Reviewerly/exHarmony huggingface dataset:
Efficient Color Image Segmentation of Low Light and Night Time Image Enhancement Using Novel 2DTU-Net and FM2CM Segmentation Algorithm

There is a huge recognition for the significance of Semantic Segmentation (SS) and it has been actively researched recently. For bright and clear images, the prevailing top-notch segmentation techniques exhibit superior performance. Nevertheless, owing to the camera sensor’s nature, images are blurred and noise increases in Low Light (LL) or night time environments; hence, it is complex for performing segmentation for several objects. By utilizing a new Double Decomposition of Tanish U-Network (2DTU-Net) and Fuzzy Maximum Metric C-Means (FM2CM) segmentation algorithm, an effective color image segmentation of LL and night time image enhancement is proposed for handling those issues. Initially, the input LL or night time images are resized. Next, by employing 2DTU-NET, the resized images are developed. Then, by deploying FM2CM, image segmentation is performed. Here, the openly accessible KITTI datasets are wielded. The proposed frameworks’ attained outcomes are analogized to the existing state-of-art algorithms. The experimental results depict that the proposed system segments the LL images with superior accuracy along with surpasses the state-of-the-art techniques.
29 Data-driven identification of suspicious test patterns based on serial values from laboratory and point of care testing

<h3>Introduction</h3> Digital health data forms the foundation for most research and many patient treatment decisions. Whether developing a new clinical decision-making tool or carrying out data-driven research, it is essential that the data itself can be trusted and relied upon. Laboratory tests are a common data source, which generate results based on patient samples, with minimal user input. Point of Care Tests (POCT) rely more heavily on user-entered values, increasing the potential for introducing human error, systematic biases, and approximation. We analysed a statistical method to detect tests that show the most non-random variation, indicative that the data may be less credible and require further investigation. <h3>Methods</h3> Laboratory data for 2020-2021 at the Great Ormond Street Hospital were extracted from the electronic patient record system. These data were explored with the programming language R using the secure Aridhia Digital Research Environment (DRE). Using runs analysis on a per-test per-day basis, the Anhøj criteria were used to quantitatively generate a ‘suspicion index’. The average suspicion index per test over the year was used for comparison between tests. A high suspicion index indicated that the values observed were less likely due to random variation and therefore potentially less credible for further analyses. <h3>Results</h3> Based on 4,749,278 test results across 2,371 distinct test types, when ranked in order of credibility, the 23 least credible tests were all POCT. The suspicion indices ranged from 0.8 to 15.6. There was a clear separation of POCT from the laboratory tests, with the latter being generally more credible. <h3>Impact</h3> Low credibility tests could adversely affect clinical tools designed to utilise them. By calculating the credibility of tests, low scoring tests could be identified and targeted for improvement or excluded from, or modified for, clinical tool development.
103 Analysis of clinical procedure activity by diagnosis

<h3></h3> Great Ormond Street Hospital NHS Trust (GOSH) has gathered a range of electronic health record data on diagnoses, interventions, and outcomes for its patients. These resources represent a database of experience that can be used to develop innovative data-based tools that could complement more traditional information sources for clinicians to provide care. We explored how a data processing and analysis pipeline could be created that provides information to clinicians about how the current diagnoses of a patient affect probable future procedures. In particular, which procedures are most likely to be required, and when. Based on dummy data (i.e., not from real patients) of diagnoses and procedures generated by the GOSH Digital Research Environment (DRE), a machine learning model was developed. The model was based on an array of logistic regression models, each trained to predict the probability of one procedure. The weights learned by the models were used to identify which diagnoses made a specific procedure more likely. The pipeline also used survival analysis methods to predict and present the time scale in which a procedure might be required. This information is displayed on plots of how the risk of a procedure changes over time after a diagnosis. The modelling and analyses were carried out in Python on the Aridhia secure DRE. The model was trained on 4000 dummy patients and validated on 1000 dummy patients, achieving an average AUC score of 0.72 in the validation set. This study shows how routinely acquired electronic health record data can produce personalised predictions of which procedures a patient is likely to require, and when, to support clinical decision making, resource planning and patient education. These tools were developed using dummy data that respect real patient data properties, which showcases the potential of dummy data techniques to innovate while keeping high privacy standards.
Convolutional Networks

