Reviewerly/exHarmony huggingface dataset:
The Role of VR Shopping in Digitalization of SCM for Sustainable Management: Application of SOR Model and Experience Economy

Shopping malls in virtual reality (VR) environments have created a new paradigm of distribution environments as a strategy of digital consumption experience for sustainable management in the fast-changing digitalization of SCM. This study implemented a VR shopping environment to examine how telepresence and interactivity affect the consumers’ usage behavior after experiencing a VR shopping environment by applying the SOM model and experience economy, and suggests the role of VR shopping in the digitalization of SCM for sustainable management. First, this study examined the impact of telepresence and interactivity on time distortion and enjoyment based on the experience economy and flow theory. Second, the relationship between time distortion and perceived enjoyment during the VR shopping experience was tested. The study also examined whether the perceived enjoyment affected behavioral intention. As a research method for this study, we directly implemented a VR supermarket, and had 120 people participate in the survey after experiencing the VR supermarket. Additionally, a structural equation model was used to verify the proposed hypothesis model. The study findings show that both telepresence and interactivity have a significant relationship with time distortion and enjoyment. Time distortion was verified to have partial mediating effects on the relationship between telepresence and enjoyment. Time distortion had a positive impact on enjoyment, which in turn was verified to have a significant effect on the consumers’ behavioral intention. In other words, in order to digitize SCM for sustainable management in a VR shopping environment, it is necessary to implement an environment in which consumers are immersed and to track consumer behavior during the experience. Through this, it is possible to create a core management strategy that creates a competitive advantage.
Large Margin Cotangent Loss for Deep Similarity Learning

Deep Convolutional Neural Networks (DCNN) models have become popular in feature extraction tasks. One of the best approaches to effectively classify the features is to utilize the loss function. Softmax loss is one of the losses initiated from One-Shot Learning method; however, using Softmax loss is considered impractical for real-world tasks since the number of labels can change frequently and require re-train when new labels emerge. In contrast, many types of loss function from Similarity Learning method such as Intra loss, Inter loss, Triplet loss, and Margin loss can tackle those drawbacks. Among them, the Margin-Loss-based losses have recently claimed to be the most effective strategies; such as CosFace, by adding a fixed parameter as a cosine margin to eliminate the dependence of the normal distribution to the cosine of weights and feature vectors; as well as ArcFace, by adding addictive angular penalty margins in the cosine function to simultaneously enhance class compactness and class differentiation. However, we evaluated that cosine functions used from these strategies do not fully represent the angle when they get smaller due to the limitation on the range of values. We propose Large Margin Cotangent Loss (LMCot), which uses cotangent function instead of cosine function since cotangent perform better by the unlimited on the range of values. As a result, our proposed method has great potential to improve performance on verification and identification tasks. Furthermore, we also experimented on various datasets from accredited competitions, the results of LMCot advanced the state-of-the-art in some measurements.
Permissioned Blockchain-Based Solution to Document Processing in the Real Estate Industry

For Blockchain Technology, Real Estate is a particularly excellent target since it has a complicated transaction process that is designed to prevent fraud and enable stringent ownership protection. The real estate industry demands these characteristics, and this is where blockchain excels. Blockchain Technology can be used to enhance System openness making it possible for regulators to identify and stop fraudulent activity. Blockchain technology and the fundamentals of the Indian real estate market are introduced in the first section of the article. The second section summarizes recent research in the area of interest and points out research gaps. The framework and suggested algorithms for document processing in the Indian real estate sector are shown in the third part. The implementation of the proposed algorithms with tools and technologies is suggested in the final section. Using the permission blockchain, the proposed algorithms can be put into practice as chain code. The suggested algorithm’s ultimate purpose is to preserve transparency, record integrity, and trust factor in the targeted area to encourage openness, integrity, availability, and trust.
Comparative Analysis of Several Approaches of Encoding Audio Files

