Reviewerly/exHarmony huggingface dataset:
A Survey of Deep Learning-Based Image Restoration Methods for Enhancing Situational Awareness at Disaster Sites: The Cases of Rain, Snow and Haze

This survey article is concerned with the emergence of vision augmentation AI tools for enhancing the situational awareness of first responders (FRs) in rescue operations. More specifically, the article surveys three families of image restoration methods serving the purpose of vision augmentation under adverse weather conditions. These image restoration methods are: (a) deraining; (b) desnowing; (c) dehazing ones. The contribution of this article is a survey of the recent literature on these three problem families, focusing on the utilization of deep learning (DL) models and meeting the requirements of their application in rescue operations. A faceted taxonomy is introduced in past and recent literature including various DL architectures, loss functions and datasets. Although there are multiple surveys on recovering images degraded by natural phenomena, the literature lacks a comprehensive survey focused explicitly on assisting FRs. This paper aims to fill this gap by presenting existing methods in the literature, assessing their suitability for FR applications, and providing insights for future research directions.
A Fuzzy Approach to Fault Tolerant in Cloud using the Checkpoint Migration Technique

Fault tolerance is one of the most important issues in cloud computing to provide reliable services. It is difficult to implement due to dynamic service infrastructures, complex configurations and different dependencies. Extensive research efforts have been made to implement fault tolerance in the cloud environment. Many studies focus only on fault detection and do not consider fault tolerance. For this reason, in this paper, in addition to recognizing the nature of the fault, a fuzzy logic-based approach is proposed to provide an appropriate response and increase the fault tolerance in the cloud environment. Checkpoint-based migration technique is used to increase fault tolerance. Using a checkpoint during migration can reduce time and processing costs and balance the load between virtual machines in the event of a fault. The simulation is performed according to the data center of Vietnam Telecommunications Company (VDC). The results of the proposed method in a period of 60 minutes show 98.03% fault detection accuracy, which is 4.5% and 4.1% superior to FLPT and PLBFT algorithms, respectively.
Robust optimization for quantum reinforcement learning control using partial observations

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of the quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of the quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and the quantum approximate optimization algorithm (QAOA). It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for a QAOA with a noisy control Hamiltonian. This robust control optimization model can be trained to compensate for the uncertainties in practical quantum computing.
The design of viscometer with smartphone controlling

New design of a viscometer based on the s tokes viscosity measurement method is proposed. The principle of operation of this viscosimetr is based on the use of ball periodic alternated movement in a horizontally positioned cuvette that filled with the test liquid. The movement appears under the in fluence of a magnetic field that created by two electromagnets. Registration of the ball movement inside the cuvette is carried out using an optoelectronic pair. A distinctive feature of the proposed design is control by using a program that installed on t he user's smartphone, which also carries out the primary data processing. Data transmission is carried out over the radio channel using a Bluetooth module. Disposable cuvettes are used for measurements. This approach makes it possible to significantly redu ce both the device production costs and operating costs by eliminating most of the operations for the device preparing for working (the vast majority of existing types of viscometers require thorough flushing of all units in contact with the test medium). In addition, the proposed approach excludes the occurrence of measurement errors associated with insufficiently thorough preparation of the device for operation.
Hybrid model for software development: an integral comparison of DevOps automation tools

DevOps is a fine fusion of development and operations teams together to deliver more efficient, reliable and quality software. DevOps make use of alternative set of automation tools for different development stages of software. The research presented in th is paper analyses selective automation tools to provide comprehensive and comparative tabular analysis followed by graphical comparison according to latest Google Trends. Best performer tools out of these alternative tool sets are grouped together into int egrated tool chain (ITC) in order to escalate DevOps performance in future. A hybrid automated model for software development using these selective automation tools from the ITC is also proposed. This analytic comparison will prove to be a big utility for young researchers, students and software developers to be cognizant with DevOps automation cul ture. Study of other tools or enhancement of the proposed hybrid model could also be considered as a part of future research.
The Portrayal of Cross-Cultural Aspects in Digital Toothpaste Advertisements: A Semiotic Study

