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A computational analysis using Artificial Intelligence techniques has drawn its attention in Industrial Revolution 4.0. There are self-regulating and integrating processes involved. The commonly used control method involves single-loop feedback control. Embedding feedforward algorithm is aimed to improve the steady state response of the controlled process without affecting the performance of transient response. Apart of it, two optimal tunings are respectively applied to suit to the different control objectives. Correlation PID tunings are applied to respective objective somehow is very relying to the engineering experience and skills. This paper proposed using Genetic Algorithm for optimization analysis to search a trade-off optimized PI controller settings, which reduces dependency on skills as well as provide better insights to all practical engineers for commending effective PI tunings to the real control practices of plants. Initially, the respective process and disturbance models of LOOP-PRO software for both Jacketed-reactor and Pumped-tank were developed. Then, the simulation analysis of PI tunings was conducted to feedback control, feedforward and feedback and Genetic Algorithm scheme. Relative performance was compared in terms of graphs, performance index, and performance indicator. It is concluded that Genetic Algorithm has consistently provided a trade-off optimized PI controller settings for both Jacketed-reactor and Pumped-tank.

Edge computing is projected to become the dominant form of cloud computing in the future because of the significant advantages it brings to both users (less latency, higher throughput) and telecom operators (less Internet traffic, more local management). However, to fully unlock its potential at scale, system designers and automated optimization systems alike will have to monitor closely the dynamics of both processing and communication facilities. Especially the latter is often neglected in current systems since network performance in cloud computing plays only a minor role. In this paper, we propose the architecture of MECPerf, which is a solution to collect network measurements in a live edge computing domain, to be collected for offline provisioning analysis and simulations, or to be provided in real-time for on-line system optimization. MECPerf has been validated in a realistic testbed funded by the European Commission (Fed4Fire+), and we describe here a summary of the results, which are fully available as open data and through a Python library to expedite their utilization. This is demonstrated via a use case involving the optimization of a system parameter for migrating clients in a federated edge computing system adopting the GSMA platform operator concept.

The wide availability of embedded sensor platforms and lowcost cameras—together with the developments in wireless communication—make it now possible the conception of pervasive intelligent systems based on vision. Such systems may be understood as distributed and collaborative sensor networks, able to produce, aggregate and process images in order to understand the observed scene and communicate the relevant information found about it. In this paper, we investigate the peculiarities of visual sensor networks with respect to standard vision systems and we identify possible strategies to accomplish image processing and analysis tasks over them. Although the rather strong constraints in computational and transmission power of embedded platforms that may prevent the use of state of the art computer vision and pattern recognition methods, we argue that multinode processing methods may be envisaged to decompose a complex task into a hierarchy of computationally simpler problems to be solved over the nodes of the network. These ideas are illustrated by describing an application of visual sensor network to infomobility. In particular, we consider an experimental setting in which several views of a parking lot are acquired by the sensor nodes in the network. By integrating the various views, the network is capable to provide a description of the scene in terms of the available spaces in the parking lot.

none 12 si In this paper we present D.A.V.I.D.E. (Development for an Added Value Infrastructure Designed in Europe), an innovative and energy efficient High Performance Computing cluster designed by E4 Computer Engineering for PRACE (Partnership for Advanced Computing in Europe). D.A.V.I.D.E. is built using best-in-class components (IBM’s POWER8-NVLink CPUs, NVIDIA TESLA P100 GPUs, Mellanox InfiniBand EDR 100 Gb/s networking) plus custom hardware and an innovative system middleware software. D.A.V.I.D.E. features (i) a dedicated power monitor interface, built around the BeagleBone Black Board that allows high frequency sampling directly from the power backplane and scalable integration with the internal node telemetry and system level power management software; (ii) a custom-built chassis, based on OpenRack form factor, and liquid cooling that allows the system to be used in modern, energy efficient, datacenter; (iii) software components designed for enabling fine grain power monitoring, power management (i.e. power capping and energy aware job scheduling) and application power profiling, based on dedicated machine learning components. Software APIs are offered to developers and users to tune the computing node performance and power consumption around on the application requirements. The first pilot system that we will deploy at the beginning of 2017, will demonstrate key HPC applications from different fields ported and optimized for this innovative platform. open Abu Ahmad, Wissam; Bartolini, Andrea; Beneventi, Francesco; Benini, Luca; Borghesi, Andrea; Cicala, Marco; Forestieri, Privato; Gianfreda, Cosimo; Gregori, Daniele; Libri, Antonio; Spiga, Filippo; Tinti, Simone Abu Ahmad, Wissam; Bartolini, Andrea; Beneventi, Francesco; Benini, Luca; Borghesi, Andrea; Cicala, Marco; Forestieri, Privato; Gianfreda, Cosimo; Gregori, Daniele; Libri, Antonio; Spiga, Filippo; Tinti, Simone

This paper presents a kind of design scheme about radar echo wave signal processing system based on digital signal processor (TMS320C6416) and FPGA (EP2C8Q). This scheme is used to timely monitor radar echo data and then calculates it. Distance of target items can be obtained by this method. Take advantage of flexibility and easy-processing complicated data of FPGA and DSP, we promote property of this system to a large extent

