Reviewerly/exHarmony huggingface dataset:
A New Class of Strongly Asymmetric PKA Algorithms: SAA-5

A new class of public key agreement (PKA) algorithms called strongly-asymmetric algorithms (SAA) was introduced in a previous paper by some of the present authors. This class can be shown to include some of the best-known PKA algorithms, for example the Diffie–Hellman and several of its variants. In this paper, we construct a new version of the previous construction, called SAA-5, improving it in several points, as explained in the Introduction. In particular, the construction complexity is reduced, and at the same time, robustness is increased. Intuitively, the main difference between SAA-5 and the usual PKA consists of the fact that in the former class, B (Bob) has more than one public key and A (Alice) uses some of them to produce her public key and others to produce the secret shared key (SSK). This introduces an asymmetry between the sender of the message (B) and the receiver (A) and motivates the name for this class of algorithms. After describing the main steps of SAA-5, we discuss its breaking complexity assuming zero complexity of discrete logarithms and the computational complexity for both A and B to create SSK.
A Participation Degree-Based Fault Detection Method for Wireless Sensor Networks

In wireless sensor networks (WSNs), there are many challenges for outlier detection, such as fault detection, fraud detection, intrusion detection, and so on. In this paper, the participation degree of instances in the hierarchical clustering process infers the relationship between instances. However, most of the existing algorithms ignore such information. Thus, we propose a novel fault detection technique based on the participation degree, called fault detection based on participation degree (FDP). Our algorithm has the following advantages. First, it does not need data training in labeled datasets; in fact, it uses the participation degree to measure the differences between fault points and normal points without setting distance or density parameters. Second, FDP can detect global outliers without local cluster influence. Experimental results demonstrate the performance of our approach by applying it to synthetic and real-world datasets and contrasting it with four well-known techniques: isolation forest (IF), local outlier factor (LOF), one-class support vector machine (OCS), and robust covariance (RC).
A Comparative Study of Face Recognition under Pose Variation

Face recognition algorithms enable computational devices to recognize faces. It has a widespread application in commerce, law enforcement and can be effectively used in criminal identification, healthcare, advertising, access and security, payments and other different areas. Face recognition has many advantages over other biometric techniques, such as intrusiveness, user consent, surveillance, and it is completely software-based. However, it also has some limitations, such as large template size, distinctiveness, accuracy, and stability. The purpose of this work is to find and compare the accuracy of three different facial recognition algorithms against the face datasets having considerable pose variation in the x-axis (left and right variation). The methodology for this study covers data collection (the process of collecting facial datasets), pre-processing (the process of cleaning data and making it ready for further operations), processing (to apply learning algorithms either to design a template or extract features), training, and classification. To train and test a model with every single image instance available in the dataset, we have used the K-Fold cross-validation method where the dataset is divided into k folds, and each fold will go for training as well as for testing. Based on the conducted experiments, we have observed that the Local Binary Pattern Histogram algorithm has outperformed the other two algorithms by obtaining 79.806 accuracy against the FEI dataset of faces and 61.477 of accuracy against the CVL dataset of faces.
Improvement of face image super-resolution by high-precision skin color detection

When super-resolution processing is applied to facial images, wrinkle and stains are emphasized, so super-resolution processing on skins part is not suitable. Therefore, in the previous study, we proposed a method to perform facial correction on skin parts. However, we confirmed that there was a problem that image quality deteriorated according to the skin color detection accuracy. Therefore, in this paper, we propose a novel skin color detection method and an experimental result demonstrates that a high quality super-resolution image is obtained for a facial image.
A Brief Survey of Text Mining Applications for the Oil and Gas Industry

Abstract Data bases of numerous oil and gas companies embrace very promising potential for more informed decision-making processes. Furthermore, there is an exponential growth in the influx of generated data from an escalating parade of systems encompassing Enterprise Resource Planning (ERP), machine instrumentation, sensory networks, and escalating mixed-media and different unlabeled data. Despite that, extracting meaningful value from zettabyte-sized datasets remains problematic given the uncontrollable wealth of data and its subsequent noise caveats. Amongst those data warehouses, are a multitude of textual information. Accordingly, Text mining has garnered worldwide interest, as it is a crucial phase in the process of knowledge discovery automatically extracting unstructured to semi-structured information. The following survey covers Text Mining methods and approaches to explain their effectiveness in information retrieval from textual databases from various sources. Moreover, the situational types where each technique may be beneficial are explored.
Developing intercultural communication competence of operating staff within corporate training program

