The Image Annotation Refinement in Embedding Feature Space based on Mutual Information

The ever-increasing size of images has made automatic image annotation one of the most important tasks in the fields of machine learning and computer vision. Despite continuous efforts in inventing new annotation algorithms and new models, results of the state-of-the-art image annotation methods are often unsatisfactory. In this paper, to further improve annotation refinement performance, a novel approach based on weighted mutual information to automatically refine the original annotations of images is proposed. Unlike the traditional refinement model using only visual feature, the proposed model use semantic embedding to properly map labels and visual features to a meaningful semantic space. To accurately measure the relevance between the particular image and its original annotations, the proposed model utilize all available information including image-to-image, label-to-label and image-to-label. Experimental results conducted on three typical datasets show not only the validity of the refinement, but also the superiority of the proposed algorithm over existing ones. The improvement largely benefits from our proposed mutual information method and utilizing all available information.

The application of VOI method to shape a strategy of additional exploration

Background. An optimal exploration strategy creates a significant share in value of project on exploration stage. The paper describes an example of solving the following tasks: determining the feasibility of additional exploration drilling; evaluating the value of drilling of one or more exploration wells; determining the optimal placement for exploration wells and drilling order. Authors presenting the modification of VoI (Value of Information) method and its application. Materials and methods. Complex probabilistic models were created summarizing main uncertainties and limitations, both geological, technical and technological. At the first stage three equiprobable geological concepts were made. For each concept probabilistic geological modelling was proceeded and then realizations corresponding to values of reserves P10, P50, and P90 were selected. Further, detailed production forecasts and economic estimates were performed. The analysis used the well pad and the corresponding area for exploration drilling as a unit of calculation. In the article the authors introduced the concept of remaining uncertainty. Application of modified VoI method allowed to form ‘dynamic’ (i.e. depending on exploration wells drilling order) range of areas for additional exploration which provide the best decrease of remaining uncertainty. An additional exploration strategy has been formed, which includes the necessary and sufficient number of wells and their drilling order. A decision tree was created depending on the success or failure of each subsequent exploration well. Results. The use of the modified VoI approach made it possible to achieve the objectives and obtain economical estimates, all of which combined to facilitate the adoption of decisions. As a result, a program for two exploration well drilling was created which would reduce the uncertainty by 90% from its initial value. Conclusions: The adopted VoI method could be applied to fields at the stage of additional exploration as well as to fields at early exploration stage to develop an exploration drilling strategy.

Ph.D training by specialty «Law» in Ukraine (on the example of Zaporizhzhia National University)

The process of training candidates for the degree of Doctor of Philosophy (PhD) in Ukraine, which is aimed at the formation of theoretical and applied competencies, consists of two blocks: theoretical and practical. The authors have considered the main stages of training of applicants. The purpose of this work is to analyze the peculiarities of the training of doctors of philosophy in the specialty "Law" in Ukraine. The object of research is the peculiarities of the training of doctors of philosophy in the specialty "Law". The subject of the research is the peculiarities of training doctors of philosophy in the specialty "Law" on the example of Zaporizhzhia National University. The following methods were used during the study: analysis and synthesis, extrapolation, modeling, comparison, observation, generalization, the ascent from the abstract to the concrete, induction, and deduction, morphological analysis method, information method, commission method. During the study, the features of theoretical and practical aspects of training doctors of philosophy in the specialty "Law" were analyzed, trends in training doctors of philosophy were identified, relevant areas for further study, and improvement of the process of obtaining the degree of doctor of philosophy were identified.

Effects of Zeros in Phase Space Reconstruction for Small and Large Solar Radiation Data Points during Wet and Dry Seasonal Modeling and Prediction

The effect of zeros in the behaviour of nature system has been a major global concern which have been reported to bias the output of the analysis. This study examines the effect of zeros on small and large solar radiation data points in Nsukka from a nonlinear dynamic perspective. The solar radiation data used were collected from National Research for Space and Development Agency (NARSDA) and covers the period of two years (January 2012–December 2013). The influence of zeros on average mutual information method for delay time (𝜏), False nearest neighbour (FNN) for embedding dimension (𝑚), and phase space reconstruction is investigated by considering two different cases (one hour and five minutes interval for small and large data points on monthly basis respectively). The results reveal that the phase space trajectories of the raw and non-zero small data points for dry and wet seasons show evidence of an attractorin a well-defined region while raw and non-zero large data points have no attractor like shape but regular patterns and well-defined shapes are visible in dry and wet seasons. These imply low-dimensional and deterministic chaotic nature of the underlying dynamics of raw and non-zero data for small and large data points during wet and dry seasons. It is observed that there is little or no significant difference in the phase space reconstruction of raw and non-zero data for both small and large data points due to the low percentage of zeros in the time series data. Keywords: mutual information method, Phase space reconstruction, False nearest neighbour, chaotic nature.