Reviewerly/exHarmony huggingface dataset:
Ontologies of the Fire Safety Domain

This article considers recent research dealing with developing ontologies of fire safety. The authors have made brief overview of ontologies existing in this field and consider possible ways to use them. The latest researches have explored the problem from different angles. It is shows various applications of ontology: there are projects on wildfires, forest fire risk management, fire in buildings and visualization of the spread of smoke. The article draws our attention to the main characteristics of ontologies and the applicability of a particular ontology in different areas. Criteria for ontology comparison have been formulated. According to the research the most important criterions are availability of ontology and scope of use. The type of ontology as a selection criterion will help at the initial stage to make a list of ontologies. The authors presented the results of the analysis in the form of a comparative table. The results can be used to help developers to make the right choice of ontologies.
Geophysics in Archeology

This paper contains information about possibilities of exploration geophysics in solving archeological problems. High effectiveness of electromagnetic sounding at revealing ancient buildings remnants in urban conditions, dating the layers of anthropogenic formations and tracing their bottoms using Impulse-auto M-1/0-20 electromagnetic surveying system is demonstrated herein. The measurement procedure and developed computer technologies for processing geophysical information, which allow solving particular archeological problems and evaluating their veracity, are stated in this paper. The results of conducted researches on the examples of archeological objects of the Kazan Kremlin, Bogoroditsky Monastery, Bilyar and Bulgar settlements of the Republic of Tatarstan are considered here.
Automatic Image Annotation at ImageCLEF

Automatic image annotation is the task of automatically assigning some form of semantic label to images, such as words, phrases or sentences describing the objects, attributes, actions, and scenes depicted in the image. In this chapter, we present an overview of the various automatic image annotation tasks that were organized in conjunction with the ImageCLEF track at CLEF between 2009–2016. Throughout the 8 years, the image annotation tasks have evolved from annotating Flickr photos by learning from clean data to annotating web images by learning from large-scale noisy web data. The tasks are divided into three distinct phases, and this chapter will provide a discussion for each of these phases. We will also compare and contrast other related benchmarking challenges, and provide some insights into the future of automatic image annotation.
Accessing Data in Healthcare Application

Data centric applications generate large amount of data in no time. Depending on the application, the data may require protection from unauthorized use. Hence in a system with heterogeneous users data privacy is a challenge. Sensitive data protection may not only need valid authentication but also proper authorization. In case of healthcare application considered in the present work, authorization may not be the direct fallout of valid authentication. Instead, authorization may have to be provided keeping the relationship between user and information under consideration. We propose a novel way of designing a system that contains and protects sensitive information based on the relationship. This design allows storage and retrieval of information of multiple sensitivity levels in a hierarchical manner.
Template attack vs. stochastic model: An empirical study on the performances of profiling attacks in real scenarios

Template Attack (TA) and Stochastic Model (SM) are two classical profiling attacks. Even if both TA and SM use the maximum likelihood principle (MLP) as the statistical tool to recover the secret key used by the target crypto device, their leakage characterization styles are different, which results in different working efficiencies of TA and SM in different scenarios. Considering that both TA and SM are important tools to evaluate the physical security of crypto devices, the right one should be used to achieve the goal of accurate evaluation. In order to do this, merits and shortcomings of TA and SM in different scenarios should be empirically learned. In fact, previous works have compared working efficiencies of TA and SM in either simulated scenarios or unprotected software scenarios. However, working efficiencies of TA and SM are not systematically evaluated and compared in either unprotected hardware scenarios or protected software scenarios yet. Therefore, evaluators do not have an empirical knowledge on merits and shortcomings of TA and SM in these scenarios, and they do not know which one should be used as the evaluation tool in these scenarios. In light of this, leakage traces provided by DPA Contest v2 and v4 are used to systematically evaluate and compare working efficiencies of TA and SM in either unprotected hardware scenarios or protected software scenarios. Then, merits and shortcomings of TA and SM in these two scenarios can be empirically learned. Therefore, evaluators can choose the right tool to accurately evaluate the physical security of a crypto device in these scenarios.
Research and implementation of perceptual hash algorithm for image retrieval

With the extensive development of the Internet, digital image resources are more and more abundant. How to effectively retrieve and utilize a large amount of image information has become a hot issue in the current research. In this paper, a perceptual hash algorithm based on SIFT features is proposed by combining the perceptual hash algorithm with the SIFT feature of the image, which helps to overcome the influence of the perceptual hash algorithm on geometric attacks, image scale changes and other abnormal conditions. Improve the accuracy of the perceptual hash algorithm. By studying the application scene of perceptual hash algorithm, using the image-aware hash algorithm to perceive the target object in the image scene, matching the relevant features of the image, the image retrieval technology based on the hash algorithm is realized.
More Fun with L-Trominoes in Three and Higher Dimensions