Difference types of DC-DC converter are used in various electronic devices and applications for so many years. But conventional converter cannot afford in high voltage and high current applications. Many researchers have been tried to full-fill the requirements. In this paper, dual stage double boost DC-DC converter is used for data center, high voltage vehicles and renewable system. Many control methods are developed for the control of DC-DC converter. To obtain a control method has the best performance under any condition is always demand. The main objective of this paper is to study the voltage mode control of proposed converter based on fuzzy logic controller (FLC) under the response of different parameters of proposed converter using Matlab/Simulink Software. &nbsp

A major drawback of nonlinear dimensionality reduction (DR) techniques is their inability to preserve some authentic information from the source domain, leading to projections that are often hard to interpret when it comes to observing anything other than the topological structure of the data. In this paper, we propose a nonlinear DR approach enforcing projection constraints resulting from an a priori knowledge about the structure of the data in multivariate periodical time series. We then propose several ways of exploiting this constrained projection to extract user-relevant information, such as the nominal behavior of a periodical dynamical system or the deviant behaviors which may occur at different time scales. The techniques are demonstrated on both a synthetic dataset composed of simulated multivariate data exhibiting a periodical behavior, and a real dataset corresponding to six months of sensor data acquisitions and recordings inside experimental buildings. 1 We would like to thank the Institut National de l'Energie Solaire (INES) and the CEA, LITEN, Laboratoire Energetique du Bâtiment for providing the data.

International audience Parsing facade images requires optimal handcrafted grammar for a given class of buildings. Such a handcrafted grammar is often designed manually by experts. In this paper, we present a novel framework to learn a compact grammar from a set of ground-truth images. To this end, parse trees of ground-truth annotated images are obtained running existing inference algorithms with a simple, very general grammar. From these parse trees, repeated subtrees are sought and merged together to share derivations and produce a grammar with fewer rules. Furthermore, unsupervised clustering is performed on these rules, so that, rules corresponding to the same complex pattern are grouped together leading to a rich compact grammar. Experimental validation and comparison with the state-of-the-art grammar-based methods on four diff erent datasets show that the learned grammar helps in much faster convergence while producing equal or more accurate parsing results compared to handcrafted grammars as well as grammars learned by other methods. Besides, we release a new dataset of facade images from Paris following the Art-deco style and demonstrate the general applicability and extreme potential of the proposed framework.

Noun Compounds are a frequently occurring multiword expression in English written texts. English noun compounds are translated into varied syntactic constructs in Hindi. The performance of existing translation system makes the point clear that there exists no satisfactorily efficient Noun Compound translation tool from English to Hindi although the need of one is unprecedented in the context of machine translation. In this paper we integrate Noun Compound Translator [13], a statistical tool for Noun Compound translation, with the state-of-the-art machine translation tool, Moses [10]. We evaluate the integrated system on test data of 300 source language sentences which contain Noun Compounds and are translated manually into Hindi. A gain of 29% on BLEU score and 27% on Human evaluation has been observed on the test data.

In this paper we describe a cyberattack against 2 well-known wearable devices. The attacker presented in this paper is an “honest but curious” Internet Service Provider (ISP) sitting somewhere in the path between the device and the cloud. The ISP launches the attack when the smartbands try to synchronize their data with the permanent cloud storage. By launching its attack, this “honest but curious” ISP is able to learn much personal information about the users of the smartbands, including the frequency of measuring the users’ heart rate and weight; the number and duration of workouts; as well as whether (i) sleep or (ii) steps were recorded on a given day. We show that privacy leaks might occur even when the transferred data are fully encrypted, and the representative mobile application utilizes state-of-the-art security mechanisms: certificate pinning, and source code obfuscation.

This paper proposes a novel hybrid intelligent system denoted as genetic algorithm and rough set incorporated neural fuzzy inference system (GARSINFIS). Its network structure dynamically changes along with the evolving genetic algorithm based rough set clustering (GARSC) technique. When input data set is applied, only the most essential information is retained in the clustering result, as knowledge reduction is done using rough set approximations and the most optimal solution is selected by genetic algorithm. The system not only obtains promising accuracy but also possesses a great level of interpretability to meet the increasing need of understanding the inference process. In terms of TSK type of fuzzy inference system, better structural interpretability is typically manifested as employing less number of input features, less number of rules, less number of fuzzy membership functions in each feature, and less complex rules in both antecedent and consequent parts. Extensive simulations on various data sets were conducted, and the performance of GARSINFIS was benchmarked against other well established neural and neural-fuzzy systems. Experimental results have shown that GARSINFIS performs well in both accuracy and interpretability.

A clustering ensemble aims to combine multiple clustering models to produce a better result than that of the individual clustering algorithms in terms of consistency and quality. In this paper, we propose a clustering ensemble algorithm with a novel consensus function named Adaptive Clustering Ensemble. It employs two similarity measures, cluster similarity and a newly defined membership similarity, and works adaptively through three stages. The first stage is to transform the initial clusters into a binary representation, and the second is to aggregate the initial clusters that are most similar based on the cluster similarity measure between clusters. This iterates itself adaptively until the intended candidate clusters are produced. The third stage is to further refine the clusters by dealing with uncertain objects to produce an improved final clustering result with the desired number of clusters. Our proposed method is tested on various real-world benchmark datasets and its performance is compared with other state-of-the-art clustering ensemble methods, including the Co-association method and the Meta-Clustering Algorithm. The experimental results indicate that on average our method is more accurate and more efficient.

