Reviewerly/exHarmony huggingface dataset:
Towards a Novel Phase Unwrapping Model for Three Dimensional Measurement

In order to obtain a higher quantity absolute phase, a novel phase unwrapping model by combination of dual-frequency heterodyne and segmented quantifization phase coding method is proposed in this research. This model also provides a generalized phase order error correction method for eliminate phase order jump errors. Only ten fringe patterns are used for the projection in this study. The workflow of the devised model starts with acquisition of the low frequency wrapped phase by using the classic double-frequency heterodyne method, and then it moves to obtain the fringe order of low frequency wrapped phase based on the projected segmented quantifization phase coding patterns. In this step, two-dimensional scanning lookup method is used to eliminate the noise points caused by the rounding operation. Then, new fringe order can be obtained by combination of the obtained fringe order of low frequency wrapped phase and the new projected binary fringe pattern with complementarity, and this new fringe order combined with the previous fringe order can be used to unwrap the heterodyne phase. Finally, the wrapped phase of the dual-frequency is unwrapped through unwrapped heterodyne phase and the highest frequency phase. This study applies metal workpiece measurements as case studies to test the validity of the proposed model. The experimental results show that the error effects of fringe order can be eliminated, and the high frequency fringes retain more information on the measure workpiece surfaces.
Life Cycle Information Models: Parameterized Linked Data Structures to Facilitate the Consistent Use of Life-Cycle Assessment in Decision Making

The recent enactment of the Buy Clean Act (2017) in California requires highway construction contractors to produce an environmental product declaration (EPD) at the point of installation for a list of all eligible construction materials. An EPD is an instrument that communicates life cycle assessment (LCA) outcomes for a product or process in compliance with the ISO Standard 14025. LCA methodology requires the use of life cycle data from interconnected industries and sectors that contribute to the supply chain of a product. This provides a challenge of consistent and reliable use of inventory data for the LCA of pavements, specifically when considering the geographic expanse of energy profiles, supply chain diversity of pavement materials, and additives. Hence, the objective of this paper is to develop life-cycle information models (LCIMs) that serve two purposes: first, allows for pavement LCA domain-specific knowledge representation, management, and sharing; and second, allows for consistent, reliable, and transparent use of life cycle inventory data. The first purpose is achieved by developing an ontological representation of key components and relationships underlying a pavement LCA to support formal parametric data structures. The second purpose is achieved by identifying life cycle inventories to be mapped to the data structures through a collaboration with the Federal LCA Commons. As LCA software tools are developed to support decision making for departments of transportation, the ontological representation of the pavement domain can serve as a specification outlining a common set of protocols and LCIM designed for reuse to support reliable decision support applications.
Improved RC6 Block Cipher Based on Data Dependent Rotations

This paper introduces an Improved RC6 (IRC6) cipher for data encryption based on data-dependent rotations. The proposed scheme is designed with the potential of meeting the needs of the Advanced Encryption Standard (AES). Four parameters are used to characterize the proposed scheme. These parameters are the size of the word (w) in bits, the number of rounds (r), the length of the secret key (b) in bytes, and the size of the block (L) in bits. The main feature of IRC6 is the variable number of working registers instead of just four registers as in RC6, resulting in a variable block size for plaintext and ciphertext. The IRC6 cipher is designed to improve the robustness against attacks by increasing the diffusion for each round and providing greater security with fewer rounds. The effectiveness of the proposed IRC6 scheme is verified against theoretical attacks. The proposed IRC6 scheme depends on full diffusion and confusion mechanisms regardless of the utilized block size. The proposed IRC6 scheme saves 70% of the encryption time and 64% of the decryption time of RC6. The simulation results prove that the IRC6 achieves a better encryption/decryption time compared to the traditional RC6. Therefore, the proposed IRC6 is anticipated to fulfill the market needs and system security requirements.
Four Years of Earthquake Early Warning in Southern Iberia: 2016–2019

