Reviewerly/exHarmony huggingface dataset:
The SeaLiT Ontology – An Extension of CIDOC-CRM for the Modeling and Integration of Maritime History Information

We describe the construction and use of the SeaLiT Ontology, an extension of the ISO standard CIDOC-CRM for the modelling and integration of maritime history information. The ontology has been developed gradually, following a bottom-up approach that required the analysis of large amounts of real primary data (archival material) as well as knowledge and validation by domain experts (maritime historians). We present the specification of the ontology, RDFS and OWL implementations, as well as knowledge graphs that make use of this data model for integrating information originating from a large and diverse set of archival documents, such as crew lists, sailors registers, naval ship registers, and payrolls. We also describe an application that operates over these knowledge graphs and which supports historians in exploring and quantitatively analysing the integrated data through a user-friendly interface. Finally, we discuss aspects related to the use, evolution, and sustainability of the ontology.
The Impact of Performance Management on Employee Performance

In order to discuss the most significant findings of those studies in four main axes—the impact of PMS on employee performance, the components of the PMS, and the literature review—this paper reviews the literature on the impact of performance management practices on employee performance during the period (2015–2020). Additionally, the traits of a successful PMS and the challenges faced when implementing the system. The article came to the conclusion that the performance system has a significant moral impact on how well workers perform in firms. These studies provided conclusive evidence that, in addition to the PMS, working conditions, compensation, and promotion are organizational elements that influence employees' performance. Performance monitoring is carried out with performance assessment by management since, according to the research; performance planning has a limited impact on workers' performance while performance appraisal has been the most precise of employee performance.
Deep learning and expert knowledge based underwater acoustic OFDM receiver

In this paper, a deep learning and expert knowledge based receiver is proposed for underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM). Different from the existing deep learning based UWA OFDM receivers, the proposed receiver combines deep learning with the classical expert knowledge of block-based signal processing in UWA OFDM to improve system performance and interpretability. It performs joint channel estimation and signal detection by designing skip connection (SC) convolutional neural network (CNN) cascaded attention mechanism (AM) enhanced bi-directional long short-term memory (BiLSTM) network, abbreviated as SC-CNN-AM-BiLSTM network (SCABNet). Specifically, the channel estimation subnet is designed with SC-CNN to utilize the thought of image super-resolution to reconstruct the entire channel frequency response of all subcarriers. The signal detection subnet is designed with AM-BiLSTM to extract the correlations of received sequential data for signal detection. Especially with the AM, the signal detection subnet can focus more on effective information of the received distorted signal to train the optimal network weights to improve the accuracy of data recovery. The proposed SCABNet is evaluated by experimental data, and the results have demonstrated that the SCABNet has the lowest BER and robust performance compared to the traditional linear algorithm, deep learning based black-box receiver, and ComNet receiver. And the proposed SCABNet is effective and robust when multiple nonideal factors co-exist.
K-DGHC: A hierarchical clustering method based on K-dominance granularity

Existing hierarchical clustering (HC) algorithms generally depend on the Euclidean characteristic metric (Euclidean distance, Manhattan distance, Chebyshev distance, etc.) on Euclidean space to describe the similarity between objects, which makes the clustering process oriented to data sets with uniform and regular distribution in Euclidean space. Although such methods can visually distinguish the cluster distribution of data, it is not effective for the data sets which are densely distributed, interlaced and complex in Euclidean space. As a scalable, efficient and robust method, granular computing generally analyzes data from the perspective of similarity and proximity. In consideration of the advantages of granular computing in extracting data information from a multi-level perspective, in order to reduce the limitations of HC methods based on Euclidean features on non-Euclidean data, this paper proposes a novel HC method based on non-Euclidean feature structure. First, this paper constructs the similarity between objects based on K-dominance granularity and neighborhood search, and considers the environmental information of data points from both global and local perspectives. Secondly, a new HC method based on non-Euclidean feature structure is designed on the basis of the similarity measurement constructed in this paper. Finally, through comparative analysis, the experimental results prove that our method can more accurately identify the densely distributed and interlaced data sets in Euclidean space; it is significantly better than comparison algorithms using different Euclidean features to measure similarity; it has good robustness when additional Gaussian noise is added.
Applying Access Control Enabled Blockchain (ACE-BC) Framework to Manage Data Security in the CIS System