Global integration and globalization processes forced numerous companies in non-English-speaking countries, including Russia, to enter foreign markets and establish joint ventures. Such state of affairs stimulated companies' reliance on English as the language of corporate discourse and international cooperation as concepts, terminology and legal documentation in English have already been shaped and do not cause discrepancies. Therefore joint ventures began to pay special attention to advanced training of production personnel in terms of mastering intercultural professional competence. The current paper deals with the ways of improving efficiency of production personnel within corporate vocational training. The paper is an attempt to present a multi-faceted model of mastering intercultural professional competence and describe its implementation in three modules converging sociocultural knowledge, linguistic skills and ability to cooperate effectively in English. The paper emphasizes the corporate program learning content, technologies of mastering sociocultural, lexico-grammatical and communicative components of intercultural competence. The paper presents the results of experimental teaching and confirms a greater efficiency of the developed model.
Research on Data Classification Method Based on Class Attribute Mathematical Expectation

In order to solve the problem of complex data in large data era, the classification method of spatial data based on mathematical expectation theory of category attributes is improved and optimized. By optimizing the arithmetic of mathematical expectation function of data attributes, the mapping value of discrete attributes can be obtained, the mapping value features can be mined and the spatial dimension can be classified, and the mathematical expectation attributes of data can be used as parameter coordinates to determine the classification of data, and the spatial distance of data can be calculated according to the data. Feature attributes are used to plan the spatial scope of data classification, so as to finish the accurate classification of data effectively. Finally, the experiment proves that the classification method is real and effective, and the accuracy is obviously improved compared with the traditional classification method.
Parallel Computing in Deep Learning: Bioinformatics Case Studiesa

In the last two decades deep learning has attracted a lot of attention internationally, solving problems in different application domains and achieving results beyond expectations. For example it has been applied in bioinformatics, game playing, imaging processing, object detection, robotic and drug discovery. One of the main reasons for the incremented use of deep learning algorithms is the need to implement approaches for the analysis of the large amount of data produces in every field, bringing researchers to dedicate their work to deep learning development. One of the main topics discussed up today is the possibility to run the training of deep models in a parallel fashion, so to reduce the time otherwise needed to find the hyperparameters and to make the achievement of the result faster.
Efficient Vulnerability Management Process in the Military

Reducing vulnerabilities is one of the most effective ways to minimize the cyber risks that can occur to information systems. Given the characteristics of the military environment, particularly in operating a wide variety of information systems and dealing with critical information on national security, clear / concise management procedures are needed that enable more realistic and direct action to identify and address vulnerabilities. Also, five requirements for the efficient vulnerability management procedure in the military are proposed as follows: (i) quickness, (ii) continuousness, (iii) clearness, (iv) interdependence, and (v) completeness. By considering all information, this paper suggested 5 phases for the vulnerability management process in military: (i) Framing; (ii) Identification; (iii) Assessment; (iv) Remediation; and (v) Verification. In addition, the three-tiered concept was applied to the efficient management of the vulnerabilities, taking into consideration the characteristics of the organization with clear hierarchical relationships. As a result, it will contribute to reduce the cyber risk in the defense area, by presenting the specific procedures for vulnerability management in each hierarchical organization.
Parallel Searching on Biological Networks

Software applications for biological networks analysis rely on graphs to model the structure interactions. A great part of them requires searching for subgraphs in a target graph or in collections of graphs. Even though very efficient algorithms have been defined to solve such a subgraph isomorphisms problem, the complexity of current real biological networks make their sequential execution time prohibitive. On the other hand, parallel architectures, from multi-core to manycore, have become pervasive to deal with the problem of the data size. Nevertheless, the sequential nature of the graph searching algorithms makes their implementation for parallel architectures very challenging. This paper presents three different parallel solutions for the graph searching problem. The first two target the exact search for multi-core CPUs and manycore GPUs, respectively. The third one targets the approximate search for GPUs, which handles node, edge, and node label mismatches. The paper shows how different techniques have been developed in all the solutions to reduce the search space complexity. The paper shows the performance of the proposed solutions on representative biological networks containing antiviral chemical compounds and protein interactions networks.
Leveraging Library Technology: Non-Library Uses of Library Technology

