Reviewerly/exHarmony huggingface dataset:
Sedenion polar harmonic Fourier moments and their application in multi-view color image watermarking

In recent years, the research on image moments has made significant progress, transitioning from gray image moments to color image moments and from gray stereo image moments to color stereo image moments. However, the application of stereo image moments is limited to binocular stereo images with two views, and cannot handle images with more than two views. Multi-view images are more promising but are difficult to handle due to their intricate structure. Based on the sedenion theory and polar harmonic Fourier moments (PHFMs), this paper proposes the sedenion polar harmonic Fourier moments (SPHFMs) for processing multi-view color images. The imaginary part of sedenion encodes all views of multi-view color images, thus allowing all views to be processed as a single unit and internal correlations between all views to be maintained. The results of the stability experiment and image reconstruction experiment demonstrate that good stability and high image reconstruction performance are achieved when the SPHFMs are applied to processing multi-view color images. Since existing multi-view color image watermarking methods are not resistant to geometric attacks, we use SPHFMs for multi-view color image watermarking, which exhibits good imperceptibility and robustness.
Integrating machine learning with linguistic features: A universal method for extraction and normalization of temporal expressions in Chinese texts

With the rapid development of information dissemination technology, the amount of events information contained in massive texts now far exceeds the intuitive cognition of humans, and it is hard to understand the progress of events in order of time. Temporal information runs through the whole process of beginning, proceeding, and ending of events, and plays an important role in many natural language processing applications, such as information extraction, question answering, and text summary. Accurately extracting temporal information from Chinese texts and automatically mapping the temporal expressions in natural language to the time axis are crucial to understanding the development of events and dynamic changes in them. This study proposes a method integrating machine learning with linguistic features (IMLLF) for extraction and normalization of temporal expressions in Chinese texts to achieve the above objectives. Linguistic features are constructed by analyzing the expression rules of temporal information, and are combined with machine learning to map the natural language form of time onto a one-dimensional timeline. The web text dataset we build is divided into five parts for five-fold cross-validation, to compare the influence of different combinations of linguistic features and different methods. In the open medical dialog dataset, based on the training model obtained from the web text dataset, 200 disease descriptions are randomly selected each time for three rounds of experiments. The F1 of multi-feature fusion is 95.2%, which is better than the single-feature and double-feature combination. The results of experiments showed that the proposed IMLLF method can improve the accuracy of recognition of temporal information in Chinese to a greater extent than classical methods, with an F1-score of over 95% on the web text dataset and medical conversation dataset. In terms of the normalization of time expressions, the accuracy of the IMLLF method is higher than 93%. IMLLF has better results in extracting and normalizing time expressions on the web text dataset and the medical conversation dataset, which verifies the universality of IMLLF to identify and quantify temporal information. IMLLF method can accurately map the time information to the time axis, which is convenient for doctors to intuitively see when and what happened to the patient, and helps to make better medical decisions.
UCR-SSL: Uncertainty-Based Consistency Regularization for Semi-Supervised Learning

Recently, semi-supervised learning methods are being actively developed to increase the performance of neural networks by using large amounts of unlabeled data. Among these techniques, pseudo-labeling methods have the advantage of low computational complexity, but are vulnerable to missing annotations. To solve this problem, we propose a method called uncertainty-based consistency regularization (UCR). UCR models a detection head to obtain different outputs for input images and computes a feature map of each. Subsequently, these feature maps are matched with the original and filtered ground truth (GT), and are classified as positive and negative samples, respectively. In this process, missing samples are generated by the filtered GT; therefore, we use a specialized loss function designed to reduce the logit difference of the samples for robustness against missing annotations. We also use the uncertainty extracted through Gaussian modeling as a criterion for annotation filtering to train the network to focus on reliable results. As a result of experiments with an SSD model on the Pascal VOC dataset, the proposed approach achieved an improvement of 0.7% in terms of mAP compared to a baseline method.
CNN-based Hand Gesture Recognition for Contactless Elevator Button Control

Recently, with the outbreak of the COVID-19 pandemic, various quarantine measures have been implemented to reduce the spread of the virus. As a part of efforts, the preference for touchless technology has been emerging. In this paper, we propose a touchless elevator control system using CNN-based hand gesture recognition. Experimental results show that the hand recognition AP and FPS on the Jetson TX2 board are 81.87% and 11.8FPS, respectively. We demonstrate that an elevator model could be controlled by virtual elevator buttons utilizing CNN-based hand gesture recognition. The proposed method can be applied to commercial elevators as an approach to prevent the spread of viruses from elevator buttons.
A Two-Tier GAN Architecture for Conditioned Expressions Synthesis on Categorical Emotions

