Computing Support Research Articles

The role of big data, cloud computing and IoT to make cities smarter

Smart city enhanced the quality of life for the citizen through utilising information technology, networking and proper data management. It could involve the proper management of natural resources, infrastructure, power, transportation, healthcare, education, public safety and government, etc. If a city becomes smarter, the living standard improves. To make a city smarter, multiple technologies need to be strengthened in the area of various services. It reduces costs and resource consumption as well as more effective use of them. To enhance smart city services, big data, cloud computing and internet of things (IoT) plays a huge potential role. As digitisation was growing, huge data are obtained in every second. Effective data collection, storage, analysis and utilisation are a big key factor for successful smart cities. This paper, reviews the role of big data, cloud computing and IoT support smart cities. In addition, it identifies different perspectives, challenges and use of these technologies.

Index-assisted hierarchical computations in main-memory RDBMS

We address the problem of expressing and evaluating computations on hierarchies represented as database tables. Engine support for such computations is very limited today, and so they are usually outsourced into stored procedures or client code. Recently, data model and SQL language extensions were proposed to conveniently represent and work with hierarchies. On that basis we introduce a concept of structural grouping to relational algebra, provide concise syntax to express a class of useful computations, and discuss algorithms to evaluate them efficiently by exploiting available indexing schemes. This extends the versatility of RDBMS towards a great many use cases dealing with hierarchical data.

Rescue Wings: Mobile Computing and Active Services Support for Disaster Rescue

During disaster events, timely and targeted information provision and exchange could provide great help to the stricken population in difficult and complicated environments. This paper reports a service-oriented system, called Rescue Wings, for providing emergency support to sufferers and rescuers in disasters. The system utilizes mobile services to acquire real-time information about the users and environment, and constructs service agents (servants) to provide active services for mobile users. To perform their functions, the servants frequently invoke a set of intelligent services of Rescue Wings, which can further access a number of public services from government and other public organizations. We identify the most frequent request sequence patterns (FRSP) of Rescue Wings, and develop a new bio-inspired algorithm for efficiently scheduling the requests to minimize the response delay. The system has been tested in several disaster rescue drills, and has been successfully applied to the 2013 Ya'an Earthquake in Southeast China.

Physiological mouse: toward an emotion-aware mouse

Human-centered computing is rapidly becoming a major research direction as new developments in sensor technology make it increasingly feasible to obtain signals from human beings. At the same time, the pervasiveness of computing devices is also encouraging more research in human---computer interaction, especially in the direction of personalized and adaptive user interfaces. Among the various research issues, affective computing, or the ability of computers to understand and react according to what a user "feels," has been gaining in importance. In order to recognize the human affect (feeling), computers rely on the analysis of signal inputs captured by a multitude of means. This paper proposes the use of human physiological signals as a new form of modality in determining human affects, in a non-intrusive manner. The principle of non-invasiveness is very important, since it imposes no extra burden on the user, which improves user accessibility and encourages user adoption. This goal is realized via the physiological mouse, as a first step toward the support of affective computing. The conventional mouse is converted with a small optical component for capturing user photoplethysmographic (PPG) signal. With the PPG signal, it is possible to compute and derive human physiological signals. A prototype of the physiological mouse was built and raw PPG readings measured. The accuracy of the approach was evaluated through empirical studies to determine human physiological signals from the mouse PPG data. Finally, pilot experiments to correlate human physiological signals with various modes of human---computer interaction, namely gaming and video watching, were conducted. The trend in physiological signals could be used as feedback to the computer system which in turn adapts to the needs or the mood of the user, for instance change the volume and the light intensity when watching a video or playing a game based on current user emotion. The authors argue that this research will provide a new dimension for multimodal affective computing research, and the pilot study has already shed some light toward this research goal.