Cybersecurity information sharing (CIS) is important in different business processes to secure data transmission, because it comprises Internet of Things (IoT) connectivity, workflow automation, collaboration, and communication. The shared information is influenced by intermediate users and alters the originality of the information. Although risk factors such as confidentiality and privacy of the data are reduced when using a cyber defense system, existing techniques rely on a centralized system that may be damaged during an accident. In addition, private information sharing faces rights issues when accessing sensitive information. The research issues influence trust, privacy, and security in a third-party environment. Therefore, this work uses the Access Control Enabled Blockchain (ACE-BC) framework to enhance overall data security in CIS. The ACE-BC framework uses attribute encryption techniques to manage data security, while the access control mechanism limits unauthorized user access. The effective utilization of blockchain techniques ensures overall data privacy and security. The efficiency of the introduced framework was evaluated using experimental results, and the experimental outcome indicated that the recommended ACE-BC framework enhanced the data confidentiality ratio (98.9%), the throughput ratio (98.2%), the efficiency ratio (97.4%), and the latency rate (10.9%) when compared to other popular models.
Robust-EQA: Robust Learning for Embodied Question Answering With Noisy Labels

Embodied question answering (EQA) is a recently emerged research field in which an agent is asked to answer the user's questions by exploring the environment and collecting visual information. Plenty of researchers turn their attention to the EQA field due to its broad potential application areas, such as in-home robots, self-driven mobile, and personal assistants. High-level visual tasks, such as EQA, are susceptible to noisy inputs, because they have complex reasoning processes. Before the profits of the EQA field can be applied to practical applications, good robustness against label noise needs to be equipped. To tackle this problem, we propose a novel label noise-robust learning algorithm for the EQA task. First, a joint training co-regularization noise-robust learning method is proposed for noisy filtering of the visual question answering (VQA) module, which trains two parallel network branches by one loss function. Then, a two-stage hierarchical robust learning algorithm is proposed to filter out noisy navigation labels in both trajectory level and action level. Finally, by taking purified labels as inputs, a joint robust learning mechanism is given to coordinate the work of the whole EQA system. Empirical results demonstrate that, under extremely noisy environments (45% of noisy labels) and low-level noisy environments (20% of noisy labels), the robustness of deep learning models trained by our algorithm is superior to the existing EQA models in noisy environments.
Performance Benchmarking and Analysis of Blockchain Platforms

The popularity of Blockchain among researchers, developers, and tech-savvy is reaching the sky. Due to the Blockchain's growing attractiveness to the new crowd, anyone familiar with cryptocurrencies must be aware of Blockchain. Blockchain technology provides trust in an untrusted environment of computation and technology. Blockchain has the potential to store, manage and share information in a decentralized manner. This decentralized blockchain property makes this technology robust and immutable to external malicious activities. In this paper, we have conducted a study to measure and assess the performance of various blockchain platforms named Ethereum (private Deployment), Hyperledger Besu Ethereum Client, and Hyperledger Fabric. The performance analysis, such as throughput, resource utilization, and latency, are also calculated. The results show that the Hyperledger Fabric performed very well compared to Ethereum and Hyperledger Besu in all the performance measures.
Real or Fake Identity Deception of Social Media Accounts using Recurrent Neural Network

Identity fraud is a widespread issue across online social networks in recent days. Current research effort is directed to develop technologies to detect identity fraud. The effectiveness of the existing strategies is uncertain. We describe a study of detecting identity fraud by using clustering and classification techniques. We define traditional methodological shortcomings in detection of identity fraud for these methods and suggest ways that can enhance their efficacy in real-world contexts. Initially, we collect data from social media accounts and applied preprocessing and filtration techniques like Natural Language Process (NLP), vectorization, dimensionality reduction, data normalization, etc. Features are extracted, based on the behavioral analysis, and characteristics of each profile. The clustering approaches are used to detect each profile, either real or fake, and similar approach has been carried out for deep learning classification. The Recurrent Neural Network (RNN) has been used to categorize each profile based on module training and testing. In the experimental analysis, we show the system's effectiveness when applied in the real-world social media environment.
Domain Decomposition Algorithms for Physics-Informed Neural Networks

Domain decomposition algorithms are widely used as fast solutions of algebraic equations arising from the discretization of partial differential equations. The original algebraic equations are partitioned and solved in each subdomain combined with an iterative procedure.
Diffractive vs holographic vs reflective waveguides: what should the industry target?

