their company and customers' confidential data, thereby reducing the cost and risk of cyber-attacks.
Architecture for Collecting and Analysing Data from Sensor Devices

A sensor device measures one or many health indicators for the user wearing it. The vital parameters are monitored and displayed to an individual or to a medical person in real or orchestrated time. This paper focuses on creating a general architecture that enables collecting, analyzing and transmitting data depending on the user needs. It can be used to track changes in vital parameters, without the need of the individual being physically present. With the focus on smartwatch data, as an example of a wearable device, this paper shows the possibilities for its application in finding accuracy of true authentic emotions that a person with autism spectrum disorder is feeling, by measuring the person’s vital parameters.
Sentiment Analysis Applied to IBOVESPA Prediction

Social media is increasingly being used as a source of news, a trend which has resulted in large amounts of data. This work presents an evaluation strategy for assessing the impact that social media has on the Bovespa Index (IBovespa), the benchmark index of the Brazilian stock market. A total of 105000 tweets were collected from the twitter profile of “G1 Economia”, one of the main Brazilian finance portals. This data was processed using sentiment analysis methods which were then incorporated into the development of an artificial neural network whose objective was to predict the IBovespa. A hyperparameter optimization study is also presented. The experimental results show that of the 1279 topologies studied, $$82.4\%$$ exhibited better performance when using sentiment analysis in conjunction with historical data, against the baseline of using only the latter. Curiously, even though the average performance was higher, the absolute best result was obtained without the use of NLP techniques. In the context of the method developed and the data used, it appears that approaches using sentiment analysis alongside historical records may be more effective than using only one or the other.
Developing a New DevOps Modelling Language to Support the Creation of Infrastructure as Code

The deployment of cloud applications and the correct management of their lifecycle is a colossal task. Infrastructure as Code (IaC) tools make this task easier; however, they require the user to have a deep knowledge of both the IaC language and the characteristics of various cloud services providers. The PIACERE project has developed a DevOps Modelling Language (DOML), aiming at describing cloud applications that are agnostic of the specificities of the different providers and IaC tools used for provisioning, deployment and configuration. DOML provides several modeling perspectives in a multi-layer approach. An application can be described in three layers: application layer, abstract and concrete infrastructure layer. It allows developers to describe how cloud applications are structured in an abstract manner, mapping the different software components to the concrete infrastructure elements, enabling the usage of different concretizations to match one particular deployment. This paper provides an overview of the DOML language: its layers and extension mechanisms, as well as an example to showcase its modeling capabilities.
Augmented Reality for Computer-Guided Interventions

With the development of augmented reality devices, new imaging hardware, and better computer simulations, the field of computer-assisted surgery is rapidly evolving. From an augmented visualization of medical images during an intervention to a fully automatic fusion of the patient’s virtual anatomy with the surgical view, many ways of augmenting a surgery can be envisioned. This chapter provides a description of the main challenges, state of the art, and results in this revolutionary area by looking at the contributions of augmented reality in several medical application fields with an emphasis on deformable techniques. We will address several key questions, such as the creation of patient-specific models of the anatomy, the registration of the virtual model onto the actual view, or the real-time computation of biophysical phenomena on this anatomy.
Classifying Me Softly: A Novel Graph Neural Network Based on Features Soft-Alignment

Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. In this paper we propose a new graph neural network architecture based on the soft-alignment of the graph node features against sets of learned points. In each layer of the network the input node features are transformed by computing their similarity with respect to a set of learned features. The similarity information is then propagated to other nodes in the network, effectively creating a message passing-like mechanism where each node of the graph individually learns what is the optimal message to pass to its neighbours. We perform an ablation study to evaluate the performance of the network under different choices of its hyper-parameters. Finally, we test our model on standard graph-classification benchmarks and we find that it outperforms widely used alternative approaches, including both graph kernels and graph neural networks.
The Augmented Reality Internet of Things: Opportunities of Embodied Interactions in Transreality