The rise of elastically scaling applications that frequently deploy new machines has led to the adoption of DevOps practices across the cloud engineering stack. So-called configuration management tools utilize scripts that are based on declarative resource descriptions and make the system converge to the desired state. It is crucial for convergent configurations to be able to gracefully handle transient faults, e.g., network outages when downloading and installing software packages. In this paper we introduce a conceptual framework for asserting reliable convergence in configuration management. Based on a formal definition of configuration scripts and their resources, we utilize state transition graphs to test whether a script makes the system converge to the desired state under different conditions. In our generalized model, configuration actions are partially ordered, often resulting in prohibitively many possible execution orders. To reduce this problem space, we define and analyze a property called preservation, and we show that if preservation holds for all pairs of resources, then convergence holds for the entire configuration. Our implementation builds on Puppet, but the approach is equally applicable to other frameworks like Chef, Ansible, etc. We perform a comprehensive evaluation based on real world Puppet scripts and show the effectiveness of the approach. Our tool is able to detect all idempotence and convergence related issues in a set of existing Puppet scripts with known issues as well as some hitherto undiscovered bugs in a large random sample of scripts.

A new system for constructing virtual community integrated GE and multi-applications is introduced in this paper. Although GE provides a good platform of 3D space for virtual community, only 3D scene is not enough. How to embed the interesting web-applications into GE to construct the large virtual community is studied. A system combines the e-commerce, 3D objects, entertainment and sight-seeing with GE is also implemented. The architecture of virtual community system and detailed design is illustrated. Both ecommerce and entertainment such as online game, blog, chatting and video on demand are implemented based on the COM API provided by GE. The experimental indicates have shown that the system is more flexible and easier to satisfy users' requirement compared with the most of virtual community.

Localization of sensor nodes in wireless sensor networks (WSNs) is very important since it associates spatial context with the data collected by sensor nodes and used in applications. Rapid development of wireless sensor technologies and wide applications of wireless sensor networks have also made security in sensor localization a primary concern as well as a great challenge. Without adequate security measures, the performance of sensor localization, e.g., the accuracy of localization results, cannot be ensured in hostile environments. In this paper, we propose a trust-based secure sensor localization scheme (TSLS) for WSNs following the theory of neural network (NN). The proposed TSLS scheme can ensure that unknown sensor nodes will get credible information to perform localization through the evaluation of beacon nodes in a WSN. The evaluation model is comprised of the evaluation of both the identity and the behavior of beacon nodes as well as a filtering mechanism to deal with the slander behaviors between beacon nodes. Simulation results show that the proposed TSLS scheme can improve the accuracy of sensor localization in hostile environments for both static and dynamic WSNs.

This paper is aimed to present the design and development of Augmented Reality (AR) book based application for collaborative learning purposed. AR interfaces contain a rich and complex type of object and interaction. The concept of co-existence of virtual information and physical objects of AR enhance the learning environment. However, the potential of AR to support collaborative learning is just begin to be explored. To design and develop an effective AR book based learning application; social interaction, communication and engagement are considered as key successful factors in developing an application to support collaborative learning process since collaboration involves the mutual engagement of group members in social interaction where the collaborators shared their knowledge and skills to achieve or solve the problem together.

The following are abridged versions of position papers being used by the IEEE/DASS in developing the IEEE VHDL standard. These were supplied by Jim Armstrong who is Chairman of the VHDL Model Subgroup. Complete versions of the papers and minutes of the IEEE VHDL Subgroup are archived at the IEEE Computer Society. For further information call Rick Cain at 202-371-0101. I believe that sometime in the fall of 89 Design and Test will publish a special issue on the VHDL standard based on the final resolutions of the DASS. Special thanks to J. W. Smith for help in obtaining this material.

This paper proposes an efficient learning method for the layered neural networks based on the selection of training data and input characteristics of an output layer unit. Comparing to recent neutral networks; pulse neural networks, quantum neuro computation, etc, the multilayer neural network is widely used due to its simple structure. When learning objects are complicated, the problems, such as unsuccessful learning or a significant time required in learning, remain unsolved. The aims of this paper are to suggest solutions of these problems and to reduce the total learning time. The total learning time means the total computational time required to learn certain objects including adjusting parameter values and restarting the learning from the beginning. Focusing on the input data during the learning stage, we undertook an experiment to identify the data that makes large errors and interferes with the learning process. Our method devides the learning process into several stages. In general, input characteristics to an output layer unit show oscillation during learning process for complicated problems. Focusing on the oscillatory characteristics, it is determined whether the learning will move on to the next stage or the learning will restart from the beginning. Computational experiments suggest that the proposed method has the capability of higher learning performance and needs less learning time compared with the conventional method.

