Reviewerly/exHarmony huggingface dataset:
Expertise Fee Appreciation

Experts in engineering discipline work hard to prepare an expertise report. This work is paid as a fee. The expert fee is not a compensation; it is a judgement expense. When the expert fee is appreciated, the following factors are taken into account: The value of the labor of the body and the mind that the expert spends, the quality of the examination and the length of study how much required, scope of files and attachments, and expenditures required by the examiner such as transportation and accommodation, the amount of the matter of the case, the level of knowledge, experience, and professional performance of the expert, or difficulty in achieving it. The expert fee is determined in consideration of the labor and time that the expert has spent according to the Expert Regulation in Turkey in the view of the quality and the importance of examining which is appointed to the expert on the basis of the tariff by the authority. In this study, a brief explanation is given about the expertise fee in Turkey.
Pipeline architectures of Three-dimensional daubechies wavelet transform using hybrid method

&lt;span&gt;The application of three-dimensional (3-D) medical image compression systems uses several building blocks for its computationally intensive algorithms to perform matrix transformation operations. Complexity in addressing large medical volumes data has resulted in vast challenges from a hardware implementation perspective. This paper presents an approach towards very-large-scale-integration (VLSI) implementation of 3-D Daubechies wavelet transform for medical image compression. Discrete wavelet transform (DWT) algorithm is used to design the proposed architectures with pipelined direct mapping technique. Hybrid method use a combination of hardware description language (HDL) and G-code, where this method provides an advantage compared to traditional method. The proposed pipelined architectures are deployed for adaptive transformation process of medical image compression applications. The soft IP core design was targeted on to Xilinx field programmable gate array (FPGA) single board RIO (sbRIO 9632). Results obtained for 3-D DWT architecture using Daubechies 4-tap (Daub4) implementation exhibits promising results in terms of area, power consumption and maximum frequency compared to Daubechies 6-tap (Daub6).&lt;/span&gt;
High-speed high dynamic range 3D shape measurement with digital micro-mirror device

The paper proposes a new method for measuring the 3D shape of fast-moving highly reflective surfaces by using the transitioning state of the digital micro-mirror device (DMD). 1-bit binary patterns are used to achieve kHz measurement speeds and to overcome the rigid camera-projection synchronization requirement. More than one captures of the fringes are made during each cycle of the pattern projection. The dark period between DMD’s ON time is used to alleviate saturation problem of highly reflective surfaces. Experiments conducted demonstrate the success of the proposed method for measuring fast-moving shiny surfaces with an image acquisition speed of 1,000 Hz
A Case Study of Content Development on Interactive Goal-Based Scenarios for Smart Education In Fisheries and Marine Education

The purpose of this study is to develop the contents of the Fisheries and marine education on applicating interactive Goal-Based Scenario for smart education. In order to complete the purpose, using to authoring tool, articulate storyline. The contents development has performed plan-design-development and usability evaluation text. This contents development has given implications that Learning to use smart devices, the interaction between learners and contents is applied at all level from passive learning to immersion learning using Goal-based scenario learning strategy. In order to improve the quality of content development, evaluation questionnaires and content usability test were conducted through the both stage.
Analysis and Importance of Deep Learning for Video Aesthetic Assessments

Deep Learning is one of the active analysis topic obtaining a great deal of analysis attention recently. This increase in analysis interest is driven by several area as that are being worked on like machine-based reality finding, good over-seeing, sensory activity recognition, online learning, world of advertisement, text analysis and so on. Videos have specific characteristics that make their method unique. Visual aesthetic typically: Remember what they see, understand and learn rather than what they hear. This paper principally emphasizes deep learning on basics of automatic video aesthetic assessments.
TV AND NEWSPAPER TEXTS: SIMILARITIES AND DIFFERENCES

Recently the problem of traditional editorial offices transition to convergent ones has acquired special relevance. In new editorial offices, “universal journalists” often prepare materials for different media platforms. To create TV or newspaper texts it’s necessary to have experience of working in such editorial offices and to understand the specificity of text preparation methods. The article examines TV and newspaper journalistic texts. The materials are analysed by the example of the same news events, which allows identifying similarities and differences between them. The author examines approaches to text creation, vocabulary, syntax.
A Project-Oriented Approach to Practicum on Software Engineering Methodology Courses

This chapter suggests a method for practicum on software engineering methodology course using a project-oriented approach. The chapter features basic organization principles of the approach and examples of methodical support for laboratory works based on these principles, and provides recommendations on choice and use of methodologies and technologies of software engineering for the development of distributed information systems. The experience of using this technique for teaching students studying for a Master's degree in Software Engineering in Bauman Moscow State Technical University is presented.
Motion Planning and Control of Social Mobile Robot – Part 2. Experimental Research