Human society is encountering a new wave of advancements related to smart connected technologies with the convergence of different traditionally separate fields, which can be characterized by a fusion of technologies that merge and tightly integrate the physical, digital, and biological spheres. In this new paradigm of convergence, all the physical and digital things will become more and more intelligent and connected to each other through the Internet, and the boundary between them will blur and become seamless. In particular, augmented/mixed reality (AR/MR), which combines virtual content with the real environment, is experiencing an unprecedented golden era along with dramatic technological achievements and increasing public interest. Together with advanced artificial intelligence (AI) and ubiquitous computing empowered by the Internet of Things/Everything (IoT/IoE) systems, AR can be our ultimate interface to interact with both digital (virtual) and physical (real) worlds while pervasively mediating and enriching our lives. In this chapter, we describe the concept of transreality that symbiotically connects the physical and the virtual worlds, incorporating the aforementioned advanced technologies, and illustrate how such transreality environments can transform our activities in it, providing intelligent and intuitive interaction with the environment while exploring prior research literature in this domain. We also present the potential of virtually embodied interactions – e.g., employing virtual avatars and agents – in highly connected transreality spaces for enhancing human abilities and perception. Recent ongoing research focusing on the effects of embodied interaction are described and discussed in different aspects, such as perceptual, cognitive, and social contexts. The chapter will end with discussions of potential research directions in the future and implications related to the user experience in transreality.
Efficient Leave-One-Out Evaluation of Kernelized Implicit Mappings

End-to-end learning is discussed in the framework of linear combinations of reproducing kernels associated with training samples. This paper shows that the leave-one-out (LOO) technique can be executed very efficiently in this framework. It is a simple extension of previous fast LOO algorithms for scalar-valued functions to vector-valued functions, but opens the door for multiple analyses of the same data with almost no cost. With a newly defined LOO matrix, we demonstrate the effectiveness and the universality of this approach.
Air-Drawing

Computer vision is a branch of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs to conduct actions or offer suggestions in response to that information. If AI gives computers the ability to think, computer vision gives them the ability to see, observe, and comprehend. Computer vision is used in many applications such as image recognition, face recognition, fingerprint recognition, medical diagnosis, and self-driving. In this study, we have designed and implemented a hands-on air drawing software, just by holding the index finger and drawing in the air. This software may be used in the video conferencing field to draw in the air using a single finger and to display the drawing on the screen.
Feature Extraction and Dimensionality Reduction of Cancer Data Using Folded LDA

Linear Discriminant Analysis is a less commonly applied dimensionality reduction technique in cancer data classification. This could be due to the inability of LDA to achieve good classification results when applied on small training data-a common characteristics of cancer data. F-LDA is an extension of LDA and was recently proposed in another application to overcome the challenge posed by the lack of enough samples for training. This paper therefore evaluates the effectiveness of F-LDA as a dimensionality reduction technique in cancer data classification. Experimental results obtained are promising and demonstrate the ability of F-LDA to effectively reduce the dimensionality of cancer data in small training sample scenarios.
Identity, Authentication and Authorization in Forestry 4.0 Using OAuth 2.0

The Internet of Things (IoT), one of the widely discussed topics in the context of Industry 4.0, denotes a novel communication network founded on several federated Internet technologies. In this virtual space, diverse Things are interconnected to enable service and information identification as well as exchange between stakeholders. In forestry, the application of the IoT deduces Forestry 4.0, which aims to digitalize a variety of value-added chains and mapped processes. Nevertheless, with an increasingly growing number of Things in the IoT, the challenge of ensuring the confidentiality, integrity, and availability (CIA rules) of communication becomes inevitable. A general approach to cover these aspects incorporates, among others, the use of authentication and authorization processes. Hence, this paper proposes an authentication and authorization approach using OAuth 2.0, designed explicitly for so-called Forestry 4.0 Things, connected over an IoT infrastructure termed the Smart Systems Service Infrastructure (S3I).
Computational Method for Determining Optimal Dribbling Routes in Basketball

In this study, we propose a method to compute optimal dribbling routes to areas from which the ball handler can shoot. We also equip a visualization tool with a function for visualizing the optimal dribbling routes computed with the proposed method. In addition, we validate this visualization function by using real location data of the players and the ball.
Is there any Correlation between Users' Star Ratings and Usability?