The performance of an earthquake early warning system (EEWS) for southern Iberia during the period of 2016–2019 is analyzed. The software PRESTo (PRobabilistic and Evolutionary early warning SysTem; the University of Naples Federico II, Italy) operating at the Universidad Complutense de Madrid has detected 728 events (2 &amp;lt; M w &amp;lt; 6.3), with 680 earthquakes occurring in southern Iberia. Differences between the EEWS origin time and epicenter and those of the Instituto Geográfico Nacional (IGN) catalog are less than 2 s and 20 km, respectively, for 70% of the detected earthquakes. The main differences correspond to the EEWS magnitude that is underestimated for earthquakes that occurred at the west of the Gibraltar Strait (M w differences larger than 0.3 for 70%). To solve this problem, several relationships have been tested, and a modification to those that currently use PRESTo is proposed. Other improvements, such as to densify the network or to use 3D Earth models, are proposed to decrease the time needed to issue the alert and avoid the false alerts (19 events over a total of 728 events). The EEWS has estimated the depth for 680 events and compared to those from the IGN (491 events). The performance of PRESTo during the 2020–2021 Granada swarm is analyzed. The hypocentral locations for the three largest earthquakes are close to those from the IGN (differences from 1 to 7 km for the epicenter and 0 s for the time origin), although there are some differences in their magnitude estimations that varies from 0.2 to 0.5. The PRESTo first times are 17, 25, and 41 s after the origin time. This study shows that the actual PRESTo EEWS configured for the southern Iberia may generate effective warnings despite the low seismicity rate in this region. To decrease the warning time, the geometry and density of the seismic network must be improved together with the use of 3D Earth models and on-site system approaches.
Revisiting German two-way prepositions

Abstract German two-way prepositions have long troubled grammar writing. Unlike most other German prepositions, they occur with both accusative and dative case. Their case is difficult to predict and has been attributed to different underlying meaning construals. Recent exploratory corpus studies propose that, in addition, their case depends on multiple co-occurring contextual variables. Following this approach, this study uses multivariate regression and collostructional analysis to investigate what determines the case of two-way prepositions in a large sample of authentic language use. Based on the results, this study then attempts to provide a usage-based description of the case of two-way prepositions. Contrary to expectations, none of the proposed variables had much influence on case, suggesting that the effects observed in the literature only hold for specific contexts. Instead, the results indicated associations of accusative and dative with individual prepositions and specific lexical items in the context. Framed in terms of usage-based construction grammar, this is interpreted as item-specific constructional prototypes that emerge from typical usage patterns and, once established, determine case based on form-meaning overlap with the current context of use. In line with recent usage-based research on grammar, a first attempt is made to describe case as part of a network of associative links between constructions and lexical items.
Data Visualization System Based on Big Data Analysis

In order to improve the user experience in the process of analyzing data information, and improve the intuitiveness of data information, this paper proposes a construction strategy of data visualization system based on big data analysis technology. The construction strategy combines big data analysis technology, relying on the Internet platform, to show the dynamic of data in a variety of ways, providing convenience for users to analyze and study the information behind the data. The results show that the construction strategy can improve the user experience, enhance the intuitiveness of data information, and improve the efficiency of data analysis.
The Research of Application of Hidden Markov Model in the Speech Recognition

In today’s society, speech recognition technology has become the focus of research. Similarly, speech recognition is inseparable from our lives, introducing voice assistants for some mobile phones, such as Apple company’s Siri. People now are no longer unfamiliar with this field. More and more people are engaged in research in this field. Now the area of speech recognition is trendy. The main work of this paper is to introduce the three main development stages of speech recognition, including template matching, statistical models, and deep learning. And we present the basic process of speech recognition and introduce the Hidden Markov Model that often uses in traditional speech recognition. In the paper, we will analyze three algorithms commonly applied in the Hidden Markov Model, such as forward algorithm, Viterbi algorithm, and Baum-Welch algorithm. At the end of the paper, we will state some of the shortcomings of this article, and we’ll regard these as future research directions.
Architecture Level Fault Tolerance Modeling for SOA Based Systems

A system based on service-oriented architecture is distributed in nature. Services can be obtained from various sources and assembled through loose coupling. This provides flexibility and scalability in development. On the other hand, involvement of multiple components in a systems causes new sources of faults. Faults may come due to individual services and their interactions during service composition. For reliable systems, one has to consider these newer faults and possibility of system functionality in presence of those faults. Hence, for avoiding failure of critical services in faults presence, fault tolerance needs to be incorporated in the system. In this paper, architecture analysis and design language (AADL) has been used to model different types of errors in a SOA based systems. The AADL is supplemented with error model annex (EMV) that helps to demonstrate error propagation, failure behavior of the design errors. These facilities of EMV are exploited for obtaining error propagations among components in SOA based systems. The proposed fault tolerance modeling may provide useful information for fault tolerance and software testing.
Underwater polarized image processing based on active illumination and image fusion of circular polarized light

Compared with the traditional optical imaging and measurement technology, polarization imaging and polarization measurement technology is a new type of optical detection technology combines polarization measurement with image processing. It can not be solved by traditional optics in the field of target detection and recognition. This paper mainly studies the restoration technology of polarization image in turbid underwater environment, and uses the method of rotating polarizer to collect polarization image by active circular After calculating the Stokes vector, the polarization degree image, polarization angle image, intensity image and visible light image are fused by NSCT image fusion method. Finally, the fusion image is processed by LSCN image enhancement method to improve the quality and effect of the image.
Blockchain for Healthcare Medical Records Management System with Sharing Control

