Reviewerly/exHarmony huggingface dataset:
GPU Accelerated Bilateral Filter for MR Image Denoising

Background: Magnetic resonance (MR) imaging plays a significant role in the computer- aided diagnostic systems for remote healthcare. In such systems, the soft textures and tissues within the denoised MR image are classified by the segmentation stage using machine learning algorithms like Hidden Markov Model. Thus, the quality of the MR image is of extreme importance and is decisive in the accuracy of the process of classification and diagnosis. Objective: To provide real-time medical diagnostics in the remote healthcare intelligent setups, the research work proposes CUDA GPU based accelerated bilateral filter for fast denoising of 2D high- resolution knee MR images. Methods: To achieve optimized GPU performance with better speed-up, the work implements an improvised technique that uses on-chip shared memory in combination with a constant cache. Results: The speed-up of 382x is achieved with the new proposed optimization technique which is 2.7x as that obtained with the shared memory only approach. The superior speed-up is along with 90.6%occupancy index indicating effective parallelization. The work here also aims at justifying the appropriateness of bilateral filter over other filters for denoising magnetic resonance images. All the patents related to GPU based image denoising are revised and uniqueness of the proposed technique is confirmed. Conclusion: The results indicate that even for a 64Mpixel image, the execution time of the proposed implementation is 334.91 msec only, making the performance almost real time. This will surely contribute to the real-time computer-aided data diagnostics requirement under remote critical conditions.
Performance-driven Control System using Off-line Learned Recurrent Neural Networks

To maintain high performance of controlling and minimize the total cost for readjustments of the controller parameters, determination the appropriate timing for readjustment the controller parameters is important. This paper proposes a design of performance-driven control system to maintain the system performance by detecting the system change using the recurrent neural networks and readjusting the control parameters based on Fictitious Reference Iterative Tuning (FRIT). This paper conducts some numerical experiments to verify the availability of the proposed method to some systems.
Logic-Based Models for Social Media Retrieval

The huge amount of information that is available in social media today has resulted in a pressing demand for information retrieval models to support users in different retrieval tasks in this area. Evaluation of products or services based on other consumers' reviews are examples of retrieval tasks where users benefit from the information in social media. However, as information in online reviews is provided from different customers, the reviews differ in their degree of credibility. Furthermore, not every detail of a product is reviewed but needs to be taken care when validating a product. Finally, reviews contain contradictions. In this work, we propose a logic-based pipeline to develop a social media retrieval model in which issues of credibility, omissions and contradictions are considered. For modeling these criteria, our approach depends on a probabilistic four-valued logic combined with an open world assumption which employs the unknown knowledge in retrieval tasks.
The Attacker Does not Always Hold the Initiative: Attack Trees with External Refinement

Attack trees provide a structure to an attack scenario, where disjunctions represent choices decomposing attacker’s goals into smaller subgoals. This paper investigates the nature of choices in attack trees. For some choices, the attacker has the initiative, but for other choices either the environment or an active defender decides. A semantics for attack trees combining both types of choice is expressed in linear logic and connections with extensive-form games are highlighted. The linear logic semantics defines a specialisation preorder enabling trees, not necessarily equal, to be compared in such a way that all strategies are preserved.
Dwarfs of Cryptography

Here the aim is to develop better performance optimized hardware implementations for the domain of cryptography. Towards this goal, the current chapter first focuses on the necessity of classifying and identifying a range of operations that are representative of a whole range of algorithms for a specific application domain, namely, cryptography.
Best from Top k Versus Top 1: Improving Distant Supervision Relation Extraction with Deep Reinforcement Learning

Distant supervision relation extraction is a promising approach to find new relation instances from large text corpora. Most previous works employ the top 1 strategy, i.e., predicting the relation of a sentence with the highest confidence score, which is not always the optimal solution. To improve distant supervision relation extraction, this work applies the best from top k strategy to explore the possibility of relations with lower confidence scores. We approach the best from top k strategy using a deep reinforcement learning framework, where the model learns to select the optimal relation among the top k candidates for better predictions. Specifically, we employ a deep Q-network, trained to optimize a reward function that reflects the extraction performance under distant supervision. The experiments on three public datasets - of news articles, Wikipedia and biomedical papers - demonstrate that the proposed strategy improves the performance of traditional state-of-the-art relation extractors significantly. We achieve an improvement of 5.13% in average F$$_1$$-score over four competitive baselines.
Application of Information Network and Control Network Integration Technology in Central Air Conditioning Data Management System

