Reviewerly/exHarmony huggingface dataset:
Lossy Image Compression in a Preclinical Multimodal Imaging Study

Abstract The growing use of multimodal high-resolution volumetric data in pre-clinical studies leads to challenges related to the management and handling of the large amount of these datasets. Contrarily to the clinical context, currently there are no standard guidelines to regulate the use of image compression in pre-clinical contexts as a potential alleviation of this problem. In this work, the authors study the application of lossy image coding to compress high-resolution volumetric biomedical data. The impact of compression on the metrics and interpretation of volumetric data was quantified for a correlated multimodal imaging study to characterize murine tumor vasculature, using volumetric high-resolution episcopic microscopy (HREM), micro-computed tomography (µCT), and micro-magnetic resonance imaging (µMRI). The effects of compression were assessed by measuring task-specific performances of several biomedical experts who interpreted and labeled multiple data volumes compressed at different degrees. We defined trade-offs between data volume reduction and preservation of visual information, which ensured the preservation of relevant vasculature morphology at maximum compression efficiency across scales. Using the Jaccard Index (JI) and the average Hausdorff Distance (HD) after vasculature segmentation, we could demonstrate that, in this study, compression that yields to a 256-fold reduction of the data size allowed to keep the error induced by compression below the inter-observer variability, with minimal impact on the assessment of the tumor vasculature across scales.
Implementation Of Grover's and Shor's Algorithms In Quantum Machine Learning

Algorithms for quantum machine learning are responsible for identifying essential information and predicting outcomes of the raw data samples. Unlike other mathematical methods that rely on a predetermined model to make predictions, these algorithms use existing data to continuously improve their ability to predict outcomes. Quantum computing algorithms have the capability to solve complex problems by leveraging the power of parallel computing, which allows them to perform many calculations simultaneously. The effectiveness of quantum computing algorithms in machine learning allows quantum technology to reach an advanced level of improvement. This study examines application-based algorithms, namely Grover's algorithm and Shor's algorithm, which are essential and most widely used quantum machine learning algorithms.
Review on the Impact of Financial Institutions’ Systems on SMEs’ Access to Finance

The importance of Small and Medium Enterprises (SMEs) in economic growth is becoming increasingly acknowledged.In Ghana, 85% of manufacturing jobs are provided by SME's, making up 92 percent of firms and contributing 70% of the country's GDP.Funding is a major barrier for many SMEs, particularly in Sub-Saharan African nations.The formal financial sector in Ghana comprises commercial banks, rural and community banks, savings and loan companies, and non-bank financial institutions.Legislative efforts have been put in place to help SMEs, but financial institutions have been cautious with lending to SMEs due to high default rates and risks associated with the sector.This paper seeks to review the impact of financial institution systems on SMEs access to finance in Ghana.A systematic review of literatures related to requirements and systems of financial institutions on SMEs access to finance in Ghana was conducted with the aid of online research journal websites.Seven articles were identified to be useful to the study, and six factors were identified to impact SMEs access to finance: collateral pledges, conditions attached to bank loans, high transaction costs, inadequate information about SMEs, Commercial Bank's Profits Orientation and short period for repayment.SMEs in Ghana are not able to access funds due to their small size and lack of assets and capital, and lack the capacity to meet financial institutions.SMEs are important for the growth and development of a nation, but their access to finance is hindered by financial institutions.To address this, policy makers, government officials and legislators should make laws that favour SMEs in accessing finance to reduce requirements and increase access to finance.
An Adaptive Feedback Strategy for Enlarging the Frequency Dynamic Range of DMZI Sensors

A vibration-state-dependent feedback strategy is proposed to enlarge the frequency dynamic range of feedback-based Dual Mach-Zehnder Interferometer sensors. A 10-fold lowering of the minimum measurable frequency with respect to conventional methods is experimentally demonstrated.
A study on total quality management consultant selection using TOPSIS method

Since its inception, quality management has undergone tremendous growth.The pharaohs of Egypt have sometimes used to date its genesis.However, it is frequently asserted that scientific administration, which was at its height in the late nineteenth and early twentieth centuries, is where contemporary quality assurance began.a wide range of enterprises, there is now much more interest in designing and implementing "total quality management (TQM) programmes" to improve performance and efficiency.Although top management backing for improvement initiatives is well acknowledged, there have been few tools to help management contribute to the creation of such programmes.The goals, resources, and preferences of the organization all influence consultant choice.Numerous variables that may be at odds with one another and adhere to ambiguous and imprecise data are having an impact on this issue.This study employs the "technique for order of preference by similarity to ideal solution (TOPSIS)" since choosing a comprehensive "quality management consultant" requires significant thought.According to how closely the favored choice resembles the ideal answer, this method employs an order of preference.Different criteria are given specific weights of importance according to the "equal weights method (EWM)".The rank of alternative consultants using the TOPSIS method for Consultant A is fourth, Consultant B is second, Consultant C is fifth, Consultant D is first and Consultant E is third.The result indicated that a consultant with high technical skills, average management skills and work experience in a related field is the best candidate among the selected consultants.
Evaluation Security Analysis and Portfolio Management using EDAS Method