Abstract Emotions are an effective communication mode during human–human and human–robot interactions. However, while humans can easily understand other people’s emotions, and they are able to show emotions with natural facial expressions, robot-simulated emotions still represent an open challenge also due to a lack of naturalness and variety of possible expressions. In this direction, we present a two-tier Generative Adversarial Networks (GAN) architecture that generates facial expressions starting from categorical emotions (e.g. joy, sadness, etc.) to obtain a variety of synthesised expressions for each emotion. The proposed approach combines the key features of Conditional Generative Adversarial Networks (CGAN) and GANimation, overcoming their limits by allowing fine modelling of facial expressions, and generating a wide range of expressions for each class (i.e., discrete emotion). The architecture is composed of two modules for generating a synthetic Action Units (AU, i.e., a coding mechanism representing facial muscles and their activation) vector conditioned on a given emotion, and for applying an AU vector to a given image. The overall model is capable of modifying an image of a human face by modelling the facial expression to show a specific discrete emotion. Qualitative and quantitative measurements have been performed to evaluate the ability of the network to generate a variety of expressions that are consistent with the conditioned emotion. Moreover, we also collected people’s responses about the quality and the legibility of the produced expressions by showing them applied to images and a social robot.
Zero Trust Architecture Patterns for Cyber-Physical Systems

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Zero trust (ZT) is an emerging initiative that focuses on securely providing access to resources based on defined policies. The core tenet of ZT is “never trust, always verify”, meaning that even within trusted zones of operation, resource access must be explicitly granted. ZT has proven effective in improving the security posture in domains such as information technology infrastructure; however, additional research and development is needed to define and apply zero trust principles to cyber-physical system domains. To work toward this objective, we have identified an initial set of ZT architectural patterns targeted specifically at cyber-physical systems. We created ZT architecture patterns in the Architecture Analysis and Design Language (AADL), a modeling language that enables engineers to describe the key elements of embedded system architectures using a well-defined semantics. The patterns are implemented as a library of ZT components that can be made available to system engineers. Utilizing AADL capabilities, engineers can model a system in AADL and apply one or more of these ZT patterns to improve the system security posture based on specific system requirements. To demonstrate our approach, we apply the ZT patterns to an unmanned aerial vehicle surveillance application. The resulting design provides inherent protection from a variety of attacks affecting system confidentiality, integrity, and availability.&lt;/div&gt;&lt;/div&gt;
Emergence of non-Gaussian coherent states through nonlinear interactions

Light-matter interactions that are nonlinear with respect to the photon number reveal the quantum nature of coherent states. We characterize how coherent states depart from Gaussian by the emergence of negative values in their Wigner function during the evolution while maintaining their characteristic Poissonian photon statistics. Such states have nonminimum uncertainty yet present a metrological advantage that can reach the Heisenberg limit. Non-Gaussianity of light arises as a general property of nonlinear interactions, which only requires a polarizable media, resonant or dispersive. Our results highlight how useful quantum features can be extracted from the seemingly most classical states of light, a relevant phenomenon for quantum optics applications.
Generalizability of Abusive Language Detection Models on Homogeneous German Datasets

Abstract Abusive language detection has become an integral part of the research, as reflected in numerous publications and several shared tasks conducted in recent years. It has been shown that the obtained models perform well on the datasets on which they were trained, but have difficulty generalizing to other datasets. This work also focuses on model generalization, but – in contrast to previous work – we use homogeneous datasets for our experiments, assuming that they have a higher generalizability. We want to find out how similar datasets have to be for trained models to generalize and whether generalizability depends on the method used to obtain a model. To this end, we selected four German datasets from popular shared tasks, three of which are from consecutive GermEval shared tasks. Furthermore, we evaluate two deep learning methods and three traditional machine learning methods to derive generalizability trends based on the results. Our experiments show that generalization is only partially given, although the annotation schemes for these datasets are almost identical. Our findings additionally show that generalizability depends solely on the (combinations of) training sets and is consistent no matter what the underlying method is.
A Sketch-Based Interface for Facial Animation in Immersive Virtual Reality