Millions of users rely on star ratings to decide which app is the best. However, it is not clear if there is a correlation between star ratings and how usable the apps are. In other words, there is a need to study if a better star rating means a better level of usability. The current study conducted an experimental study with 30 participants who were given three different apps with different star ratings (low-medium and high). Three sessions were recorded for every participant. In every session, an array of three tasks was performed. During each session of the usability experiment, quantitative data about the time taken to complete each task, task success, and the number of errors was collected to evaluate the factors: efficiency and effectiveness. After each session, the participant evaluated the app by answering a System Usability Scale (SUS) survey to evaluate the satisfaction factor. The results showed no correlation between user star rating and the usability of the tested apps. Additionally, the results showed no correlation between user star rating and any of the tested factors taken separately.
Design of a Deep Learning-based Detection System for Criminal Activities

In the last six years, Manila crime rate improvement was significantly low compare to other ASEAN cities. The Philippines' crime rate & statistics was recorded 8.93 in 2017 which has a percentage of 23.55% decline from 2016, then the Philippines' crime rate & statistics for 2018 was 6.47, a 22.96% decline from 2017. In 2019, there were more than 1,200 deliberate shooting deaths and more than 7000 cases of assault injuries in the nation. The project aims to design a deep learning-based detection system for criminal activities such as shooting and assault. Also, to design a web application that includes features such as a dashboard showing the number of confirmed detections, a notification page, and a live feed page for streaming the footage from the CCTV camera. The model is used for the system to work together with the Python framework. The system obtained an accuracy of 95% percentage of accuracy for crime detection along with a 100% accuracy percentage for the other project objectives. The design of the deep learning-based detection system has shown a significant potential to introduce a more active response to crime by supporting police officers in preventing violence and upholding a safer environment for all.
Exploring adaptive attention in memory transformer applied to coherent video paragraph captioning

A coherent description is the ultimate goal regarding video captioning via a couple of sentences because it might also affect the consistency and intelligibility of the generated results. In this context, a paragraph describing a video is affected by the activities used to both produce its specific narrative and provide some clues that can also assist in decreasing textual repetition. This work proposes a model, named Adaptive Transformer, that uses attention mechanisms to enhance a memory -augmented transformer. This new approach increases the coherence among the generated sentences, assessing data importance (about the video segments) contained in the self-attention results and uses that to improve readability. The test results show the potential of this new approach as it provides higher coherence among the various video segments, decreasing the repetition in the generated sentences and improving the description diversity in the ActivityNet Captions dataset.
Comparing Traditional Rendering Techniques to Deep Learning Based Super-Resolution in Fire and Smoke Animations

The following work explores and compares the differences between rendering fire and smoke simulations in high resolution vs rendering these same simulations in low resolution and using deep learning based neural networks to up-scale the output via super-resolution. Several simulations are created at different levels of detail, both with and without post-processing noise added to them. The simulations are then rendered in both high and low resolutions, the lower of which is used for the super-resolution step. The results are then compared in terms of quality and time cost, to determine whether such a computationally expensive task can be improved with deep learning methods. The evaluation shows that using low resolution inputs does not create comparable results to classic high resolution renders, however using a high resolution render of a lower detail simulation creates similar results to high resolution renders of more detailed simulations.
Fusing Label Relations for Chinese EMR Named Entity Recognition with Machine Reading Comprehension

Chinese electronic medical records named entity recognition (NER) is a core task in medical knowledge mining, which is usually viewed as a sequence labeling problem. Recent works introduce the machine reading comprehension (MRC) framework into this task and extract named entities in a question-answering manner, resulting in state-of-the-art performance. However, they extract entities of different types independently, ignoring the fact that entities presented in the context might highly correlate with each other. To address this issue, we extend the MRC-based model and propose Fusion Label Relations with MRC (FLR-MRC). The method implicitly models the relations between labels through graph attention networks and fuse label information with text for named entity recognition. Experimental results on the benchmark datasets CMeEE and CCKS2017-CNER demonstrate FLR-MRC outperform existing clinical NER methods, with F1-score reaching 0.6652 and 0.9101, respectively.
Augmented Reality-Based Home Interaction Layout and Evaluation