The library has experienced many conversations with colleagues at the college in different departments lately related to the library tools and services it offers. Some of these conversations have included senior administrators, who have at some point directly supervised the library. In order for senior administrators to advocate for the library, librarians must educate them on the tools and services the library offers to its customers. Springshare's LibApps is one such tool that has been integral to library operations. In the few years since the library has subscribed to Springshare's LibApps' suite of tools, Office of Institutional Research & Training (OIRT) has successfully utilized this library-specific tool, which has justified the annual renewal of this software tool by senior administration. For more departments at the college to adopt these tools and integrate them into their day-to-day tasks, librarians must educate customers (student, staff, and faculty) as well as administration about this and other library-specific tools. Since Springshare markets to and for librarians and as more librarians write about how this tool can be used for a variety of different applications, then other departments college-wide will become aware of and then adopt Springshare's LibApps' suite of tools.
Structural analysis of Iranian educational technologies

The purpose of this research is to identify the key driving forces that have an impact on educational technologies to find out the effects of these drivers on each other and the system. This research is a special attention to driving forces which seem they are able to change the educational space paradigms. This study explored the identification of medium term investment opportunities in the field of educational technology. It also helps to plan for the creation and development of new educational institutions and equip universities and schools based on future needs. In this regard, after the identification of effective driving forces, the relationships between these drivers were determined by the experts including university professors, leading educational college managers, and product developers in the field of educational technology and then the results of cross-impact analysis were analyzed by Mick Mac software. Finally, the drivers of virtual learning and learning space as strategic drivers in this space were identified and the factors of reinforcement and system verification indicators were presented.
Identification of efficient algorithms for web search through implementation of learning-to-rank algorithms

Today, amount of information on the web such as number of publicly accessible web pages, hosts and web data is increasing rapidly and exhibiting an enormous growth at an exponential rate. Thus, information retrieval on web is becoming more difficult. Conventional methods of information retrieval are not very effective in ranking since they rank the results without automatically learning the model. Machine learning domain called learning-to-rank comes to the aid to rank the obtained results. Different state-of-the-art methodologies have been developed for learning-to-rank to date. This paper focuses on finding out the best algorithm for web search by implementation of different state-of-the-art algorithms for learning-to-rank. Our work in this paper marks the implementation of learning-to-rank algorithms and analyses effect of topmost performing algorithms on respective datasets. It presents an overall review on the approaches designed under learning-to-rank and their evaluation strategies.
Bilingual Printed Document Image Retrieval Based on SIFT Feature

This paper present a printed document retrieval system which can retrieve Chinese and Uyghur keywords from printed document images. In this paper we introduce the framework of the printed document retrieval system and processing step behind it which can be based line for the word spotting based document retrieval system, we also describe the extraction algorithm that use local feature as SIFT to extract the feature from image and use Euclidean based matching algorithm to query the matching word in printed document image. Some novel idea applied in this system might be helpful for some Other Bilingual printed document image retrieve system.
Extending Flow Graphs for Handling Continuous−Valued Attributes

This paper describes a research work that focuses on a suitable data structure, capable of summarizing a given supervised training data set into a weighted and labeled digraph. The data structure, named flow graph (FG), has been proposed not only for representing a set of supervised training data aiming at its analysis but, also, for supporting the extraction of decision rules, aiming at a classifier. The work described in this paper extends the original FG, suitable for discrete data, for dealing with continuous data. The extension is implemented as a discretization process, carried out as a pre-processing step previously to learning, in a two-step hybrid approach named HFG (Hybrid FG). The results of the conducted experiments were analyzed with focus on both, the induced graph-based structure and the performance of the associated set of rules extracted from the structure. Results obtained using the J48 are also presented, for comparison purposes.
A Hardware–Software Co-Design Framework for Real-Time Video Stabilization

Live digital video is a valuable source of information in security, broadcast and industrial quality control applications. Motion jitter due to camera and platform instability is a common artefact found in captured video which renders it less effective for subsequent computer vision tasks such as detection and tracking of objects, background modeling, mosaicking, etc. The process of algorithmically compensating for the motion jitter is hence a mandatory pre-processing step in many applications. This process, called video stabilization, requires estimation of global motion from consecutive video frames and is constrainted by additional challenges such as preservation of intentional motion and native frame resolution. The problem is exacerbated in the presence of local motion of foreground objects and requires robust compensation of the same. As such achieving real-time performance for this computationally intensive operation is a difficult task for embedded processors with limited computational and memory resources. In this work, development of an optimized hardware–software co-design framework for video stabilization has been investigated. Efficient video stabilization depends on the identification of key points in the frame which in turn requires dense feature calculation at the pixel level. This task has been identified to be most suitable for offloading the pipelined hardware implemented in the FPGA fabric due to the involvement of complex memory and computation operations. Subsequent tasks to be performed for the overall stabilization algorithm utilize these sparse key points and have been found to be efficiently handled in the software. The proposed Hardware–Software (HW–SW) co-design framework has been implemented on Zedboard FPGA platform which houses Xilinx Zynq SOC equipped with ARM A9 processor. The proposed implementation scheme can process real-time video stream input at 28 frames per second and is at least twice faster than the corresponding software-only approach. Two different hardware accelerator designs have been implemented using different high-level synthesis tools using rapid prototyping principle and consume less than 50% of logic resources available on the host FPGA while being at least 30% faster than contemporary designs.
DSL Based Automatic Generation of Q&amp;A Systems