Many real world applications consist of skewed datasets which result in class imbalance problem. During classification, class imbalance cause underestimation of minority classes. Researchers have proposed a number of algorithms to deal with this problem. But recent research studies have shown that some skewed datasets are unharmful and applying class imbalance algorithms on these datasets lead to degenerated performance and increased execution time. In this research paper, we have pre-estimated the degree of harmfulness of class imbalance for skewed classification problems, using two of the data complexity measures: scatter matrix based class separability measure and ratio of intra-class versus inter-class nearest neighbours. Also the performance of oversampling-based class imbalance classification algorithms have been analysed with respect to these data complexity measures. The experiments are conducted using k-nearest neighbour (k-nn) and naive Bayes as the base classifiers for this study. The obtained results illustrate the usefulness of these measures by providing the prior information about the nature of the imbalance datasets that help us to select the more efficient classification algorithm.

In tunnel construction, it is important to ensure economic efficiency and safety when collecting data on ground condition variations during construction. Therefore, in recent years an increasing number of exploration methods using rock drills in front of tunnel faces have been developed. In this paper, a new exploration method called TFS-learning was proposed; this method uses machine data of a rock drill. Further, TFS identifies the relationship between the drilling machine data during the perforation of the blast hole and the assessment score of the tunnel face through machine learning to predict the distribution of assessment scores on the tunnel face. In this study, TFS-learning is focused on drilling data of the applied A tunnel. Based on the analysis result, a regression equation is derived, from which, the accuracy of the predicted result of the forward face assessment scores is verified.

The Scalable Adaptive Multicast Research Group (SAM RG) within the Internet Research Task Force (IRTF) is chartered to define a hybrid multicast protocol that opportunistically combines overlay multicast protocols and native multicast protocols to create and maintain hybrid multicast trees. A design for SAM hybrid multicast trees has been proposed that leverages the AMT multicast tunneling protocol. Evaluating hybrid multicast protocols is difficult for two reasons. The simulation environment must combine both a scalable overlay and a detailed network layer that includes routers with native multicast support. Suitable metrics are needed to compare the tree quality with pure overlay multicast trees. This paper presents our approach to simulating and evaluating hybrid multicast protocols.

Multi-label classification, where each instance is assigned with multiple labels, has been an attractive research topic in data mining. The annotations of multi-label instances are typically more difficult and time consuming, since they are simultaneously associated with multiple labels. Therefore, active learning, which reduces the labeling cost by actively querying the labels of the most valuable data, becomes particularly important for multi-label learning. Study reveals that methods querying instance-label pairs are more effective than those query instances, since for each sample, only some effective labels need to be annotated while others can be inferred by exploring the label correlations. However, with the high dimensionality of label space, the instance-label pair selective algorithm will be affected since the computational cost of training a multi-label model may be strongly affected by the number of labels. In this paper we propose an approach that combines instance sampling with optimal label subset selection, which can effectively improve the classification model performance and substantially reduce the annotation cost. Experimental results demonstrate the superiority of the proposed approach to state-of-the-art methods on three benchmark datasets.

During the past few years there has been a considerable amount of confusion with respect to the terminology used for adrenocorticotropins (ACTH) isolated from the pituitary glands of various species. In this paper ( 1 ) a unified system of nomenclature for these hormones is proposed. It is hoped that this system will furnish readily and at a glance, from the terminology itself, pertinent information about the source of the particular preparation as well as the chemical formula of any active degraded product derived from the natural hormone.

Relation extraction is one of the most important parts of natural language processing. It is the process of extracting relationships from a text. Extracted relationships actually occur between two or more entities of a certain type and these relations may have different patterns. The goal of the paper is to find out the noisy patterns for relation extraction of Bangla sentences. For the research work, seed tuples were needed containing two entities and the relation between them. We can get seed tuples from Freebase. Freebase is a large collaborative knowledge base and database of general, structured information for public use. But for Bangla language, there is no available Freebase. So we made Bangla Freebase which was the real challenge and it can be used for any other NLP based works. Then we tried to find out the noisy patterns for relation extraction by measuring conflict score.

Screening mammograms is the gold standard for detecting breast cancer early. While a good amount of work has been performed on mammography image classification, especially with deep neural networks, there has not been much exploration into the confidence or uncertainty measurement of the classification. In this paper, we propose a confidence measure-based evaluation metric for breast cancer screening. We propose a modular network architecture, where a traditional neural network is used as a feature extractor with transfer learning, followed by a simple Bayesian neural network. Utilizing a two-stage approach helps reducing the computational complexity, making the proposed framework attractive for wider deployment. We show that by providing the medical practitioners with a tool to tune two hyperparameters of the Bayesian neural network, namely, fraction of sampled number of networks and minimum probability, the framework can be adapted as needed by the domain expert. Finally, we argue that instead of just a single number such as accuracy, a tuple (accuracy, coverage, sampled number of networks, and minimum probability) can be utilized as an evaluation metric of our framework. We provide experimental results on the CBIS-DDSM dataset, where we show the trends in accuracy-coverage tradeoff while tuning the two hyperparameters. We also show that our confidence tuning results in increased accuracy with a reduced set of images with high confidence when compared to the baseline transfer learning. To make the proposed framework readily deployable, we provide (anonymized) source code with reproducible results at https://git.io/JvRqE., Comment: To be presented at the IEEE ICHI 2020

In this paper, we highlight three issues that limit performance of machine learning on biomedical images, and tackle them through 3 case studies: 1) Interactive Machine Learning (IML): we show how IML can drastically improve exploration time and quality of direct volume rendering. 2) transfer learning: we show how transfer learning along with intelligent pre-processing can result in better Alzheimer's diagnosis using a much smaller training set 3) data imbalance: we show how our novel focal Tversky loss function can provide better segmentation results taking into account the imbalanced nature of segmentation datasets. The case studies are accompanied by in-depth analytical discussion of results with possible future directions.

Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through machine learning have been a subject of intense research in recent years. Recent success of deep learning in computer vision have progressed such research further. However, common limitations with such algorithms are reliance on a large number of training images, and requirement of careful optimization of the architecture of deep networks. In this paper, we attempt solving these issues with transfer learning, where state-of-the-art architectures such as VGG and Inception are initialized with pre-trained weights from large benchmark datasets consisting of natural images, and the fully-connected layer is re-trained with only a small number of MRI images. We employ image entropy to select the most informative slices for training. Through experimentation on the OASIS MRI dataset, we show that with training size almost 10 times smaller than the state-of-the-art, we reach comparable or even better performance than current deep-learning based methods., Comment: Presented at IEEE BIBM 2017

Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through machine learning has been a subject of intense research in recent years. Recent success of deep learning in computer vision has progressed such research further. However, common limitations with such algorithms are reliance on a large number of training images, and requirement of careful optimization of the architecture of deep networks. In this paper, we attempt solving these issues with transfer learning, where the state-of-the-art VGG architecture is initialized with pre-trained weights from large benchmark datasets consisting of natural images. The network is then fine-tuned with layer-wise tuning, where only a pre-defined group of layers are trained on MRI images. To shrink the training data size, we employ image entropy to select the most informative slices. Through experimentation on the ADNI dataset, we show that with training size of 10 to 20 times smaller than the other contemporary methods, we reach state-of-the-art performance in AD vs. NC, AD vs. MCI, and MCI vs. NC classification problems, with a 4% and a 7% increase in accuracy over the state-of-the-art for AD vs. MCI and MCI vs. NC, respectively. We also provide detailed analysis of the effect of the intelligent training data selection method, changing the training size, and changing the number of layers to be fine-tuned. Finally, we provide Class Activation Maps (CAM) that demonstrate how the proposed model focuses on discriminative image regions that are neuropathologically relevant, and can help the healthcare practitioner in interpreting the model's decision making process.

Background: Empirical evidence pertaining to the influence of treatment preferences on attrition, adherence and outcomes in intervention evaluation trials is inconsistent. The inconsistency can be explained by the method used for allocating treatment and measuring preferences. The current methodological study is designed to address these factors by implementing the two-stage partially randomized or preference trial design, and administering a validated measure to assess participants’ preferences for the treatments under evaluation. It aims to compare attrition, adherence and outcomes for participants allocated randomly or by preference to treatment. The study is in its final stages of data collection; its protocol is presented in this paper. Methods/Design: A partially randomized clinical or preference trial is used. Eligible participants are randomized to two trial arms. First is the random arm involving random assignment to treatments, and second is the preference arm involving allocation to the chosen treatment. Participants with chronic insomnia are targeted. Two behavioral treatments are offered, stimulus control therapy and sleep restriction therapy, in the same format (small group) and dose (two sessions given over a 4-week period). A participant log is used to collect data on attrition. Adherence is evaluated in terms of exposure and enactment of treatment. Sleep-related outcomes (sleep parameters and perceived insomnia severity) are measured at pretest, posttest, 6 and 12 month follow-up. Treatment preferences, adherence and outcomes are assessed with reliable and valid measures. Discussion: The advantages and limitations of the preference trial design are highlighted. The challenges in implementing the trial are discussed relative to the distribution of participants in the groups defined by treatment received and method of treatment allocation. Trial registration: ClinicalTrials.gov Registry NCT02513017

Finding an appropriate transfer function (TF) for mapping color and opacity values in direct volume rendering (DVR) can be a daunting task. This paper presents a novel approach towards TF generation for DVR, where the traditional low-level color and opacity parameter manipulations are not necessary. The TF generation process is hidden behind a simple and intuitive spherical self-organizing map (SSOM) visualization. The SSOM represents a visual form of the topological relations among the clusters. The user interacts with the SSOM lattice to find interesting regions in the volume. The color and opacity values are generated automatically from the voxel features based on the user's perception. We also use harmonic colors to present a visually pleasing result. Due to the independence of SSOM from feature type, our proposed method is flexible in nature and can be integrated with any set of features. The GPU implementation provides real-time volume rendering and fast interaction. Experimental results on several benchmark volume datasets show the effectiveness of our proposed method.

Fast and precise spectrum opportunity (SOP) exploration is a fundamental and vital procedure for dynamic spectrum management. In this paper, the temporal rather than geographical feature of spectrum database is investigated. The spectrum prediction is formulated and taken as a function of spectrum database. Furthermore, spectrum sensing is also a choice for SOP exploration to cope with the inefficiency of the solely spectrum database approach on freshness of the information and the precise of the prediction. On that consideration, a joint SOP exploration (JSE) scheme is proposed which determines SOP based on joint spectrum sensing and the spectrum prediction enabled spectrum database. The advantages of spectrum database, spectrum prediction and spectrum sensing can be taken jointly to ascertain SOP. Extensive simulation results demonstrate that the proposed scheme can accommodate to diverse scenarios with different sensing ability of SUs and prediction ability of spectrum database, while the advantages of existing SOP exploration approaches can be jointly taken.