Creating facial animations using 3D computer graphics represents a very laborious and time-consuming task. Among the numberless approaches for animating faces, the use of blendshapes remains the most common solution because of their simplicity and the ability to produce high-quality results. This approach, however, is also characterized by important drawbacks. With the traditional animation suites, to select the blendshapes to be activated animators are generally requested to memorize the mapping between the blendshapes and the influenced mesh vertices; alternatively, they need to adopt a trial-and-error search within the whole library of available blendshapes. Moreover, the level of expressiveness that can be reached may be lower than expected; this is due to the fact that the possibility to apply transformations to vertices different than just linear translations and mechanisms for adding, e.g., exaggerations, are typically not integrated into the same animation environment. To tackle these issues, this article proposes an immersive virtual reality-based interface that leverages sketches for the direct manipulation of blendshapes. Animators can draw both linear and curved strokes, which are used to automatically extract information about the blendshape to be activated and its weight, the trajectories that associated vertices have to follow, as well as the timing of the overall animation. A user study was carried out with the aim of evaluating the proposed approach on several representative animations tasks. Both objective and subjective measurements were collected. Experimental results showed the benefits of the devised interface in terms of task completion time, animation accuracy, and usability.
Open data-set identifier for open innovation and knowledge management

Purpose This paper aims to highlight the importance of open data and the role that knowledge management and open innovation can play in its identification and use. Open data has great potential to create social and economic value, but its main problem is that it is often not easily reusable. The aim of this paper is to propose a unique identifier for open data-sets that would facilitate search and access to them and help to reduce heterogeneity in the publication of data in open data portals. Design/methodology/approach Considering a model of the impact process of open data reuse and based on the digital object identifier system, this paper develops a proposal of a unique identifier for open data-sets called Open Data-set Identifier (OpenDatId). Findings This paper presents some examples of the application and advantages of OpenDatId. For example, users can easily consult the available content catalogues, search the data in an automated way and examine the content for reuse. It is also possible to find out where this data comes from, solving the problems caused by the increasingly frequent federation of data in open data portals and enabling the creation of additional services based on open data. Originality/value From an integrated perspective of knowledge management and open innovation, this paper presents a new unique identifier for open data-sets (OpenDatId) and a new concept for data-set, the FAIR Open Data-sets.
Determinants of Repurchase Intentions of Hospitality Services Delivered by Artificially Intelligent (AI) Service Robots

The current study examines how subjective norms, effort expectations, and performance expectations affect perceived value and quality of hospitality service experiences provided by service robots. Later, the experience quality and perceived value on customers’ overall satisfaction determine the plans to repurchase AI (Artificial Intelligence) services in the hotels. A total of 331 valid responses were gathered from hotel customers who had experience with service robots using a purposive sampling strategy. The salient findings of PLS-SEM indicate that subjective norms, effort expectations, and performance expectations all considerably improve the perceived value and quality of experiences. Furthermore, hotel customers’ overall satisfaction with services provided by robots is significantly impacted by experience quality and perceived value. Finally, overall satisfaction considerably increases customers’ preference to repurchase those services. This present study added significance for hotels on customer AI service robots repurchase intention that may deliver a preliminary blueprint for further research.
Kernel Reverse Neighborhood Discriminant Analysis

Currently, neighborhood linear discriminant analysis (nLDA) exploits reverse nearest neighbors (RNN) to avoid the assumption of linear discriminant analysis (LDA) that all samples from the same class should be independently and identically distributed (i.i.d.). nLDA performs well when a dataset contains multimodal classes. However, in complex pattern recognition tasks, such as visual classification, the complex appearance variations caused by deformation, illumination and visual angle often generate non-linearity. Furthermore, it is not easy to separate the multimodal classes in lower-dimensional feature space. One solution to these problems is to map the feature to a higher-dimensional feature space for discriminant learning. Hence, in this paper, we employ kernel functions to map the original data to a higher-dimensional feature space, where the nonlinear multimodal classes can be better classified. We give the details of the deduction of the proposed kernel reverse neighborhood discriminant analysis (KRNDA) with the kernel tricks. The proposed KRNDA outperforms the original nLDA on most datasets of the UCI benchmark database. In high-dimensional visual recognition tasks of handwritten digit recognition, object categorization and face recognition, our KRNDA achieves the best recognition results compared to several sophisticated LDA-based discriminators.
Strategies for Enhancing Assessment Information Integrity in Mobile Learning

Mobile learning is a global trend, which has become more widespread in the post-COVID-19 pandemic era. However, with the adoption of mobile learning comes new assessment approaches to evaluate the understanding of the acquired information and knowledge. Nevertheless, there is scant knowledge of how to enhance assessment information integrity in mobile learning assessments. Due to the importance of assessments in evaluating knowledge, integrity is the sine qua non of online assessments. This research focuses on the strategies universities could use to improve assessment information integrity. This research adopts a qualitative design, employing interviews with academics as well as teaching and learning support staff for data collection. The findings reveal five strategies that academics and support staff recommend to enhance assessment information integrity in mobile learning. The theoretical and practical implications are discussed, as well as future research directions.
Cybersecurity Test Bed for Smart Contracts