Nowadays, with large quantities of data in every industry and the advancement of technology, solutions to a wide range of problems can be resolved. In this paper, Machine Learning and Blockchain are used to propose a solution to difficulties linked to healthcare data management systems. Machine Learning allows the extraction of needed-only information that is relevant from data. This is achieved using trained algorithms that provide an intelligent decision strategy based on Convolutional Neural Networks (CNN) to automatically extract high-level semantic information from electronic medical records and then perform automatic diagnosis. Once medical data is saved, the next issue is data sharing and reliability. This is where Blockchain technology comes into play. The Blockchain with consensus protocol ensures that information is authentic, and transactions are safe. By putting the patient at the center of the healthcare system and boosting the privacy and interoperability of health data, this proposed solution can improve health care administration. This paper focuses on using Blockchain technology to solve healthcare data management problems while also incorporating some essential Machine Learning features. The expected result of the proposed system 98.67% accuracy and 96.02% recall, demonstrating that employing a convolutional neural network to learn high-level semantic aspects of electronic medical records and then undertake assist diagnosis is feasible and valuable.
The Value and Applications of Blockchain Technology in Business: A Systematic Review of Real Use Cases

This work provides an empirical study to identify the specific objective pursued by companies that are currently investing to develop blockchain technologies to improve their processes. Unlike other studies based on the theoretical potential application of blockchain technology in different sectors, the main objective of this paper is to analyse real projects and investment of companies in blockchain technology to identify the main value or use that managers prioritize in their technology development efforts. More than one hundred blockchain projects from different sectors have been examined with the aim of extracting the perceived value of blockchain technology by managers, customers, and partners. Identifying the business value that is most demanded in each sector and company size, as well as the relationship between the values that are jointly demanded. This article assesses the main values attributed to blockchain, highlighting those really appreciated by companies to invest in them and identifying new applications of blockchain technology in different sectors, and generating organisational change.
Maximum Likelihood Estimation (MLE) Approach for Determining Most Representative Solar Resource Data Set for United States

Solar projects in the US benefit from having many irradiation data sources available for comparative analysis. Not many countries in the world have a database extending back to the 1960s as well as multiple providers of satellite-derived irradiance estimates. The National Solar Resource Database ("NSRDB") program envisioned to support the growth of the solar market by laying a strong foundation for irradiation measurement. The "site-specific" NSRDB modeling approach transitioned in 2014 to use a satellite-derived Physical Solar Model ("PSM"). Currently, several additional satellite-derived data sets are commercially available which provide more recent estimates of irradiation data. The key question is: which one of these data sets is likely to be most representative of the 'long term' resource for a site? This paper explores using the concept of Maximum Likelihood Estimation (MLE) to be applied to several equally relevant data sources to provide feedback on long-term weather selection.
ESPN: Extremely Sparse Pruned Networks

Deep neural networks are often highly over-parameterized, prohibiting their use in compute-limited systems. However, a line of recent works has shown that the size of deep networks can be considerably reduced by identifying a subset of neuron indicators (or mask) that correspond to significant weights prior to training. We demonstrate that a simple iterative mask discovery method can achieve state-of-the-art compression of very deep networks. Our algorithm represents a hybrid approach between single-shot network pruning methods (such as SNIP) with Lottery-Ticket type approaches. We validate our approach on several datasets and outperform several existing pruning approaches in both test accuracy and compression ratio.
Hybrid Attention-based Approach for Arabic Paraphrase Detection

The growth of data across the web and the ambiguous structure of Arabic language have favored the act of paraphrase. It is defined as a restatement of the original text, giving the same meaning in another form without mentioning its source. Its detection requires calculating semantic textual similarity, which is an important research area in Natural Language Processing (NLP) tasks. Following the literature, deep neural network models have gained satisfactory results in sentence modeling and similarity computing. In this context, a hybrid Siamese neural network architecture is proposed that is composed of the following main components: First, salient features are extracted by applying Global Vectors Representation (GloVe). Then, Convolutional Neural Networks (CNN) capture and learn the contextual meaning of words due to their outstanding performance that has been achieved in different NLP tasks. Then, the output of CNN is combined with an attention model to distinguish the most important words representing the meaning of the sentence. The similarity score between sentences was subsequently computed by applying the cosine measure. Experiments were carried out on a proposed Arabic paraphrased corpus using the Open-Source Arabic Corpora (OSAC). To validate its quality, the SemEval benchmark is used.
Residual Decoder based U-Net for Semantic Segmentation