Cloud computing is a great design of technology ever made which provides services, applications, and resources through a network. Cloud computing gives the opportunity to use a very large amount of resources on demand. There are many cloud infrastructures as a service (IaaS) frameworks that exist for users, developers, and administrators and they have to make a decision about which environment is best suited for them. In this paper, we focus to analyze and evaluate the performance of the open-source OpenStack for IaaS cloud computing, and give the comparison between OpenStack and VMware. We outline some of our initial findings by providing such a testbed on OpenStack. The experimental results showed the advantages of OpenStack solution in cloud computing where infrastructure is provided as a service.

Motivation: The emergence of network medicine not only offers more opportunities for better and more complete understanding of the molecular complexities of diseases, but also serves as a promising tool for identifying new drug targets and establishing new relationships among diseases that enable drug repositioning. Computational approaches for drug repositioning by integrating information from multiple sources and multiple levels have the potential to provide great insights to the complex relationships among drugs, targets, disease genes and diseases at a system level. Results: In this article, we have proposed a computational framework based on a heterogeneous network model and applied the approach on drug repositioning by using existing omics data about diseases, drugs and drug targets. The novelty of the framework lies in the fact that the strength between a disease–drug pair is calculated through an iterative algorithm on the heterogeneous graph that also incorporates drug-target information. Comprehensive experimental results show that the proposed approach significantly outperforms several recent approaches. Case studies further illustrate its practical usefulness . Availability and implementation: http://cbc.case.edu Contact: jingli@cwru.edu Supplementary information: Supplementary data are available at Bioinformatics online.

With the application of wireless sensor network (WSN) in healthcare field, online sharing of medical data has attracted more and more attention. However, wearable sensor nodes in WSN are limited in energy, storage space and data processing capacity, which largely restricts their deployment in resource demand application scenarios. Fortunately, cloud storage services can enrich the capabilities of wearable sensors and provide an effective method for people to share data within a group. However, as medical data directly relates to patients' health and privacy information, ensuring the integrity and privacy of medical records stored in cloud servers becomes a key issue to be urgently solved. Many public data auditing schemes have been put forward to address the above issues. Unfortunately, most of them have security vulnerabilities or poor functionality and performance. In this paper, we come up with a secure and efficient certificateless public auditing scheme for cloud-assisted medical WSNs, which not only supports dynamic data sharing and privacy protection, but also achieves efficient group user revocation. Security analysis and performance evaluation demonstrate that our scheme significantly reduce the total computation cost while achieving a higher security level. Compared with other related schemes, our new proposal is more suitable for group user data sharing in cloud-assisted medical WSNs.

Recent Linux[7] kernel introduced an interrupt mitigation method called NAPI to reduce the overhead of interrupt handling procedures caused by frame arrival and transmission. However, NAPI caused serious delay and jitter in such procedures. On the other hand, many conventional real-time communication mechanisms were implemented independently to the existing network stack, and most of such mechanisms disabled the stack. There-fore, it is difficult to utilize both of real-time communication and IP communication in parallel. This paper proposed a parallel implementation method to realize both of real-time communication and IP communication simultaneously in a single computer unit. This method implement real-time network stack and IP network stack in parallel by using multiple queue functionality in a NIC (network interface card). Also, this method applied the interrupt mitigation method only to the IP stack. Therefore, this method achieved to reduce the overhead of the wide bandwidth of IP traffic, also to keep delay and jitter of real-time traffic enough small. Some experiments were conducted to measure the performance of the proposed method. The RTT (round trip time) delay of realtime communication was kept lower than 100μs even in high traffic load of 800 Mbit/s with small jitter; while the RTT of IP communication varied between 100μs to 20 ms. This paper introduced the design, implementation, and evaluation of the proposed method.

To explore the conjunction of abnormal changes among different processes is a key and challenging technique problem in processes monitoring, in faults analysis, and in faults location. In this paper, an indication series is used to symbolize the abnormal change in sampling series, two kinds of conjunction test indices are constructed to measure the conjunction degrees, which rely on the indication series of multidimensional synchronization sampling series and the abnormal change percentage series of multidimensional asynchronous sampling series separately. What is more, these conjunction-test indices are successfully used to set up the clustering algorithms of abnormal changes in multidimensional series. Some Monte Carlo results show that algorithms given in this paper are efficient. The idea and technological methods of this paper are helpful for us to get viable approaches to analyze abnormal changes and to diagnose faults in large-scale dynamic system.

This paper describes several experiments performed by our research group to show the use of smartphones in physics teaching. We document our work on the development of apps for smartphones and on how to use recorded sensor data. As a final result, some interesting outcomes are obtained from the surveys conducted on the students about the use of these applications and their mLearning experience in general. These results show that the students are very interested in using smartphones as a complement to a more traditional learning. Finally, the influence of using the developed applications on students' grades and engagement was also analyzed. The results of this analysis prove that the use of smartphones highly improves students' engagement.