Contemporary life has been governed by advertisements that have cultural depictions to attract region bound target customers.Multinational companies design different advertisements for different cultural settings, and although it increases the advertising cost, yet the survival of multinational companies depends on fascinating indigenous cultural elements in advertisements.This study aims to unravel those hidden cultural signs in advertisements of two toothpaste brands Oral-b and Sensodyne across six continents.It explores the cross-cultural aspects through the lens of Roland Barthes' semiotic theory.This qualitative study reveals that advertisements are designed keeping in mind the socio-cultural aspects of the target audience.These ads become appealing to the target customers of that culture, but the same advertisement may not influence the audience of another culture.The study contributes to a better understanding of cross-cultural advertisements; hence, it motivates to avoid cultural shock elements which can ruin any industry.
An Online Program for Teacher Learning to Enhance Students' Media Literacy Skills

“An Online Program for Teacher Learning to Enhance Students' Media Literacy Skills” was a product of employing Research and Development (R&amp;D) methodology. It consisted of the teachers' learning development project and the utilized the teachers’ learning outcomes for the student development project. The first project yielded six teachers’ learning handbooks, whereas the second project was composed of one action handbook that utilized the teachers’ learning outcomes to foster student development. The handbooks were evaluated in a school that represented the opportunity expansion schools under the Commission on Basic Education. In the experimental research model, which was designed with a one group pre-test/post-test, there was an experimental group of 12 teachers and 59 students. The results revealed that the invented online program had been effective and consistent with the study’s assumptions. The findings illustrated the following: 1) the post-experimental test for teachers had met the standard of 90/90, 2) the teachers’ post-test mean scores had been statistically significantly higher than before the experiment, and 3) after the experiment, the students’ mean score on media literacy skills assessment had been statistically significantly higher than before the experiment. In addition, the results verified that the created online program would be appropriate for the dissemination in the target opportunity expansion schools under the Office of the Basic Education Commission throughout the country.
Adaptation of Grad-CAM Method to Neural Network Architecture for LiDAR Pointcloud Object Detection

Explainable Artificial Intelligence (XAI) methods demonstrate internal representation of data hidden within neural network trained weights. That information, presented in a form readable to humans, could be remarkably useful during model development and validation. Among others, gradient-based methods such as Grad-CAM are broadly used in an image processing domain. On the other hand, the autonomous vehicle sensor suite consists of auxiliary devices such as radars and LiDARs, for which existing XAI methods do not apply directly. In this article, we present our adaptation approach to utilize Grad-CAM visualization for LiDAR pointcloud specific object detection architectures used in automotive perception systems. We try to solve data and network architecture compatibility problems and answer the question whether Grad-CAM methods could be used with LiDAR sensor data efficiently. We showcase successful results of our method and all the benefits that come with a Grad-CAM XAI application to a LiDAR sensor in an autonomous driving domain.
Machine learning model for malaria risk prediction based on mutation location of large-scale genetic variation data

Abstract In recent malaria research, the complexity of the disease has been explored using machine learning models via blood smear images, environmental, and even RNA-Seq data. However, a machine learning model based on genetic variation data is still required to fully explore individual malaria risk. Furthermore, many Genome-Wide Associations Studies (GWAS) have associated specific genetic markers, i.e., single nucleotide polymorphisms (SNPs), with malaria. Thus, the present study improves the current state-of-the-art genetic risk score by incorporating SNPs mutation location on large-scale genetic variation data obtained from GWAS. Nevertheless, it becomes computationally expensive for hyperparameter optimization on large-scale datasets. Therefore, this study proposes a machine learning model that incorporates mutation location as well as a Genetic Algorithm (GA) to optimize hyperparameters. Besides that, a deep learning model is also proposed to predict individual malaria risk as an alternative approach. The analysis is performed on the Malaria Genomic Epidemiology Network (MalariaGEN) dataset comprising 20,817 individuals from 11 populations. The findings of this study demonstrated that the proposed GA could overcome the curse of dimensionality and improve resource efficiency compared to commonly used methods. In addition, incorporating the mutation location significantly improved the machine learning models in predicting the individual malaria risk; a Mean Absolute Error (MAE) score of 8.00E−06. Moreover, the deep learning model obtained almost similar MAE scores to the machine learning models, indicating an alternative approach. Thus, this study provides relevant knowledge of genetic and technical deliberations that can improve the state-of-the-art methods for predicting individual malaria risk.
On Overlap and Otherness: A Comparison of Three Vocabularies’ Approaches to LGBTQ+ Identity