With the rapid development of intelligent buildings and the continuous improvement of management requirements, demand for real-time information management and rationality of the storage strategy design in a central air conditioning system are increased simultaneously. Aiming at the characteristics of the central air conditioning data management system with large numbers of detecting points, large capacity of storage, variety in subsystems, and management complexity in intelligent buildings, the data acquisition module was constructed and the data management system was designed and implemented in the research based on OPC remote access server technology and the zero configuration and high efficiency of SQLite database. The experimental and practical results show that the designed system can be used for data acquisition and information management in central air conditioning monitoring systems and improve the level and efficiency of management to a certain degree.
Search Results Diversification based on Subtopics Attention Network

The retrieval of relevant documents by search engine mostly depends on how the users construct their queries. But in reality, most of the search queries received by search engines are either underspecified or ambiguous. Search result diversification is among the sensible solutions to disambiguate a search query by returning a ranked list of documents covering many aspects of the query topics as possible. This paper proposes a novel approach considering user query intent as a hierarchy. Specifically, we employed Recurrent Neural Networks (RNN) encoder-decoder attention mechanism to construct a diverse ranked list of documents. The model computes the relevance of each document in relation to its subtopics and compares it with previously selected documents in the rank list to ensure that its information is not covered by the previously selected documents. The experimental results, obtained by applying the proposed framework on Clueweb09 datasets, show that the proposed solution outperforms most of the existing methods.
Multiple Reference Images Color Transfer Based on Improved GMM Model

Color transfer is to change the image color according to one or more reference images to match people’s cognition. Usually, there are two problems deserved to be researched further. One is to find suitable reference colors for a complex image. The other is to lessen the details loss in the process. In the paper, GMM model is improved by considering the geometric position relation of the color clustering to construct color transfer intentions between source image and multiple reference images. The geometric coordinate smoothing parameter is introduced to improve EM algorithm to reduce the details loss. Based on Baidu data set and the reference paper data sets, a comparison experiment between typical color transfer algorithms and our method was presented. The experimental results can demonstrate that the algorithm proposed in the paper is reasonable and effective.
Formal modeling and verification of cloud‐based web service composition

Summary Web service composition is a key technology to resolve cross‐organizational business process integration, and it faces many constraints and requirements as unexpected bugs can cause shutdown of the software. A high degree of reliability is expected from such systems, and formal modeling of web service composition is an active area of research. In this work, we propose a new approach for the formal modeling of diverse web service composition. Our approach verifies the terminate property on all possible runs of the system to ensure deadlock‐free workings. For a verification of the properties of interest, we use the NuSMV model checker and specify the properties in CTL*. The approach can be used to verify the soundness property of diverse web service composition to ensure live and deadlock‐free web services.
A New Approach for Cloud Security Using Hybrid Querying System Over Cloud Scenario

Cloud computing is one of the most important and remarkable innovations of recent times. Since all the data is stored in servers located at different locations operating on different hardware so data consistency, availability, and security while accessing becomes a challenging task. Cloud introduces different data access management techniques that make sure of information security within the cloud even in the scenarios of outsourcing and untrusted cloud servers. Information access management in the cloud is a difficult issue due to the distributed architecture of cloud storage systems. Low trust of such a storage paradigm leads to limited proliferation and acceptability. These challenges further soar while addressing the issues of security, since it remains a massive risk. In this scenario, hybrid information prove a meaningful solution as it assists and stores all these varied information depending on privacy, needs of information and initial supply and offered capability. In this context, this paper proposes a cloud security hybrid querying system (CSHQS) that help to overcome data storage complexity and its subsequent access. The query input mechanism help in data computation, its processing, and deriving output with input test. The experimental results show the efficiency of the proposed CSHQS as it outperforms the existing state of art.
School Without Walls, Expanding School Curricula Outside the School Walls with an Innovative Education Tool