The paper is the second part of the work concerned with the motion planning and control of PIAP IRYS social robot mobile platform, which is the adapted NOMAD 200 robot with synchronously driven and steered wheels. In the first part of the work developed hardware and software architectures of the research object, as well as its kinematic structure were described. In the part two of the paper, state of the art in mobile robot navigation systems is described and the problem of motion planning and control of a mobile platform is considered. The experiments were conducted with a real robot in a real environment. For experimental research the most representative and relevant local motion planning algorithm was chosen. During each experimental traverse of the robot a map of indoor environment was created using Hector SLAM algorithm. For robot localization the same SLAM algorithm was used. The experiments were carried out for the selected local motion planning algorithm. During some experiments additional static and dynamic obstacles were introduced, that were not present before. Results of selected experiments for various environments and additional obstacles were presented and discussed.
Ranking in Genealogy

Genealogy research is the study of family history using available resources such as historical records. Ancestry provides its customers with one of the world's largest online genealogical index with billions of records from a wide range of sources, including vital records such as birth and death certificates, census records, court and probate records among many others. Search at Ancestry aims to return relevant records from various record types, allowing our subscribers to build their family trees, research their family history, and make meaningful discoveries about their ancestors from diverse perspectives.
Object Detection on Base of Modified Convolutional Network

The work involves a new object detector using a convolutional network with a kernel of type NiN (Network in Network). Detection refers to the simultaneous localization of objects on an image and their recognition. The operation of the detector is possible on images of arbitrary size. To learn the network images $$100\times 100$$ pixels are used. The proposed method has a high computational efficiency, so processing time of HD frame on a single CPU core is about 300 ms. As will be seen from the paper, a high degree of uniformity of network operations creates conditions for streaming parallel processing of data on the GPU, with an estimated operating time of less than 10 ms. Our method is resistant to small overlaps, the average quality of images of detected objects and represents the end-to-end learner model, the output of which is delimited by the boundaries and classes of objects throughout the image. In work, an open dataset of images obtained from car recorders is used to evaluate the algorithm for detecting objects. A similar approach can be used to detect and count other types of objects, for example people’s faces. This method is not limited to the use of one type of objects, it is possible to simultaneously detect a mixture of objects. The algorithm of the detector was tested on our own a3net framework, without using third-party neural network programs.
Modern Trends of the Russian Educational System Amid Global Changes

World globalization is gradually penetrating into all spheres of social life and, in particular, into education. Differences between Russia’s traditional institutional social norms and market rules don’t make it possible to fully benefit from globalization, while Russia’s social and economic underdevelopment draws it back to ever-overtaking modernization, even in the educational sphere. Thus, this problem is still relevant.
Reinterpreting and Improving the Cryptanalysis of the Flash Player PRNG

Constant blinding is an efficient countermeasure against just-in-time (JIT) spraying attacks. Unfortunately, this mitigation mechanism is not always implemented correctly. One such example is the constant blinding mechanism found in the Adobe Flash Player. Instead of choosing a strong mainstream pseudo-random number generator (PRNG), the Flash Player designers chose to implement a proprietary one. This led to the discovery of a vulnerability that can be exploited to recover the initial seed used by the PRNG and thus, to bypass the constant blinding mechanism. Using this vulnerability as a starting point, we show that no matter the parameters used by the previously mentioned PRNG it still remains a weak construction. A consequence of this study is an improvement of the seed recovering mechanism from previously known complexity of $$\mathcal O(2^{21})$$ to one of $$\mathcal O(2^{11})$$ .
LMS Use in Primary School as an Internet-Accessible Notice Board

The educational use of LMSs in primary schools where all students entered the platform using their own e-mail account in order to enter in the platform and get access to homework or tasks assigned to them by the teacher has been presented and analysed before. However, there are practical problems involved in enrolling very young students into any type of platform, which resulted in the present trial to use it without such restrictions. The school use of the LMS as (effectively) an internet-accessible notice board came to provide solutions to the following educational needs: (a) Open communication between teacher and parents e.g. to inform them for upcoming events, assignments, and instructions on a day-to-day basis. (b) Help students who missed lessons to find reliable information as to what was taught, and download supporting material. (c) Offer extra teaching material (or supporting material) to students. (d) Attempt to build a sense of three-part educational community between students, parents, and teacher with obvious benefits to all. Specifically, the parents get the supervisory role to their child’s education that they deserve. Details of the actual LMS deployment and its actual use at a primary school are given. The results from the analysis of a student and parent questionnaire are analysed and presented graphically herein, while conclusions are drawn.
Identity-based TLS for Cloud of Chips

