Reviewerly/exHarmony huggingface dataset:
Identification of data elements for blood gas analysis dataset: a base for developing registries and artificial intelligence-based systems

One of the challenging decision-making tasks in healthcare centers is the interpretation of blood gas tests. One of the most effective assisting approaches for the interpretation of blood gas analysis (BGA) can be artificial intelligence (AI)-based decision support systems. A primary step to develop intelligent systems is to determine information requirements and automated data input for the secondary analyses. Datasets can help the automated data input from dispersed information systems. Therefore, the current study aimed to identify the data elements required for supporting BGA as a dataset.This cross-sectional descriptive study was conducted in Nemazee Hospital, Shiraz, Iran. A combination of literature review, experts' consensus, and the Delphi technique was used to develop the dataset. A review of the literature was performed on electronic databases to find the dataset for BGA. An expert panel was formed to discuss on, add, or remove the data elements extracted through searching the literature. Delphi technique was used to reach consensus and validate the draft dataset.The data elements of the BGA dataset were categorized into ten categories, namely personal information, admission details, present illnesses, past medical history, social status, physical examination, paraclinical investigation, blood gas parameter, sequential organ failure assessment (SOFA) score, and sampling technique errors. Overall, 313 data elements, including 172 mandatory and 141 optional data elements were confirmed by the experts for being included in the dataset.We proposed a dataset as a base for registries and AI-based systems to assist BGA. It helps the storage of accurate and comprehensive data, as well as integrating them with other information systems. As a result, high-quality care is provided and clinical decision-making is improved.
Multilayer Photonic Spiking Neural Networks: Generalized Supervised Learning Algorithm and Network Optimization

We propose a generalized supervised learning algorithm for multilayer photonic spiking neural networks (SNNs) by combining the spike-timing dependent plasticity (STDP) rule and the gradient descent mechanism. A vertical-cavity surface-emitting laser with an embedded saturable absorber (VCSEL-SA) is employed as a photonic leaky-integrate-and-fire (LIF) neuron. The temporal coding strategy is employed to transform information into the precise firing time. With the modified supervised learning algorithm, the trained multilayer photonic SNN successfully solves the XOR problem and performs well on the Iris and Wisconsin breast cancer datasets. This indicates that a generalized supervised learning algorithm is realized for multilayer photonic SNN. In addition, network optimization is performed by considering different network sizes.
Retinex Based on Weaken Factor with Truncated AGCWD for Backlight Image Enhancement

This paper proposes a new backlight image enhancement algorithm based on an Adaptive Gamma Correction with Weighting Distribution (AGCWD) algorithm. The proposed algorithm is based on Multi-scale Retinex (MSR). And it is composed of HSV color space, surrounding luminance blur(blurred-surrounding luminance), MSR-based just-noticeable difference (JND) curve weakening factor, and AGCWD. The proposal MSR&#x0027;s surrounding luminance was simulated by the Gaussian filter was replaced with the mean filter to reduce the complexity of the proposed algorithm. In order to avoid over-enhancing the areas with high luminance by AGCWD and keep the detail information of local areas, a restriction is added to the Cumulative Distribution Function curve in the proposed algorithm. The proposed algorithm has better performance than previous studies in the backlight image, especially shadow areas. In addition, it also reduces the complexity of the MSR&#x0027;s algorithm. Decreased 86.65&#x0025; at the time of simulating surrounding luminance.
Interrelation of Bitcoin and Some Traditional Assets

In the research, the causal relationships between Bitcoin, gold and oil prices were examined. The data of the research covers the period from 2015 to July 2020 and consists of daily price values. Augmented Dickey-Fuller Unit Root Test was used to see whether the stochastic process changes with time. Bitcoin and gold series do not contain a unit root since the oil series is stationary at the level while the difference is stationary. The reason why the series containing unit roots are not stationary is due to structural breaks or not, was investigated by Bai-Perron Unit Root Test with Multiple Structural Breaks. According to the test, it was determined that the Bitcoin series has one break and two regimes, while the gold series has two structural breaks and three different regimes. Whether the research series are cointegrated or not was investigated with the Gregory and Hansen test. The causality between the series was examined with the Toda-Yamamoto causality test, which is based on the VAR (Vector Autoregression) model and examines the causality in the series regardless of the unit root. A two-way causality relationship was determined between the eight lag-long Gold series and the Bitcoin series. In other cases, a causal relationship has not been established. As a result, we give an evidence that Bitcoin and gold prices series followed a parallel pattern while with oil not. Therefore, investors can add Bitcoin into their portfolios to make balance of the risk and return.
Goal-Oriented Software Design Reviews