Bibliographic records can include information from controlled vocabularies to capture identities about individuals, especially about authors or intended audiences; personal name authority records can also contain information about identity. Employing a systematic analysis of the overlap of the Homosaurus, Library of Congress Subject Headings (LCSH), and Library of Congress Demographic Group Terms (LCDGT), this article explores the extent to which LGBTQ+ identities are represented in the three vocabularies. Despite LCSH’s long, iterative history of development and the faceted, post-coordinate nature of LCDGT, neither vocabulary was found to be adequate in covering the complex, LGBTQ+ identities represented in the Homosaurus.
Imperceptible Image Steganography Using Symmetry-Adapted Deep Learning Techniques

Digital image steganography is the process of embedding information within a cover image in a secure, imperceptible, and recoverable way. This research extends a symmetry-adapted deep-learning approach to identify hidden patterns of images using two-dimensional convolutional neural networks (CNN). The proposed method (SteGuz) uses three CNNs to implement the different phases of the steganography process on digital image covers. SteGuz introduced a gain function, based on several image similarity metrics, to maximize the imperceptibility of the hiding process. Using 10 different pairs of cover-secret images, the performance of the proposed method was measured in terms of standard metrics such as peak signal to noise ratio (PSNR) and structured similarity index measurement (SSIM). The results showed that the proposed methodology outperformed the original method in terms of both imperceptibility and recoverability. In addition, when compared with some existing methods, SteGuz proved the outstanding performance of achieving a very high PSNR value while maintaining high accuracy of the extracted image.
Vehicle Make Detection Using the Transfer Learning Approach

Vehicle detection and classification is an important part of an intelligent transportation surveillance system. Although car detection is a trivial task for deep learning models, studies have shown that when vehicles are visible from different angles, more research is relevant for brand classification. Furthermore, each year, more than 30 new car models are released to the United States market alone, implying that the model needs to be updated with new classes, and the task becomes more complex over time. As a result, a transfer learning approach has been investigated that allows the retraining of a model with a small amount of data. This study proposes an efficient solution to develop an updatable local vehicle brand monitoring system. The proposed framework includes the dataset preparation, object detection, and a view-independent make classification model that has been tested using two efficient deep learning architectures, EfficientNetV2 and MobileNetV2. The model was trained on the dominant car brands in Lithuania and achieved 81.39 % accuracy in classifying 19 classes, using 400 to 500 images per class.
Adaptable Cryptographic Primitives in Blockchains via Smart Contracts

Blockchain-based platforms utilise cryptographic protocols to enforce the correct behaviour of users, as well as to guarantee a sufficient level of protection against malicious adversaries. Cryptography is, however, an ever-evolving discipline, and any breakthrough would have immediate consequences on the security of blockchain-based applications. A possible threat currently under investigation is given by the development of quantum computers, since several wide-adopted cryptographic protocols have been proved to be unsafe against quantum-capable adversaries. In this work, we propose a novel approach for the management of cryptographic primitives in smart-contract-based ledgers, discussing how it fits in both a (partially) permissioned and a fully permissionless setting. The cryptographic protocols are managed in a flexible manner via a set of smart-contracts defined on the ledger itself, in this way the choice of algorithms and parameters can change quickly. Among the advantages of this approach, we remark how it allows designing an adaptive post-quantum-based blockchain that keeps up with ongoing technological advances. In general, the introduction of new features and the application of fixes to a blockchain cause forks in the chain, which may cause major disruptions. The use of smart contracts in blockchain management allows to avoid this problem, dynamically introducing new protocols or deprecating old ones without compromising previous data. The Cryptographic Kernel approach has been adopted by Quadrans, an open-source, public, decentralised smart-contract-based blockchain with a specific focus on the needs of industry, complex supply chains, and IOT devices.
Unveiling photon statistics with a 100-pixel photon-number-resolving detector

Abstract Single-photon detectors are ubiquitous in quantum information science and quantum sensing. They are key enabling technologies for numerous scientific discoveries and fundamental tests of quantum optics. Photon-number-revolving detectors are the ultimate measurement tool of light. However, few detectors to date can provide high-fidelity photon number resolution at few-photon levels. Here, we demonstrate an on-chip detector that can resolve up to 100 photons by spatiotemporally multiplexing an array of superconducting nanowires along a single waveguide. The unparalleled photon number resolution paired with the high-speed response exclusively allows us to unveil the quantum photon statistics of a true thermal light source for the first time, which is realized by direct measurement of high-order correlation function g ( N ) with N up to 15, observation of photon-subtraction-induced photon number enhancement, and quantum-limited state discrimination against a coherent light source. Our detector provides a viable route towards various important applications, including photonic quantum computation and quantum metrology.
A Business-to-Business Collaboration System That Promotes Data Utilization While Encrypting Information on the Blockchain