In this work, we implement an identity-based Transport Layer Security (ID-TLS) protocol and integrate it on scalable multiprocessor system-on-chip (MPSoC), namely Cloud-of-Chips (CoC), in order to secure the SDN communication on this platform. We select two identity-based encryption schemes that are more likely to meet the performance and resource constraints on the target platform. The schemes are Sakai-Kasahara’s identity-based encryption (SK-IBE) and the optimized identity-based encryption (OIBE) for lightweight devices by Guo et al.. The results assert that both the schemes have their computation vs storage trade-off. The SK-IBE algorithm is significantly more computationally efficient than its OIBE counterpart while SK-IBE uses around 30 percent more memory than OIBE. However, the performance results of ID-TLS favor SK-IBE over OIBE. Finally, ID-TLS is integrated in the existing OpenFlow switch and controller implementations. This brings us to a fully functional and secure ID-TLS implementation on CoC, keeping the platform constraints in consideration.
Light-by-light control at quantum level mediated by flying atoms

Quantum interface between photons is a long- standing goal of fundamental significance, and also serves as powerful tools for quantum technologies. Remarkable advances in quantum optics have recently developed in several platforms to demonstrate the generation of optical nonlinearities at the level of individual photons, which enable a number of unique applications such as light-by-light control at quantum level. Here, we present two examples in the realm of continuous variables based on the platform of flying atoms where thermal motion of atoms with long-lived coherence of ground state mediate the coupling between the spatially separated optical channels.
Outsourcing Modular Exponentiation in Cryptographic Web Applications

Modern web applications using advanced cryptographic methods may need to calculate a large number of modular exponentiations. Performing such calculations in the web browser efficiently is a known problem. We propose a solution to this problem based on outsourcing the computational effort to untrusted exponentiation servers. We present several efficient outsourcing protocols for different settings and a practical implementation consisting of a JavaScript client library and a server application. Compared to browser-only computation, our solution improves the overall computation time by an order of magnitude.
Sequence learning using content and consumption patterns for user path prediction

We introduce a novel model for future/next page prediction in online user journeys that uses a combination of doc2vec webpage representations with an LSTM-based neural network to mine patterns from users' online navigational paths combined with their content preferences. Empirical explorations show promise towards creating customized user experiences leveraging this work.
Local Field Response Method Working as Quasi-Quantum Annealing

The local field response (LFR) method is a method of searching for the ground state in a similar manner to quantum annealing. However, it operates on a classical machine, and quantum effects are phenomenologically introduced: (1) an average quantum effect is incorporated in the response function of the spins to the effective field through a priori information; (2) the effect of linear superposition is introduced by taking two successive time points into account in the spin response, reproducing 〈σz〉 ≈ 0 at band anticrossing points; (3) spin correlations, as an effect of entanglement, are introduced by resequencing the calculation order of the spins at their response to the effective field. In this report, we review these phenomenological methods and their effects.
Uncertain Schema Matching

Data integration has been the focus of research for many years now. At the heart of the data integration process is a schema matching problem whose outcome is a collection of correspondences between different representations of the same real-world construct. In recent years, data integration has been facing new challenges as a result of the presence of big data. These challenges require the development of a set of methods to support a matching process using uncertainty management tools to quantify the inherent uncertainty in the process. This chapter is devoted to the introduction of uncertain schema matching. It also discusses existing and future research, as well as possible applications.
Missing Data Augmentation for Bayesian Exponential Random Multi-Graph Models

In this paper we present an estimation algorithm for Bayesian exponential random multi-graphs (BERmGMs) under missing network data. Social actors are often connected with more than one type of relation, thus forming a multiplex network. It is important to consider these multiplex structures simultaneously when analyzing a multiplex network. The importance of proper models of multiplex network structures is even more pronounced under the issue of missing network data. The proposed algorithm is able to estimate BERmGMs under missing data and can be used to obtain proper multiple imputations for multiplex network structures. It is an extension of Bayesian exponential random graphs (BERGMs) as implemented in the Bergm package in R. We demonstrate the algorithm on a well-known example, with and without artificially simulated missing data.
Providing Cognitive Scaffolding Within Computer-Supported Adaptive Learning Environment for Material Science Education