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
A composite Literature review on Impact of Artificial Intelligence on Jobs Profiling

The employment impact of advances in artificial intelligence has become a major concern in today's world in recent years. It is often discussed at conferences that rapid advances in machine learning, robotics, and other AI-related technologies could lead to massive unemployment in the country. This study focuses on comparing various research papers published by different authors based on the impact of Artificial Intelligence on human jobs. Various authors investigate the extent to which computerization and automation have the potential to replace available jobs. My research paper is based on thorough examination of research papers published by different authors and also online survey done by me via google forms. We studied the current and foreseeable effects of trends in job availability and human survival due to increase in automation in almost every sector. We examine the jobs most likely to be eliminated, the financial impact of automation, and how people may still be productive in a world when robots perform the majority of the labour.
Research on Computer–aided Translation of Vector Mixed Semantic Network in Information Age

This study aimed at the shortcomings of traditional rule-based machine translation models such as inaccurate English translation and difficulty in accurately describing the relationship between words, designed and improved machine translation models used by semantic networks. This model adopts the semantic algorithm of vector mixing. In the conversion model, the algorithm is adopted for getting the correlation between vectors. After the calculation of weighted vector addition, it becomes easier to identify the character of sentences, obtain English translation results, and implement weight training on the sentences with the main phrases which make up the sentences. The translation results summarize the central idea of the sentence. The semantic network machine translation model is improved to introduce big data according to the needs of users and let linguists participate in the procedure. Thus, English translation results not only explain sentences independently but accurately describe the similarity of vectors. The experimental data illustrate that the framework can translate English accurately and efficiently.
Block Propagation in Blockchain-Based 6G Network: From a Communication Perspective

The 6th generation (6G) communication network in the future will be participated by multi-parties (e.g., cellular network, satellite network, vehicular network, etc.). Integrating blockchain into 6G can provide a credible decentralized environment between untrusted parties. Most existing works assume that the block propagation time is ignorable as it is insignificant compared with the block mining time in consensus. However, the block propagation time is no longer negligible in such a wireless and heterogeneous 6G network. To be specific, the deterministic communication in the wired network changes to the opportunistic communication resulting from the moving nodes (e.g., vehicles and satellites) that adopt wireless communication. In this work, we study block propagation in the 6G network. Specifically, we establish the analytical model with the discrete-time Markov chain from the microscopic view to portray the block propagation process in three specific cases. On this basis, we present a hybrid-communication-modes-based block propagation mechanism (HCM) to optimize the propagation time. Finally, the simulation results demonstrate the effectiveness of the proposed mechanism.
Passive Optical Black Box

Nowadays the development and implementation of optical networks is unstoppable. Up-to-date knowledge in this area is essential for engineers. The training of this subject is accomplished both in secondary and upper levels at several institutions in Hungary. In connection with my current work - in higher education - my task was to create a passive optical measuring box, which provides an opportunity to learn the measurement techniques of the subject field with the help of several measurement exercises. The design of the measuring box and the measuring environment was made according to samples taken from a real network. The prospective engineering students can measure in a complex measurement environment that includes the measurement box, instruments, and associated measuring instructions.
A Flexible Real-Time Stereo Vision Architecture for Multiple Data Streams with Runtime Configurable Parameters

It is significant for a stereo vision real-time computing system to flexibly adapt to different parameters of stereo matching without re-customizing hardwares. In this paper, a configurable pipelined hardware architecture based on the sum of absolute differences (SAD) algorithm is proposed. We split the SAD calculation into two parts to accommodate pipelined computing. The architecture can be configured with different resolutions, window sizes, and disparity levels without stopping and restarting. In addition, it can be configured as a multiple-data-stream mode and we have developed a configuration gen-eration algorithm for the mode. The presented architecture is synthesized and implemented on a Xilinx ZCUI04 board. The evaluation results demonstrate that the real-time computing of 480P, 720P, and 1080P video streams can be process at 250MHz with the peak computing performance of 480P/784fps at the disparity level of 125. It uses 60% LUTs, 34% registers, and 39 % BRAM, producing flexible configurability and superior computing performance than the other similar work.
Does Information Technology Influence Processes at Universities? – Teacher's Perspective