Semantic segmentation is the preliminary step in several computer vision tasks and therefore is a hot topic of research. Convolutional Neural Networks have invaded the domain by their representation power and proved their efficacy in the recent years. This paper proposes an encoder–decoder-based segmentation model, with skip connections established across encoder and decoder blocks in a U-shape. When most of the existing models focus on the encoder part, the proposed model attempts to enhance the decoder module to recover the spatial information effectively using residual connection. Pretrained VGG16 is employed to capture the high-level features of the image via the encoder module. In the proposed decoder module, two variants of convolutional blocks with residual connection are designed, in which one uses serial convolutional layers and the other uses parallel convolutional layers. The use of dilated convolutions in an optimal manner improved the performance further. Experimental analysis on a dataset of road images shows that the model outperforms the basic U-net considerably, with a mIoU of 77.86% and 76.74% for serial and parallel variants, respectively. It shows promising performance in separating the semantically relevant objects in the image. Despite using convolutions with large-sized kernels, the model is able to be trained with less number of parameters with the proposed parallel variant of the residual decoder-based model.
A Comparative Study of $$\text {L}_1$$ and $$\text {L}_2$$ Norms in Support Vector Data Descriptions

The Support Vector Data Description ( $$\text {L}_1$$ SVDD) is a non-parametric one-class classification algorithm that utilizes the $$\text {L}_1$$ norm in its objective function. An alternative formulation of SVDD, called $$\text {L}_2$$ SVDD, uses a $$\text {L}_2$$ norm in its objective function and has not been extensively studied. $$\text {L}_1$$ SVDD and $$\text {L}_2$$ SVDD are formulated as distinct quadratic programming (QP) problems and can be solved with a QP-solver. The $$\text {L}_2$$ SVDD and $$\text {L}_1$$ SVDD’s ability to detect small and large shifts in data generated from multivariate normal, multivariate t, and multivariate Laplace distributions is evaluated. Similar comparisons are made using real-world datasets taken from various applications including oncology, activity recognition, marine biology, and agriculture. In both the simulated and real-world examples, $$\text {L}_2$$ SVDD and $$\text {L}_1$$ SVDD perform similarly, though, in some cases, one outperforms the other. We propose an extension of the SMO algorithm for $$\text {L}_2$$ SVDD, and we compare the runtimes of four algorithms: $$\text {L}_2$$ SVDD (SMO), $$\text {L}_2$$ SVDD (QP), $$\text {L}_1$$ SVDD (SMO), and $$\text {L}_1$$ SVDD (QP). The runtimes favor $$\text {L}_1$$ SVDD (QP) versus $$\text {L}_2$$ SVDD (QP), sometimes substantially; however using SMO reduces the difference in runtimes considerably, making $$\text {L}_2$$ SVDD (SMO) feasible for practical applications. We also present gradient descent and stochastic gradient descent algorithms for linear versions of both the $$\text {L}_1$$ SVDD and $$\text {L}_2$$ SVDD. Examples using simulated and real-world data show that both methods perform similarly. Finally, we apply the $$\text {L}_1$$ SVDD and $$\text {L}_2$$ SVDD to a real-world dataset that involves monitoring machine failures in a manufacturing process.
Multilevel filtering image denoising algorithm based on edge information fusion

Edges are critical important for the visual appearance of images. The traditional denoising algorithms are difficult to preserve the edges of the image while removing the noise of ICCD sensing image. At the same time, it is difficult to eliminate the problems of image darkness and low resolution caused by uneven illumination. This paper proposes a multilevel filtering image denoising algorithm based on edge information fusion. The target edges detection of the image after non-local means (NL-means) filtering is carried out based on the eight-direction Sobel operator. In order to filter the false edge points and residual noise, an adaptive threshold is determined according to the mean and variance of the eight neighborhood pixels of the detected pixel. Meanwhile, homomorphic filtering is used to enhance the image contrast and uniformity. By comparing the pixel values of the edge image and the homomorphic filtered image, the final denoised image is obtained by fusing the two images. The results indicate that, compared with the traditional algorithms, the edge preserving ability of the proposed algorithm is improved by more than 20%, and the denoising ability is improved by 63.5% for building target. For specific targets (vehicle), the results demonstrate that the proposed algorithm have the maximum edge preserving index and contrast, and the minimum non-uniformity. This algorithm lays a foundation for target segmentation and recognition.
Handwriting With Sound-Speed Imaging Using Ultrasound Computed Tomography

This letter proposes the use of ultrasound computed tomography (USCT) for handwriting by imaging sound velocity changes in a medium caused by a rod. The rod was used as a writing object, and the tomographic instrument was able to process the data over time with a temporal frequency of 4 frames per second. Tests are carried out in writing various words and letters using USCT data and automatic imaging software. The work shows the functional imaging capability offered by the USCT system combining qualitative and temporal imaging features.