Reviewerly/exHarmony huggingface dataset:
Effect of Augmented Reality on the Academic Performance of Undergraduate Students in Econometrics in Northwest, Nigeria

The paper investigated the effect of augmented reality on the academic performance of undergraduate students in econometrics in the northwest, Nigeria. The paper sought to determine whether there was a difference in the academic performance of the students taught using AR and those taught the same using a traditional approach. The study adopted a quasi-experimental design. The population of the study involved all 300-level students in the two universities under the study. Intact classes of 41 and 31 students were selected as experimental and control groups respectively. An augmented reality (Simple Boy) was used as the treatment, and an econometrics test instrument (ETI) was used as a research instrument for the study. The instrument was validated by experts, and subjected to a pilot study. A reliability index of 0.76 was obtained using KR-21. The study found that there was a significant difference in the academic performance of the students taught using the AR and those taught the same content using the traditional approach in favour of those taught using the AR. There was also a significant difference in the academic performance of the students taught using the AR-based on gender, in favour of female students. The study concluded that augmented reality is an emerging technology that if used diligently in the instructional process is capable of revolutionizing education in the 21st century. There is no doubt that the technology is effective in improving students’ active participation in the learning process which in turn promotes students’ academic performance at all levels of education. It was recommended that Based on the findings of the study, it is recommended that researchers should design and develop augmented reality apps and test their effectiveness in different scenarios and different learning environments. This would help to further ascertain its strengths and weaknesses for quality assurance, control, and integration in the education sector.
Human Recognition Based Decision Virtualization for Effecting Safety-as-a-Service Using IoT Enabled Automated UV-C Sanitization System

To meet the increasing demand of disinfection in public places across the country, we thought of coming up with the idea of a disinfectant car. The goal of this work is to serve public healthcare sectors by disinfecting large areas with Ultraviolet-C (UV-C) lights. Emerging technologies like Internet of Technology (IoT) and robotics have been recognized as promising ways to tackle the current challenges and achieve the goal. To avoid human touch, the overall process, including movement of the robotic car is controlled by a mobile application.
Predicate Annotation for Chinese Intent Detection

Intent detection is to automatically classify the intentions expressed by users’ language in many scenes, such as human-computer dialogue, question and answer, etc. It is the most critical and basic task in natural language processing tasks. To train a more universal and transferable Chinese intent detection model based on the multi-point semantic representation framework, we focuses on the predicate annotation of users’ language in Q&A scenarios. As far as we know, it is the first job to introduce the predicate corpus. Firstly, the predicates are defined under the multi-point semantic framework, and a set of standards of predicate annotation for intent detection is proposed based on the definition. Based on the annotation standards, we select appropriate data. The data annotation and quality control are carried out according to the double annotation method. The constructed corpus includes 14,000 sentences. Finally, through the above data fine-tuning, we use BERT to achieve automatic predicate annotation. The automatic trained annotation model can obtain 89.84% in F-score on the same domain and 81.97% F-score on out-of-domain test sets. The experiment shows the predicate corpus can train the model for processing general domain data with high accuracy, while the accuracy in a specific domain needs to be improved.
Unsupervised Single-Image Reflection Removal

Reflections often degrade the quality of images by obstructing the background scenes. This is not desirable for everyday users, and it negatively impacts the performance of multimedia applications that process images with reflections. Most current methods for removing reflections utilize supervised learning models. These models require an extensive number of image pairs of the same scenes with and without reflections to perform well. However, collecting such image pairs is challenging and costly. Thus, most current supervised models are trained on small datasets that cannot cover the numerous possibilities of real-life images with reflections. In this paper, we propose an unsupervised method for single-image reflection removal. Instead of learning from a large dataset, we optimize the parameters of two cross-coupled deep convolutional neural networks on a target image to generate two exclusive background and reflection layers. In particular, we design a network model that embeds semantic features extracted from the input image and utilizes these features in the separation of the background layer from the reflection layer. We show through objective and subjective studies on benchmark datasets that the proposed method substantially outperforms current methods in the literature. The proposed method does not require large datasets for training, removes reflections from single images, and does not impose impractical constraints on the input images.
A Theory of Injection-Based Vulnerabilities in Formal Grammars

