Popular Engines Research Articles

Crowdsourcing a cyclist perspective on suggested recreational paths in real-world networks

ABSTRACTRouting and navigation services for leisure activities are conditioned by special needs and trade-offs. The advent of online communities and large crowdsourced datasets offers opportunities to improve the adoption of a user’s perspective in these suggested paths. This paper focuses on achieving two goals. First, the presented methodology analyses a dataset of 190,610 historical GPS traces to gain insights into the appreciation or attractiveness of each edge in a real-world network for a specific leisure activity (i.e. road cycling). Second, as literature on these leisure activities is still sparse, we want to create a thorough understanding of the activities at hand for future work. An appreciation model is proposed and the spread of this score is analyzed in shortest-path alternatives of popular routing engines for this activity. This analysis successfully discriminates these shortest paths based on the scoring value and three morphological parameters of the path. However, the robustness of the model should be improved to ensure the viability of the proposed approach in future work. More specifically, further research on the local optimality of the route choices will be imperative.

Visualizing relativity: The OpenRelativity project

We present OpenRelativity, an open-source toolkit to simulate effects of special relativity within the popular Unity game engine. Intended for game developers, educators, and anyone interested in physics, OpenRelativity can help people create, test, and share experiments to explore the effects of special relativity. We describe the underlying physics and some of the implementation details of this toolset with the hope that engaging games and interactive relativistic “laboratory” experiments might be implemented.

Robot Cloud: Bridging the power of robotics and cloud computing

Cloud computing is shaping the cyber world and evolves as a key computing and service platform for sharing resources including platforms, software applications and everything in the form of services. This is known “X as a Service”. Although it brings our age unparalleled computing ability and economic benefits, the application of cloud computing is still limited currently in the cyberspace due to the cloud services can only reside in cloud instead of our daily life environment. In fact, there are still a plethora of physical position based on-site service demands that cloud computing could help little due to the “cyber limitation”. In this paper, we aim to integrate the cyber world and the physical world by bringing up the idea of “Robot Cloud” to bridge the power of robotics and cloud computing. To make it possible, we design a novel Robot Cloud stack to support our idea and adopt the service-oriented architecture (SOA) to make the functional modules in the Robot Cloud more flexible, extensible and reusable. Then we develop a prototype of Robot Cloud using the popular Google App Engine to demonstrate our design method. Finally, we conduct the simulation experiments with a “robot show” application scenario to evaluate our scheduling policy and identify the effect of different request distributions and robot center solutions.

Low-cost virtual reality environment for engineering and construction

Abstract Background Presenting significant building or engineering 3D-models is a crucial part of the planning, construction and maintenance phases in terms of collaboration and understanding. Especially in complex or large-scale models, immersion is one of the major key factors for being able to intuitively perceive all aspects of the scene. A fully immersive system needs to give the user a large field-of-view with reduced latency for lifelike impression. Technologies such as VRwalls and shutter glasses can deliver high refresh rates, yet fail to give a large field-of-view. Head-mounted-devices for virtual reality fill this gap. Head tracking mechanisms translate movements of the user’s head into virtual camera movements and enable a natural way of examining models. Unlike a stereoscopic representation with projectors, point-of-view tracking can be achieved separately for each individual user. Hardware costs for such systems were very high in the past, but have dropped due to virtual reality systems now gaining traction in the mainstream gaming community. Methods In this paper we present a way to build a low-cost, highly immersive virtual reality environment for engineering and construction applications. Furthermore, we present a method to simplify and partly automate the process of reusing digital building models, which are already used in construction, to create virtual scenes, instead of having to do parallel content creation for visualization. Using the Oculus Rift head-mounted display and the Leap Motion hand-tracking device, we show the possibilities of naturally interacting within a virtual space in different use cases. The software, based on the popular game engine Unreal Engine 4, will be used as a basis for further research and development. Results Building Information Modeling data can be imported to UE4 with our presented plugin. Using an automated database for mapping materials to the geometry simplifies the process of importing Building Information Modeling entities. The refresh rate of the system stays within acceptable margins needed for virtual reality applications using head-mounted devices. Conclusions Head-mounted devices present a great potential for the Architecture, Engineering and Construction industry, as a person can experience realistic first-person situations without having to care about injuries. Automated processes for the simplification of content creation, leveraging existing models, and the usage of visual programming languages enable even nonprogrammers to create scenarios to their needs.