Ensuring the reliability of data gathering from every connected device is an essential issue for promoting the advancement of the next paradigm shift, i.e., Industry 4.0. Blockchain technology is becoming recognized as an advanced tool. However, data collaboration using blockchain has not progressed sufficiently among companies in the industrial supply chain (SC) that handle sensitive data, such as those related to product quality, etc. There are two reasons why data utilization is not sufficiently advanced in the industrial SC. The first is that manufacturing information is top secret. Blockchain mechanisms, such as Bitcoin, which uses PKI, require plaintext to be shared between companies to verify the identity of the company that sent the data. Another is that the merits of data collaboration between companies have not been materialized. To solve these problems, this paper proposes a business-to-business collaboration system using homomorphic encryption and blockchain techniques. Using the proposed system, each company can exchange encrypted confidential information and utilize the data for its own business. In a trial, an equipment manufacturer was able to identify the quality change caused by a decrease in equipment performance as a cryptographic value from blockchain and to identify the change one month earlier without knowing the quality value.
A Bibliometric Analysis of Virtual Reality-Aided Vision Therapy

While Virtual Reality (VR) has gained significant attention in different domains of health care and promises benefits for managing vision disturbances, no bibliometric analysis focuses on its use in vision therapy. This study aims to analyze and visualize the scientific literature indexed in the Web of Science databases by visualizing bibliometric indicators illustrating publication trends from 2001. The findings provide a better understanding of the state-of-the-art of existing vision therapy research utilizing VR.
Prediction of Retweets Based on User, Content, and Time Features Using EUSBoost

Twitter is one of the popular microblogs that allow users to write posts. Retweeting is one of the mechanisms for the diffusion of information on Twitter. One way to understand the spread of information is to learn about retweet predictions. This study focuses on predicting retweets using Evolutionary Undersampling Boosting (EUSBoost) based on user, content, and time-based features. We also consider the vector of text as a predictive feature. Models with EUSBoost are able to outperform models using the AdaBoost method. The evaluation results show that the best model can achieve an AUC performance score of 77.21% and a GM score of 77.18%. While the Adaboost-based models achieved AUC scores ranging from 68% to 69% and GM scores ranging from 62% to 63%. In addition, we found that there was no significant difference between using numeric features only and combining numeric and text features.&#x0D;
Blockchain Adoption in Accounting and Auditing: A Qualitative Inquiry in Sri Lanka

Regardless of the benefits blockchain provides to the accounting information systems, its adoption and application is limited. This study: (1) investigated the underlying reasons for this limited adoption of blockchain in a developing country context; (2) explored the potential benefits and challenges of adopting blockchain; and (3) proposed how accounting and auditing should be facilitated to adopt blockchain technology. The study utilized a qualitative general inquiry approach by conducting 13 in-depth interviews with professionals and practitioners involved in the adoption process in organizations that had adopted blockchain technology, or are researching this technology and have plans to adopt this technology in the future. Findings revealed five reasons for the lack of blockchain adoption, contributing to Technology-Organization-Environment (TOE) framework and Institutional theory. In addition, the study identified the benefits and challenges of introducing blockchain into accounting and auditing while proposing a framework to facilitate blockchain adoption in these disciplines.
Improving Deliberation by Text-Only and Semi-Supervised Training

Text-only and semi-supervised training based on audio-only data has gained popularity recently due to the wide availability of unlabeled text and speech data. In this work, we propose incorporating text-only and semi-supervised training into an attention-based deliberation model. By incorporating text-only data in training a bidirectional encoder representation from transformer (BERT) for the deliberation text encoder, and large-scale text-to-speech and audio-only utterances using joint acoustic and text decoder (JATD) and semi-supervised training, we achieved 4%-12% WER reduction for various tasks compared to the baseline deliberation. Compared to a state-of-the-art language model (LM) rescoring method, the deliberation model reduces the Google Voice Search WER by 11% relative. We show that the deliberation model also achieves a positive human side-by-side evaluation compared to the state-of-the-art LM rescorer with reasonable endpointer latencies.