Reviewerly/exHarmony huggingface dataset:
Effectiveness and resource utilization of hospital-based contact tracing of patients and health care workers with COVID-19 at a tertiary teaching hospital in Thailand

BackgroundThe benefits of contact tracing in health care workers (HCWs) and patients in hospital have rarely been evaluated. This study aimed to assess the yield of contact tracing in order to evaluate the usefulness of resource utilization.MethodsData were collected from 1 April 2021 to 31 March 2022 at teaching hospital in Thailand. The team was notified if any HCW or patient admitted outside COVID-19 designated areas were tested positive. Those with high-risk exposure identified by the team needed to leave from work for 14 days and 2 PCR tests as early as possible and day 14 after the exposure.ResultA total of 4,292 HCWs and 422 patients were exposed to 575 COVID-19 cases and were identified as high-risk contacts, leading to 50,312 days of quarantine and 9,333 PCR tests. The secondary attack rate was 3.42%, which was more common in patients than in HCWs (OR = 1.82, 95%CI = 1.17–2.85, p = 0.0082). Close contact with patients had more secondary attack rates than with HCWs (OR = 1.51, 95%CI:1.01–2.25, p = 0.045). Among those who had close contact with patients, patients but not HCWs had increased risk of infection (OR = 2.86; 95%CI:1.24–6.62; p = 0.0138, OR = 1.06; 95%CI:0.63–1.77; p = 0.834, respectively). The positivity rate of high-risk close contact HCWs was 2.28 per 1,000 person-days after contact, as compared to the positivity rate of low-risk close contact with symptoms at 0.35 per 1,000 person-days during the same period.ConclusionsHospital-based contact tracing could identify more infected HCWs than symptomatic testing alone. Identifying more infected HCWs could potentially prevent infection in both HCWs and patients.
A novel approach suggestion for assessing the impact of topographic shading on the estimation of the floating photovoltaic technical potential

This study presents a novel approach for the implementation of floating photovoltaic (FPV) systems at the Ayvalı hydroelectric power plant (HPP) in Türkiye. The method proposed in this study accounts for dynamic changes in water levels to accurately calculate the shading effects induced by topography. First, the minimum reservoir surface for the FPV system was calculated using remote sensing (RS). The minimum reservoir surface area, which was determined as 504.69 ha using 60 Sentinel-2 satellite images, was calculated using machine learning algorithms on the Google Earth Engine (GEE) platform, support vector machines (SVM) and automatic water extraction index (AWEI). In the second stage, new digital elevation model (DEM) maps were produced by overlapping monthly changes in water height with ALOS PALSAR data and solar analysis was performed on them. An annual global horizontal irradiance (GHI) map was produced using these maps, and it was divided into five classes to emphasize differences in production potential. The results revealed that 1083.45 GWh of electricity can be produced annually by installing FPV in very high and high potential areas. However, as the moderate, low, and very low regions represent only 5.02% of the reservoir surface and there is a 1.68-fold difference in production potential between the highest and lowest areas due to topography-induced shading near the coastline, it was concluded that FPV installation would not be efficient in those regions. This study highlights the significance of incorporating topography-induced shading and emphasizes the importance of employing RS and geographic information system (GIS) techniques to achieve this objective.
OXI: An online tool for visualization and annotation of satellite time series data

Satellite telemetry data is a special case of multivariate time series characterized by large volumes (in terms of both the number of series and samples), varying sampling rates (including time gaps), redundant sensors, and many interconnections between the series. Special tools are needed to handle visualization, analysis, and, most importantly, annotation of such data. Manually prepared annotations are crucial when designing and evaluating algorithms for satellite telemetry analysis, including anomaly detection in telemetry data. Although there are many applications for time series analysis, there are no tools that would smoothly handle typical satellite telemetry and at the same time provide a free user-friendly interface accessible from any computer without the need for installation, registration, data sharing, or special training. As a solution to this technology gap, we propose the web-based OXI tool written in JavaScript and publicly available at https://oxi.kplabs.pl/. Based on the TRAINSET application (https://trainset.geocene.com/), we optimized each part of the interface to handle real satellite telemetry data from European Space Agency (ESA) missions. The most important improvements include the redesigned data loading and visualization to effectively handle large datasets (up to 2,000,000 samples), different annotation modes (1D/2D and single/multi-series), autosaving annotation status in the browser storage, and dozens of user experience enhancements (autoscaling of axes, color-coded selectors, additional hotkeys, and many others). The tool was designed and evaluated in cooperation with domain experts from Airbus and ESA. Finally, we provide an extensive description of the software, design choices, and their impact on creating and evaluating machine learning models for anomaly detection in satellite telemetry.
Criterias for the formation of large ore deposits in Uzbekistan