The use of cryptography is essential to ensure confidential communication across networks. Concerns about the safety of digital data as a result of its fast expansion has increased the need for the development of more sophisticated methods of cryptography. Cryptography is a procedure that rearranges and replaces content inside the information in order to scramble it, making it unintelligible to anybody other than the person who is capable of deciphering it. This becomes cryptography a sort of information security. This process is known as encoding. Cryptanalysis, sometimes known as "breaking the code," refers to the process of deciphering a communication even when the decipherer has no idea how the information was encrypted in the first place. The use of encryption is no longer restricted to just textual information. There are algorithms for a variety of data types, including images, audio, and video, amongst others. A few of the criteria that are considered while evaluating cryptographic algorithms are throughput, speed, CPU time, battery power consumption, and memory requirements. In this work, an investigation into many commonly used algorithms like as RC6, RC4, ThreeFish, Twofish, Blowfish, AES, 3DES, and DES is carried out on the aforementioned parameters in order to locate the most effective solution. This work's objective is to provide a comprehensive introduction to a few of the already – present cryptographic approaches and their respective work quality for a wide variety of data types, with a particular emphasis on audio file encryption.
Phishing URLs Detection Using Machine Learning

Nowadays, internet user numbers are growing steadily, covering online services, and goods transactions. This growth can lead to the theft of users' private information for malicious purposes. Phishing is one technique that can cause users to be redirected to sites with malicious content and steal all of their information. The main purpose of phishing is to steal user identities such as online credentials, bank transaction details, etc. As technology advances, the mechanism of phishing attacks begins to take place, so to prevent it from happening, some mechanism anti-phishing is used to detect phishing links or URLs Machine learning is the most solutions tools against phishing offensive, and with its algorithms, we can rank all content and determine whether it is phishing or not. We tested cross-validation as well as the correlation between features. Using Logistic Regression, we determined the importance of the features. Finally, we tested the Multinomial Naïve Baye classifier. We found that the Logistic Regression classifier had better accuracy for the best accuracy.
Self‐attention based progressive generative adversarial network optimized with arithmetic optimization algorithm for kidney stone detection

Summary Self‐attention based progressive generative adversarial network optimized with arithmetic optimization algorithm (AOA) is proposed in this manuscript for kidney stone detection. Initially, the input kidney stone images are gathered via CT kidney dataset. Then, the input image is preprocessed by utilizing APPDRC filtering approach. Also, the preprocessed images are given to the multi‐level thresholding segmentation technique for segmented the image. Then the segmented images are given to the TF‐IDF feature extraction method for extracting the features. Then the extracted features are fed to feature selection using Weibull distributive generalized multidimensional scaling methods for selecting the features. Then the selected features are SPGGAN classification method for kidney stone detection. Generally, SPGGAN not reveal any adoption of optimization methods compute optimum parameters for assuring correct kidney stone detection. Thus, AOA is used for optimizing the weight parameters of SPGGAN and it is implemented in python, its performance is examined under certain performances metrics, such as accuracy, precision, sensitivity, specificity, F‐measure, computational time, and ROC. The proposed SPGGAN‐AOA‐KSD method attains 54.78%, 34.89%, and 20.96% higher accuracy and 3.45%, 4.08%, and 5.06% greater AUC compared with existing methods, such as a ANN‐OGGA‐KSD, HMANN‐BPA‐KSD, and ANN‐CSOA‐KSD respectively.
High probability of successive occurrence of Nankai megathrust earthquakes

Great earthquakes along the Nankai megathrust in south-western Japan feature in the top priority list of Japan's disaster management agenda. In May 2019, an alert system was incepted to issue public warnings when the probability of an earthquake occurrence along the Nankai megathrust became higher than usual. One of the cases that trigger the issuance of public warnings is when a great earthquake occurred and another one of the same scale is anticipated within a short period of time. Although such "twin ruptures" have occurred multiple times along the Nankai megathrust, the quantification of the probability of such twin ruptures has never been attempted. Based on global statistics and local earthquake occurrence history, we estimated the probability of a successive occurrence of two M8 or larger earthquakes within 3 years globally and along the Nankai megathrust to be 5.0-18% and 4.3-96%, respectively. The timing of the second earthquake followed the Omori-Utsu law in global statistics, which allowed the estimation of the probability for the successive occurrence of Nankai megathrust earthquakes in arbitrary time frames. The predicted probability for the one-week timeframe was 100-3600-fold higher than that of the norm, endorsing the necessity for the warning scheme.
Scale Invariant Feature Transform with Crow Optimization for Breast Cancer Detection