The rapid development of information technology (IT) both forces and supports the transformation of universities in almost all their operations (strategic planning, budgeting, education, research, quality control, cooperation with business and society, etc.). The paper presents the research results related to the digitalization of different processes at universities – more specifically, teachers’ opinions on the effect of digitalization on different processes. The survey was conducted among the University of Mostar, Bosnia, and Herzegovina (BH) teachers. The authors identified processes at the university and investigated the perceived impact of information technology on them. The findings show that the impact of digitalization is positive on most processes, whereby a strong influence is determined for research projects financed by the Ministry of Education, quality management, teaching, and evaluation of acquired knowledge (examination).
Total Generalized Variation for Piecewise Constant Functions on Triangular Meshes with Applications in Imaging

We propose a novel discrete concept for the total generalized variation (TGV), which was originally derived to reduce the staircasing effect in classical total variation regularization, in image denoising problems. We describe discrete, second-order TGV for piecewise constant functions on triangular meshes, thus allowing the TGV functional to be applied to more general data structures than pixel images, and in particular in the context of finite element discretizations. Particular attention is given to the description of the kernel of the TGV functional, which, in the continuous setting, consists of linear polynomials. We discuss how to take advantage of this kernel structure using piecewise constant functions on triangular meshes. Numerical experiments include denoising and inpainting problems for images defined on nonstandard grids, including data from a three-dimensional scanner.
People Factors Influencing Project Success in Software Development: A Survey of Agile Development Teams in Indonesia

Currently, agile is one method often used in software development. However, implementing agile is not easy. Many factors can affect the successful implementation of agile software development (ASD). One of the factors that often arise is individuals/people. Previous research has proven that the people factor is one of the factors that can increase the success of ASD implementation. However, only one study focuses on the people factor that researchers in Portugal have done. Therefore, this study attempts to identify the effect of people's factors on the success of ASD in Indonesia. Respondents in this study are individuals who have or are currently using ASD. Data collection in this study used a questionnaire and validated the data using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The findings of this study indicate that team capability influences the success of ASD implementation, while personal characteristics, societal culture, and customer involvement have no effect. In addition, other findings personal characteristics can affect team capabilities and customer involvement.
Semi-Supervised Event Extraction Incorporated With Topic Event Frame

Supervised Meta-event extraction suffers from two limitations: (1) The extracted meta-events only contain local semantic information and do not present the core content of the text; (2) model performance is easily degraded because of labeled samples with insufficient number and poor quality. To overcome these limitations, this study presents an approach called frame-incorporated semi-supervised topic event extraction (FISTEE), which aims to extract topic events containing global semantic information. Inspired by the frame-based knowledge representation, a topic event frame is developed to integrate multiple meta-events into a topic event. Combined with the tri-training algorithm, a strategy for selecting unlabeled samples is designed to expand the training sets, and labeling models based on conditional random field (CRF) are constructed to label meta-events. The experimental results show that the event extraction performance of FISTEE is better than supervised learning-based approaches. Furthermore, the extracted topic events can present the core content of the text.
Clinical management by telemonitoring for people with chronic disease in a Health Plan Operator

Objective: to report and analyze the implementation of clinical management by telemonitoring for people with chronic noncommunicable disease in a health insurance provider. Method: an evaluative, single-case study conducted in 2019, based on the models of description and reapplication of interventions and attention to chronic conditions. Clinical management by telemonitoring was the studied intervention. Results: adoption or maintenance of a healthy lifestyle was the goal of this intervention that assisted, on a monthly basis, up to 200 beneficiaries with non-communicable chronic disease, with low and medium clinical risk, from a medium-sized supplementary health operator located in the Center-south of Paraná-PR. Professionals from the multidisciplinary health team, trained in counseling for behavior change, carried out actions of health education and self-management supported by monthly, synchronous and previously scheduled telecontact, during 12 months. The phone calls were recorded and stored by the operator, and the activities and self-referred clinical data were recorded in electronic medical records. Conclusion: the clinical management by telemonitoring described was implemented in a favorable context, met the proposed objective and most elements of clinical management, innovated the care for beneficiaries with chronic noncommunicable diseases, and can be reproduced.
Correction to Vanadium Speciation in Ancient Shales Revealed through Synchrotron-Based X-ray Spectroscopy