School Without Walls - SWW is a tool conceived to address some of the challenges presented by today’s Education in a digital world. Its educational features should promote students active role in their own learning experience, and improve their school engagement. Through the use of SWW, teachers and educators will create, access, use and reuse free educational material. Learning will no longer be confined to the school walls but will include any spot of natural surroundings or urban environment that has previously been geotagged with an educational content. Learning in and outside school walls is one of the 11 promising Educational Technology Trends for 2017, as students will develop a greater interest for their environment and wider surroundings [1]. Using the tool should help students make the connection between what is being taught at school and its relationship with everyday life, situations, problems. The tool is presently used in extracurricular educational context, but will evolve to be included in the classrooms.
Enhancing the Healthcare Retrieval with a Self-adaptive Saturated Density Function

The proximity based information retrieval models usually use the same pre-define density function for all of terms in the collection to estimate their influence distribution. In healthcare domain, however, different terms in the same document have different influence distributions, the same term in different documents also has different influence distributions, and the pre-defined density function may not completely match the terms' actual influence distributions. In this paper, we define a saturated density function to measure the best suitable density function that fits the given term's influence distribution, and propose a self-adaptive approach on saturated density function building for each term in various circumstance. Particularly, our approach utilizing Gamma process is an unsupervised model with no requirements for external resources. Then, we construct a density based weighting method for the purpose of evaluating the effectiveness of our approach. Finally, we conduct our experiment on five standard CLEF and TREC datasets, and the experimental results show that our approach is promising and outperforms the pre-defined density functions in healthcare retrieval.
Safety at the Workplace – Selected Issues of Personal Data Safety

Abstract The paper presents the problem domain related to data safety management in the face of the threats that organisations of all types encounter in this scope. The Author’s particular concern are personal data management issues, which are of key importance for contemporary enterprises as they frequently determine wining the market advantage and growth in their competitiveness. Yet, incidents of personal data breaches, aimed at economic organisations have been on the increase in the recent years, leading not only to substantial financial losses, but what is worse, frequently resulting in damage to their reputation. Therefore, a vital issue for all enterprises is to make their employees acquainted with threats to data security and their potential harmful effects on the operations and financial results of organisations. The paper presents an analysis of breaches to personal data in organisations in a global dimension as well as analyses of their negative effects to their image and trust of their customers.
Dynamic Allocation of Cloud Resources Using Skewness and SVM Algorithm

Exploring huge information applications realize huge data and also difficulties to modernize group and so the genius community. Cloud computing with its huge opportunity is the way to deal these issues. Let that be as it is, this cannot play its part on the off beat that we do not expert in fine allocation for cloud foundation resources. In this paper, we introduce a multi-target advancement calculation to exchange off the execution of Big Data and accessibility of Big Data, thereby reducing the cost of application running on Cloud. In the view of splitting and showcasing the interweaved relations among these destinations, we plan and execute our approach on trial condition. At long last, three sets of analyses demonstrate that our approach can keep running our application quicker than other regular improvement techniques and can accomplish the process at an higher execution rate than other heuristic calculations, while also having reduction in the cost of the system due to lesser usage of resources.
Object codetection based on a higher-order conditional random field

In recent years, codetecting objects through the use of contextual information across multiple images has attracted considerable attention. We introduce an object codetection method that exploits contextual information among multiple images through a higher-order conditional random field (CRF). First, we obtain object candidates from each image of a test set by using a pretrained detector. Second, we feed the object candidates into a higher-order CRF that captures the appearance similarity using pairwise potentials and object category co-occurrence constraints using higher-order potentials. Finally, we jointly predict the category labels of all object candidates through the mean field inference in the CRF. Experimental results on the Caltech Pedestrian, PASCAL VOC 2007, PASCAL VOC 2012, and COCO datasets demonstrate the effectiveness of the proposed method compared to the baseline method.
Project Teamwork Assessment and Success Rate Prediction Through Meta-Heuristic Algorithms

In this chapter, machine learning algorithms along with association rule analysis are applied to measure how the project teamwork success rate depends on various technical and soft skill factors of a software project. A real-life dataset is taken form UCI archive on project teamwork, which comprises of 84 features or attributes with 64 samples. The most effective feature set is therefore selected using meta-heuristic algorithms (i.e., particle swarm optimization [PSO] and simulated annealing [SA]) and then the data are given to support vector machine (SVM) and k-nearest neighbor (KNN) classifier for classification. Association rule mining is also used for rule generation among the different features of software project team to determine support and confidence. This chapter deals with how the project-based learning helps to manifest the students towards professionalism.
Analysis on Key Points of Quality Control of Waterproof Engineering in Residential Buildings