Some software requirements are omitted or ambiguous depending on the design context, although these requirements would not necessarily be omitted or ambiguous when viewed as requirements alone.The design context sometimes causes inconsistencies among implementations that realize the same requirement.Existing detection and analysis methods do not limit evaluation of review materials to implementations of context-dependent design.An evaluation technique that limits the evaluated parts to the parts describing context-dependent design implementations is expected to be efficient.This paper proposes a method for detecting inconsistent implementations (context-dependent requirement defects) caused by context-dependent requirement omissions and ambiguities in design reviews.The proposed method defines goal-oriented check items for design review using a goal tree obtained by goal-oriented requirements analysis.Reviewers use the goal-oriented check items to detect inconsistent implementations that realize the same requirement.This paper also evaluates the proposed method through a case study.The results of the case study showed that the proposed method defined five goal-oriented check items and that reviewers detected 24 context-dependent requirement defects with goal-oriented check items.The results also showed that the sum of the estimated additional effort to define goal-oriented check items and perform design reviews with goal-oriented check items was 19.6 person-hours.Furthermore, the results showed that an engineer with general skills and knowledge of software development but without system-specific skills and knowledge could define a goal tree and the corresponding goal-oriented check items.
Consistent attentive dual branch network for person re-identification

Abstract Several recent person re-identification methods are focusing on learning discriminative representations by designing efficient metric learning loss functions. Other approaches design part based architectures to compute an informative descriptor based on local features from semantically coherent parts. Few efforts learn the relationship between distant similar regions and parts by adjusting them to their most feasible positions with the help of soft attention. However, they focus on calibrating distant similar parts features and ignore to learn the noise (blur) free and distinct feature representations as the person re-identification datasets contain degraded images. To tackle these issues, we propose a novel Consistent Attention Dual Branch Network (CadNet) that has ability to model long-range dependencies (correlations) between channels as well as feature maps. We adopt multiple classifiers trained to learn the most discriminative global features for a unique representation of a person. Correlation between channels are consistently computed by using channel attention mechanism to make the learned feature noise free and distict from noisy and blurry data. Feature correlations interpret the relationship between distant similarities in the images computed by the self attention mechanism. The proposed CadNet significantly enhances the performance with respect to the baseline on the person re-identification benchmarks.
AABLSTM: A Novel Multi-task Based CNN-RNN Deep Model for Fashion Analysis

With the rapid growth of online commerce and fashion-related applications, visual clothing analysis and recognition has become a hotspot in computer vision. In this paper, we propose a novel AABLSTM network, which is based on deep CNN-RNN, to solve the visual fashion analysis of clothing category classification, attribute detection, and landmark localization. The designed fashion model is leveraged with the multi-task driven mechanism as follows: firstly, a bidirectional LSTM (Bi-LSTM) branch is proposed for efficiently mining the semantic association between related attributes so as to improve the precision of clothing category classification and attribute detection; then, an imitated hourglass sub-network of “down-up sampling” is constructed for boosting the accuracy of fashion landmark localization; and finally, a specially designed multi-loss function is constructed to better optimize the network training. Extensive experimental results on large-scale fashion datasets demonstrate the superior performance of our approach.
Design of an optimized fuzzy system for edge detection in images

Edge Detection is the first stage in the image division into separate parts. Image division is the partitioning of a digital image to the different zones or the set of pixels. Edge detection is one of the techniques applied in digital image processing often. The purpose of detecting pixels is to match the edges in the image. Filtering, Enhancement, and Detection are three steps in edge detection. Images are usually destroyed by casual changes in intensity intervals called noise or confusion. some noise variations include salt and pepper, pulse, and Gaussian. However, there is a relation between edge detection power and noise reduction. Using filters to the noise reduction causes the loss of edge detection power. For facilitating the edges detection, it is essential for the determination of pixels’ intensity constraints in their neighborhood. Many points in an image have a nontransparent slope, and all of them are not the edges of the joint space. Therefore, some of the linear and nonlinear methods such as Sobel, Prewitt, and Robert have to be used to determine the edge points. The fuzzy logic and the system based on it, is one of the most effective methods for edge detection. This paper presents an optimized rule-based fuzzy inference system and designs the efficiency mask matric. The simulation results for edge detection are presented using the traditional edge detection techniques, including Binary Filter, Sobel Filter, Prewitt Filter, and Robert Filter. Also, it is presented using the fuzzy approach. The simulation results show that the designed fuzzy system has been able to detect the edges of the image more accurately and help to increase the sharpness and quality of the edges. Therefore, the proposed method has more accurate and more reliable results and reduces false edge detection comparison to the traditional methods.
Enhancing Moon Crescent Visibility Using Contrast-Limited Adaptive Histogram Equalization and Bilateral Filtering Techniques

