Unsolved Research Problems Research Articles

Fourier feature network for 3D vessel reconstruction from biplane angiograms

3D reconstruction of biplane cerebral angiograms remains a challenging, unsolved research problem due to the loss of depth information and the unknown pixelwise correlation between input images. The occlusions arising from only two views complicate the reconstruction of fine vessel details and the simultaneous addressing of inherent missing information. In this paper, we take an incremental step toward solving this problem by reconstructing the corresponding 2D slice of the cerebral angiogram using biplane 1D image data. We developed a coordinate-based neural network that encodes the 1D image data along with a deterministic Fourier feature mapping from a given input point, resulting in a slice reconstruction that is more spatially accurate. Using only one 1D row of biplane image data, our Fourier feature network reconstructed the corresponding volume slices with a peak signal-to-noise ratio (PSNR) of 26.32 ± 0.36, a structural similarity index measure (SSIM) of 61.38 ± 1.79, a mean squared error (MSE) of 0.0023 ± 0.0002, and a mean absolute error (MAE) of 0.0364 ± 0.0029. Our research has implications for future work aimed at improving backprojection-based reconstruction by first examining individual slices from 1D information as a prerequisite.

New Insights in Smooth Occluding Contours for Nonphotorealistic Rendering.

Computing occluding contours is often a crucial step in stroke-based artistic 3-D stylization for movies, video games, and visualizations. However, many existing applications use only simple curve stylization techniques, such as thin black lines or hand-animated strokes. This is because sophisticated procedural stylization requires accurate curve topology, which has long been an unsolved research problem. This article describes a recent theoretical breakthrough in the topology problem. Specifically, the new theory points out that existing contour algorithms often generate curves that cannot have any valid visibility, and new algorithms show how to correct the problem. This article surveys classes of algorithms that can compute contours accurately and identifies new research opportunities.

МЕНЕДЖМЕНТ КІБЕРБЕЗПЕКИ В СИСТЕМІ ЛОЯЛЬНОСТІ ПОКУПЦІВ

The need to find new methods of management of trading enterprises, where the management of cyber security of trading operations should occupy a key position, is becoming highly relevant. The search and implementation of these methods is a priority task for management practitioners and theoreticians, public administration specialists. That is why the purpose of the article is to find and research effective methods of cyber security management in the customer loyalty system of domestic enterprises, to build an effective organizational chart of the customer loyalty cyber security management mechanism, which is based on the protection of all components of the commodity-economic interaction and its proper resource provision. As part of the study, the following general scientific theoretical methods were used: system analysis and synthesis - to build a management scheme for the loyalty cyber protection system; induction method - for accumulation, generalization and processing of information of relevance and unsolved research problems; the method of deduction - to distinguish the main components of the problem; methods of abstraction and concretization - for a schematic and practically oriented description of the problems of cyber protection of trade enterprises; abstract logical method - for identifying logical problems of the loyalty cyber protection system, assessing the risks of cyber incidents, identifying resource needs of cyber protection. As a result of research on the security of informatization of trade and economic processes, the significant impact of cyber incidents on the decrease in customer loyalty of trade enterprises has been confirmed. However, in the case of domestic sellers and buyers, there is a low interest in issues of information and cyber security of trade operations. The main problem remains the lack of a unified approach to managing customer loyalty cyber security. The paper proposes a scheme for cyber protection of customer loyalty, which provides for a comprehensive mechanism for assessing, monitoring and solving cyber risks and cyber incidents. The main component of the effectiveness of such a scheme is the Information Security Policy and the prompt response to cyber incidents. The practical significance of the obtained results lies in the fact that the recommendations and proposals outlined in the study provide justification for the implementation of an effective system of cyber protection of the loyalty of buyers of domestic trade enterprises. Prospects for further research in this direction are the segmentation of the organizational chart of the buyer's loyalty cyber security management mechanism for the cases of domestic trade enterprises of various types and sizes.

Separable Paravector Orientation Tensors for Enhancing Retinal Vessels.

Robust detection of retinal vessels remains an unsolved research problem, particularly in handling the intrinsic real-world challenges of highly imbalanced contrast between thick vessels and thin ones, inhomogeneous background regions, uneven illumination, and complex geometries of crossing/bifurcations. This paper presents a new separable paravector orientation tensor that addresses these difficulties by characterizing the enhancement of retinal vessels to be dependent on a nonlinear scale representation, invariant to changes in contrast and lighting, responsive for symmetric patterns, and fitted with elliptical cross-sections. The proposed method is built on projecting vessels as a 3D paravector valued function rotated in an alpha quarter domain, providing geometrical, structural, symmetric, and energetic features. We introduce an innovative symmetrical inhibitory scheme that incorporates paravector features for producing a set of directional contrast-independent elongated-like patterns reconstructing vessel tree in orientation tensors. By fitting constraint elliptical volumes via eigensystem analysis, the final vessel tree is produced with a strong and uniform response preserving various vessel features. The validation of proposed method on clinically relevant retinal images with high-quality results, shows its excellent performance compared to the state-of-the-art benchmarks and the second human observers.