Mammography is considered a significant image for accurate breast cancer detection. Content-based image retrieval (CBIR) contributes to classifying the query mammography image and retrieves similar mammographic images from the database. This CBIR system helps a physician to give better treatment. Local features must be described with the input images to retrieve similar images. Existing methods are inefficient and inaccurate by failing in local features analysis. Hence, efficient digital mammography image retrieval needs to be implemented. This paper proposed reliable recovery of the mammographic image from the database, which requires the removal of noise using Kalman filter and scale-invariant feature transform (SIFT) for feature extraction with Crow Search Optimizationbased the deep belief network (CSO-DBN). This proposed technique decreases the complexity, cost, energy, and time consumption. Training the proposed model using a deep belief network and validation is performed. Finally, the testing process gives better performance compared to existing techniques. The accuracy rate of the proposed work CSO-DBN is 0.9344, whereas the support vector machine (SVM) (0.5434), naïve Bayes (NB) (0.7014), Butterfly Optimization Algorithm (BOA) (0.8156), and Cat Swarm Optimization (CSO) (0.8852).
From video summarization to real time video summarization in smart cities and beyond: A survey

With the massive expansion of videos on the internet, searching through millions of them has become quite challenging. Smartphones, recording devices, and file sharing are all examples of ways to capture massive amounts of real time video. In smart cities, there are many surveillance cameras, which has created a massive volume of video data whose indexing, retrieval, and administration is a difficult problem. Exploring such results takes time and degrades the user experience. In this case, video summarization is extremely useful. Video summarization allows for the efficient storing, retrieval, and browsing of huge amounts of information from video without sacrificing key features. This article presents a classification and analysis of video summarization approaches, with a focus on real-time video summarization (RVS) domain techniques that can be used to summarize videos. The current study will be useful in integrating essential research findings and data for quick reference, laying the preliminaries, and investigating prospective research directions. A variety of practical uses, including aberrant detection in a video surveillance system, have made successful use of video summarization in smart cities.
A Rapidly-Exploring Random Trees Motion Planning Algorithm for Hybrid Dynamical Systems

This paper proposes a rapidly-exploring random trees (RRT) algorithm to solve the motion planning problem for hybrid systems. At each iteration, the proposed algorithm, called HyRRT, randomly picks a state sample and extends the search tree by flow or jump, which is also chosen randomly when both regimes are possible. Through a definition of concatenation of functions defined on hybrid time domains, we show that HyRRT is probabilistically complete, namely, the probability of failing to find a motion plan approaches zero as the number of iterations of the algorithm increases. This property is guaranteed under mild conditions on the data defining the motion plan, which include a relaxation of the usual positive clearance assumption imposed in the literature of classical systems. The motion plan is computed through the solution of two optimization problems, one associated with the flow and the other with the jumps of the system. The proposed algorithm is applied to a walking robot so as to highlight its generality and computational features.
Research on reinforcement learning-based safe decision-making methodology for multiple unmanned aerial vehicles

A system with multiple cooperating unmanned aerial vehicles (multi-UAVs) can use its advantages to accomplish complicated tasks. Recent developments in deep reinforcement learning (DRL) offer good prospects for decision-making for multi-UAV systems. However, the safety and training efficiencies of DRL still need to be improved before practical use. This study presents a transfer-safe soft actor-critic (TSSAC) for multi-UAV decision-making. Decision-making by each UAV is modeled with a constrained Markov decision process (CMDP), in which safety is constrained to maximize the return. The soft actor-critic-Lagrangian (SAC-Lagrangian) algorithm is combined with a modified Lagrangian multiplier in the CMDP model. Moreover, parameter-based transfer learning is used to enable cooperative and efficient training of the tasks to the multi-UAVs. Simulation experiments indicate that the proposed method can improve the safety and training efficiencies and allow the UAVs to adapt to a dynamic scenario.
Image-to-image generative adversarial networks for synthesizing perfusion parameter maps from DSC-MR images in cerebrovascular disease

Stroke is a major cause of death or disability. As imaging-based patient stratification improves acute stroke therapy, dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) is of major interest in image brain perfusion. However, expert-level perfusion maps require a manual or semi-manual post-processing by a medical expert making the procedure time-consuming and less-standardized. Modern machine learning methods such as generative adversarial networks (GANs) have the potential to automate the perfusion map generation on an expert level without manual validation. We propose a modified pix2pix GAN with a temporal component (temp-pix2pix-GAN) that generates perfusion maps in an end-to-end fashion. We train our model on perfusion maps infused with expert knowledge to encode it into the GANs. The performance was trained and evaluated using the structural similarity index measure (SSIM) on two datasets including patients with acute stroke and the steno-occlusive disease. Our temp-pix2pix architecture showed high performance on the acute stroke dataset for all perfusion maps (mean SSIM 0.92-0.99) and good performance on data including patients with the steno-occlusive disease (mean SSIM 0.84-0.99). While clinical validation is still necessary for future studies, our results mark an important step toward automated expert-level perfusion maps and thus fast patient stratification.
Independent Natural Policy Gradient Methods for Potential Games: Finite-time Global Convergence with Entropy Regularization