Building waterproofing engineering mainly aims at roof waterproofing, basement waterproofing and floor waterproofing (toilet bathroom, kitchen) waterproofing, which is an important part of building waterproofing engineering. Therefore, on the basis of briefly expounding the importance of roof waterproofing, basement waterproofing and floor waterproofing (toilet bathroom, kitchen), this paper analyzes the main points of quality control of different waterproofing projects, and the methods and measures to solve these problems are discussed. It is expected that through the analysis and research of this paper, it can provide reference and reference for the effective control of the quality of building waterproofing engineering.
Meet the new SEG Library

The SEG Library relaunched on 27 February with a new name, a new look, friendlier navigation, and technology upgrades that include responsive design. Take a look at your earliest opportunity, and check back frequently, as this release marks the beginning of a wave of discoverability improvements, feature additions, and content infusions that will find their way to the browsers of site visitors during 2019.
Non‐Figurative Art in Russia in the Late 20th to 21st Centuries

Abstract The article explores Russian non‐figurative art as an overlooked dimension in art history and the role that work played in global art history. Special attention is given to identifying theoretical and practical aspects of the interaction between philosophy and art as specific types of spirituality, understanding their special role in worldview searches of contemporary humans, the formation of a new worldview paradigm. The practical significance of the results obtained consists of the possibility of their use in educational activities, in particular, in preparation of lecture courses on the history of Russian art of the 20th century, as well as the development of special courses on issues of contemporary fine and visual arts in art academies. The materials in the study present practical value for the formation of museum collections and the holding of exhibitions.
Research on Interactive Design of Three-Way Interactive Platform in Scenic Spots—Based on the Theory of User Experience Elements: A Case Study of Danxia Mountain Scenic Area

In recent years, due to the promotion of government policies and the continuous development of tourism information, various tourism application platforms have emerged. At the same time, the sustainable development of “the scenic spot” requires the concerted efforts of tourists, residents and the spots themselves. However, there is no application platform to integrate the three resources in the market, and related tourism applications have problems such as poor user experience and serious homogenization. Therefore, the development of a three-way interactive platform with excellent user experience is a new direction for tourism application research. Based on the theory of user experience elements, this paper analyzes the key points and relationships in the interaction design of user experience elements based on five levels. Using Danxia Mountain Scenic Area as an example, the analysis and design of the three-way interactive platform in the scenic spot were completed according to the theory of user experience elements, and the feasibility of the method was demonstrated.
Evaluating DASH Player QoE with MPTCP in Presence of Packet Loss

MPTCP aggregates multiple network interfaces for a single TCP session for enhanced transfer rates and improved reliability to applications. Due to this property, MPTCP has the potential to provide better end-user viewing experience (QoE) for DASH video when used instead of TCP on a multi-homed device. This paper evaluates the performance of DASH on MPTCP and its usability in topologies with independent links between two end-hosts having different packet loss rates and varying DASH video segment sizes. We found that MPTCP provides better playback bitrate than TCP in most cases, at the cost of lower utilization of available bandwidth. MPTCP is also resistant to playback interruptions. Moreoever, the optimal video segment size for QoE is a tradeoff between lesser number of down switches and higher average playback bitrate. So, with the proper choice of video segment size, MPTCP is preferable over TCP for DASH video QoE on a multi-homed wireless mobile device.
The modern concept of selecting publications for the research library collections

In recent decade, the collection development expert system has been introduced into acquisition practice at the RAS Library for Natural Sciences CLS. Its major components are: the «experts’ institute», information base (Russian Book Chamber database, publishers’ information), and evaluation system. The system long-term successful operation is provided by simple and friendly interface, both for experts and acquisition librarians and applied technological solutions. Implementation of the system enables to solve a series of problems of research libraries: prompt informing scholars on the works published in the country, developing library collection that meets users’ information needs to full extent. The statistical data obtained from the expert system enables acquisition librarians to clarify selection criteria for nonperiodic domestic publications, to form the list of publishers, whose products are mostly demanded by scholars, important subjects and documents corresponding them. The statistical data for 2008–2017 (the experts’ roaster, the scope of their research interests; information of participants of domestic market of scholarly literature; data on scholarly publications demanded by scholars, etc.) are presented in the paper and conclusions are made upon them.