Portfolio management and security analysis.Security analysis, portfolio management, and financial derivatives are three fields of contemporary investment analysis that are merged.It gives a fair treatment of theories, organizations, markets, academic research, and real-world applications while presenting both basic concepts and sophisticated principles.Instead of investing in a single security, a portfolio refers to a collection of securities.Finding out the potential risk and rewards of holding different combinations of individual securities is the goal of portfolio analysis.Security analysis has a variety of goals.They are the expansion of capital, dependable income, preservation of capital, defense against inflation, and liquidity.It is a method for figuring out an asset's intrinsic worth and researching factors that can affect its price in the future.Security analysis is the study of tradable financial instruments also known as securities.It concentrates on figuring out the fair value of particular securities (i.e., stocks and bonds).These are typically categorized as stocks, debentures, or some combination of the two.When choosing between several investment options, such as real estate, bonds, debentures, equity shares, fixed deposits, gold, jewelry, etc., security analysis can help.Portfolio management is the process of selecting, prioritizing, and managing an organization's activities and programs according to its strategic goals and capacity to carry them out.It's crucial to establish a balance between implementing change efforts and carrying on with business as usual to optimize return on investment.Economic analysis, industry analysis, and basic analysis are the three core subfields of security analysis.Security analysis is important because it enables the investor to examine a stock's options while calculating the expected return and risk.Organizations may install real-time monitoring of servers, endpoints, and network traffic, integrate and integrate a variety of event data from application and network logs, do forensic analysis to better understand attack patterns, and more using security analytics technology.EDAS Evaluation Based on Distance from Average Solution method for Fundamental Weighting, Minimum Variance, and Stable ROE Portfolio Alternative and Volatility, Sharpe Ratio, Excess Return Over Benchmark Evaluation.Fundamental Weighting, Minimum Variance, and Stable ROE Portfolio.Volatility, Sharpe Ratio, Excess Return Over Benchmark.Minimum Variance has the highest rank whereas Stable ROE Portfolio has the lowest rank.
Mobile and Adaptive Learning Application for English Language Learning Using the SPSS Method

Mobile studying, also called m-Learning, is a way of having access to learning content material through mobile devices.This technique leverages on-demand mastering, allowing customers to access content anywhere it suits them.Camera telephones provide an amazing manner to ask freshmen to 'look at' the grammar around them.Students can be recommended to take pictures of street signs and symptoms, menus, classified ads, or examples of English writing they see round them.My favorites are spotting misuse of apostrophe(s) or misspellings.Mobile-friendly strategies permit newbies to get entry to information fast and easily and tailor getting to know to fit them.As a result, phone newcomers complete course fabric forty-five% quicker than computer pc users.Mobile getting to know is time-sensitive from a management angle.Learner, trainer, context, content material and evaluation are the fundamental additives of holistic mobile gaining knowledge of.Key traits of cell getting to know are ubiquitous, compact length of cellular devices, blended, private, interactive, collaborative, and immediately facts.An know-how of getting to know concepts helps instructors hook up with all varieties of students.Teachers can awareness on one-of-a-kind learning styles to attain distinctive college students, creating teaching that focuses at once on students' wishes and talents Mobile-friendly methods allow novices to get entry to information speedy and without difficulty, and tailor gaining knowledge of two suit them.As a result, phone newbies enter the entire direction of cloth forty-five% faster than computing device laptop customers.Mobile studying is time-sensitive from a management attitude.SPSS statistics is multivariate analytics, business intelligence, and criminal investigation data management, advanced analytics, developed by IBM for a statistical software package.A long time, spa inc.Was created by IBM purchased it in 2009.The brand name for the most recent versions is IBM SPSS statistics.Evaluation parameters: Vocabulary practicing, Collocation practicing, Listening practice, Entertainment, Grammar, Speaking practice, Conversation.The Cronbach's Alpha Reliability result.The overall Cronbach's Alpha value for the model is .744which indicates 74% reliability.From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis.The Cronbach's Alpha Reliability result.The overall Cronbach's Alpha value for the model is .744which indicates 74% reliability.From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis.
Metafeature Selection via Multivariate Sparse-Group Lasso Learning for Automatic Hyperparameter Configuration Recommendation