These days adaptivity is the cutting edge of modern education. Technologies are being developed rapidly and bringing new possibilities to educators. Thus, diverse types of adaptive learning environment have appeared during these last decades. Material Science and Engineering Education (MSEE) have a solid formalized foundation, which consists of standards, recommendations and clear rules. Moreover, investigators report on growing role of computer in teaching and learning in MSEE. These brings great perspectives to computer adaptive learning system based on a material science and engineering ontology. This paper aims to justify general pedagogical foundations of adaptivity and to collect requirements to a computer adaptive learning system. As an extra result we introduce the architecture of ontology-based adaptive learning system to MSEE.
The image catalogs of the libraries of RAS Pushchino Science Center and RAS Library for Natural Sciences: Generation and use

The experience of building and use of the image catalogs at the Library of Pushchino Science Center of theRussianAcademyof Sciences and RAS Library for Natural Sciences is examined. The differences between the catalogs are identified: the former comprises the scanned systematic catalog, and the latter – the alphabet catalog of foreign books. Two different approaches toward retroconversion are analyzed. In Pushchino, the Library preferred hand input into the ecatalog to retroconversion on the basis of the completed systematic image catalog. The systematic image catalog is heavily used in the library services and available via the Library’s www-site. Instead, the RAS Library for Natural Sciences after having scanned and edited the alphabet catalog of foreign books, implemented the function of book search and ordering from the image catalog. The prospects and tasks for acquiring retro-entries to the e-catalogs are discussed. The Library of RAS Pushchino Science Center is to complete the retroconvesion of the alphabet card catalog and editing of the systematic image catalog. The RAS Library for Natural Sciences is to generate the image catalog of national book publications.
Fast Adapting Mixture Parameters Schemes for Probability Density Difference-Based Deformable Model

This paper presents a new region-driven active contour using the pdf difference to evolve. The pdf estimation is done via a new and fast Gaussian mixture model (GMM) parameters updating scheme. The experiments performed on synthetic and X-ray images have shown not only an accurate contour delineation but also outstanding performance in terms of execution speed compared to the GMM estimation based on EM algorithm and to non-parametric pdf estimations.
Image Inpainting Based on Local Patch Search Supported by Image Segmentation

Image inpainting can be defined as a restoration process in which damaged or selected regions are repaired by taking into account the image content. In this work, we employ a local-based strategy instead of a global one to identify the best existing patch with information to replace the damaged/selected patch. In order to properly identify the most representative patches, we propose a method based on the (i) creation of a local graph using a similarity of patches in the original image and (ii) partition of the image into regions according to hierarchical image segmentation to support the local patch identification. The experimental results demonstrate that our local search outperformed the results of image inpainting in terms of both qualitative and quantitative aspects, when compared to global search of patches.
An Improved Data Hiding Technique Using Context Aware System

Objectives: The study presents a novel DH (Data Hiding) technique which is the combination of DH and context aware system (CAS) for developing/improving the conventional DH scheme. It ensures that the decoder/receiver authenticity before proceeding into the decode process. Methods/Statistical Analysis: This validation operation is performing in three different layers using the CAS properties. The decode action is performed based on the validation result by the proposed system at receiver side. In detail, if the decoder validation is success then he/she may permit to decode the encoded data by Modified Steganography for Image Decode (MSID) process else the system will give another chance to make their validation. If the validation is failed maximum of three times then the system will be deleted the encoded without the knowledge of the receiver. Findings: This feature has been added with the conventional DH system. The proposed system is divided into two different stages of DH and validation using CAS. The secret image is hiding inside of the known image using Modified Steganography for Image encode (MSIE) process and the validation parameters of the user name, location where the decode should be performed and time schedule are appended and it sent to the receiver in the sender side. He/ she must perform the validation process and the CAS will take a decision based on the validation result once the receiver/ decoder received the data. As the outcome of this system, the intruder can’t be access the encoded data 99%, the strengthen ratio is improved up to 95% than the existing scheme, the secret data is reconstructed without any loss, it can ensure the reliability, availability and confidentiality of the secret data up to 98%. Application/Improvements: It has implemented for the clandestine image/data transmission between sender and receiver using CAS. Keywords: Context Aware System, Data Hiding, Data Hiding Technique, Receiver, Secret Data, Sender
Formal Application Description of Autonomous and Cooperative M2M Application Services

Abstract-This publication presents a novel concept for designing M2M applications on end user level.A formal description language is introduced that enables formal description of M2M application semantic based on statemachines.The evaluation of several modelling languages for describing statemachine-based application semantic are analysed with the result that UML Statemachine Diagrams form the best fitting approach for the introduced formal description language.The defined concept of behavioural modelling M2M application through end users by means of statemachines forms a generalised, intuitive and platform independent methodology to define the semantic of M2M applications.