This paper proposes an indoor scene layout interactive assessment method based on augmented reality (AR) to solve the problems of lacking an intuitive and systematic assessment of indoor scene layout and inefficient human moving for layout readjustment. This paper evaluates the indoor layout from accessibility, ventilation, and illumination and proposes the home interactive evaluation system AR-IHES (Augmented Reality Based Interactive Home Evaluation System) to give evaluation opinions and layout adjustment methods. The accessibility is assessed by using an avatar free-roaming in the scene, ventilation by simulating the natural wind field with a particle system, and illumination by calculating the cumulative influence of each light source at each point of the room. Then, we project the layout into AR, and users wearing AR devices obtain rich evaluation opinions, and freely adjust the design through gestures. The experimental results show that the assessment method can provide real-time feedback of objective and scientific evaluation results and provide users with corresponding modification opinions, which solve both the problems of tricky indoor scene layout evaluation and inefficient adjustment.
A Survey Based on Online Voting System Using Blockchain Technology

India could be an equitable country which suggests individuals have the control to choose their pioneers. For choice there’s a decision handle which is inclined to extortion and has numerous drawbacks. India is losing the genuine meaning of Democracy as the percentage of voting is diminishing definitely day by day. In order to overcome this problem there’s ought to give a simple and secured preparation by developing a Mobile/Web Application. This must be considered as each people group's vote plays a noteworthy part in choosing the correct pioneers. Block chain innovation offers the straightforwardness and security requisites for the fair-minded race. It may be a total decentralized, permanent record framework. The online voting framework permits the voters to cast their vote from any place at any time which leads to expanding the voter support check. The objective of the survey is to review a voting framework which gives straightforwardness and security utilizing block chain innovation. The Blockchain framework will permit the citizens to cast their votes in spite of their area. Each year an expansive bunch of Indians closes up the country due to which they are unfit to sharpen their voting rights most of the time. Blockchain system will be a medium for them casting their votes undoubtedly in showing disdain toward the reality that they are not physically displayed in India among race time.
Sparse projection infinite selection ensemble for imbalanced classification

Imbalanced datasets pose frequent and challenging problems to many real-world applications. Classification models are often biased towards the majority class when learning from class-imbalanced data. Typical imbalanced learning (IL) approaches, e.g., SMOTE, AdaCost, and Cascade, often suffer from poor performance in complex tasks where class overlapping or a high imbalance ratio occurs. In this paper, we systematically investigate the IL problem and propose a novel framework named sparse projection infinite selection ensemble (SPISE). SPISE iteratively resamples balanced subsets and combines the classifiers trained on these subsets for imbalanced classification. The diversity of classifier ensembles and the similarity between the subsets and the whole dataset are considered in this process. Specifically, we present a graph-based approach named infinite subset selection to adaptively sample diverse and similar subsets. Additionally, a random sparse projection is combined with feature selection at the beginning of each iteration to augment the training features and enhance the diversity of the generated subsets. SPISE can be easily adapted to most existing classifiers (e.g., support vector machine and random forest) to boost their performance for IL. Quantitative experiments on 26 imbalanced benchmark datasets substantiate the effectiveness and superiority of the proposed model compared with other popular approaches.
Revisiting the Security of Salted UOV Signature

Due to the recent attack of Beullens on Rainbow, the crypto community looks back again at the unbalanced oil-and-vinegar (UOV) signature. The original UOV does not have any formal security reduction. It was Sakumoto et al. who added a random salt to the original UOV signature to give a reduction under the UOV-inversion (UOVI) problem in the classical random oracle model (CROM). In this paper, we revisit the security of salted UOV signature. We start by identifying some issues related to programming the random oracle and the distribution of the salt. Then provide a security reduction of the salted UOV signature in the CROM that clearly addresses these issues. One crucial requirement of our reduction is that the field size needs to be asymptotically superpolynomial in the security parameter. We also give a security reduction of the salted UOV under the UOVI problem in the quantum random oracle model. This work is hoped to aid further concrete security analysis and thereby guide parameter choice of UOV-based schemes in the context of future standardization of post-quantum signature.