DAC-Mobi: Data-Assisted Communications of Mobile Images with Cloud Computing Support

This research proposes a novel data assisted image transmission scheme, which utilizes a large amount of correlated images stored in the cloud to improve the spectrum efficiency and visual quality. First, a two-layer Coset coding is proposed for the DCT coefficients transmission. The most significant bits (MSB) of the coefficients are generated by the first layer Coset and together with a few low frequency coefficients are transmitted through the most reliable channel coding and digital modulation. The middle bits generated by the second layer Coset are discarded by the sender and the residual bits are transmitted through amplitude modulation. Based on the MSB and the residual bits, an approximation of the original image is reconstructed. With this approximation, a lot of correlated images can be retrieved from the cloud, which are used to recover the discarded middle bits. The two layer Coset coding can significantly decrease the data energy so as to improve the transmission power efficiency. Hence, the end to end distortion of amplitude modulation can be reduced. Second, the image quality can be further improved by joint internal and external denoising with the retrieved images. Simulations show that the proposed scheme outperforms conventional digital schemes about 4 dB in peak signal to noise power ratio (PSNR) and achieves 2 dB gain over the state-of-the-art uncoded transmission. At low signal to noise power ratio (SNR), an additional 2-3 dB gain is achieved. The visual quality comparison also validates the objective image assessment result.

Bridging approaches to reduce the gap between classical and quantum computing

In this paper, we are discussing the comparison between classical and Quantum computing with Influence on different bridging approaches which are necessary to support the classical computing on Quantum chips or Quantum computing on classical chips. We have also tried to find the possibilities of Hardware level support for the classical computation and software level simplification and supportiveness of classical software on Quantum chips.

Operational cost-aware resource provisioning for continuous write applications in cloud-of-clouds

The emergence of cloud computing has made it become an attractive solution for large-scale data processing and storage applications. Cloud infrastructures provide users a remote access to powerful computing capacity, large storage space and high network bandwidth to deploy various applications. With the support of cloud computing, many large-scale applications have been migrated to cloud infrastructures instead of running on in-house local servers. Among these applications, continuous write applications (CWAs) such as online surveillance systems, can significantly benefit due to the flexibility and advantages of cloud computing. However, with specific characteristics such as continuous data writing and processing, and high level demand of data availability, cloud service providers prefer to use sophisticated models for provisioning resources to meet CWAs' demands while minimizing the operational cost of the infrastructure. In this paper, we present a novel architecture of multiple cloud service providers (CSPs) or commonly referred to as Cloud-of-Clouds. Based on this architecture, we propose two operational cost-aware algorithms for provisioning cloud resources for CWAs, namely neighboring optimal resource provisioning algorithm and global optimal resource provisioning algorithm, in order to minimize the operational cost and thereby maximizing the revenue of CSPs. We validate the proposed algorithms through comprehensive simulations. The two proposed algorithms are compared against each other to assess their effectiveness, and with a commonly used and practically viable round-robin approach. The results demonstrate that NORPA and GORPA outperform the conventional round-robin algorithm by reducing the operational cost by up to 28 and 57 %, respectively. The low complexity of the proposed cost-aware algorithms allows us to apply it to a realistic Cloud-of-Clouds environment in industry as well as academia.

A Test of Various Partial Atomic Charge Models for Computations on Diheteroaryl Ketones and Thioketones

The effective use of partial atomic charge models is essential for such purposes in molecular computations as a simplified representation of global charge distribution in a molecule and predicting its conformational behavior. In this work, ten of the most popular models of partial atomic charge are taken into consideration, and these models operate on the molecular wave functions/electron densities of five diheteroaryl ketones and their thiocarbonyl analogs. The ten models are tested in order to assess their usefulness in achieving the aforementioned purposes for the compounds in title. Therefore, the following criteria are used in the test: (1) how accurately these models reproduce the molecular dipole moments of the conformers of the investigated compounds; (2) whether these models are able to correctly determine the preferred conformer as well as the ordering of higher-energy conformers for each compound. The results of the test indicate that the Merz-Kollman-Singh (MKS) and Hu-Lu-Yang (HLY) models approximate the magnitude of the molecular dipole moments with the greatest accuracy. The natural partial atomic charges perform best in determining the conformational behavior of the investigated compounds. These findings may constitute important support for the effective computations of electrostatic effects occurring within and between the molecules of the compounds in question as well as similar compounds.