Abstract The article presents new data on the study of ore deposits in Uzbekistan in recent years in the laboratory “Structure of the lithosphere”. Based on the complex of regional geophysical data, a three-dimensional geological and geophysical analysis of the relief, basement and Mohorovicic surfaces was carried out in software ArcGIS 10.1. A three-dimensional spatial dynamic model of the Central Kyzylkum Ore-Magmatic concenter was developed on the basis of a complex methodology, including an integrated analysis of the placement of deep structures, the relationships of objects and phenomena using modern methods of spatial analysis and geomodelling. New potentially promising areas for gold mineralization of the Muruntau type have been elucidated within the Central Kyzylkum Ore-Magmatic Concentre (CKOMC).
Benefits and challenges of digital pathology use for primary diagnosis in gynaecological practice: a real-life experience

Digital pathology has developed rapidly in the last decade to the point where it is now perceived as mainstream. However, there are still limited numbers of mature deployments with significant experience of primary digital diagnosis. In this article, we will outline our personal experience of transitioning to digital pathology from the joint viewpoint of an experienced consultant gynaecological pathologist and a senior pathology trainee. We will examine the drivers for going digital including efficiency, quality and patient safety from the perspective of a real life deployment. Flexibility of a fully digital workflow include the ability to radically redesign service delivery, which has the potential to support new models of subspecialisation and deliver quality gains. We will describe our experience of using digital pathology for pathology training and wider teaching. Finally, we will consider some of the deficiencies of primary reporting in digital pathology and some of the challenges in delivering a large digital pathology service. While overall our experiences are largely positive, there are challenges to be overcome and issues to be considered for future digital pathology deployments.
Un adverb scos la iveală din vechile scrieri româneşti: adevăr. Contribuţii etimologice, lexicale şi gramaticale

"In the current study, the author aims to continue the detailed analysis of the inherited Latin adjectives used adverbially in Romanian in order to highlight the lexical-semantic and functional features of the word adevăr ‘really’ in the ancient period, when it had different uses. The starting point for the analysis was some previous contributions, in whose pages aspects concerning adjectives with similar morphosyntactic behavior have been described, some of them, at that time, being presented sequentially, revealing the typological characteristics of the class and less the morphological or the phraseological (implicitly prepositional) variation of each lexical unit. The choice of this adverbial adjective as the main topic of the current analysis is also explained by the fact that, in ancient texts, its number of occurrences was relatively significant because it was often found in the structure of adverbial phraseological units. For a proper interpretation, the author uses a corpus of ancient language (SOR Corpus), which facilitates an objective analysis of language facts. It also refers to similar studies and dictionaries of Romance languages to show the similarities between them, more exactly similar uses, confirming the unity of the Neolatin idioms regarding the adverbials. In this paper, the author will prioritise several perspectives: the etymological, the Romance, the morphological, the structural and the syntactic. The main aim is to illustrate the dynamics of this adverb in the ancient language, which was, naturally, much more striking than nowadays. This analysis will undoubtedly make it possible to identify certain constants and relate them to other adverbial adjectives in order to confirm or refute some trends in the language or to seek the fixing of some structures or the generalization of some particular phraseological variants."
Development of the Co-reference Resolution Tagged Data set in Assamese @ A Semi-Automated Approach

Co-reference resolution (CR) is an essential task in several Natural Language Processing (NLP) applications. It implies finding all the linguistic expressions (known as mentions) in a given text and the references of those mentioned. Consider the following text: Ishwar Chandra Bandyopadhyay was a great man in India in the nineteenth century. He received the honorable title Vidyasagar. In this text, each italics phrase is a mention and all are co-references. That means both expressions He and Vidyasagar refer to the same discourse entity Ishwar Chandra Bandyopadhyay. It is used in various NLP applications like Text Summarization, Information Retrieval (IR), Question Answering systems, Machine Translation (MT), etc. To develop a co-reference system using any algorithm must be needed the data. For a resource-scarce language like Assamese, there is no such data set or supportive tools to prepare the data. So, the data preparation is the primary task of the research in co-reference resolution. This paper attempt a semi-automated approach to develop a tagged data set for the co-reference resolution. This resource will boost the researchers in NLP research in Assamese languages.
Translation of Low Illumination On-road Images to Well Illuminated Ones for Assisting Driving using Deep Auto-Encoders

Low illumination can be dangerous for driving, as human perception depends upon ambient illumination. This paper attempts to aid driving in poor illumination using illumination-correction techniques. We propose an approach of low-light image enhancement using a de-noising convolutional auto-encoder (DNCAE) dedicated to on-road conditions. We prepare a custom data set consisting of paired images of well-lit and synthetically darkened images. A sample of this data set is used to train a neural network model that produces a better-illuminated estimate of the scene. We solve the color constancy issue of our model using a color space conversion designed for human perception of vision. The method has shown effectiveness against existing illumination correction techniques regardlna specific metrics viz. PSNR, SSIM, MSE and MAE.
Secure Video Streaming Techniques: A Performance Overview