Image enhancement is becoming increasingly important with the advancement of space exploration techniques and the technological development of more durable and scientifically sound observatories equipped with more powerful telescopes. The enhancement of images helps astronomers analyze the results and act toward determining the dates of religious festivals. This work describes a technique known as contrast-limited adaptive histogram equalization (CLAHE) with grayscale contrast enhancement and bilateral filtering. We apply CLAHE on the L component of the CIE-Lab color space to adjust lightness contrast. Subsequently, grayscale contrast enhancement is performed to increase the visibility of the moon crescent. Noise caused by grayscale contrast enhancement is reduced using bilateral filtering. Two quantitative measures are selected (PSNR and MSE) to show the visual improvement achieved by the proposed algorithm.
Variationally Autoencoded Dynamic Policy Programming for Robotic Cloth Manipulation Planning based on Raw Images

This paper proposes a framework for model learning and action planning of cloth manipulation tasks based on such high-dimensional and redundant sensor observations as raw pixel images. This framework is called the variationally autoencoded dynamic policy programming (VAE-DPP), which is a combination of variationally autoencoded hidden Markov decision process (VAE-HMDP) and dynamic policy programming (DPP). At first, VAE-HMDP is learned, which consists of two disentangled latent spaces: for the image with VAE and for the hidden Markov decision process (HMDP). Then DPP is applied to the learned HMDP to plan action sequences efficiently. After investigating its effectiveness through a simple simulation of a toy problem, we applied VAE-DPP to two robotic cloth-folding tasks: (1) folding a handkerchief and (2) folding trousers. We successfully generated appropriate actions that reached the target folding state.
Security proof for round-robin differential-quadrature-phase-shift quantum key distribution

By introducing four-phase modulations into round-robin-differential-phase-shift (RRDPS) quantum key distribution (QKD) protocol, the round-robin-differential-quadrature-phase-shift (RRDQPS) protocol is proposed. According to the security proof of RRDQPS protocol, it is able to tolerate a higher error rate of key bits. However, its performance with a practical weak coherent source is still unknown since that security proof only applies to a single photon source. In this article, we give the security proof for the general $n$-photon RRDQPS protocol, by which RRDQPS with a practical weak coherent source becomes feasible in the experiment. Through numerical simulation, it is verified that RRDQPS protocol has an advantage over RRDPS protocol on transmission distance, especially when the pulse number is small.
Toward sustainable development? Trend analysis of environmental policy in Korea from 1987 to 2040

It is crucial to observe how international policy initiatives are implemented in each country's environmental policies to ensure global environmental conservation and sustainable development. This study aims to comprehensively analyze the trend of environmental policy in Korea over time. To this aim, we use semantic network analysis to explore the main keywords and to identify the characteristics of the key themes in each period based on the national environmental comprehensive plan. Our results show that the value of economic growth and technological development are regarded as priorities in Korean environmental policy because the economy and technology are closely related to the environment. Similarly, when it comes to the fundamental concept of environmental policy, the concept of "green growth" is applied in preference to the concept of "sustainability." The results contribute to expanding the approach of existing policy research by providing the potential for semantic network analysis based on policy documents.
Target Sentiment Analysis Ensemble for Product Review Classification

Abstract— Machine learning can be used to provide systems the ability to automatically learn and improve from experiences without being explicitly programmed. It is fundamentally a multidisciplinary field that draws on results from Artificial intelligence, probability and statistics, information theory and analysis, among other fields that impact the field of Machine Learning. Ensemble methods are techniques that can be used to improve the predictive ability of a Machine Learning model. An ensemble comprises of individually trained classifiers whose predictions are combined when classifying instances. Some of the currently popular ensemble methods include Boosting, Bagging and Stacking. In this paper, we review these methods and demonstrate why ensembles can often perform better than single models. Additionally, some new experiments are presented to demonstrate the computational ability of Stacking approach.
Detection of Face Morphing Attacks Based on Patch-Level Features and Lightweight Networks

Aiming at the problem of face morphing attack detection under mobile and resource-constrained conditions, a face morphing detection method based on patch-level features and lightweight networks is proposed. It utilizes the combination of three blocks’ structures for learning. By outputting the probability of each bona fide or morphed face patches, the whole face features are integrated for recognition. Experimental results and analysis show this method can significantly improve the detection accuracy of face morphing attacks. Compared with the existing methods, this method has the characteristics of high computational efficiency and strong robustness. It has great application potential in enhancing the security of the face recognition system.
An intelligent voice recognition system based on fuzzy logic and the bag-of-words technique