Achieving Secure and Efficient Data Access Control for Cloud-Integrated Body Sensor Networks

Body sensor network has emerged as one of the most promising technologies for e-healthcare, which makes remote health monitoring and treatment to patients possible. With the support of mobile cloud computing, large number of health-related data collected from various body sensor networks can be managed efficiently. However, how to keep data security and data privacy in cloud-integrated body sensor network (C-BSN) is an important and challenging issue since the patients’ health-related data are quite sensitive. In this paper, we present a novel secure access control mechanism MC-ABE (Mask-Certificate Attribute-Based Encryption) for cloud-integrated body sensor networks. A specific signature is designed to mask the plaintext, and then the masked data can be securely outsourced to cloud severs. An authorization certificate composed of the signature and related privilege items is constructed which is used to grant privileges to data receivers. To ensure security, a unique value is chosen to mask the certificate for each data receiver. Thus, the certificate is unique for each user and user revocation can be easily completed by removing the mask value. The analysis shows that proposed scheme can meet the security requirement of C-BSN, and it also has less computation cost and storage cost compared with other popular models.

Analysis of quantum-mechanical states of the ring-shaped Mie-type diatomic molecular model via the Fisher's information

Recently, the information theory of quantum-mechanical systems has aroused the interest of many theoretical physicists. This is due to the fact that it provides a deeper insight into the internal structure of the system. Also, it is the strongest support of the modern quantum computation and information, which is a basic theory for numerous technological developments. This study reports the solution of Schrödinger equation with the ring-shaped Mie-type potential. The rotational-vibrational spectroscopic study of some few selected diatomic molecules are given. The probability distribution density of the system which gives the probability density for observing the electron in the state characterized by the quantum numbers (n, l, m) in the ring-shaped Mie-type potential is obtained. Finally, the analysis for this distribution via a complementary information measures of a probability distribution known as the Fisher's information have been presented.

High-Speed General Purpose Genetic Algorithm Processor.

In this paper, an ultrafast steady-state genetic algorithm processor (GAP) is presented. Due to the heavy computational load of genetic algorithms (GAs), they usually take a long time to find optimum solutions. Hardware implementation is a significant approach to overcome the problem by speeding up the GAs procedure. Hence, we designed a digital CMOS implementation of GA in [Formula: see text] process. The proposed processor is not bounded to a specific application. Indeed, it is a general-purpose processor, which is capable of performing optimization in any possible application. Utilizing speed-boosting techniques, such as pipeline scheme, parallel coarse-grained processing, parallel fitness computation, parallel selection of parents, dual-population scheme, and support for pipelined fitness computation, the proposed processor significantly reduces the processing time. Furthermore, by relying on a built-in discard operator the proposed hardware may be used in constrained problems that are very common in control applications. In the proposed design, a large search space is achievable through the bit string length extension of individuals in the genetic population by connecting the 32-bit GAPs. In addition, the proposed processor supports parallel processing, in which the GAs procedure can be run on several connected processors simultaneously.

The Cloud Computing: A Systematic Review

These days, the Cloud Computing is probably the most considerable technology in the IT sector. It is broadly made use to deliver services well over the internet for both low priced and technical motives. Cloud Computing support virtualization technique over the internet to meet the stretchy demand of the users with less interaction by having service provider. For that reason users are progressively engaging toward this trend. In this paper, we presented brief review of Cloud Computing; it also includes comparative study between Cloud Computing, Distributed, Utility, Cluster, and Grid Computing and also three deployment model and service model. This paper also lay out brief discussion of the main characteristics of cloud computing, benefits, challenges and its applications.