Energy‐efficient adaptive optical character recognition for wearable devices

As the computing power of mobile/wearable devices increases, computation-intensive real-time optical character recognition (OCR) becomes feasible for high-resolution images. Developing mobile/wearable OCR applications is challenging because they should perform highly accurate OCR within users' tolerable waiting time and achieve low energy consumption. An adaptive power management scheme is presented that predicts the execution time for OCR and minimises its energy consumption via dynamic voltage and frequency scaling while meeting its time constraint. Tesseract, a popular open source OCR engine, is used to verify the proposed scheme. The experimental results show up to 48.25% reduction in energy consumption (with an average reduction of 34.53%).

ONLINE HEALTH INFORMATION SEEKING DURING ADOLESCENCE: A QUANTITATIVE STUDY REGARDING ROMANIAN TEENAGERS

How Internet is used by individuals from different age groups to keep their health in check has become one of the major issue of both academic researchers and policy makers. The topic derives mainly from 2000-2014 data which converge towards an Internet accessing pattern as source of information regarding health. Previous studies showed that teenagers are the main consumers of the Internet and they often start surfing for online health concerns on social media (Facebook, Twitter) and popular engines (Google, Yahoo). The current paper describes how Romanian teenagers (N=161, aged 14-19) browse for online topics to keep their health in check. Based on a questionnaire, the data revealed that the Internet is used to a certain extent by more than a third of the respondents for health topics and over half of them consider that the health related information helped them to achieve a good trim. Overall, the research outcomes showed that the adolescents seem less interested in using Internet for health information and sometimes challenge the credibility of online health content.

Prediction of the Optical Character Recognition Accuracy based on the Combined Assessment of Image Binarization Results

In the paper the problem of reliable evaluation of the effects of image binarization is discussed in view of image recognition accuracy. Considering the Optical Character Recognition methods, typically used for document images obtained by cameras or scanners, their accuracy is strongly dependent on the results of image binarization. Unfortunately, metrics typically used for the evaluation of binarization results, such as Peak Signal to Noise Ratio, Distance Reciprocal Distortion or Misclassification Penalty Metric, are not always well correlated with the recognition accuracy of individual characters. Therefore, a novel approach related to the use of combined metric for the assessment of binarization results is proposed and verified for the binary images obtained using some popular histogram-based methods from the original images with degraded quality. For the experimental prediction of the character recognition accuracy, the popular open source engine supported by Google, known as Tesseract, has been used.DOI: http://dx.doi.org/10.5755/j01.eee.21.6.13764

Prediction of the Optical Character Recognition Accuracy based on the Combined Assessment of Image Binarization Results

In the paper the problem of reliable evaluation of the effects of image binarization is discussed in view of image recognition accuracy. Considering the Optical Character Recognition methods, typically used for document images obtained by cameras or scanners, their accuracy is strongly dependent on the results of image binarization. Unfortunately, metrics typically used for the evaluation of binarization results, such as Peak Signal to Noise Ratio, Distance Reciprocal Distortion or Misclassification Penalty Metric, are not always well correlated with the recognition accuracy of individual characters. Therefore, a novel approach related to the use of combined metric for the assessment of binarization results is proposed and verified for the binary images obtained using some popular histogram-based methods from the original images with degraded quality. For the experimental prediction of the character recognition accuracy, the popular open source engine supported by Google, known as Tesseract, has been used. DOI: http://dx.doi.org/10.5755/j01.eee.21.6.13764

Thermodynamic performance analysis and optimization of DMC (Dual Miller Cycle) cogeneration system by considering exergetic performance coefficient and total exergy output criteria