This panel will welcome several industry players involved in waveguides for augmented reality applications, be they reflective or diffractive. During the panel discussion, we will exchange around the technology choices, the challenges, the trade-offs and the possible way that the industry should follow towards making augmented reality for the consumer a reality.
An augmented reality workflow for creating 'live' wine labels

Augmented reality (AR) technologies are constantly developing in various fields of communication such as entertainment, education, information, and marketing and other fields such as industrial product design among others. This paper aims to present an integrated AR workflow for the labelling of wine products. The proposed wine label enhancement workflow may work as follows: the wine business comes up with the idea of creating an AR experience for its wine products, and then an AR expert designs the AR experience, and develops the AR application. As a final step in the process, the application is distributed to the users with the use of various platforms and the experience can then be activated by pointing the camera of a mobile device to the bottle label. The AR content is then generated and displayed to the user who can interact with the digital product on a whole new level.
Image quality assessment based on the perceived structural similarity index of an image

&lt;abstract&gt; &lt;p&gt;Image quality assessment (IQA) has a very important role and wide applications in image acquisition, storage, transmission and processing. In designing IQA models, human visual system (HVS) characteristics introduced play an important role in improving their performances. In this paper, combining image distortion characteristics with HVS characteristics, based on the structure similarity index (SSIM) model, a novel IQA model based on the perceived structure similarity index (PSIM) of image is proposed. In the method, first, a perception model for HVS perceiving real images is proposed, combining the contrast sensitivity, frequency sensitivity, luminance nonlinearity and masking characteristics of HVS; then, in order to simulate HVS perceiving real image, the real images are processed with the proposed perception model, to eliminate their visual redundancy, thus, the perceived images are obtained; finally, based on the idea and modeling method of SSIM, combining with the features of perceived image, a novel IQA model, namely PSIM, is proposed. Further, in order to illustrate the performance of PSIM, 5335 distorted images with 41 distortion types in four image databases (TID2013, CSIQ, LIVE and CID) are used to simulate from three aspects: overall IQA of each database, IQA for each distortion type of images, and IQA for special distortion types of images. Further, according to the comprehensive benefit of precision, generalization performance and complexity, their IQA results are compared with those of 12 existing IQA models. The experimental results show that the accuracy (PLCC) of PSIM is 9.91% higher than that of SSIM in four databases, on average; and its performance is better than that of 12 existing IQA models. Synthesizing experimental results and theoretical analysis, it is showed that the proposed PSIM model is an effective and excellent IQA model.&lt;/p&gt; &lt;/abstract&gt;
Secure-e-Share: Data leakage Detection and Prevention with Secured Cloud Storage

Data privacy and security has always been a prime concern of any user or an organization, especially when it comes to sharing of confidential files either for different end users or in any organization. Secure-e-Share is one such file-sharing system that creates a secure environment for sharing files of different formats (i.e., zip, Docx, txt, CSV, pdf, etc.). The key protection point of this system is that it encrypts the file and then stores it in the cloud storage which makes the database secure, as only encrypted files are stored. The decryption of a file is done only when the authorized individual accesses it with a unique key provided by the sender. For detection, after every successful transfer, the counter of successful transfer is increased which shows the success rate of the web app, and every case of data leakage is also prevented and detected and recorded creating a threat-free environment.
The International Handbook of Physics Education Research: Teaching Physics

The International Handbook of Physics Education Research: Teaching Physics covers the topics of educational technologies, learning environments, teacher education, and assessment in relation to physics education. As the field of physics education research grows, it is increasingly difficult for newcomers to gain an appreciation of the major findings across all sub-domains, discern global themes, and recognize gaps in the literature. The current volume incorporates the understanding of both physics and education concepts and provides an extensive review the literature in a wide range of important topics. The International Handbook of Physics Education Research: Teaching Physics includes:Various teaching perspectives including active learning, the modeling method, and teaching environments.Distance/online education, formative and summative assessment, and assessment in online education.Gender, race, and ethnicity equity. Readers will find this comprehensive treatment of the literature useful in understanding physics education research and extending to all the physical sciences including chemistry, mathematics, astronomy, and other related disciplines.
Investigating the Intelligibility of Plural Counterfactual Examples for Non-Expert Users: an Explanation User Interface Proposition and User Study