On the Sixth International Olympiad in Cryptography NSUCRYPTO

NSUCRYPTO is the unique cryptographic Olympiad containing scientific mathematical problems for professionals, school and university students from any country. Its aim is to involve young researchers in solving curious and tough scientific problems of modern cryptography. From the very beginning, the concept of the Olympiad was not to focus on solving olympic tasks but on including unsolved research problems at the intersection of mathematics and cryptography. The Olympiad history starts in 2014. In 2019, it was held for the sixth time. In this paper, problems and their solutions of the Sixth International Olympiad in cryptography NSUCRYPTO'2019 are presented. We consider problems related to attacks on ciphers and hash functions, protocols, Boolean functions, Dickson polynomials, prime numbers, rotor machines, etc. We discuss several open problems on mathematical countermeasures to side-channel attacks, APN involutions, S-boxes, etc. The problem of finding a collision for the hash function Curl27 was partially solved during the Olympiad.

On the Sixth International Olympiad in Cryptography NSUCRYPTO

NSUCRYPTO is the unique cryptographic Olympiad containing scientific mathematical problems for professionals, school and university students from any country. Its aim is to involve young researchers in solving curious and tough scientific problems of modern cryptography. From the very beginning, the concept of the Olympiad was not to focus on solving olympic tasks but on including unsolved research problems at the intersection of mathematics and cryptography. The Olympiad history starts in 2014. In 2019, it was held for the sixth time. We present the problems and their solutions of the Sixth International Olympiad in cryptography NSUCRYPTO $$^{\prime} $$ 2019. Under consideration are the problems related to attacks on ciphers and hash functions, protocols, Boolean functions, Dickson polynomials, prime numbers, rotor machines, etc. We discuss several open problems on mathematical countermeasures to side-channel attacks, APN involutions, S-boxes, etc. The problem of finding a collision for the hash function Curl27 was partially solved during the Olympiad.

Sustainable Development and Industry 4.0: A Bibliometric Analysis Identifying Key Scientific Problems of the Sustainable Industry 4.0

The main aim of the manuscript is the identification of key research problems in the field of sustainable development, in the era of implementing the Industry 4.0 concept. The manuscript presents results of the bibliometric analysis in the subject: “Sustainable Industry 4.0”. The bibliometric analysis was realized in three segments: Sustainability, Industry 4.0 and Sustainable Industry 4.0. In the analysis, the following databases were used: Web of Science (WoS), Scopus, Google Scholar. The main purpose of the analysis was to outline the dynamics of publications in the categories: citation, author, country, type document, science field, research area. The review of sources carried out in this way allowed us to identify key research areas and confirm the research thesis adopted in the manuscript. The research thesis: Sustainable Industry 4.0 allows the integration of the Industry 4.0 concept with sustainable development goals. The article is dedicated especially to scientists looking for still unsolved research problems in the implementation of sustainable Industry 4.0. Furthermore, the manuscript could be an inspiration for scientists, stakeholders, practitioners and governments to complete today’s knowledge about the problems of sustainability in Industry 4.0.

Quantifying the Influence of Cloud Radiative Feedbacks on Arctic Surface Warming Using Cloud Locking in an Earth System Model

AbstractUnderstanding the influence of clouds on amplified Arctic surface warming remains an important unsolved research problem. Here, this cloud influence is directly quantified by disabling cloud radiative feedbacks or “cloud locking” within a state‐of‐the‐art and well‐documented model. Through comparison of idealized greenhouse warming experiments with and without cloud locking, the influence of Arctic and global cloud feedbacks is assessed. Global cloud feedbacks increase both global and Arctic warming by around 25%. In contrast, disabling Arctic cloud feedbacks has a negligible influence on both Arctic and global surface warming. Interestingly, the sum of noncloud radiative feedbacks does not change with either global or Arctic‐only cloud locking. Notably, the influence of Arctic cloud feedbacks is likely underestimated, because, like many models, the model used here underestimates high‐latitude supercooled cloud liquid. More broadly, this work demonstrates the value of regional and global cloud locking in a well‐characterized model.