Miller cycle engines are one of the popular engine concepts that are available for improving performance, reducing fuel consumption and NOx emissions. There are many research studies that investigated the modification of existing conventional engines for operation on a Miller cycle. In this context, a comparative performance analysis and optimization based on exergetic performance criterion, total exergy output and exergy efficiency has been carried out for an irreversible Dual–Miller Cycle cogeneration system having finite-rate of heat transfer, heat leak and internal irreversibilities. The EPC (Exergetic Performance Coefficient) criterion defined as the ratio of total exergy output to the loss rate of availability. Performance analysis has been also extended to the Otto–Miller and Diesel-Miller cogeneration cycles which may be considered as two special cases of the Dual–Miller cycle. The effect of the design parameters such as compression ratio, pressure ratio, cut-off ratio, Miller cycle ratio, heat consumer temperature ratio, allocation ratio and the ratio of power to heat consumed have also been investigated. The results obtained from this paper will provide guidance for the design of Dual–Miller Cycle cogeneration system and can be used for selection of optimal design parameters.

Topic Categorization of Biomedical Abstracts

Nowadays, people frequently use search engines in order to find the information they need on the Web. Especially, Web search constitutes of a basic tool used by million researchers in their everyday work. A very popular indexing engine, concerning life sciences and biomedical research, is PubMed. PubMed is a free database accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. The present search engines usually return search results in a global ranking, making it difficult for the users to browse in different topics or subtopics. In this work we propose a novel system to address the issues of clustering biomedical search engine results according to their topic. A methodology that exploits semantic text clustering techniques in order to group biomedical document collections in homogeneous topics, is presented and evaluated. In order to provide more accurate clustering results, we utilize various biomedical ontologies, likeMeSH and GeneOntology. Finally, we embed the proposed methodology in an online system that post-processes the PubMed online database so as to provide users the retrieved results according to well formed topics. So as to expose our method as an online real-time service with reasonable response times, we performed a wide range of engineering optimizations along with experimentation on preprocessing time to precision of results trade off.

New fully single layer QCA full-adder cell based on feedback model

Quantum-dot cellular automata QCA is one of the pioneer nanoelectronics for possible substitution of conventional CMOS circuits. In this paper, a nanoelectronic-compatible gate-level design for one-bit full-adder cell is proposed. This design is appropriate to apply in most of majority gate-based technologies. Hence, a fully single layer QCA structure for one-bit full-adder cell is implemented using this cell. The addition operation in the presented cell is accomplished using a feedback loop. This loop saves carry value through a synchronised four-phase QCA wire to produce sum value at the next level. The proposed one-bit full-adder cell excels most of previously reported structures by removing wire crossing overhead and decreasing cell complexity. QCA Designer tool as a popular simulation engine in QCA area is used for endorsing the proper functionality of the proposed circuit.

Complete Cycle Experiments Using the Adiabatic Gas Law Apparatus

The ability of our society to make informed energy-usage decisions in the future depends partly on current science and engineering students retaining a deep understanding of the thermodynamics of heat engines. Teacher imaginations and equipment budgets can both be taxed in the effort to engage students in hands-on heat engine activities. The experiments described in this paper, carried out using the Adiabatic Gas Law Apparatus1 (AGLA), quantitatively explore popular complete cycle heat engine processes.