Plural counterfactual examples have been proposed to explain the prediction of a classifier by offering a user several instances of minimal modifications that may be performed to change the prediction. Yet, such explanations may provide too much information, generating potential confusion for the end-users with no specific knowledge, neither on the machine learning, nor on the application domains. In this paper, we investigate the design of explanation user interfaces for plural counterfactual examples offering comparative analysis features to mitigate this potential confusion and improve the intelligibility of such explanations for non-expert users. We propose an implementation of such an enhanced explanation user interface, illustrating it in a financial scenario related to a loan application. We then present the results of a lab user study conducted with 112 participants to evaluate the effectiveness of having plural examples and of offering comparative analysis principles, both on the objective understanding and satisfaction of such explanations. The results demonstrate the effectiveness of the plural condition, both on objective understanding and satisfaction scores, as compared to having a single counterfactual example. Beside the statistical analysis, we perform a thematic analysis of the participants’ responses to the open-response questions, that also shows encouraging results for the comparative analysis features on the objective understanding.
Multiview Subspace Clustering via Low-Rank Symmetric Affinity Graph

Multiview subspace clustering (MVSC) has been used to explore the internal structure of multiview datasets by revealing unique information from different views. Most existing methods ignore the consistent information and angular information of different views. In this article, we propose a novel MVSC via low-rank symmetric affinity graph (LSGMC) to tackle these problems. Specifically, considering the consistent information, we pursue a consistent low-rank structure across views by decomposing the coefficient matrix into three factors. Then, the symmetry constraint is utilized to guarantee weight consistency for each pair of data samples. In addition, considering the angular information, we utilize the fusion mechanism to capture the inherent structure of data. Furthermore, to alleviate the effect brought by the noise and the high redundant data, the Schatten p-norm is employed to obtain a low-rank coefficient matrix. Finally, an adaptive information reduction strategy is designed to generate a high-quality similarity matrix for spectral clustering. Experimental results on 11 datasets demonstrate the superiority of LSGMC in clustering performance compared with ten state-of-the-art multiview clustering methods.
Analysis of density-dependent bead and cell structure of expanded polypropylene bead foams from X-ray computed tomography of different resolution

Closed-cell bead foams with their hierarchical geometrical structure are a challenge for statistical reconstruction and finite element modelling. For the purpose of providing the fundamental micro- and meso-structural descriptors - wall thickness, cell as well as bead volume and sphericity - of expanded polypropylene bead foams of different density, 3D-images from X-ray computed tomography are analyzed. A detailed description of development and application of an image analysis methodology for the determination of feature distributions from CT-scans of different level of detail is provided. The methods are based on off-the-shelf algorithms provided by the open-source package distribution FIJI. It should be highlighted, that beside essential methods such as thresholding, euclidean distance and watershed transformation here the Trainable WEKA segmentation is applied for separating material phases in the images. Although the methods elaborated are generally very case sensitive, the reader benefits from the validation strategies applied, so that development of individual methods into the direction of reliability, repeatability and automation is supported.
IDCF: information distribution composite feature for multi-modal image registration

Automatic registration of multi-modal remote sensing data is a challenging task. Due to the significant nonlinear radiation difference and noise between multi-mode images, the register points extracted by traditional gradient algorithms (such as SIFT) have low repetition rate and poor feature similarity, which makes it difficult to register multi-mode images. To solve these problems, this paper proposes a method which is called Multi-modal Image Matching Based on Information Distribution Composite Feature (IDCF). Firstly, an adaptive information entropy map (AIEM) is proposed. AIEM not only describes the information distribution characteristics of the image, but also the distribution of the contour features of the image. Compared with traditional contour feature extraction operators, AIEM extraction results are clearer, more comprehensive and more detailed. In addition, AIEM is more robust to NRD compared to gradient, which is greatly affected by NRD. Then, IDCF extracts corner points on AIEM as feature points because corner points have better repeatability. After that, a composite feature description model based on maximum information index map (MIIM) and information trend graph (ITM) is defined. MIIM describes the main change direction of image information and ITM describes the overall change trend of image information. Finally, the similarity criterion SAD is used to match the feature points. The proposed IDCF aims to capture the structural similarity between images and has been tested with a variety of optical, Lidar, SAR, and map data. The results show that IDCF is robust against complex NRD outperforms the advanced algorithms (i.e. RIFT, PSO-SIFT and OS-SIFT) in matching performance.