Many systems are controlled via commands built upon user inputs. For systems that deal with structured commands, (SQL queries, XML documents, etc.), such commands are generally constructed in a “fill-in-the-blank” fashion: the user input is concatenated with a part written by the developer. However, the user input can be crafted to modify the command’s semantics intended by the developer and lead to the system’s malicious usages. These injection-based attacks are considered one of the most severe threat to web applications. Our approach addresses these vulnerabilities from the formal language theory’s point of view. We formally define “intent-equivalence”, which guarantees that a developer’s template cannot lead to malicious injections, and “intent-security”, which guarantees that every possible template is intent-equivalent, and therefore that the programming language itself is secure. We analyze the decidability of these properties for the most common grammar classes. We conclude by highlighting the technical implications of these results.
Ai-Powered Public Surveillance Systems: Why We (Might) Need Them and How We Want Them

In this article, we address the introduction of AI-powered surveillance systems in our society by looking at the deployment of real-time facial recognition technologies (FRT) in public spaces and public health surveillance technologies, in particular contact tracing applications. Both cases of surveillance technologies assist public authorities in the enforcement of the law by allowing the tracking of individual movements and extrapolating results towards monitoring and predicting social behavior. Therefore, they are considered as potentially useful tools in response to societal crises, such as those generated by crime and health related pandemics. To approach the assessment of the potentials and threats of such tools, we offer a framework with three dimensions: a function dimension, examines the type, quality and quantity of data the system needs to employ to work effectively; the consent dimension considers the user’s right to be informed about and reject the use of surveillance, questioning whether consent is achievable and whether the user can decide fully autonomously/independently; and a societal dimension that frames vulnerabilities and the impacts of the increased empowerment of established political regimes through new means to control populations based on data surveillance. Our analysis framework can assist public authorities in their decisions on how to design and deploy public surveillance tools in a way that enables compliance with the law while highlighting individual and societal tradeoffs.
Application of A$$^*$$ to the Generalized Constrained Longest Common Subsequence Problem with Many Pattern Strings

This paper considers the constrained longest common subsequence problem with an arbitrary set of input strings and an arbitrary set of pattern strings as input. The problem has applications, for example, in computational biology, serving as a measure of similarity among different molecules that are characterized by common putative structures. We develop an exact A $$^*$$ search to solve it. Our A $$^*$$ search is compared to the only existing competitor from the literature, an Automaton approach. The results show that A $$^*$$ is very efficient for real-world benchmarks, finding provenly optimal solutions in run times that are an order of magnitude lower than the ones of the competitor. Even some of the large-scale real-world instances were solved to optimality by A $$^*$$ search.
Histolab: A Python Library for Reproducible Digital Pathology Preprocessing with Automated Testing

Deep Learning (DL) is rapidly permeating the field of Digital Pathology with algorithms successfully applied to ease daily clinical practice and to discover novel associations. However, most DL workflows for Digital Pathology include custom code for data preprocessing, usually tailored to data and tasks of interest, resulting in software that is error-prone and hard to understand, peer-review, and test. In this work, we introduce histolab, a Python package designed to standardize the preprocessing of Whole Slide Images in a reproducible environment, supported by automated testing. In addition, the package provides functions for building datasets of WSI tiles, including augmentation and morphological operators, a tile scoring framework, and stain normalization methods. histolab is modular, extensible, and easily integrable into DL pipelines, with support of the OpenSlide and large_image backends. To guarantee robustness, histolab embraces software engineering best practices such as multiplatform automated testing and Continuous Integration.
Multivariate Beta-Based Hierarchical Dirichlet Process Hidden Markov Models in Medical Applications

Considering the increasing demand for analyzing sequential data in various fields of our daily lives, finding hidden patterns in a continuous flow of data is one of the interesting topics in research. Hidden Markov models (HMMs) are one of the most powerful statistical models applied for modeling the continuous flow of new data. In this chapter, we will focus on the hierarchical Dirichlet process of HMM (HDP-HMM) that has a nonparametric structure and is an advanced and elegant extension of standard parametric HMM. In the HDP-HMM process, we define transition matrices over infinite state spaces. Defining the proper number of states in HMM is one of the essential parameters that has a great impact on the inferred model. Moreover, we construct our nonparametric model based on multivariate Beta distribution. We applied variational learning approach that provides a promising strategy for inference and has been applied successfully on various domains.
Impact of climate change on wind and solar energy sources complementarity: a case study of the northeast Brazilian region