Istar: A Web Platform for Large-Scale Protein-Ligand Docking

Protein-ligand docking is a key computational method in the design of starting points for the drug discovery process. We are motivated by the desire to automate large-scale docking using our popular docking engine idock and thus have developed a publicly-accessible web platform called istar. Without tedious software installation, users can submit jobs using our website. Our istar website supports 1) filtering ligands by desired molecular properties and previewing the number of ligands to dock, 2) monitoring job progress in real time, and 3) visualizing ligand conformations and outputting free energy and ligand efficiency predicted by idock, binding affinity predicted by RF-Score, putative hydrogen bonds, and supplier information for easy purchase, three useful features commonly lacked on other online docking platforms like DOCK Blaster or iScreen. We have collected 17,224,424 ligands from the All Clean subset of the ZINC database, and revamped our docking engine idock to version 2.0, further improving docking speed and accuracy, and integrating RF-Score as an alternative rescoring function. To compare idock 2.0 with the state-of-the-art AutoDock Vina 1.1.2, we have carried out a rescoring benchmark and a redocking benchmark on the 2,897 and 343 protein-ligand complexes of PDBbind v2012 refined set and CSAR NRC HiQ Set 24Sept2010 respectively, and an execution time benchmark on 12 diverse proteins and 3,000 ligands of different molecular weight. Results show that, under various scenarios, idock achieves comparable success rates while outperforming AutoDock Vina in terms of docking speed by at least 8.69 times and at most 37.51 times. When evaluated on the PDBbind v2012 core set, our istar platform combining with RF-Score manages to reproduce Pearson's correlation coefficient and Spearman's correlation coefficient of as high as 0.855 and 0.859 respectively between the experimental binding affinity and the predicted binding affinity of the docked conformation. istar is freely available at http://istar.cse.cuhk.edu.hk/idock.

The Earth System Grid Federation: An open infrastructure for access to distributed geospatial data

The Earth System Grid Federation (ESGF) is a multi-agency, international collaboration that aims at developing the software infrastructure needed to facilitate and empower the study of climate change on a global scale. The ESGF’s architecture employs a system of geographically distributed peer nodes, which are independently administered yet united by the adoption of common federation protocols and application programming interfaces (APIs). The cornerstones of its interoperability are the peer-to-peer messaging that is continuously exchanged among all nodes in the federation; a shared architecture and API for search and discovery; and a security infrastructure based on industry standards (OpenID, SSL, GSI and SAML). The ESGF software stack integrates custom components (for data publishing, searching, user interface, security and messaging), developed collaboratively by the team, with popular application engines (Tomcat, Solr) available from the open source community. The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the entire Fifth Coupled Model Intercomparison Project (CMIP5) output used by the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and a suite of satellite observations (obs4MIPs) and reanalysis data sets (ANA4MIPs). This paper presents ESGF as a successful example of integration of disparate open source technologies into a cohesive, wide functional system, and describes our experience in building and operating a distributed and federated infrastructure to serve the needs of the global climate science community.

Diamond dicing

In OLAP, analysts often select an interesting sample of the data. For example, an analyst might focus on products bringing revenues of at least $100,000, or on shops having sales greater than $400,000. However, current systems do not allow the application of both of these thresholds simultaneously, selecting products and shops satisfying both thresholds. For such purposes, we introduce the diamond cube operator, filling a gap among existing data warehouse operations.Because of the interaction between dimensions the computation of diamond cubes is challenging. We compare and test various algorithms on large data sets of more than 100million facts. We find that while it is possible to implement diamonds in SQL, it is inefficient. Indeed, our custom implementation can be a hundred times faster than popular database engines (including a row-store and a column-store).

Efficient big data processing in Hadoop MapReduce

This tutorial is motivated by the clear need of many organizations, companies, and researchers to deal with big data volumes efficiently. Examples include web analytics applications, scientific applications, and social networks. A popular data processing engine for big data is Hadoop MapReduce. Early versions of Hadoop MapReduce suffered from severe performance problems. Today, this is becoming history. There are many techniques that can be used with Hadoop MapReduce jobs to boost performance by orders of magnitude. In this tutorial we teach such techniques. First, we will briefly familiarize the audience with Hadoop MapReduce and motivate its use for big data processing. Then, we will focus on different data management techniques, going from job optimization to physical data organization like data layouts and indexes. Throughout this tutorial, we will highlight the similarities and differences between Hadoop MapReduce and Parallel DBMS. Furthermore, we will point out unresolved research problems and open issues.