Anyone familiar with the game Tetris is familiar with polyominoes—they are finite collections of edge-connected, equal-sized, squares in the plane. In three dimensions, we adjust the definition and...
PERSONALITY’S PATRIOTIC CONSCIOUSNESS DEVELOPMENT

The article substantiates the topicality of addressing the problem of a personality’s patriotic consciousness development. Connection between the existence of the Native Land and the Home Country and a personality is affirmed. Scientific novelty of the research is in the formulation of the basic principles and the definition of the key means of patriotic education and self-education as a process of patriotic consciousness development. The author’s conclusions are based on the Native Land and the Home Country understanding as spiritual categories, the analysis of the historical and modern socio-cultural trends of development, systematization and results of philosophical and scientific research.
Deep Distance Transform to Segment Visually Indistinguishable Merged Objects

We design a two stage image segmentation method, comprising a distance transform estimating neural network and watershed segmentation. It allows segmentation and tracking of colliding objects without any assumptions on object behavior or global object appearance as the proposed machine learning step is trained on contour information only. Our method is also capable of segmenting partially vanishing contact surfaces of visually merged objects. The evaluation is performed on a dataset of collisions of Drosophila melanogaster larvae manually labeled with pixel accuracy. The proposed pipeline needs no manual parameter tuning and operates at high frame rates. We provide a detailed evaluation of the neural network design including 1200 trained networks.
Adaptive Fuzzy Logic Controllers Using Hybrid Genetic Algorithms

In this paper, an integration of fuzzy logic controllers (FLCs) with hybrid genetic algorithms (HGAs) is developed with a view to make the design process fully automatic, without requiring any human expert and numerical data. Our approach consists of two phases: first phase involves selection and definition of fuzzy control rules as well as adjustment of membership functions parameters, while the second phase performs an optimal selection of membership function types corresponding to fuzzy control rules. Learning both parts concurrently represents a way to improve the accuracy of the FLCs to minimize the errors. It has been argued that the performance of FLCs greatly depends on the parameters as well as types of membership functions. Thus, the aforementioned HGAs are a viable solution for designing an efficient adaptive FLCs system. To demonstrate the effectiveness of the proposed method for optimal design of the FLCs, the proposed approach is applied to a well-known benchmark controller design tasks, car and truck-and-trailer like robot system. Simulation results illustrates that proposed optimization approach can find optimal fuzzy rules and their corresponding membership functions types with a high rate of accuracy. The new HGAs optimized adaptive FLCs outperforms not only a passive control strategy but also human-designed FLCs, a neural coded controller with clustering and a neural-fuzzy control algorithm.
Effective Validity: A Generalized Logic for Stable Approximate Inference

The traditional approach in philosophy of using logic to reconstruct scientific theories and methods operates by presenting or representing a scientific theory or method in a specialized formal language. The logic of such languages is deductive, which makes this approach effective for those aspects of science that use deductive methods or for which deductive inference provides a good idealization. Many theories and methods in science, however, use non-deductive forms of approximation. Approximate inferences, which produce approximately correct conclusions and do so only under restricted conditions before becoming unreliable, behave in a fundamentally different way. In the interest of developing accurate models of the structure of inference methods in scientific practice, the focus of this paper, we need conceptual tools that can faithfully represent the structure and behaviour of inference in scientific practice. To this end I propose a generalization of the traditional notion of logical validity, called effective validity, that captures the form of approximate inferences typically used in applied mathematics and computational science. I provide simple examples of approximate inference in mathematical modeling to show how a logic based on effectively valid inference can directly, faithfully represent a wide variety of the forms of inference used in scientific practice. I conclude by discussing how such a generalized logic of scientific inference can provide a richer understanding of problem-solving and mathematical modeling processes.
Cloud Computing: Security and Privacy Issues

