A Tool for Improving Decision Quality on Upstream Consortia

This article aims to provide a guideline to better decision quality on multi-company upstream projects. The scope is to provide a high-level overview of what should be included in a decision quality process, when companies with different levels of ownership and influence on the decision-making process naturally tend to have different approaches towards risks and decision management. It is well known in our industry that there is a predominance of multicompany participation in these projects and the paper will provide guidelines that will ultimately provide better decision quality and participant's alignment. Normally high-risk upstream projects have multi-company ownership. However, it has been noticed that companies tend to face decision management differently, which causes unnecessary delays on budget approvals and even operational timeline. Procedures to normalize the definition of an initial decision frame and creation of solutions based on a good set of alternatives are paramount to facilitate discussions and drive final recommendations. In summary, companies’ alignment on decision-making processes is key to quality, speed, and effectiveness of those decisions and critical to project success. Alignment between partners on pivotal decisions can significantly improve project delivery. The main results are practical guidelines for generating (a) decision framing, (b) strategies, (c) alternatives and potential consequences, and (d) logical analysis, partners’ alignment and commitment to action.

Resolving the Covid Vaccination Debate by Understanding Diversity and Standardization

To help resolve the current debate on whether countries should vaccinate the whole population against Covid, we offer a new perspective by looking at the issue as a case of diversity versus standardization, which, we believe, is at the heart of the issue, moving away from the politics. While diversity and standardization are, to a large extent, opposites, they are also complementary in social, economic and technological development. Over-promoting or artificially suppressing one or the other will harm the creativity and efficiency of an economy. The optimal balance between diversity and standardization is that when facing a new problem of an unknown nature, we must diversify and create a competition to find the best methods for solving it. In the current covid crisis, we have done just that, and now we have a good set of vaccines, some better than others. Once the best methods are found, the government of a country should concentrate and adopt them as the standard for the entire nation for implementation. When we develop vaccines, we need some diversity, a diversity of ideas, approaches and chemistry. Once the best vaccines have been found, we need to have standardization to quickly vaccinate the entire population and realize the benefits of the vaccines.

Alternative Definitions of Complexity for Practical Applications of Model Selection Criteria

Defining and quantifying complexity is one of the major challenges of modern science and contemporary societies. This task is particularly critical for model selection, which is aimed at properly identifying the most adequate equations to interpret the available data. The traditional solution of equating the complexity of the models to the number of their parameters is clearly unsatisfactory. Three alternative approaches are proposed in this work. The first one estimates the flexibility of the proposed models to quantify their potential to overfit. The second interprets complexity as lack of stability and is implemented by computing the variations in the predictions due to uncertainties in their parameters. The third alternative is focused on assessing the consistency of extrapolation of the candidate models. All the upgrades are easy to implement and typically outperform the traditional versions of model selection criteria and constitute a good set of alternatives to be deployed, depending on the priorities of the investigators and the characteristics of the application.

THE NEED TO REVIEW CULTURAL POLICIES IN BULGARIA

Cultural policy guarantees freedom of expression, creates conditions for equal participation in the cultural life of the country, preserves and promotes the culture of different ethnic groups and religions, supports education, intercultural exchange and expands intercultural communication. In this context, the influence of European cultural policies on national ones is of particular importance for the development of art and the preservation of cultural values. The choice of priorities, goals and tasks, a good set of measures, funding mechanisms, accessibility to citizens, their recognition by society are of particular importance and favors the development of culture. In the context of the social isolation caused by COVID-19, cultural policies need to be updated. This report is based on an analysis of statistics relating to the expenditure on culture and the arts at the European and national level, a comparative analysis of European cultural policies and their impact on national ones. The obtained results outline guidelines for the development of cultural policies at the regional level and can be a basis for practical application and further research in this direction.

Invasive Weed Optimization Based Ransom-Ware Detection in Cloud Environment

Cloud provide support to manage, control, monitor different organization. Due to flexible nature f cloud chance of attack on it increases by means of some software attack in form of ransomware. Many of researcher has proposed various model to prevent such attacks or to identify such activities. This paper has proposed a ransomware detection model by use of trained neural network. Training of neural network was done by filter or optimized feature set obtained from the feature reduction algorithm. Paper has proposed a Invasive Weed Optimization algorithm that filter good set of feature from the available input training dataset. Proposed model test was performed on real dataset, have set sessions related to cloud ransomware attacks. Result shows that proposed model has increase the comparing parameter values.