A major challenge in multi-agent systems is that the system complexity grows dramatically with the number of agents as well as the size of their action spaces, which is typical in real world scenarios such as autonomous vehicles, robotic teams, network routing, etc. It is hence in imminent need to design decentralized or independent algorithms where the update of each agent is only based on their local observations without the need of introducing complex communication/coordination mechanisms. In this work, we study the finite-time convergence of independent entropy-regularized natural policy gradient (NPG) methods for potential games, where the difference in an agent’s utility function due to unilateral deviation matches exactly that of a common potential function. The proposed entropy-regularized NPG method enables each agent to deploy symmetric, decentralized, and multiplicative updates according to its own payoff. We show that the proposed method converges to the quantal response equilibrium (QRE)— the equilibrium to the entropy-regularized game—at a sublinear rate, which is independent of the size of the action space and grows at most sublinearly with the number of agents. Appealingly, the convergence rate further becomes independent with the number of agents for the important special case of identical-interest games, leading to the first method that converges at a dimension-free rate. Our approach can be used as a smoothing technique to find an approximate Nash equilibrium (NE) of the unregularized problem without assuming that stationary policies are isolated.
Blockchains as a means to promote privacy protecting, access availing, incentive increasing, ELSI lessening DNA databases

Not all blockchains are created equal, and many cannot accommodate all of the primary characteristics of big data: Variety, Velocity, Volume and Veracity. Currently, public blockchains are slow and clunky, it can be expensive to keep up with the velocity of genomic data production. Further, the transparent and universally accessible nature of public blockchain doesn't necessarily accommodate all of the variety of sequence data, including very private information. Bespoke private permissioned blockchains, however, can be created to optimally accommodate all of the big data features of genomic data. Further, private permissioned chains can be implemented to both protect the privacy and security of the genetic information therein, while also providing access to researchers. An NFT marketplace associated with that private chain can provide the discretized sale of anonymous and encrypted data sets while also incentivizing individuals to share their data through payments mediated by smart contracts. Private blockchains can provide a transparent chain of custody for each use of the customers' data, and validation that this data is not corrupted. However, even with all of these benefits there remain some concerns with the implementation of this new technology including the ethical, legal and social implications typically associated with DNA databases.
Finite-Time Complexity of Online Primal-Dual Natural Actor-Critic Algorithm for Constrained Markov Decision Processes

We consider a discounted cost constrained Markov decision process (CMDP) policy optimization problem, in which an agent seeks to maximize a discounted cumulative reward subject to a number of constraints on discounted cumulative utilities. To solve this constrained optimization program, we study an online actor-critic variant of a classic primal-dual method where the gradients of both the primal and dual variables are estimated using samples from a single trajectory generated by the underlying time-varying Markov processes. This online primal-dual natural actor-critic algorithm maintains and iteratively updates three variables: a dual variable (or Lagrangian multiplier), a primal variable (or actor), and a critic variable used to estimate the gradients of both primal and dual variables. These variables are updated simultaneously but on different time scales (using different step sizes) and they are all intertwined with each other. Our main contribution is to derive a finite-time analysis for the convergence of this algorithm to the global optimum of a CMDP problem. Specifically, we show that with a proper choice of step sizes the optimality gap and constraint violation converge to zero in expectation at a rate O(1/K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/6</sup> ), where K is the number of iterations. To our knowledge, this paper is the first to study the finite-time complexity of an online primal-dual actor-critic method for solving a CMDP problem. We also validate the effectiveness of this algorithm through numerical simulations.
A Teacher-Student Markov Decision Process-based Framework for Online Correctional Learning

A classical learning setting typically concerns an agent/student who collects data, or observations, from a system in order to estimate a certain property of interest. Correctional learning is a type of cooperative teacher-student framework where a teacher, who has partial knowledge about the system, has the ability to observe and alter (correct) the observations received by the student in order to improve the accuracy of its estimate. In this paper, we show how the variance of the estimate of the student can be reduced with the help of the teacher. We formulate the corresponding online problem – where the teacher has to decide, at each time instant, whether or not to change the observations due to a limited budget – as a Markov decision process, from which the optimal policy is derived using dynamic programming. We validate the framework in numerical experiments, and compare the optimal online policy with the one from the batch setting.