Reviewerly/exHarmony huggingface dataset:
Dataset and ANFIS model prediction of the performance of graphene nano-LPG in domestic refrigerator system

LPG's steady-state performance in a base lubricant and a graphene nanolubricant was investigated in this study. Step-by-step processes and procedures for preparing graphene nanolubricant concentrations and replacing them for the base lubricant in a domestic refrigerator system were presented as the measuring devices necessary and their uncertainties. The experimental dataset and the training and testing datasets for Adaptive Neuro-fuzzy Inference System. (ANFIS) are available. The use of an ANFIS approach model to forecast graphene nanolubricant performance in a domestic refrigerator is described. The Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE) are also available as statistical performance indicators for the ANFIS model prediction.
Face Liveness Detection Based on Stereo Imaging

Face recognition is used extensively in authentication systems because it offers contactless and fast use. Face liveness detection algorithms have become of vital importance, especially with its widespread use in important areas such as banking and digital wallets. In this study, a real-time liveness detection method that can be applied to face recognition systems is proposed. The method detects faces in two images taken simultaneously with stereo imaging and extracts landmarks from the detected faces. A sparse 3D face model is created with the help of stereo calibration matrices obtained from these points and cameras. Using a deep learning architecture trained with the created 3D face models, the input images are classified whether they belong to an actual face or a photograph. In this way, deception of the face recognition based authentication system is prevented. According to the quantitative test results obtained, it has been shown that the proposed algorithm is much more successful against photo deception compared to existing methods. The method achieved an accuracy of up to 99% in experiments performed on the expanded version of the EPFL Stereo Face Dataset.
Voice-Zikr: A Speech Recognition System Implementation for Hands-Free Zikr Based on Deep Learning

Speech recognition is a branch of pattern recog-nition that has been widely implemented in products. Some well-known products that used speech recognition systems include Google Assistant, Apple Siri, and Alexa which have high accuracy to produce output with user expectations. Recently, deep learning is one of the techniques that is often used to build models in speech recognition systems. The technique works to keep information in its hidden layers from audio frames as input features and phones as output labels respectively. Zikr is one of the Muslim worship activities that can be done at any time. Several tools and applications have been created to count the zikr words while repeatedly speaking them. In this research, the speech recognition system is implemented to create an application called voice-zikr that is used to count the zikr words spoken by Muslim people. The speech recognition model is trained using time delay neural networks with 5 hidden layers. The dataset was collected from different ages of speakers who read “Subhanallah”,” Alhamdulillah”, “Lailahaillallah”, and”Allahuakbar”. The model performance can reach 1.04 %WER on recorded audio testing and work perfectly on microphone testing,
Collaborative Mixed Reality Annotations System for Science and History Education Based on UWB Positioning and Low-Cost AR Glasses

In this research, the authors designed a low-cost mixed reality (MR) collaborative annotations system for science and history education based on Ultra-wideband (UWB) communication technology. The position of the user is provided by the gyroscope in AR Glasses and the UWB antenna tag which is carried with users. The system is suitable for science education in developing countries that lack quality science teaching resources. It can provide comparably sized things or scenes that may be difficult to see clearly or do not exist in daily life for users to observe and experience. While using the system, the users can interact with each other. The system has some unique advantages, such as low-cost, accurate positioning in areas where GPS signal is weak, virtuality, and reality combination in large indoor spaces.
IoT-Fog-Blockchain Framework

Exploring the unique blockchain-internet of things (IoT) framework may be an attractive structure for enhancing communications efficiency in the 5G networks. The wireless communication would have been the largest research area that allows users to communicate with each other. Nowadays, high-speed, smart, efficient with many technologies, such as low power consumption, and so on, appear to be available to communicate with each other in today's globe. Throughout this framework, the expansion of fog features is enabled for physical objects within IoT. Several of the challenging issues in the field of wireless communications would be to build a new blockchain-based virtualization system across the IoT architecture. The main purpose of this framework is to connect blockchain technology to the IoT and fogging or maintains the IoT cryptography secured when transactions occurred. This strengthens blockchain and fog to build an effective IoT communication system. The recommended method is an important estimation of the extensive work.
Traces in Virtual Environments: A Framework and Exploration to Conceptualize the Design of Social Virtual Environments