Agile enterprises and networks are required to continuously evolve so as to adequately respond to a dynamic global and very competitive business environment. Unfortunately, this continuous change requirement affects the capacity of the enterprise to effectively interoperate internally and externally. Adding to this problem, the standards typically used as pillars for enterprise and network management and (inter)operation are themselves subject to continuous change and often have their own interoperability, inconsistency and overlap problems. It is essential that these concerns are identified and addressed in a sustainable manner, i.e. taking into account the life cycles of all entities involved as their interoperability capabilities and requirements change along their lives. This paper focuses on the issues specific to the standards and proposes a way forward based on principles tested in previous case studies combining elements from the Enterprise Interoperability, Architecture and Integration, Collaborative Networks and Artificial Intelligence bodies of knowledge.

It is difficult to extract trading rules for financial trading systems. One reason is that the data often leads to conflicting rules, i.e., rules that have the same left-hand side but different right hand side. In this paper, we explore ways to deal with the problem of inconsistent rules using US ten-years bond time series data. We explore Wang's method, and identify a particular shortcoming. In addition, we explore the use of frequency, and the last-in-sequence methods in choosing among inconsistent rules. We conclude that frequency can be used to develop short-term systems, while last-in-sequence produces overly biased rules. >

Intrusion detection systems are devoted to monitor a network with aims at finding and avoiding anomalous events. In particular, we focus on misuse detection systems, which are trained to identify several known types of attacks. These can be unauthorized accesses, or denial of service attacks, among others. Whenever it scans a trace of a suspicious event, it is programmed to trigger an alert and/or to block this dangerous access to the system. Depending on the security policies of the network, the administrator may seek different requirements that will have a strong dependency on the behavior of the intrusion detection system. For a given application, the cost of raising false alarms could be higher than carrying out a preventive access lock. In other scenarios, there could be a necessity of correctly identifying the exact type of cyber attack to proceed in a given way. In this paper, we propose a multi-objective evolutionary fuzzy system for the development of a system that can be trained using different metrics. By increasing the search space during the optimization of the model, more accurate solutions are expected to be obtained. Additionally, this scheme allows the final user to decide, among a broad set of solutions, which one is better suited for the current network characteristics. Our experimental results, using the well-known KDDCup’99 problem, supports the quality of this novel approach in contrast to the state-of-the-art for evolutionary fuzzy systems in intrusion detection, as well as the C4.5 decision tree.

In aspect-oriented programming languages, advice evaluation is usually considered as part of the base program evaluation. This is also the case for certain pointcuts, such as if pointcuts in AspectJ, or simply all pointcuts in higher-order aspect languages like AspectScheme. While viewing aspects as part of base level computation clearly distinguishes AOP from reflection, it also comes at a price: because aspects observe base level computation, evaluating pointcuts and advice at the base level can trigger infinite regression. To avoid these pitfalls, aspect languages propose ad-hoc mechanisms, which increase the complexity for programmers while being insufficient in many cases. After shedding light on the many facets of the issue, this paper proposes to clarify the situation by introducing levels of execution in the programming language, thereby allowing aspects to observe and run at specific, possibly different, levels. We adopt a defensive default that avoids infinite regression in all cases, and give advanced programmers the means to override this default using level shifting operators. We formalize the semantics of our proposal, and provide an implementation. This work recognizes that different aspects differ in their intended nature, and shows that structuring execution contexts helps tame the power of aspects and metaprogramming.

Relevance feedback formulations have been proposed to refine query result in content-based image retrieval in the past few years. Many of them focus on a learning approach to solve the feedback problem. In this paper, we present an expectation maximization approach to estimate the user's target distribution through user's feedback. Furthermore, we describe how to use the maximum entropy display to fully utilize user's feedback information. We detail the process and also demonstrate the result through experiments.

Asynchronous communication is an important communication mechanism in service oriented architecture for developing dynamic large-scale applications among distributed services. Service systems which maintain large scalability and loose coupling properties need a convenient verification method for its asynchronous communication. Publish/Subscribe messaging scheme is a kind of asynchronous communication mechanisms supporting these properties. This paper provides a formal model for the publish/subscribe messaging scheme based on message brokers using π-calculus. Two patterns are provided to achieve the composition of message brokers, which generates a scalable system. Meanwhile, a complex publish/subscribe system can be simplified to an original one. From the original model reduced from the complex system, we can clearly analyze the behavior of the whole complex system and verify some properties of the publish/subscribe scheme. The composition and reduction can be applied to the service integration both within one enterprise and between enterprises.

Digital Signature of Network Segment using Flow Analysis (DSNSF) can be classified as a traffic characterization of a network segment. This paper presents a model for signature creation using a clustering approach, enabling recognition patterns and characteristics in data sets. To avoid the problem of local optimal solutions, an improved Ant Colony Optimization metaheuristic is used, whose main features include low computational complexity and reliance on small number of input parameters.