Abstract This paper describes a method for recognizing voice command based on a fuzzy logic system capable of perceiving fuzzy commands, i.e. commands containing fuzzy terms, for example, ‘close’, ‘closer’, ‘close to’, ‘closer than’, ‘further’ and ‘very far’. The developed approach has the ability to be trained for a specific user. The developed fuzzy logic system is used to recognize linguistically inaccurate commands in order to increase the expressiveness of the language for control of a moving robot.
LPCP: An efficient Privacy-Preserving Protocol for Polynomial Calculation Based on CRT

With the development of privacy-preserving techniques, the increasing demand for secure multiparty computation (MPC) of mobile devices has become a significant challenge. Unfortunately, it is inapplicable for mobile devices to implement the existing secure multiparty computation schemes that rely on costly computation and communication overhead. To solve this problem, we propose an efficient two-party computation protocol secure against semi-honest adversary based on the Chinese remainder theorem (CRT). Our protocol utilizes CRT-based encryption and re-encryption techniques to realize additional and multiplicative homomorphic encryption, which can be transformed into a two-party secure computation scheme. Then, we extend our two-party LPCP protocol into a multiparty LPCP protocol, which is much faster and more space saving than the previous works. For practical purpose, we describe a distance measurement application for mobile devices based on LPCP. In the end, we implement LPCP codes and compare the experimental results to the state-of-the-art two-party and multiparty computation protocols. The experimental result shows that the high computation and communication efficiency of LPCP makes it possible for low computing-power mobile devices to implement multiparty secure computation protocols in reality.
Development of Trace Transform using Machine Learning for reducing the tracing line calculation

An important approach presented in this paper is to apply the idea of machine learning to improve the visual processing of the trace transform algorithm by using machine learning to predict the answer. In terms of calculating the results of each tracing line, each such tracing line must be calculated to find only one solution from a single equation, which will have different forms of complex equations. There are 22 basic equations options to choose from, the results of which can effectively reduce the overall runtime of the trace transform. The idea in this research can reduce the complexity of trace's algorithm by applying the predictive results obtained from training data into the machine learning system to create a model for implementation. The training model will be used to predict the results which will replace the original function calls for repetitive calculations and use only forecasting.
Ontologically correct taxonomies by construction

Taxonomies play a central role in conceptual domain modeling, having a direct impact in areas such as knowledge representation, ontology engineering, and software engineering, as well as knowledge organization in information sciences. Despite this, there is little guidance on how to build high-quality taxonomies, with notable exceptions being the OntoClean methodology, and the ontology-driven conceptual modeling language OntoUML. These techniques take into account the ontological meta-properties of types to establish well-founded rules on the formation of taxonomic structures. In this paper, we show how to leverage the formal rules underlying these techniques in order to build taxonomies which are correct by construction. We define a set of correctness-preserving operations to systematically introduce types and subtyping relations into taxonomic structures. In addition to considering the ontological micro-theory of endurant types underlying OntoClean and OntoUML, we also employ the MLT (Multi-Level Theory) micro-theory of high-order types, which allows us to address multi-level taxonomies based on the powertype pattern. To validate our proposal, we formalize the model building operations as a graph grammar that incorporates both micro-theories. We apply automatic verification techniques over the grammar language to show that the graph grammar is sound, i.e., that all taxonomies produced by the grammar rules are correct, at least up to a certain size. We also show that the rules can generate all correct taxonomies up to a certain size (a completeness result).
Low Memory Access Video Stabilization for Low-Cost Camera SoC

Video stabilization is one of the most important features in consumer cameras. Even simple video stabilization algorithms may need to access the frames several times to generate a stabilized output image, which places a significant burden on the camera hardware. This high-memory-access requirement makes it difficult to implement video stabilization in real time on low-cost camera SoC. Reduction of the memory usage is a critical issue in camera hardware. This paper presents a structure and layout method to efficiently implement video stabilization for low-end hardware devices in terms of shared memory access amount. The proposed method places sub-components of video stabilization in a parasitic form in other processing blocks, and the sub-components reuse data read from other processing blocks without directly accessing data in the shared memory. Although the proposed method is not superior to the state-of-the-art methods applied in post-processing in terms of video quality, it provides sufficient performance to lower the cost of camera hardware for the development of real-time devices. According to my analysis, the proposed one reduces the memory access amount by 21.1 times compared to the straightforward method.