Creating social Virtual Environments (VEs) is an ongoing challenge. Traces of prior human interactions, or traces of use, are used in Physical Environments (PEs) to create more meaningful relationships with the PE and the people within it. In this paper, we explore how the concept of traces of use can be transferred from PEs to VEs to increase known success factors for social VEs, such as increased social presence. First, we introduce a conceptualization and discussion ($N=4$ expert interviews) of a “Traces in VEs” framework. Second, we evaluate the framework in two lab studies ($N=46$ in total), exploring the effect of traces in (i) VE vs. PE, and (ii) on social presence. Our findings confirm that traces increase the feeling of social presence. However, their meaning may differ depending on the environment. Our framework offers a structured overview of relevant components and relationships that need to be considered when designing meaningful user experiences in VE using traces. Thus, our work is valuable for practitioners and researchers who systematically want to create social VEs.
An Improved Image Feature Detection Algorithm Based on Oriented FAST and Rotated BRIEF for Nighttime Images

This paper presents an improved feature detection and descriptor construction method for nighttime vehicle surrounding images. The proposed algorithm is based on the Oriented FAST and Rotated Brief (ORB) algorithms. In order to improve the accuracy of the proposed algorithm, the Contrast Limited Adaptive Histogram Equalization (CLAHE), feature detection, and descriptor construction were added. In addition, the proposed feature detection description method is realized by the Very Large-Scale Integration (VLSI) technique to meet the real-time application for vehicles. Compared with previous studies, the proposed design has better performance than previous methods of detecting features in nighttime for image stitching and recognition.
CBDC: functional scope, pricing and controls

Even before their deployment in major economies, one of the concerns that have been voiced regarding central bank digital currencies (CBDC) is that they might be "too successful" and lead to bank disintermediation, which could intensify further in the case of a banking crisis. Moreover, some argue that CBDCs might unduly crowd out private sector payment solutions, i.e. beyond what would be desirable from the perspective of the respective comparative advantages of private and public sector money. This paper discusses success factors for CBDC, and how to avoid at the same time the risk that CBDC could crowd out banks and private sector payment solutions. For this, beyond recalling mitigants to an excessive take up of CBDC as store of value, the paper emphasizes the importance of the functional scope of CBDC for the payments' functions of money. The paper also recalls the risks that the usage of CBDC could be too low, if its functional scope, convenience, and store of value properties are unattractive for users. Finding an adequate functional scope of CBDC - neither too broad to crowd out private sector solutions, nor too narrow to be of too limited use - is challenging in an area with network effects like payments. In this context, the role of the incentives applied to private sector service providers involved in the distribution, usage and processing of CBDC (such as banks, wallet providers, merchants, payment processors, acquirers, etc.) is discussed, including both fees and compensations.
The Evolution of the Idiolect over the Lifetime: A Quantitative and Qualitative Study of French 19th Century Literature

The way in which authors express themselves is unique but changes over their lifetime. However, quantitative studies of this idiolectal evolution are rare. Using the Corpus for Idiolectal Research (CIDRE) that contains the dated works of 11 prolific 19th century French fiction writers, we propose new methods to identify, quantify and describe the grammatical-stylistic changes that take place using lexico-morphosyntactic patterns, also called motifs. To examine the strength of the chronological signal of change, we developed a method to calculate if a distance matrix of literary works contains a stronger chronological signal than expected by chance. Ten out of 11 corpora showed a higher than chance chronological signal, leading us to conclude that the evolution of the idiolect is in a mathematical sense monotonic, supporting the rectilinearity hypothesis previously put forward in the stylometric literature. The rectilinear property of the evolution of the idiolect found for most authors in CIDRE subsequently enabled us to propose a machine learning task: predicting the year in which a work was written. For the majority of the authors in our corpus, the accuracy and the amount of variance that is explained by the model were high and we discuss why the technique might fail for others. After applying a feature selection algorithm, we examined the most important features, i.e. the motifs that have the greatest influence on idiolectal evolution. We find that some of those features are stylistic and have been previously identified in qualitative literature studies. We report some remarkable stylistic constructions revealed by our algorithm to illustrate which kind of stylistic patterns can be extracted using our method.
Image-Based Navigation in Real-World Environments Via Multiple Mid-Level Representations: Fusion Models, Benchmark and Efficient Evaluation