The bulk of our current knowledge about articulatory/acoustic signatures of prosodic structure comes from paradigms that elicit discrete prosodic variation intentionally produced by subjects. In this paper, we collect speech elicited through continuous variation in tempo and hypo-hyper articulation, and analyze spontaneous emergence of high-level prosodic boundaries as a means of resolving low-level tempo and precision demands. Our data show that as the area of structural affordance for a prosodic boundary comes under decreasing temporal pressure, the temporal coordination patterns of the gestures in the vicinity of this affordance get continuously rearranged. This re-arrangement is comprehensively captured with the optimization-based embodied task dynamics platform (Simko & Cummins, 2010, 2011), in which this phenomenon can be modeled in terms of localized changes in relative demands on articulatory efficiency, perceptual clarity, and minimal duration, and the optimal resolution of these demands. (C) 2014 Elsevier Ltd. All rights reserved.

Modality translation grants diagnostic value to wearable devices by translating signals collected from low-power sensors to their highly-interpretable counterparts that are more familiar to healthcare providers. For instance, bio-impedance (Bio-Z) is a conveniently collected modality for measuring physiological parameters but is not highly interpretable. Thus, translating it to a well-known modality such as electrocardiogram (ECG) improves the usability of Bio-Z in wearables. Deep learning solutions are well-suited for this task given complex relationships between modalities generated by distinct processes. However, current algorithms usually train a single model for all users that results in ignoring cross-user variations. Retraining for new users usually requires collecting abundant labeled data, which is challenging in healthcare applications. In this paper, we build a modality translation framework to translate Bio-Z to ECG by learning personalized user information without training several independent architectures. Furthermore, our framework is able to adapt to new users in testing using very few samples. We design a meta-learning framework that contains shared and user-specific parameters to account for user differences while learning from the similarity amongst user signals. In this model, a meta-learner approximated by a neural network learns how to learn user-specific parameters and can efficiently update them in testing. Our experiments show that the proposed model reduces the percentage root mean square difference (PRD) by 41% compared to training a single model for all users and by 36% compared to training independent models for each user. When adapting the model to new users, our model outperforms fine-tuning a pre-trained model through back-propagation by 40% using as few as two new samples in testing.

We consider a situation in which individual features of the input are represented in the neural system by different frequencies of periodic firings. Thus, if two of the features are presented concurrently, the input to the system will consist of a superposition of two periodic trains. In this paper we present an algorithm that is capable of extracting the individual features from the composite signal by separating the signal into periodic spike trains with different frequencies. We show that the algorithm can be implemented in a biophysically based excitatory-inhibitory network model. The frequency separation process works over a range of frequencies determined by time constants of the model's intrinsic variables. It does not rely on a "resonance" phenomenon and is not tuned to a discrete set of frequencies. The frequency separation is still reliable when the timing of incoming spikes is noisy.

Online recognition of gestures is critical for intuitive human-robot interaction (HRI) and further push collaborative robotics into the market, making robots accessible to more people. The problem is that it is difficult to achieve accurate gesture recognition in real unstructured environments, often using distorted and incomplete multisensory data. This paper introduces an HRI framework to classify large vocabularies of interwoven static gestures (SGs) and dynamic gestures (DGs) captured with wearable sensors. DG features are obtained by applying data dimensionality reduction to raw data from sensors (resampling with cubic interpolation and principal component analysis). Experimental tests were conducted using the UC2017 hand gesture dataset with samples from eight different subjects. The classification models show an accuracy of 95.6% for a library of 24 SGs with a random forest and 99.3% for 10 DGs using artificial neural networks. These results compare equally or favorably with different commonly used classifiers. Long short-term memory deep networks achieved similar performance in online frame-by-frame classification using raw incomplete data, performing better in terms of accuracy than static models with specially crafted features, but worse in training and inference time. The recognized gestures are used to teleoperate a robot in a collaborative process that consists in preparing a breakfast meal.

Cloud computing is the top issue of IT industry. In addition, the mobile platform has become a major keyword of IT technologies. Thus, we introduce the mobile cloud computing that consist of the union of two concepts. This conflated model can provide a high performance computing in mobile platform. To provide the mobile cloud computing service, it needs various IT technologies. Among these technologies, remote access protocol is one of major issues because it is directly related in a performance of mobile cloud computing. There are two major standard protocols to provide remote access. They are Remote Frame Buffer protocol(RFB protocol) and Remote Desktop Protocol(RDP protocol). RFB protocol relays the view image from a server to a client. It has a simple architecture, but it makes many network traffics structurally. On the other hand, RDP protocol sends the message of a server event to client and the client creates and shows the view image to users. It has smaller traffic than the RFB protocol, but it is only used in Windows platform due to the license problem. The other protocol called SPICE can solve problems from two standard protocols. This protocol has similar architecture to RDP protocol and it is an open-source program. However, SPICE protocol cannot support Android platform now. In this paper, we introduce the way to convert SPICE protocol to support Android platform. However, the modified protocol has some additional delay. Therefore, we also propose the way to enhance the performance of this protocol. It can provide a high performance and be suitable for the mobile cloud.

Maritime security has always been given high priority in Russia. On April 22, 2000, the Government of the Russian Federation adopted Regulation 324, approving the federal statute of protection for the maritime industry from unlawful acts against the safety of maritime navigation. The paper describes measure taken and planned to ensure implementation of Chapter XI-2 and ISPS Code in the Russian Federation.