A Comprehensive Review Towards Segmentation and Detection of Cancer Cell and Tumor for Dynamic 3D Reconstruction

Automated cancer cell and tumor segmentation and detection for 3D modeling are still an unsolved research problem in computer vision, image processing and pattern recognition research domains. Human body is complex three-dimensional structure where numerous types of cancer and tumor may exist regardless of shape or position. A three-dimensional (3D) reconstruction of cancer cell and tumor from body parts does not lead to loss of information like 2D shape visualization. Various research methodologies for segmentation and detection for 3D reconstruction of cancer cell and tumor were performed by previous research. However, the pursuit for better methodology for segmentation and detection for 3D reconstruction of cancer cell and tumor are still unsolved research problem due to lack of efficient feature extraction for details surface information, misclassification during training phases and low tissue contrast which causes low detection and precision rate with high computational complexity during detection and segmentation. This research addresses comprehensive and critical review of various segmentation and detection research methodologies for cancer affected cell and tumor in human body in the basis of three-dimensional reconstruction from MRI or CT images. At first, core research background is illustrated highlighting various aspects addressed by this research. After that, various previous methods with advantages and disadvantages followed by various phases used as frameworks exist in the previous research demonstrated by this research. Then, extensive experimental evaluations done by previous research are demonstrated by this research with various performance metrics. At last, this research summarized overall observation on previous research categorized into two aspects, i.e. observation on common research methodologies and recommended area for further research. Overall reviews proposed in this paper have been extensively studied in various research papers which can significantly contribute to computer vision research and can be potential for future development and direction for future research.

Insights on Schemes towards Image Counterfeit Attacks & their Effectiveness

This paper reviews significant schemas pertaining to image forensics where the prime emphasize has been laid towards exploring the mechanisms which identify image counterfeit with higher accuracy. The study reviewed the prime contribution published in the last four years and also addressed the unsolved research problems which are needed to be objectified. The extraction of the research gap further extensively elaborated, which identify the gap needed to be filled up. The extensive review of literature also provides better insight into the design aspects associated with the conventional techniques which are defensive against counterfeit image attacks. The future direction of this investigational study aims to come up with a solution model which can address the accuracy and complexity problems which exist in the conventional system.

Insights on Schemes towards Image Counterfeit Attacks & Their Effectiveness

This paper reviews significant schemas pertaining to image forensics where the prime emphasize has been laid towards exploring the mechanisms which identify image counterfeit with higher accuracy. The study reviewed the prime contribution published in the last four years and also addressed the unsolved research problems which are needed to be objectified. The extraction of the research gap further extensively elaborated, which identify the gap needed to be filled up. The extensive review of literature also provides better insight into the design aspects associated with the conventional techniques which are defensive against counterfeit image attacks. The future direction of this investigational study aims to come up with a solution model which can address the accuracy and complexity problems which exist in the conventional system.

3D map-guided single indoor image localization refinement

Image localization is an important supplement to GPS-based methods, especially in indoor scenes. Traditional methods depending on image retrieval or structure from motion (SfM) techniques either suffer from low accuracy or even fail to work due to the texture-less or repetitive indoor surfaces. With the development of range sensors, 3D colourless maps are easily constructed in indoor scenes. How to utilize such a 3D colourless map to improve single image localization performance is a timely but unsolved research problem. In this paper, we present a new approach to addressing this problem by inferring the 3D geometry from a single image with an initial 6DOF pose estimated by a neural network based method. In contrast to previous methods that rely multiple overlapping images or videos to generate sparse point clouds, our new approach can produce dense point cloud from only a single image. We achieve this through estimating the depth map of the input image and performing geometry matching in the 3D space. We have developed a novel depth estimation method by utilizing both the 3D map and RGB images where we use the RGB image to estimate a dense depth map and use the 3D map to guide the depth estimation. We will show that our new method significantly outperforms current RGB image based depth estimation methods for both indoor and outdoor datasets. We also show that utilizing the depth map predicted by the new method for single indoor image localization can improve both position and orientation localization accuracy over state-of-the-art methods.

Moment Features based Violence Action Detection using Optical Flow

Instantaneous detection of violence is still an unsolved research problem although artificial intelligence lives its prosperous years. The severity of injury causes due to violence can be minimized by detecting violence in real time demands for effective violence detection. Various methods were previously proposed for violence detection which could not provide robust results due many challenges, i.e. noise, motion estimation, lack of appropriate feature selection, lack of effective classification approach, complex background and variations in illumination. This research proposes an efficient method for violence detection using moment features to use motion patterns to facilitate detection in each frame and provides smaller area as region of interest. This means probability for extraction of motion intensity is getting lost because of same colored object in the background is reduced and thus minimizes background complexity. After that, proposed method uses optical flow to calculate angles and linear distances in each frame. In this context, if there is any frame loss due to noise or illumination variation, proposed method uses Kalman filter to process that frame by illuminating noise. Finally, decision for violence is determined using random forest classifier from single feature vector by generating a set of probabilities for each class. Proposed research performed extensive experimentation where accuracy rate of 99.12% was achieved using frame rate of 35 fps which is higher comparing with previous research results. Experimental results reveal the effectiveness of the proposed methodology.