Disk Layout Techniques for Online Social Network Data

Social networking applications' disk access patterns differ from those of traditional applications. However, today's disk layout techniques aren't adapted to social networking workloads, and thus their performance suffers. The authors' disk layout techniques leverage community structure in a social graph to make placement decisions that optimize read latency. Their layout manager, Bondhu, incorporates these techniques and is integrated into the popular Neo4j graph database engine. Experimental results show that Bondhu improves the median response time for online social network operations by as much as 48 percent.

Noncespaces: Using randomization to defeat cross-site scripting attacks

Cross-site scripting (XSS) vulnerabilities are among the most common and serious web application vulnerabilities. It is challenging to eliminate XSS vulnerabilities because it is difficult for web applications to sanitize all user input appropriately. We present Noncespaces, a technique that enables web clients to distinguish between trusted and untrusted content to prevent exploitation of XSS vulnerabilities. Using Noncespaces, a web application randomizes the the (X)HTML tags and attributes in each document before delivering it to the client. As long as the attacker is unable to guess the random mapping, the client can distinguish between trusted content created by the web application and untrusted content provided by an attacker. To implement Noncespaces with minimal changes to web applications, we leverage a popular web application architecture to automatically apply Noncespaces to static content processed through a popular PHP template engine. We design a policy language for Noncespaces, implement a training mode to assist policy development, and conduct extensive security testing of a generated policy for two large web applications to show the effectiveness of our technique.

DSX: A Knowledge-Based Scoring Function for the Assessment of Protein–Ligand Complexes

We introduce the new knowledge-based scoring function DSX that consists of distance-dependent pair potentials, novel torsion angle potentials, and newly defined solvent accessible surface-dependent potentials. DSX pair potentials are based on the statistical formalism of DrugScore, extended by a much more specialized set of atom types. The original DrugScore-like reference state is rather unstable with respect to modifications in the used atom types. Therefore, an important method to overcome this problem and to allow for robust results when deriving pair potentials for arbitrary sets of atom types is presented. A validation based on a carefully prepared test set is shown, enabling direct comparison to the majority of other popular scoring functions. Here, DSX features superior performance with respect to docking- and ranking power and runtime requirements. Furthermore, the beneficial combination with torsion angle-dependent and desolvation-dependent potentials is demonstrated. DSX is robust, flexible, and capable of working together with special features of popular docking engines, e.g., flexible protein residues in AutoDock or GOLD. The program is freely available to the scientific community and can be downloaded from our Web site www.agklebe.de .

Estratégias de Marketing Digital na Era da Busca

A evoluo das tecnologias de comunicao e informao TICs inauguram uma era de crescimento e disseminao exponencial de contedo relevante. Com a expanso da ubiqidade on-line, o crescimento e a facilidade de acesso web mvel, via dispositivos portteis, a busca popularizou-se e os mecanismos de busca cresceram e se sofisticaram. O presente artigo tem como foco fazer uma atualizao das publicaes mais recentes das Estratgias de Marketing Digital, ampliando a visibilidade de conceitos e tendncias importantes: i) evoluo da web 2.0 para web semntica ii) Marketing de busca, Estratgias SEM e SEO iii) mbile tagging, QR codes e Realidade Aumentada. Trata-se de uma pesquisa exploratria e uma investigao secundria, sobretudo bibliogrfica, privilegiando publicaes em sites especializados, notadamente no perodo de 1995 a 2010. Algumas inovaes tecnolgicas se tornaram marcantes e se transformaram em fortes tendncias, impactando os profissionais de marketing e de Tecnologia de Informao (TI). A migrao do consumo para a web, as facilidades dos mecanismos de buscas por melhores produtos e referncias e o aumento do consumo em tempo real, assumem-se como tendncias irreversveis, que exigem das organizaes estratgias mercadolgicas eficientes. Assim, torna-se imperativo gerenciar a comunicao empresarial na Era da busca exigindo uma postura de atualizao permanente e estratgias de marketing dirigidas e sustentveis. DOI: 10.5585/remark.v10i1.2199