The need for secure video streaming has become increasingly important in the age of digitalization. With video surveillance systems being widely adopted across the world, video data has become a valuable target for unauthorized access and tampering. Both video coding and encryption techniques are critical in ensuring the security of video streams. In this review paper, we discuss existing works on video encoding and encryption techniques to provide an analysis of their merits and limitations. We analyze H.264 Advanced Video Coding (AVC), H.265 High Efficiency Video Coding (HEVC) and VP9 video encoding techniques in terms of quality factors like PSNR, SSIM and VMAF. We observe that H.265 offers higher compression whereas H.264 is more computationally efficient while VP9 shows better performance with high-resolution footage. We also compare the performance of block ciphers and stream ciphers such as AES, Blowfish, 3DES, ChaCha and Salsa. Our analysis shows that block ciphers provide more robust security compared to stream ciphers but at the cost of speed. We also evaluate Elliptical Curve Cryptography (ECC) over traditional cryptographic schemes in terms of security enhancement. From our analysis, we conclude that the combination of these techniques helps achieving a balance between computational efficiency, bandwidth consumption and the level of security offered to video streams.
Secure Image Encryption using a New Bowl-shaped Chaotic Attractor

Image encryption using a chaotic signal is significant because it provides high security for digital images. Chaotic signals are highly unpredictable and difficult to replicate. Thus, they are suitable for the image encryption process. A new chaotic system is developed in this paper. The new system generates a bowl shape, chaotic attractor. The properties of the new chaotic system are studied by using the Lyapunov exponent diagram and bifurcation diagram. Then, the developed system is used to encrypt images of cameraman, apple, and peppers. Subsequently, the security analysis is done for the cameraman image. The resulting security level is investigated through correlation coefficient, histogram analysis, entropy analysis, signal-to-noise ratio, and differential attack analysis. It is found that the proposed encryption algorithm based on the developed chaotic system is efficient in protecting the vital image information transmitted through a communication channel.
Evaluation on User Equipment Chip for Deep Learning based Channel Estimation in 5G Advanced System

Nowadays, the integration of conventional 5G communication systems and artificial intelligence (AI) has become one of the important trends for the evolution of future advanced systems. With the employment of AI, many works have verified that significant performance gains can be achieved for varieties of tasks. However, these works mainly focus on the performance improvement and ignore the practical feasibility. In this paper, we investigate the performance of a convolutional neural network (CNN) based channel estimation scheme to run on two powerful mobile terminal chips with different quantization types. In particular, the accuracy of channel estimation and the computation capability of terminal chips for supporting model inference are evaluated. Simulation results show that the accuracy losses caused by both quantization types are small in the mobile scenario of speed at 120 km/h. However, in the case of speed at 350 km/h, the INT8 quantization type leads to a performance degradation while the $\Gamma$P16 quantization type can still maintain a satisfying performance. In addition, the results also show that further optimization for AI based computing is essential for guaranteeing a promising pratical deployment more reliably. Finally, the achievable demodulation performance gain is much smaller than the channel estimation gain, which should be explored fully in the future works.
Quantum Integrated (C+G+Q)PU Split Architecture

Computing today requires a lot of processing power, an immersive environment, and excellent precision. In this paper, we propose a hybrid multiprocessor architecture with quantum integration for data-intensive applications like a metaverse. Regular non-computationally intensive applications are handled by the central processing unit (CPU) and video rendering, graphically intensive programs by the graphics processing unit (GPU), and enormous, parallel, and computationally intensive applications by the quantum processing unit (QPU). To achieve higher permanence computing, we provide combined CPU, GPU, and QPU architecture in this work. This paper provides a roadmap for truly high-performance computing for massive real-time systems. In addition, we present an onsite and cloud-based architecture for scalability. Also, we present multimodality security protection systems for internal and external communication based on the split computing paradigm.
Leveraging Blockchain for Device Registration and Authentication in tSIP-Based Phone-of-Things (PoT) Systems