Robust Drowsiness Detection for Vehicle Driver using Deep Convolutional Neural Network

Drowsiness detection during driving is still an unsolved research problem which needs to be addressed to reduce road accidents. Researchers have been trying to solve this problem using various methods where most of these solution lacks behind in accuracy, real-time performance, costly, complex to build, and has a higher computational cost with low frame rate. This research proposes robust method for drowsiness detection of vehicle drivers based on head pose estimation and pupil detection by extracting facial region initially. Proposed method used frame aggregation strategy in case of face region cannot be extracted in any frame due to shortcomings, i.e. light reflection, shadow. In order to improve identification under highly varying lighting conditions, proposed research used cascade of regressors cutting edge method where each regression refers estimation of facial landmarks. Proposed method used deep convolutional neural network (DCNN) for accurate pupil detection to learn non linear data pattern. In this context, challenges of varying illumination, blurring and reflections for robust pupil detection are overcome by using batch normalization for stabilizing distributions of internal activations during training phase which makes overall methodology less influenced by parameter initialization. Proposed research performed extensive experimentation where accuracy rate of 98.97% was achieved using frame rate of 35 fps which is higher comparing with previous research results. Experimental results reveal the effectiveness of the proposed methodology.

Insights on Video Compression Strategies using Machine Learning

With the rising advancement of the multimedia technology, video compression is becoming a challenging problem. Although, there is availability of various standard compression algorithms, yet robust compression performance is yet to be seen in existing compression techniques. This paper also highlights that machine learning plays a significant contributory role in improving the performance of the video compression. Therefore, this manuscript offers a technical insight about the performance of existing video compression technique using machine learning approach. The contribution of this paper is its findings which states that machine learning approach do have significant advantage but the advantageous features are limited by the inherent and unsolved research problem. The core findings of this paper are basically to highlight the strength and limitations of existing methods as well as to highlight the research gap in terms of open-end research problems which requires immediate attention.

Przyczynowość w ekonomii. Najnowsze badania i nierozwiązane problemy

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Causality and inference in economics: An unended quest

The aim of this article is to point to the unsolved research problems connected to causation in the philosophy of economics. First, the paper defines causation and discusses two notable approaches, i.e. the realist theory of causation and the instrumentalist theory of causation. Second, it offers a review the current research activity focusing on the problem of causation in economics. Third, it discusses several case studies. On the grounds of comparison of the research practice of economists and the current issues undertaken by the philosophers of economics, the paper concludes that there is a gap between the research practice and the normative methodological analyses and indicate the research questions that need to be addressed.

Optimization-based state estimation: Current status and some new results

This paper presents an overview of the fundamentals of moving horizon state estimation and full information estimation. The paper provides a new statement of robust global asymptotic stability (RGAS) for state estimation and establishes a new result using this definition, namely that full information is RGAS for the case of a nonlinear detectable system subject to β-convergent state and measurement disturbances. Two unsolved research problems are presented: (i) suboptimal moving horizon estimation and (ii) proving RGAS for the case of bounded rather than convergent disturbances.

A Statistical Quality Model for Data-Driven Speech Animation

In recent years, data-driven speech animation approaches have achieved significant successes in terms of animation quality. However, how to automatically evaluate the realism of novel synthesized speech animations has been an important yet unsolved research problem. In this paper, we propose a novel statistical model (called SAQP) to automatically predict the quality of on-the-fly synthesized speech animations by various data-driven techniques. Its essential idea is to construct a phoneme-based, Speech Animation Trajectory Fitting (SATF) metric to describe speech animation synthesis errors and then build a statistical regression model to learn the association between the obtained SATF metric and the objective speech animation synthesis quality. Through delicately designed user studies, we evaluate the effectiveness and robustness of the proposed SAQP model. To the best of our knowledge, this work is the first-of-its-kind, quantitative quality model for data-driven speech animation. We believe it is the important first step to remove a critical technical barrier for applying data-driven speech animation techniques to numerous online or interactive talking avatar applications.

Data Confidentiality: The Next Five Years Summary and Guide to Papers

On May 1–2, 2008, the National Center for Health Statistics/CDC (NCHS) and the National Institute of Statistical Sciences (NISS) co-sponsored a workshop “Data Confi- dentiality: The Next Five Years,” which was held at NCHS’ headquarters in Hyattsville, MD. The purpose of the workshop was to bring together the academic and federal data confidentiality research communities, including mathematicians, mathematical statisticians, statisticians, computer scientists, cryptographers, and federal agency “owners” of data confidentiality problems in order to:
 
 Identify important unsolved research problems associated with data confidentiality;
 Articulate a research agenda that both addresses those problems and responds to current and emerging needs among federal statistical agencies;
 Discuss and catalyze the kinds of collaborations among mathematicians, mathematical statisticians, statisticians, computer scientists, cryptographers, domain scientists, and data-owning agencies that are needed to pursue the research agenda.