A B S T R A C TRobot visual navigation is a relevant research topic. Current deep navigation models mostly learn the navigation policies in simulation. This is convenient, given the efficiency offered by simulators to collect the required training experience. Unfortunately, the resulting models show a limited generalization ability when deployed in the real world. In this work we investigate the problem of learning robust visual navigation policies in simulation that can successfully transferred in the real world. We propose an evaluation tool to reproduce realistic navigation episodes in simulation. We then study whether the mid-level visual representations, that have proven to be effective in previous applications, can be successfully used in simulation to solve the domain gap. Our experiments, performed both in simulation and on a robotic platform, show the proposed evaluation tool can provide results which are consistent with the ones obtained in the real world.
Digital forensics evidence management based on proxy re-encryption

The growing world of digitisation has given rise to cybercrimes. Digital forensics is the process of collecting lawful evidence. Such evidence plays a very crucial role in the court of law to demonstrate the fact explicitly against the crime of the suspect. To ensure the admissibility of evidence in the court of law during trials, it is important to maintain evidence using a proper evidence management system. We propose the use of a unidirectional multi-hop proxy re-encryption scheme for authority delegation. This proposed system will help in securely delegating access to digital evidence. The re-encryption scheme perceives a clearer perception of security and validates the usefulness of proxy re-encryption as a method of adding access control to a secure evidence management system. The correctness analysis of the proposed scheme is validated by using BAN logic. The security analysis using the AVISPA tool shows that the proposed scheme is safe against various security attacks.
The Application of Artificial Intelligence in Libraries: An Overview

The Artificial Intelligence is viewed an extension of human intelligence and has taken over various sectors. Application of Artificial Intelligence in libraries has been providing a breakthrough for the information sector. It has also injected new vitality into the development of modern libraries. The adoption of Artificial Intelligence inlibrary activities is considered to provide a new online service for libraries.Librarians are always on the cutting edge of technologies to engage and enhance services for their users, some of the valid additions include virtual realities that engages users with libraries and enhance information literacy skills.
General function approximation of a class of cascade chaotic fuzzy systems

This paper presents an innovative architecture called cascade chaotic fuzzy system (CCFS) for the function approximation and chaotic modeling. The proposed model can dominate complications in the type-2 fuzzy systems and increase the chaotic performance of a whole framework. The proposed cascade structure is based on combining two or more one-dimensional chaotic maps. The combination provides a new chaotic map with more high nonlinearity than its grain maps. The fusion of cascade chaotic structure into the neurons of the membership layer of a conventional fuzzy system makes the CCFS more capable of confronting nonlinear problems. Based on the General Function Approximation and Stone-Weierstrass theorem, we show that the proposed model has the function approximation property. By analyzing the bifurcation diagram and applying the CCFS to the problem of chaotic modeling, the new model is investigated. Simulation results and analysis are demonstrated to illustrate the concept of general function approximation.
Attentional synchrony in films: A window to visuospatial characterization of events

The study of event perception emphasizes the importance of visuospatial attributes in everyday human activities and how they influence event segmentation, prediction and retrieval. Attending to these visuospatial attributes is the first step toward event understanding, and therefore correlating attentional measures to such attributes would help to further our understanding of event comprehension. In this study, we focus on attentional synchrony amongst other attentional measures and analyze select film scenes through the lens of a visuospatial event model. Here we present the first results of an in-depth multimodal (such as head-turn, hand-action etc.) visuospatial analysis of 10 movie scenes correlated with visual attention (eye-tracking 32 participants per scene). With the results, we tease apart event segments of high and low attentional synchrony and describe the distribution of attention in relation to the visuospatial features. This analysis gives us an indirect measure of attentional saliency for a scene with a particular visuospatial complexity, ultimately directing the attentional selection of the observers in a given context.
Knowledge Graph Recommendation Model Based on Feature Space Fusion

The existing recommendation model based on a knowledge graph simply integrates the behavior features in a user–item bipartite graph and the content features in a knowledge graph. However, the difference between the two feature spaces is ignored. To solve this problem, this paper presents a new recommendation model named the knowledge graph recommendation model based on feature space fusion (KGRFSF). Specifically, in the behavioral feature space, the behavioral features of users and items are constructed by extracting the behavioral feature from the user–item bipartite graph. In the content feature space, the content features related to users and items are extracted through the attention mechanism on the knowledge graph, and then the content feature vectors of users and items are constructed. Finally, through the feature space fusion model, the behavior features and content features are projected into the same preference feature space, and then the fusion of the two feature spaces is completed to construct the complete vector representations of users and items and calculate the vector similarity to predict the score of the user to the item. This paper applies the presented model to public datasets in the fields of music and film. It can be found through the experimental results that KGRFSF can effectively improve the recommendation accuracy compared with the existing models.
Determining the Best Email and Human Behavior Features on Phishing Email Classification