The performance of classification algorithms is mainly governed by the hyperparameter settings deployed in applications, and the search for desirable hyperparameter configurations usually is quite challenging due to the complexity of datasets. Metafeatures are a group of measures that characterize the underlying dataset from various aspects, and the corresponding recommendation algorithm fully relies on the appropriate selection of metafeatures. Metalearning (MtL), aiming to improve the learning algorithm itself, requires development in integrating features, models, and algorithm learning to accomplish its goal. In this article, we develop a multivariate sparse-group Lasso (SGLasso) model embedded with MtL capacity in recommending suitable configurations via learning. The main idea is to select the principal metafeatures by removing those redundant or irregular ones, promoting both efficiency and performance in the hyperparameter configuration recommendation. To be specific, we first extract the metafeatures and classification performance of a set of configurations from the collection of historical datasets, and then, a metaregression task is established through SGLasso to capture the main characteristics of the underlying relationship between metafeatures and historical performance. For a new dataset, the classification performance of configurations can be estimated through the selected metafeatures so that the configuration with the highest predictive performance in terms of the new dataset can be generated. Furthermore, a general MtL architecture combined with our model is developed. Extensive experiments are conducted on 136 UCI datasets, demonstrating the effectiveness of the proposed approach. The empirical results on the well-known SVM show that our model can effectively recommend suitable configurations and outperform the existing MtL-based methods and the well-known search-based algorithms, such as random search, Bayesian optimization, and Hyperband.
Blockchain in Educational Development: Potentialities and Issues—Towards Sophisticated Digital Education Systems

Blockchain has become an important digital technology component in the Information Technology field and applicable in diverse areas of society and community. Blockchain and its gradual development lead it to become an emerging technology and called as ‘Blockchain Technology. Blockchain simply offers a system of recording information and which are not possible to change due to robust and secure systems. Blockchain is a kind of digital ledger transaction that spreads over the entire communications or networks. Due to the adopted mechanism in Blockchain it is difficult to change or hack or check within the systems and therefore this technology is rising gradually as it is using encrypted and distributed databases connected with transactions. Blockchain is a digital ledger system and that further depends on different independent record, and keep recoded data in a particular place. Blockchain is dedicated in tangible and intangible asset management and further concerned pieces of data stored in the same place. Normally, it is being started its uses in the financial segment regarding proper digital currency transactions but gradually it has become an important tool in diverse areas such as in tangible and intangible asset management. As far as the Education sector is concerned it is useful in both onsite and online education systems. However online and e-learning are more suited with Blockchain. Today in educational institutions many third parties are being used in proper educational delivery and Blockchain is being used in proper safeguarding in newer educational and training models. This Paper is about Blockchain with its concepts, features and applications in educational systems, management and the teaching-learning process. Work also suggested the issues and concerns of Blockchain in educational development and management.
Software Engineering Defect Prediction using the SPSS Method

Software Deficiency (SDP) is among the most helpful the SDLC test step.It distinguishes volumesDeficiency is required and requires detailed testing.In this way, test sources can beUsed efficiently withoutExceeds the restrictions.Software Errors has emerged as a prominent research avenue inside this field of software engineering.Developers can detect potential issues and defects using the program's flawed predictions improvingTests the sourcesThe program improves reliability.The best way to observe and test each activity of the application and run the testSoftware, check the real softwareDevices to help you understand what problems may be for the final userGo and check the softwareDifferent test environment, briefly, notice and resolve its simple test and if notmodels for software faults are employed to automatically identify problematic classes prior to system testing.These models can lower the cost of resources, infrastructure, and the test period.To enhance the test procedure, we suggest a new defect prediction model in this study.Software systems are now more expansive and intricate than ever.It can be quite difficult to prevent software diseases with such characteristics.In order to effectively allocate scarce resources, it is required to automatically estimate the quantity of errors in software.Several methods have been put forth to quickly and cheaply find and fix these flaws.The effectiveness of these strategies needs to be significantly improved, though.In order to estimate the quantity of software system flaws, we therefore suggest a novel approach in this work that makes use of deep learning techniques.First, companies are processing the public database, which includes data normalisation and registration changes.In the interests of preparing the required data for a machine learning model, we perform the data sampling second.Lastly, we feed summary statistics to a deep neural network that has been specifically created to anticipate the number of flaws.We test the suggested strategy in two reputable datasets as well.The findings of the assessment show that the suggested approach is precise and can be improved in more advanced approaches.Ratio studies are statistical analyses of data from appraisals and property valuations.Nearly all states utilise them to produce quantitative measure of the proportion of current market price about which individually estimated taxable property is appraised as well as to offer assessment performance indicators.Software defect prediction, Deep learning, Software quality, Software metrics and Robustness evaluation The Cronbach's Alpha Reliability result.The overall Cronbach's Alpha value for the model is .658which indicates 66% reliability.From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis.Software Engineering Defect Predictionthe Cronbach's Alpha Reliability result.The overall Cronbach's Alpha value for the model is .658which indicates 66% reliability.From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis.
Appropriate media choice for e-learning effectiveness analysis Using Evaluation Based on Distance from Average Solution Method