Phone of Things (PoT) is a new paradigm in IoT that has been recently coined in the literature. PoT provides a framework for leveraging the ubiquitous phone network assets and infrastructure by making them part of the IoT architecture to manage and control IoT gadgets. PoT proposes a lightweight SIP-based messaging protocol, which we call tSIP, that we can map to its corresponding original SIP protocol messages with the help of a proxy (i.e., the PoT gateway). tSIP facilitates communication between resource-constrained smart objects and the communication server using mature phone technologies with abstractions that people are already familiar with. In this context, the paper proposes a lightweight decentralized registration and authentication mechanism based on blockchain technology for smart gadgets in the PoT system to facilitate their association with the communication server. The proposed mechanism provides a secure, trustless, and scalable environment for PoT without requiring high-end communication servers, affecting the existing SIP-based VoIP architecture, or mandating trust in third-party entities. The proposed mechanism uses the Ethereum blockchain and smart contracts to implement a programmatic, immutable access control mechanism to administer the association of IoT devices with the communication server. As a proof of concept, we provide a prototype implementation of the proposed mechanism using a private blockchain due to its privacy characteristics, fast transaction processing, and cost-effectiveness. We conduct a feasibility study of the proposed mechanism and a security analysis. The analysis demonstrates that the proposed mechanism complies with the security standards of the SIP protocol and fits the constraints of embedded smart objects.
A Hybrid CNN-SVM Prediction Approach for Breast Cancer Ultrasound Imaging

This paper discusses the development of a hybrid Convolutional Neural Network (CNN)-Support Vector Machine (SVM) model for automated breast tumor detection using ultra-sound images. The study aims to compare the accuracy of the proposed model with the AlexNet CNN and develop a generalized image detection model that can detect tumors of all types across the human anatomy. The use of ultrasound imaging is justified based on its non-invasive nature and cost-effectiveness, and the possibility of collecting large datasets. The study reviews the prior work on deep learning and CNNs for tumor detection and segmentation in medical imaging, highlighting their potential for improving accuracy and efficiency. The findings of this study have the potential to improve the prognosis and treatment of cancer, a serious health condition affecting a significant number of people worldwide. CNN-SVM model had an accuracy of 91% and AlexNet at 88% with validation accuracy at 60% and 65% respectively. Showing the hybrid model is better in terms of accuracy and has more potential as a future base for medical image modeling prediciton.
A New Scalable Distributed Homomorphic Encryption Scheme for High Computational Complexity Models

Due to the increasing privacy demand in data processing, Fully Homomorphic Encryption (FHE) has recently received growing attention for its ability to perform calculations over encrypted data. Since the data can be processed in encrypted form and the output remains encrypted, only an authorized user or a user who holds the key can decrypt the data and understand its meaning. Hence, it is possible to securely outsource data processing to untrustworthy but powerful public computing resources on the edge. However, due to the high computational complexity, FHE-based data processing experiences scalability related concerns. It is currently unclear whether FHE can be used to solve large-scale problems. In this paper, we propose a novel general distributed FHE-based data processing approach as a concrete step towards solving the scalability challenge. The main idea behind our approach is to use slightly more communication overhead for a shorter computing circuit in FHE, hence, reducing the overall complexity. We verify our new model’s efficiency and effectiveness by comparing the distributed approach with the central approach over various FHE schemes (CKKS, BGV, and BFV). This is performed using one of the most popular libraries of FHE ‘‘Microsoft SEAL by performing specific mathematical operations and observing the time consumed. The empirical results demonstrate that the proposed approach results in a significant reduction in time, up to 54% compared to the traditional central approach.
A Novel Method of Network Security Measurement Based on Indicators

Currently, the security level of systems or products can be measured by some well-known standards, such as the Common Criteria (CC). Yet, how to measure the security of the whole network is still under research. The traditional risk assessment methodology is used to measure the potential risks the network faces, but it cannot completely reflect the security status of a network. Considering the current status, this paper studies the critical factors that may have influence on network security, quantifies these factors, and provides the experimental results which can be used as references for potential future work on network security measurement.
Attribute-based Blockchain Dynamic Failure Traceability in Multi-vendor Disaggregated Optical Networks

We first proposed cross-vendor access technology based on blockchain to provide guarantee for cross-vendor fault traceability of disaggregated optical networks in the future. The feasibility and effectiveness of the proposed architecture are verified on our experimental platform.
Camera-First Form Filling

The effective reporting of climate hazards, such as flash floods, hurricanes, and earthquakes, is critical. To quickly and correctly assess the situation and deploy resou rces, emergency services often rely on citizen reports that must be timely, comprehensive, and accurate. The pervasive availability and use of smartphone cameras allow the transmission of dynamic incident information from citizens in near-real-time. While high-quality reporting is beneficial, generating such reports can place an additional burden on citizens who are already suffering from the stress of a climate-related disaster. Furthermore, reporting methods are often challenging to use, due to their length and complexity. In this paper, we explore reducing the friction of climate hazard reporting by automating parts of the form-filling process. By building on existing computer vision and natural language models, we demonstrate the automated generation of a full-form hazard impact assessment report from a single photograph. Our proposed data pipeline can be integrated with existing systems and used with geospatial data solutions, such as flood hazard maps.