Evolution of Activation Functions: An Empirical Investigation

The hyper-parameters of a neural network are traditionally designed through a time-consuming process of trial and error that requires substantial expert knowledge. Neural Architecture Search algorithms aim to take the human out of the loop by automatically finding a good set of hyper-parameters for the problem at hand. These algorithms have mostly focused on hyper-parameters such as the architectural configurations of the hidden layers and the connectivity of the hidden neurons, but there has been relatively little work on automating the search for completely new activation functions, which are one of the most crucial hyperparameters to choose. There are some widely used activation functions nowadays that are simple and work well, but nonetheless, there has been some interest in finding better activation functions. The work in the literature has mostly focused on designing new activation functions by hand or choosing from a set of predefined functions while this work presents an evolutionary algorithm to automate the search for completely new activation functions. We compare these new evolved activation functions to other existing and commonly used activation functions. The results are favorable and are obtained from averaging the performance of the activation functions found over 30 runs, with experiments being conducted on 10 different datasets and architectures to ensure the statistical robustness of the study.

Survey on Heart Disease Anticipation Through Genetic Algorithm and BP-Neural Network

Medical Diagnosis Systems play a vital role in medical practice and are used by medical practitioners for diagnosis and treatment. In this paper, a medical diagnosis system is presented for predicting the risk of cardiovascular disease. This system is built by combining the relative advantages of genetic algorithm and neural network. Multilayered feed forward neural networks are particularly suited to complex classification problems. The weights of the neural network are determined using genetic algorithm because it finds acceptably good set of weights in less number of iterations. The dataset provided by University of California, Irvine (UCI) machine learning repository is used for training and testing. It consists of 303 instances of heart disease data each having 14 attributes including the class label. First, the dataset is preprocessed in order to make them suitable for training. Genetic based neural network is used for training the system. The final weights of the neural network are stored in the weight base and are used for predicting the risk of cardiovascular disease. The classification accuracy obtained using this approach is 94.17%.

Two-loop anomalous dimensions of QCD operators up to dimension-sixteen and Higgs EFT amplitudes

We consider two-loop renormalization of high-dimensional Lorentz scalar operators in the gluonic sector of QCD. These operators appear also in the Higgs effective theory obtained by integrating out the top quark loop in the gluon fusion process. We first discuss the classification of operators and how to construct a good set of basis using both off-shell field theory method and on-shell form factor formalism. To study loop corrections, we apply efficient unitarity-IBP strategy and compute the two-loop minimal form factors of length-3 operators up to dimension sixteen. From the UV divergences of form factor results, we extract the renormalization matrices and analyze the operator mixing behavior in detail. The form factors we compute are also equivalent to Higgs plus three-gluon amplitudes that capture high-order top mass corrections in Higgs EFT. We obtain the analytic finite remainder functions which exhibit several universal transcendentality structures.

Kurdish Handwritten Classification Based on Human Personality Analysis

This study proposes a holistic technique of classifying Kurdish handwritten text documents, involving several stages. The first stage entails the sectioning of Kurdish handwritten document images into lines, words, and characters and the second stage entails the obtaining of structural and statistical features from the sectioned parts that are often utilized in human personality analysis for the examination of feature behavior and effectiveness. This is done by combining the entire potential outcomes to determine the significant features set. The third stage entails the use of F-Measure to evaluate the extracted features performance and their combination in various relationship methods, individually and in groups. The last stage entails the actual experiment using the standard KRDOH dataset of the Kurdish handwritten text, containing 1076 volunteers’ samples of different ages, genders, and education levels in a cumulative 4304 manuscripts consisting of 4 contributed pages by each writer. Based on the results obtained from multiple runs of an individual clustering method of each distance measure, a good set of features generally deliver significantly enhanced clustering of handwritten structures.

Accurate feature selection improves single-cell RNA-seq cell clustering

Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as ‘features’), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have a significant impact on the clustering accuracy. All existing scRNA-seq clustering tools include a feature selection step relying on some simple unsupervised feature selection methods, mostly based on the statistical moments of gene-wise expression distributions. In this work, we carefully evaluate the impact of feature selection on cell clustering accuracy. In addition, we develop a feature selection algorithm named FEAture SelecTion (FEAST), which provides more representative features. We apply the method on 12 public scRNA-seq datasets and demonstrate that using features selected by FEAST with existing clustering tools significantly improve the clustering accuracy.