Cloud computing has been considered a revolutionary approach towards the computing and a promising solution to increasing demand for using resources provisioned over the Internet which becomes more risky than ever. The cloud computing is a powerful technology in IT environment to provide cost effective, easy to manage, elastic, and powerful resources over the Internet. by eliminating the need to maintain expensive computing hardware, The cloud computing increases the capabilities of the hardware resources through optimal and shared utilization. Even though there are innumerable advantages to approaching the cloud computing, it also contains various problems lie in data security, data confidentiality, Authenticity and Privacy. The prominent barrier to adopt the cloud computing is security. This paper comprises the elaborated study on various security issues related to cloud storage in order to protect the data stored in the cloud.
Designing for “Raressential” Functions: Usage Patterns and UI Guidelines for Infrequent but Essential Tasks

As the number of functions offered by IT devices and services has grown with technological advances, designers have increasingly incorporated factors such as usage frequency and importance into the design of the user interface (UI). In this process, rarely used functions were pushed to the remote corners of the interface; some of these include infrequent but essential functions (“raressential” functions) that can significantly affect the user experience of a service. Despite their impact on user satisfaction, there have been few studies addressing the issues arising from the use of raressential functions. This study investigates usage patterns of representative raressential functions in order to propose design guidelines for UIs with such functions. Twenty-five participants were asked to sketch and perform the process for locating raressential functions in three different platforms. The protocol analysis and data visualization identified that (1) the corners of the mobile screen are strong visual attractors, (2) the overlap between semantic scopes of icons or names complicates the searching process in the PC environment, and (3) the lack of understanding of application software hierarchy seriously hinders orderly navigation in the TV platform. Participants also exhibited repetitive search patterns unique to each platform that delayed the completion of the task. Additional behavioral characteristics and relevant design guidelines are proposed for enhancing user experience of raressential functions.
Scale Your Information Modeling Effort Using the Power of the Crowd

Data is increasingly important for companies to get insights, allowing to better serve their customers and to generate new business opportunities. The increase of data with all its hidden potential is so large, that to keep up in understanding that data, we need to scale up by involving the power of the (business) crowd. This starts with knowing what the data is and how it relates to other data. Renowned methods like Fact Based Modeling (FBM) require the participation of specialists, and take time before business people can benefit from this data knowledge. A new approach allows the business to do self-service modeling to quickly capture and share relevant knowledge about the data. This approach uses a checklist with questions, guiding the development of an informal model. Self-service modeling enables a crowd of people to describe their knowledge about data, scaling up the use of data and its hidden potential.
SamaTulyataOne

Source to source semantic preserving transformations have been carried out by compiler or several human guided transformations. If the semantics preserving transformations carried out wrongly it gives the software bug. So it is very important to establish equivalence between the original and the counter part. This paper presents a translation validation tool for verifying optimizing and parallelizing code transformations by checking equivalence between two PRES+ models; one corresponds the source program and other corresponds its counterpart.
Reference Values Based Hardening for Bloom Filters Based Privacy-Preserving Record Linkage

Privacy-preserving record linkage (PPRL) is the process of identifying records that refer to the same entities across different data-bases without revealing any sensitive information about these entities. A popular PPRL technique that is efficient and effective is Bloom filter encoding. However, recent research has shown that Bloom filters are vulnerable to cryptanalysis attacks that aim to re-identify sensitive attribute values encoded into Bloom filters. As counter-measures, hardening techniques have been developed that modify the bit patterns in Bloom filters. One recently proposed hardening technique is BLoom-and-flIP (BLIP), which randomly flips bit values according to a differential privacy mechanism. However, while making Bloom filters more resilient to attacks, applying BLIP can lower linkage quality. We propose and evaluate a reference values based BLIP mechanism which ensures that Bloom filters for similar encoded sensitive values are modified in a similar way, resulting in improved linkage quality compared to standard BLIP hardening.
The Application of the Positive Semi-Definite Kernel Space for SVM in Quality Prediction

Background: A transformation toward 4th Generation Industrial Revolution (Industry 4.0) is being led by Germany based on Cyber-Physical System-enabled manufacturing and service innovation. Smart manufacturing is an important feature of Industry 4.0 which uses the networked manufacturing systems for smart production. Current manufacturing systems (5M1E systems) require deeper mining of the data which is generated from manufacturing process. Objective: To map low-dimensional embedding into the input space would meet the requirement of “kernel trick” to solve a problem in feature space. On the other hand, the distance can be calculated more precisely. Methods: In this research, we proposed a positive semi-definite kernel space by using a constant additive method based on a kernel view of ISOMAP. There were 6 steps in the algorithm. Results: The classification precision of KMLSVM was better than SVM in the enterprise data set, in which SVM selected the RBF kernel and optimized its parameters. Conclusion: We adopted the additive constant method in kernel space construction and the positive semi-definite kernel was built. The typical mixed data set of an enterprise was used in simulation. We compared the SVM and KMLSVM in this data set and optimized the SVM kernel function parameters. The simulation results demonstrated the KMLSVM was a better algorithm in mix type data set than SVM.
In-Database Rule Learning Under Uncertainty: A Variable Precision Rough Set Approach