EMC: Emotion-aware mobile cloud computing in 5G

With the development of 5G, the wireless world will be interconnected without barriers. This new technology will enable many challenging applications, and more personalized and interactive services are expected to be available with resourcelimited mobile terminals. Fortunately, mobile cloud computing (MCC) emerging in the context of 5G has the potential to overcome this bottleneck, which enables many resource-intensive services for mobile users with the support of mobile big data delivery and cloud-assisted computing. In this article we propose a novel framework named EMC in the context of 5G, which offers personalized emotionaware services by MCC and affective computing. With the proposed framework, the traditional MCC architecture is modified to achieve the required Quality of Experience in emotion-aware applications. Furthermore, we design a partitioning solution corresponding to the fundamental trade-off between the communication and computation in EMC. The framework would be helpful to provide personalized, human-centric, intelligent emotion-aware services in 5G.

Cellular cardiac electrophysiology modeling with Chaste and CellML.

Chaste is an open-source C++ library for computational biology that has well-developed cardiac electrophysiology tissue simulation support. In this paper, we introduce the features available for performing cardiac electrophysiology action potential simulations using a wide range of models from the Physiome repository. The mathematics of the models are described in CellML, with units for all quantities. The primary idea is that the model is defined in one place (the CellML file), and all model code is auto-generated at compile or run time; it never has to be manually edited. We use ontological annotation to identify model variables describing certain biological quantities (membrane voltage, capacitance, etc.) to allow us to import any relevant CellML models into the Chaste framework in consistent units and to interact with them via consistent interfaces. This approach provides a great deal of flexibility for analysing different models of the same system. Chaste provides a wide choice of numerical methods for solving the ordinary differential equations that describe the models. Fixed-timestep explicit and implicit solvers are provided, as discussed in previous work. Here we introduce the Rush–Larsen and Generalized Rush–Larsen integration techniques, made available via symbolic manipulation of the model equations, which are automatically rearranged into the forms required by these approaches. We have also integrated the CVODE solvers, a ‘gold standard’ for stiff systems, and we have developed support for symbolic computation of the Jacobian matrix, yielding further increases in the performance and accuracy of CVODE. We discuss some of the technical details of this work and compare the performance of the available numerical methods. Finally, we discuss how this is generalized in our functional curation framework, which uses a domain-specific language for defining complex experiments as a basis for comparison of model behavior.

Grids, virtualization, and clouds at Fermilab

Fermilab supports a scientific program that includes experiments and scientists located across the globe. To better serve this community, in 2004, the (then) Computing Division undertook the strategy of placing all of the High Throughput Computing (HTC) resources in a Campus Grid known as FermiGrid, supported by common shared services. In 2007, the FermiGrid Services group deployed a service infrastructure that utilized Xen virtualization, LVS network routing and MySQL circular replication to deliver highly available services that offered significant performance, reliability and serviceability improvements. This deployment was further enhanced through the deployment of a distributed redundant network core architecture and the physical distribution of the systems that host the virtual machines across multiple buildings on the Fermilab Campus. In 2010, building on the experience pioneered by FermiGrid in delivering production services in a virtual infrastructure, the Computing Sector commissioned the FermiCloud, General Physics Computing Facility and Virtual Services projects to serve as platforms for support of scientific computing (FermiCloud 6 GPCF) and core computing (Virtual Services). This work will present the evolution of the Fermilab Campus Grid, Virtualization and Cloud Computing infrastructure together with plans for the future.

Ecological Design in Network Learning Resources

In the rapid development of information technology, education, change of social background, the word "ecology" is more and more get the favor of education researchers. Ecological network learning environment to the learner and learning environment as a whole, focus on the interaction between learners and learning environment, attention generated in the learning process, focus on formation and maintenance of network learning ecology, is considered to be adapt to the future development trend of network learning environment. Ecology "based on" ecological learning "and" learning "learning ecosystem" supplies "as keywords to retrieve relevant literature, through to classify the literature, comparative analysis, think: the ecological network learning environment research comes from WebX.0, mix (a surgeon - up), the development of cloud computing and other technical and support; ecological psychology, Theory of complexity theory, uniform for ecological design of the network learning environment provide direct theoretical support.