In order to help the user to search for relevant information, Question and Answering (Q&A) Systems provide the possibility to formulate the question freely in a natural language, retrieving the most appropriate and concise answers. These systems interpret the user's question to understand his information needs and return him the more adequate replies in a semantic sense; they do not perform a statistical word search, thus differing from the existing search engines. There are several approaches to develop and deploy Q&A Systems, making hard to choose the best way to build the system. To turn easier this process, we are proposing a way to create automatically Q&A Systems based on DSLs (Domain-specific Languages), thus allowing the setup and the validation of the Q&A System to be independent of the implementation techniques. With our proposal, we want the developers to focus on the data and contents, instead of implementation details.
RUSSIAN SCHOLARLY JOURNALS INTERNATIONALIZATION: A STRATEGY FOR INCREASING THEIR IMPORTANCE GLOBALLY

In this research we address the issue of Russian scholarly journals being almost isolated from the global research community. We have identified 2 conditions which seem alerting: (1) the authors who publish in Russian journals are mostly Russian and (2) papers in Russian sources are cited mostly by Russian authors. In this paper we provide justification for a more intensive integration of Russian titles into international research process and suggest the most obvious ways to make it happen.
Human Detection in Crowded Situations by Combining Stereo Depth and Deeply-Learned Models

Human detection in crowded situations represents a challenging task in many practically relevant scenarios. In this paper we propose a passive stereo depth based human detection scheme employing a hierarchically-structured tree of learned shape templates for delineating clusters corresponding to humans. In order to enhance the specificity of the depth-based detection approach towards humans, we also incorporate a visual object recognition modality in form of a deeply-trained model. We propose a simple way to combine the depth and appearance modalities to better cope with complex effects such as heavily occluded and small-sized humans, and clutter. Obtained results are analyzed in terms of improvements and shortcomings introduced by the individual detection modalities. Our proposed combination achieves a good accuracy at a decent computational speed in difficult scenarios exhibiting crowded situations. Hence in our view, the presented concepts represent a detection scheme of practical relevance.
Brain volume mapping for constructing volumetric statistical shape model

We propose a new method for mapping onto a brain volume model including inner organs with complicated shapes such as lateral ventricles. The proposed method is based on a volumetric Self-organizing Deformable Model (vSDM) which allows to control the mapping positions of inner organs while preserving geometrical features before and after the mapping. The control sometimes causes the self-intersection of the volume model. The solution for the self-intersection in vSDM is to move vertices of the volume model. However, when the inner organ has complicated shape, the vertex movement cannot always correct the self-intersection. To solve this problem, we extend vSDM by introducing a new process of editing the mesh structure of the volume model. Moreover, by applying the proposed method to six brain volume models, a volumetric Statistical Shape Model (SSM) is constructed which represents the shape variations of not only brain surface but also brain inner organs. From experimental results, we confirmed the volumetric SSM has an acceptable performance compared with general surface SSMs generated by organ surface models.
ANFIS: Establishing and Applying to Managing Online Damage

Fuzzy logic (FL) and artificial neural networks (ANNs) own individual advantages and disadvantages.Adaptive neuro-fuzzy inference system (ANFIS), a fuzzy system deployed on the structure of ANN, by which FL and ANN can interact to not only overcome their limitations but also promote the ability of each model has been considered as a reasonable option in the real fields.With the vital strong points, ANFIS has been employed well in many technology applications related to filtering, identifying, predicting, and controlling noise.This chapter, however, focuses mainly on building ANFIS and its application to identifying the online bearing fault.First, a traditional structure of ANFIS as a data-driven model is shown.Then, a recurrent mechanism depicting the relation between the processes of filtering impulse noise (IN) and establishing ANFIS from a noisy measuring database is presented.Finally, one of the typical applications of ANFIS related to online managing bearing fault is shown.