Solar and wind energy systems have been widely installed in the world as a solution to reduce the emission of greenhouse gases. This massive deployment of intermittent sources makes electrical systems more vulnerable, forcing the emergence of questions about the possibility of the combined use of the sources to reduce the variability of the output signal of hybrid plants. To explore this possibility of smoothness, as well as assess the implications of certain climate change scenarios on the complementarity between sources, this work establishes a methodology and applies it to a case study in northeast Brazil using observational data from stations, in addition to data from reanalysis of a model of general atmospheric circulation over a period of 35 years and scenarios of climate change established by the Intergovernmental Panel on Climate Change over a period also of 35 years. It is observed that the methodology indicates the case study of the evaluated location, a great complementarity between the solar and wind sources during the reanalysis period, this complementarity being impacted by climate changes.
Effective VM Placement Mechanism in Cloud Computing using Cuckoo Search Optimization

Effective VM placement mechanism is needed for Cloud Computing as incoming flow of tasks were dynamic when they are coming onto cloud console. Incoming requests for any cloud console were large in number then there is a chance of decay in quality of service. Quality of service will be degraded when these number of requests were not properly handled i.e. SLA violations and more number of migrations. Quality of Service will be directly affected by these parameters. Many of the authors addressed these metrics but still there is a chance to improve the VM placement in cloud computing. In this paper, we have used a nature inspired algorithm i.e. cuckoo search to design the VM placement strategy and we have used a threshold value to identify the physical host whet her it is overloaded, under loaded or balanced based on the utilization of CPU. Cloudsim toolkit is used as a simulator to conduct simulation and our proposed mechanism minimizes violation of SLA and there is a great improvement in makespan when it was compared with PSO and GA algorithms.
Robust Intent Classification Using Bayesian LSTM for Clinical Conversational Agents (CAs)

Conversational Agents (CAs) are software programs that replicate human conversations using machine learning (ML) and natural language processing (NLP). CAs are currently being utilised for diverse clinical applications such as symptom checking, health monitoring, medical triage and diagnosis. Intent classification (IC) is an essential task of understanding user utterance in CAs which makes use of modern deep learning (DL) methods. Because of the inherent model uncertainty associated with those methods, accuracy alone cannot be relied upon in clinical applications where certain errors may compromise patient safety. In this work, we employ Bayesian Long Short-Term Memory Networks (LSTMs) to calculate model uncertainty for IC, with a specific emphasis on symptom checker CAs. This method provides a certainty measure with IC prediction that can be utilised in assuring safe response from CAs. We evaluated our method on in-distribution (ID) and out-of-distribution (OOD) data and found mean uncertainty to be much higher for OOD data. These findings suggest that our method is robust to OOD utterances and can detect non-understanding errors in CAs.
Real-Time Speech Recognition Using Convolutional Neural Network

Due to the rapid advancements in algorithms, the systems based on speech recognition are now on the verge of huge commercial success. It is proved that speakers can be identified from their speech signals. In this paper, an algorithmically improved system for speech recognition in real time with convolutional neural network (CNN) based on English language has been discussed with obtained results. The system performs on mel frequency cepstral coefficient (MFCC) technique with cepstral-based features and for feature vector matching various techniques like CNN and dynamic time wrapping (DTW). Different speech processing techniques like pre-emphasis, framing, windowing, triangular filtering, DFT, and DCT have been used to process the uttered speech, and MFCC vectors are extracted from input speech sample, and based on obtained coefficients, feature vectors for each uttered speech are stored in database. Mapping is done by DTW for speech recognition. Total system is trained with CNN. A graphical user interface (GUI) is developed for efficient user friendly use and simplicity. The overall system is divided into training phase and testing phase. While training, system is trained with input samples and while testing, test sample is mapped with stored database obtained during training phase. In this paper, 70% database is used for training and 30% database is used for testing. This system is having accuracy almost 100% for 1758 speech samples containing letters and single word speech samples like right, A, E, etc. If database goes on increasing, accuracy may decrease with increase in number of speakers and number of speech samples uttered by each speaker.
Crunch: Analysis of the phenomenon in the independent game development scene