NIH'sBig Data to Knowledgeinitiative and the advancement of biomedical informatics

Two influential reports on data and computation from advisory committees to the NIH leadership resulted in important initiatives: (1) the report from the Working Group on Biomedical Computing for the Advisory Committee to the Director (ACD) of the National Institutes of Health (NIH) in 1999i led to the Biomedical Information Science and Technology Initiative (BISTI), and (2) the more recent report from the Working Group on Data and Informatics for the ACD in 2012ii led to the Big Data to Knowledge (BD2K) initiative.iii Several AMIA members participated in this working group. Both reports recommended strong support for data science and computation in biomedical sciences and healthcare. The BD2K initiative was launched at NIH in 2013 through the development of several focused workshops, calls for proposals for centers of excellence, for a data discovery index, for training programs, and through the creation of the new position of Associate Director of Data Sciences, reporting directly to the NIH director. According to Francis Collins, the charge is to “lead an NIH-wide priority initiative to take better advantage of the exponential growth of biomedical …

Cloud Computing Support to University Business Processes in External Collaboration

Cloud Computing Support to University Business Processes in External Collaboration

Simulation as a service (SMaaS): a cloud-based framework to support the educational use of scientific software

Large manufacturers increasingly leverage modelling and simulation to improve quality and reduce cost. Small manufacturers have not adopted these techniques due to sizable upfront costs for expertise, software and hardware. The software as a service (SaaS) model provides access to applications hosted in a cloud environment, allowing users to try services at low cost and scale as needed. We have extended SaaS to include high-performance computing-hosted applications, thus creating simulation as a service (SMaaS). Polymer portal is a first-generation SMaaS platform designed to integrate access to multiple modelling, simulation and training services. Polymer portal provides a number of features including an e-commerce front end, common AAA service, and support for both cloud-hosted virtual machine (VM) images and high-performance computing (HPC) jobs. It has been deployed for six months and has been used successfully for a number of training and simulation activities. This paper describes the requirements, challenges, design and implementation of the polymer portal.

Essential Terrestrial Variable data workflows for distributed water resources modeling

This paper discusses a prototype infrastructure, HydroTerre, that provides researchers, educators and resource managers with seamless access to geospatial/geotemporal data for supporting physics-based numerical models. The prototype defines the supporting data as Essential Terrestrial Variables (ETV's) and includes data fusion tools necessary to predict and manage surface and groundwater resources that resolve important dynamics of upland stream networks. The evaluation of ecosystem and watershed services, such as the detection and attribution of the impact of climatic change, provides one of many examples of the pressing need for high resolution, spatially explicit resource assessments in upland catchments. However, the current infrastructure for supporting models and data anywhere in the continental USA (CONUS) must overcome important problems of: efficient accessibility to high resolution geospatial datasets from multiple sources, scalability of geospatial data in support of distributed models and data-intensive computation for multi-scale, multi-state simulations.We discuss data workflows for web access to ETV data processing in support of catchment modeling, as part of a larger strategy for consuming this data within a framework that enables hydrological modelers to build and test models with fast data access at a United States Geological Survey (USGS) National Hydrography Dataset Hydrological Unit Code (HUC) level-12 scale. Given the prospect of petabytes of existing high resolution environmental data (NRC, 2012), we limit our investigation to a limited set of ETV's necessary to provide the first level of support for model implementation anywhere in the CONUS, and that resolve important features of upland watersheds (e.g. hill slopes within 1st–2nd–3rd order streams). The paper demonstrates HydroTerre tools for fast ETV data access to web users, and describes the computational resources necessary for using ETV's as the basis for implementing spatially distributed models at scales approaching the native resolution of the data (≥30 m). The Penn State Integrated Hydrologic Model (PIHM) serves as an example although other models are currently being considered.