There are many email filters that have been developed for classifying spam and phishing email.However, there is still a lack of phishing email filters developed because of the complexity of feature extraction and selection of the data.There are several categories of features for classifying phishing emails, either on the email part or on the human part.The absence of which features are best for helping to classify phishing emails is one of the challenges; in the previous experiment, there was no benchmark for the features to be used for phishing email classification.This research will provide new insight into the feature selection process in the phishing email classification area.Therefore, this work extracts the features based on the category and determines which features have the most impact on classifying email as phishing or not phishing using a machine learning approach.Feature selection is one of the essential parts of getting a good classification result.Therefore, obtaining the best features from email and human behavior will significantly impact phishing classification.This research collects the public phishing email dataset, extracts the features based on category using Python, and determines the feature importance using machine learning approaches with the PyCaret library.The dataset experimented on three different experiments in which each feature category was separated, and one experiment was the combined feature selection.Binary classification is also done with the extracted features.The experiment verified that the proposed method gave a good result in feature importance and the binary classification using selected features in terms of accuracy compared to previous research.The highest result obtained is the classification with combined features with 98% accuracy.The results obtained are better compared to previous studies.Hence, this research proves that the selected features will increase the performance of the classification.
The effect of artificial intelligence and payment flexibility on operational performance: The enabling role of supply chain risk management

This paper investigates the effect of artificial intelligence (product information, recommendation, and social media exposure) and payment flexibility on the operational performance of ecommerce retailers. The study is based on Transactional Cost Analysis, Material Flow, and Technology Integration theories. It considered a sample size of 270 members out of the population of 769 employees from five ecommerce companies operating in the region (Namshi.com, Noon, Joly Chic, Extra, and Styli). The analysis involved constructing a structural equation model to examine the trickle-down effect of the variables included in the study. The study concluded that artificial intelligence and payment flexibility are the core reasons that the retailers in the region are registering operational success in the retail market.
An Emotion Cause Detection Method Based on XLNet and Contrastive Learning

Emotion cause detection is a new direction in the field of emotion research and is a fine-grained analysis of emotion. However, research on emotion cause detection is still challenging due to the extreme complexity of human emotions, the difficulty of tracing emotions back to their origins, and the fact that emotion cause detection corpus annotation requires a lot of manual involvement. An emotion cause detection method incorporating contrastive learning is proposed to address this problem, which combines the autoregressive language model XLNet and a contrastive learning approach to introduce a difficult sample generation strategy and a word repetition strategy in the positive/negative example comparison pattern in the training data,and design a loss function that incorporates the classification task and the comparison learning task. Experiments on the Weibo sports game commentary dataset show better performance in terms of accuracy, macro-average F1 values, and a 2.73 percentage point improvement in accuracy compared to the baseline XLNet, demonstrating the effectiveness of the proposed method.
Collaborative Web Service Quality Prediction via Network Biased Matrix Factorization

Facing a large number of candidate Web service with the same function, user wishes to get the most appropriate one.Quality-of-Service (QoS) which represents non-functional attributes of Web services, has become a major concern for choosing service.But it is time-consuming and resource-consuming to assess all the QoS values by invoking candidate services one by one.Thus, QoS prediction is considered an effective method to obtain QoS information.Although most of QoS prediction methods claim be able to capture the interaction between users and services, few of them take account non-interaction factors, especially the factors arising from the network environment.In this paper, the non-interaction factors from the network environment are referred as network bias, and a network biased matrix factorization (NBMF) method is proposed for QoS prediction.The method packages network bias into a linear regression model and puts the user-service interaction into a matrix factorization model, which is more sophisticated in adapting diversified circumstance, particularly in complex network environment.In addition, extensive experiments are conduct on real-world QoS dataset, and the result prove that the NBMF method achieves better performance than other state-ofthe-art methods.