ADVERTISEMENT RETURN TO ISSUEPREVAddition/CorrectionNEXTORIGINAL ARTICLEThis notice is a correctionCorrection to Vanadium Speciation in Ancient Shales Revealed through Synchrotron-Based X-ray SpectroscopyWilliam W. Bennett*William W. BennettMore by William W. Bennetthttps://orcid.org/0000-0002-8126-4555, Enzo LombiEnzo LombiMore by Enzo Lombihttps://orcid.org/0000-0003-3384-0375, Kirk G. ScheckelKirk G. ScheckelMore by Kirk G. Scheckel, Ryo SekineRyo SekineMore by Ryo Sekinehttps://orcid.org/0000-0001-9980-0603, Scott G. JohnstonScott G. JohnstonMore by Scott G. Johnstonhttps://orcid.org/0000-0002-5826-5613, Edward D. BurtonEdward D. BurtonMore by Edward D. Burtonhttps://orcid.org/0000-0002-9628-089X, Daryl L. HowardDaryl L. HowardMore by Daryl L. Howard, Peter KappenPeter KappenMore by Peter Kappen, and Donald E. CanfieldDonald E. CanfieldMore by Donald E. CanfieldCite this: ACS Earth Space Chem. 2023, 7, 3, 661Publication Date (Web):February 24, 2023Publication History Received20 February 2023Published online24 February 2023Published inissue 16 March 2023https://doi.org/10.1021/acsearthspacechem.3c00046Copyright © 2023 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views322Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (791 KB) Get e-Alertsclose Get e-Alerts
The use of Vsee videoconferencing for live telepathology in Rwanda, a potential solution for resource-limited area

The shortage of pathologists is a worldwide problem that is more severe in Africa. One of the solutions is the use of telepathology (TP); however, most of the TP systems are expensive and unaffordable in many developing countries. At the University Teaching Hospital of Kigali, Rwanda, we assessed the possibility of combining commonly available laboratory tools into a system that can be used for diagnostic TP using Vsee videoconferencing.Using an Olympus microscope (with a camera) operated by a laboratory technologist, histologic images were transmitted to a computer whose screen was shared, using Vsee, with a remotely located pathologist who made the diagnoses. Sixty consecutive small biopsies (≤6 glass slides) from different tissues were examined to make a diagnosis using live Vsee-based videoconferencing TP. Vsee-based diagnoses were compared to pre-existing light microscopy-based diagnoses. Percent agreement and unweighted Cohen's kappa coefficient of the agreement were calculated.For agreement between conventional microscopy-based and Vsee-based diagnoses, we found an unweighted Cohen's kappa of 0.77 ± 0.07SE with a 95% CI of 0.62-0.91. The perfect percent agreement was 76.6% (46 of 60). Agreement with minor discrepancy was 15% (9 of 60). There were 2 cases of major discrepancy (3.30%). We were unable to make a diagnosis in 3 cases (5%) because of poor image quality related to the instantaneous internet connectivity problems.This system provided promising results. However, additional studies to assess other parameters which can affect its performance are needed before this system can be considered an alternative method of providing TP services in resource-limited settings.
Using Heterogeneous Cloud Computing to Manage Resources in Sustainable Cyber-Physical Systems

A significant increase in heterogeneous distributed computing has allowed for significant advancements in cyber physical systems (CPS). An additional strategy for enhancing system sustainability is to combine CPS with heterogeneous cloud computing. However, there are still certain difficulties with resource management in cloud systems, such as work allocation in heterogeneous clouds and Web server capacity limitations. Service delays are frequently caused by the erratic demand for services, which is embarrassing for the businesses' ability to compete. This article tackles the NP-hard problem of job assignment in heterogeneous clouds, which has been shown. The suggested method is known as the Smart Cloud-based Optimizing Workload (SCOW) Model, which assigns jobs to heterogeneous clouds using forecast cloud capacity and sustainable parameters. We provide a few strategies, such as the Workload Resource Minimization (WRM), Smart Task Assignment (STA), and Task Mapping Algorithm, to achieve the optimization target (TMA). Our experimental analyses have looked at how well the suggested strategy performs.