Blockchain, smart contracts, and related concepts have emerged in recent years as a promising technology for cryptocurrency, NFTs, and other areas. However, there are still many security issues that must be addressed as these technologies evolve. This paper reviews some of the leading social engineering attacks on smart contracts, as well as several vulnerabilities which result from insecure code development. A smart contract test bed is constructed using Solidity and a Metamask wallet to evaluate vulnerabilities such as insecure arithmetic, denial of service, and re-entrancy attacks. Cross-chain vulnerabilities and potential vulnerabilities resulting from layer 2 side-chain processing were also investigated. Mitigation best practices are proposed based on the experimental results.
Anomaly detection for blueberry data using sparse autoencoder-support vector machine

High-dimensional space includes many subspaces so that anomalies can be hidden in any of them, which leads to obvious difficulties in abnormality detection. Currently, most existing anomaly detection methods tend to measure distances between data points. Unfortunately, the distance between data points becomes more similar as the dimensionality of the input data increases, resulting in difficulties in differentiation between data points. As such, the high dimensionality of input data brings an obvious challenge for anomaly detection. To address this issue, this article proposes a hybrid method of combining a sparse autoencoder with a support vector machine. The principle is that by first using the proposed sparse autoencoder, the low-dimensional features of the input dataset can be captured, so as to reduce its dimensionality. Then, the support vector machine separates abnormal features from normal features in the captured low-dimensional feature space. To improve the precision of separation, a novel kernel is derived based on the Mercer theorem. Meanwhile, to prevent normal points from being mistakenly classified, the upper limit of the number of abnormal points is estimated by the Chebyshev theorem. Experiments on both the synthetic datasets and the UCI datasets show that the proposed method outperforms the state-of-the-art detection methods in the ability of anomaly detection. We find that the newly designed kernel can explore different sub-regions, which is able to better separate anomaly instances from the normal ones. Moreover, our results suggested that anomaly detection models suffer less negative effects from the complexity of data distribution in the space reconstructed by those layered features than in the original space.
Fuzzy Neural Network with Ordered Fuzzy Numbers for Life Quality Technologies

The general goal of the research in this article is to devise an artificial neural network that requires less computational power than an ordinary one for assessing overall life satisfaction—a term often referred to as quality of life (QoL). The development of the mentioned ANN was possible due to the application of fuzzy logic, especially ordered fuzzy numbers (OFN). Research on the appliance of OFN aims at different issues such as the detection of an attack on a computer network, the anticipation of server load, management of multiplexing of data transmission paths, or transmission error rate forecasting that allows the improvement of the quality of life. It occurs due to, for instance, reduced energy demand, savings through better data transmission, and the distribution of computers’ power used in the cloud. Finally, the application of OFN on single neurons of a deep ANN allows achieving a network that is able to solve the same problem as a normal network, but with a lower number of neurons. Such networks in the future may be implemented easier in small solutions, such as solutions for the Internet of Things to improve the quality of human life. This approach is unique and has no equivalent in the literature. Due to the application of OFN in an ANN, fewer requirements for network architecture were needed to solve the same problems, and as a result, there is less demand for processor power and RAM.
A New Lagrangian Problem Crossover—A Systematic Review and Meta-Analysis of Crossover Standards

The performance of most evolutionary metaheuristic algorithms relies on various operators. The crossover operator is a standard based on population-based algorithms, which is divided into two types: application-dependent and application-independent crossover operators. In the process of optimization, these standards always help to select the best-fit point. The high efficiency of crossover operators allows engineers to minimize errors in engineering application optimization while saving time and avoiding overpricing. There are two crucial objectives behind this paper; first, we provide an overview of the crossover standards classification that has been used by researchers for solving engineering operations and problem representation. This paper proposes a novel standard crossover based on the Lagrangian Dual Function (LDF) to enhance the formulation of the Lagrangian Problem Crossover (LPX). The LPX for 100 generations of different pairs parent chromosomes is compared to Simulated Binary Crossover (SBX) standards and Blended Crossover (BX) for real-coded crossovers. Three unimodal test functions with various random values show that LPX has better performance in most cases and comparative results in other cases. Moreover, the LPB algorithm is used to compare LPX with SBX, BX, and Qubit Crossover (Qubit-X) operators to demonstrate accuracy and performance during exploitation evaluations. Finally, the proposed crossover stand operator results are demonstrated, proved, and analyzed statistically by the Wilcoxon signed-rank sum test.