The expansion of the e-generation is accelerated by internet progress, which has impacted all commercial and industrial activities.E-discipline is embraced by organisations due to high expense, time, or adaptability for designing courses and learners.Evaluation of electrocardiographic ability is crucial, and many assessment systems are used.Although much work has gone into evaluating e-clay skills, a common size rating model takes into account both the complexity of the psychological sense and the cross -functional and cross seen between criterion.With the aid of the way to solve relative associations from the measurement model and the averaging solution (EDAS) Method, the new novel hybrid Learning model that is being proposed in this work refers to the independence relationships of the racial criteria.The estimate with the distances from weighted average method (EDAS) approach is extended in this study.uses interval-valued Pythagorean fuzzy numbers, which offer high flexibility and a wide membership domain, to solve fuzzy inter cooperative decision-making problems.An example of an automobile selection problem is given to illustrate the efficacy and application of the suggested model, and the outcomes are contrasted with those obtained using the simple interval-valued EDAS method.E-Learning Infrastructure, Webpage Connection, Learning Record, and Instructional Material make up the evaluation parameter.Analysis, Design, Production, and execution are the alternatives.EDAS-based elearning programme development.Development is ranked first, whereas analysis is ranked last.
IMPLEMENTING AN ACADEMIC RESEARCH SOCIAL NETWORK IN THE ARAB WORLD: THE CASE OF “BAHETH” PLATFORM

Web 2.0 and 3.0 introduced online social networks to the world, which offered a new and versatile medium of communication and collaboration for different communities over the Internet.The use of specific social networks that targets a certain audience is becoming more accepted today since interactivity and collaboration between members is secured and limited to specific groups.Academic research is a field that can benefit from online social networks.The Arab Conference Network is the first research-based institution in the Middle East to adopt this new technology by creating an online Academic Research Social Network named "Arab Researcher Portal-Baheth".The network is open to all Arabic-speaking researchers and professors to collaborate on different academic and research fields.The authors investigated the viability of "Baheth" for Arab professors and researchers to collaborate on different academic and research fields An exploratory study of implementing "Baheth" is presented and discussed.The findings showed that only 19% of university professors and researchers who joined the network have completed their online profiles and participated actively in the network.As a result, the level of awareness of the true value of the online academic research social network was very low.Academic rank, specialization, computer, and Internet literacy in addition to online social skills, played a role in adopting this new channel.In conclusion, the research presents steps that can be taken to improve the use of this unique social media channel, and guidelines for motivating professors are suggested on the basis of the research findings.
Plenary talk- Empowering Health Experts to Design and Use Virtual Reality Experiences

He is heading the Games Engineering research group at the Chair for Human-Computer Interaction.Before, he had studied and worked at the Universities of Erlangen, Calgary (PhD), and Augsburg (Habilitation).By now, he has authored or co-authored more than one hundred publications, has won numerous research awards, acquired significant research grants, and has been an active member of several scientific communities (especially Games, Serious Games, Artificial Life, Organic Computing).Despite his urge towards human-
Redundant BLDC motors control system for scientific uncrewed space missions

Brushless DC (BLDC) motors are widely used in payloads of scientific space missions. To work with this type of motor it’s required to develop a complex control system considering basic technical requirements as well as the demands to reliability linked with the space application of the system. The basic technical requirements and the demands for the reliability of the system were formulated in the article. The block commutation algorithm was selected for the system since it’s simple and it doesn’t require using a high-performance processor and precision rotor position sensors. Based on the selected algorithm and the requirements structural scheme of the control system with redundancy features was designed. Further, schematics for output stages of the system were considered. They allowed connecting one set of motors and Hall sensors to both the main and the redundant control system. To test the structural and schematic solutions a prototype board was developed. The prototype was successfully tested and the solutions were verified.