The games industry suffers from a mandatory unpaid overtime issue. This often takes place in the months prior to the game’s release or an important event, and it is referred to as "crunch" or "crunch time". Through a literature review, this paper investigates the widespread phenomenon of crunch time both in the AAA and the independent industry of digital games. We establish factors that influence this behaviour and provide examples of how it can affect the personal lives and mental health of the workers. We have also found that crunch is still an issue even with lone developers, and it is related to game development culture. This paper links a high incidence of the burnout syndrome and the low expectancy of time in the AAA industry—between 3 and 9 years (Cote &amp; Harris, 2021)—among the video game workers to crunch. According to the IGDA, 42% of developers are subjected to crunch. We expect to shed a light on the issue and, by doing so, raise awareness about the unhealthy working conditions game workers are subjected to. We also hope that new and more realistic expectations can be created regarding independent game development, respecting the practice and its developers.
Topological Spaces and Continuous Maps

The subject Topology sets out its official journey in this chapter through the address of the concepts of topological spaces and their continuous maps, which are the basic objects and the basic functions in topology.
Augmented Reality Painting and Sculpture: From Experimental Artworks to Art for Sale

This chapter focuses on a use of augmented reality that is more closely related to traditional painting and sculpture than to interactive game-like AR installations. Based on an analysis of the author’s experimental paintings and sculptures, presented in his solo exhibition Hidden Realities and the outdoor installation The Enterprise Jigsaw, it deals with a particular type of augmented reality paintings that integrate gallery-quality art prints of digital paintings with augmentation by 2D and 3D objects. This type of painting can provide one easy and reliable solution to the acute problem of the saleability of augmented reality art. Alongside theoretical considerations, the first-ever augmented reality painting for sale on Amazon is presented—the author’s artwork The Half Kiss. Similar possibilities for AR sculptures are also analysed.
Virtual Reality Content Evaluation Visualization Tool Focused on Comfort, Cybersickness, and Perceived Excitement

With the development of technology, the Virtual Reality (VR) industry is expanding. However, cybersickness still remains a major concern. At this point, in this study, we aim to develop a visualization tool that comprehensively considers hardware, software, and user factors which can affect VR experience by taking objective and subjective measurement into account. In order to develop a visualization tool, we primarily refer to a standards association IEEE std 3079™-2020 documents, which develops technical guidelines that can be used to resolve VR sickness due to Head Mounted Display (HMD). Specifically, Hardware and user factors borrow measurement tools developed in previous studies, while content factors are developed and integrated in the present study. To develop content evaluation tools, Delphi and Analytic Hierarchy Process (AHP) method analysis methods are utilized by 20 experts and finally weighted through a collection of opinions that classified and defined content cybersickness-causing factors based on four steps. Along with content evaluation tools, a visualization tool is developed by combining hardware and user factor evaluation tools. The visualization tool consists of a structure in which VR content is evaluated in terms of 16 categories based on the perceived excitement level (x-axis) and the sickness level (y-axis).
Text Sentiment Analysis Based on Improved Naive Bayes Algorithm

Aiming at the lack of specific domain corpus in text sentiment polarity analysis, the inaccurate classification accuracy of the naive Bayes algorithm due to the independence assumption and the sparse word vector matrix, a text sentiment analysis method based on the improved naive Bayes algorithm is proposed. Combining machine learning methods with domain sentiment dictionary weighting methods. The improved word frequency inverse file frequency algorithm is used to extract the feature word vector of hotel review text, and the weight of the feature word vector of the domain dictionary after regression test is introduced to weaken the influence of the independence assumption. The singular value decomposition algorithm realizes the dimensionality reduction of the word vector sparse matrix and eliminates redundancy. The remaining features are used to construct a polynomial model of Naive Bayes. The results of simulation research show that this method can effectively improve the effect of text sentiment classification.
Face Expressions Recognition Based on Image Processing using Convolutional Neural Network for Human Computer Interface

Feelings, communicated in various structures, are a specific relational correspondence. The emotional state helps in an independent direction, helps inventiveness, and oversees human comprehension and human-machine correspondence. In a couple of years, the need to recognize an individual’s feelings is expanding, and interest in human feeling acknowledgment in different fields has been expanding, however not restricted to human-PC interfaces and metropolitan sound discernment. This study proposed a new self-constructed architecture named NNN-Net to compare it with famous AlexNet architecture. We use the same parameters, input size, optimizer, and learning rate in both architectures to find the best combinations that will perform the best result. The dataset that we use is CK+48, one of the famous datasets to study face expression recognition. We also augmented the dataset to increase the number of images for each class and balance the dataset. Furthermore, we found that our NNN-Net shows better results with an exact combination of parameters. The best accuracy result is 98.63%. at last, this study can be helpful as a foundation to classify students’ expression using an online meeting platform.