Relational Database Systems are the predominant repositories to store mission-critical information collected from industrial sensor devices, business transactions and sourcing activities, among others. As such, they provide an exceptional gateway for data science. However, conventional knowledge discovery processes require data to be transported to external mining tools, which is a very challenging exercise in practice. To get over this dilemma, equipping databases with predictive capabilities is a promising direction. Using Rough Set Theory is particularly interesting for this subject, because it has the ability to discover hidden patterns while founded on well-defined set operations. Unfortunately, existing implementations consider data to be static, which is a prohibitive assumption in situations where data evolve over time and concepts tend to drift. Therefore, we propose an in-database rule learner for nonstationary environments in this chapter. The assessment under different scenarios with other state-of-the-art rule inducers demonstrate the algorithm is comparable with existing methods, but superior when applied to critical applications that anticipate further confidence from the decision-making process.
Neural Modeling of Fetal Biometric Parameters for Detection of Fetal Abnormality

This paper presents an approach of neural modeling for the diagnosis of fetus abnormality using ultrasound (US) images. The proposed algorithm is a hybrid approach wherein image processing methods have been used for preprocessing the image data, and an artificial neural network has been used as a classifier to extract fetus abnormality. Initially, 350 US images were collected in DICOM format directly from the radiologist and were preprocessed to extract the fetal biometric parameters using a morphological operator and a gradient vector flow algorithm. The extracted parameters were labeled as normal and abnormal fetal parameters. The extracted parameters were then applied to a Feed-Forward Back-propagation Neural Network (FFNN) for the training and validation purpose. These neural networks are capable to provide excellent performance in the critical cases especially in the field of pattern recognition. The result found from the proposed FFNNs was in closed confirmation with the real-time results. This modeling will help radiologist to take appropriate decisions in the boundary line cases.
Experimental Study of Modified Multilevel Median Filter for Noise Reduction

Digital image enhancement is efforts to improve the quality of a declining image and one of the causes of the decline in the quality of digital images is the emergence of spots called noise. Median filter is one method that is widely used and developed to digital images noise reduction. In this paper, we conducted an experiment study to reduce noise using a standard multilevel median filter and a modified multilevel median filter. Further, we measured the images filtered quality using MSE and PNSR to find out the advantages of both methods.
Exploiting Single Image Depth Prediction for Mono-stixel Estimation

The stixel-world is a compact and detailed environment representation specially designed for street scenes and automotive vision applications. A recent work proposes a monocamera based stixel estimation method based on the structure from motion principle and scene model to predict the depth and translational motion of the static and dynamic parts of the scene. In this paper, we propose to exploit the recent advantages in deep learning based single image depth prediction for mono-stixel estimation. In our approach, the mono-stixels are estimated based on the single image depth predictions, a dense optical flow field and semantic segmentation supported by the prior knowledge about the characteristic of typical street scenes. To provide a meaningful estimation, it is crucial to model the statistical distribution of all measurements, which is especially challenging for the single image depth predictions. Therefore, we present a semantic class dependent measurement model of the single image depth prediction derived from the empirical error distribution on the Kitti dataset. Our experiments on the Kitti-Stereo’2015 dataset show that we can significantly improve the quality of mono-stixel estimation by exploiting the single image depth prediction. Furthermore, our proposed approach is able to handle partly occluded moving objects as well as scenarios without translational motion of the camera.
Numerical study on adjusting parameters to improve gaze estimation using planar approximations from electro-oculogram signal voltage ratios

Gaze or eye movements are used as a communication interface in daily life. Herein, we developed a simple method for gaze estimations based on planar approximations of voltage ratios calculated from multiple electro-oculogram signals not affected by drift phenomena, which decrease accuracy. Subsequently, we conducted simulations using an eyeball battery model and investigated the effects of adjusting electrode arrangements, determination coefficients for planar approximations of voltage ratios, and threshold values for angles between simultaneous linear equations, to improve the estimation accuracy and decreased the number of required electrodes. Numerical experiments were used to identify arrangements of six electrodes with errors that were approximately 5° less than those of nine-electrode L-shaped arrangements, indicating improved estimation accuracy with fewer electrodes.