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The sufficient utilization of the mega data acquired from operative power units is a promising way leading to high efficient, clean and safe operation of power plant. Hereinto, data filtering is a critical link to the data-driven dynamic model identification aiming at optimizing the process control. The machine learning is an advantageous approach for filtering usable data from mega databases for identification due to its effective statistical learning strategy to the field data with noises, disturbances and coupling quantities. Therefore, a data filtering method of combining Gaussian Naive Bayesian classifier and prediction error method (Gaussian NB-PEM) is proposed in this paper. Firstly, variables associated with identification model are selected by analyzing the characteristics of the process. Secondly, the GaussianNB classifier is used for coarse data filtering by calculating the priori probability of each attribute from training sample set and the probability with all possible values of the known categories for testing sample set. Thirdly, the prediction error method is used for further data filtering based on model fitting. By using the filtered closed-loop data, the dynamic characteristics of the superheated steam temperature is modeled and verified by closed-loop control simulation, showing the validity of the Gaussian NB-PEM data filtering method.

Heterogeneous, distributed multi-databases (data warehouses, grids) are growing day by day as businesses demand more economical and flexible solutions for their data storage and processing needs. One of the main issues obstructing the large scale deployment of distributed multi-databases is latencies involved in data retrieval. Such problems are usually solved with the help of persistent caches. The paper presents a semantic caching architecture which supports faster semantic matching in query processing. This paper enhances the existing architecture of semantic cache and provides the mapping of hierarchical indexing scheme over it with the help of case study in order to represent the working of architecture. This paper also provides experimental results of the query processing over updated architecture and provides the discussion over comparison of the extended architecture with the previously proposed architecture.

Contact Logics provide a natural framework for representing and reasoning about regions in several areas of computer science. In this paper, we focus our attention on reasoning methods for Contact Logics and address the satisfiability problem and the unifiability problem. Firstly, we give sound and complete tableaux-based decision procedures in Contact Logics and we obtain new results about the decidability/complexity of the satisfiability problem in these logics. Secondly, we address the computability of the unifiability problem in Contact Logics and we obtain new results about the unification type of the unifiability problem in these logics.

A common task of Web users is querying structured information from Web pages. In this paper we propose a novel query processor for systematically discovering any-k relations from Web search results with conjunctive queries. The 'any-k' phrase denotes that retrieved tuples are not ranked by the system.For realizing this interesting scenario the query processor transfers a structured query into keyword queries that are submitted to a search engine, forwards search results to relation extractors, and then combines relations into result tuples.Unfortunately, relation extractors may fail to return a relation for a result tuple. We propose a solid information theory-based approach for retrieving missing attribute values of partially retrieved relations. Moreover, user-defined data sources may not return at least k complete result tuples. To solve this problem, we extend the Eddy query processing mechanism [14] for our 'querying the Web' scenario with a continuous, adaptive routing model. The model determines the most promising next incomplete row for returning any-k complete result tuples at any point during the query execution process.We report a thorough experimental evaluation over multiple relation extractors. Our experiments demonstrate that our query processor returns complete result tuples while processing only very few Web pages.

The interest in multivariate statistical process control (MSPC) has increased as the industrial processes have become more complex. This paper presents an industrial process involving a plastic part in which, due to the number of correlated variables, the inversion of the covariance matrix becomes impossible, and the classical MSPC cannot be used to identify physical aspects that explain the causes of variation or to increase the knowledge about the process behaviour. In order to solve this problem, a Multivariate Statistical Process Control based on Principal Component Analysis (MSPC-PCA) approach was used and an R code was developed to implement it according some commercial software used for this purpose, namely the ProMV (c) 2016 from ProSensus, Inc. (www.prosensus.ca). Based on used dataset, it was possible to illustrate the principles of MSPC-PCA. This work intends to illustrate the implementation of MSPC-PCA in R step by step, to help the user community of R to be able to perform it., FCT - Fundação para a Ciência e a Tecnologia(UID/CEC/00319/2013)

Deadlock prevention policies are used to solve the deadlock problems of FMSs. The theory of regions is recognized as the unique method for obtaining maximally permissive (i.e., optimal) controllers in the existing literature. In this paper, the selective siphons and critical markings method [17] is merged in the new deadlock prevention policy. First of all, reachability graph is still needed. Second, CMTSIs are identified. Further, selective siphons and critical markings method is used to check if all dead/quasi-dead markings of CMTSIs are covered by these selective siphons. Furthermore, choose anyone CMTSI that belongs to a same selective siphon to be processed. Finally, controllers are therefore obtained. Experimental results indicate that the computational cost can be reduced again. Besides, it is the most efficient policy to obtain maximal permissive behavior of Petri net models.

The classification of color image that contain skin pixel is a challenging process. It is challenging due to certain factor such as brightness, background similar to human skin represent the obstacles thereof. The basic approach of skin detection is color based classification. The human skin image is the composition of RGB color. The data values of skin is calculated by randomly sampling RGB color values of different face images which have different age, race, genders sets. However, its performance has not really been high because of the high overlapped degree between “skin” and “non-skin” pixels. This paper describes the linear norm fuzzy based twin support vector machine (LN-FTSVM)) approach for discriminate skin and non-skin data values of skin dataset and to enhance the skin recognition performance. The concept of fuzzy is resolved the unclassified and overlapped data region problems. If no decision function is positive for a data set, this data set is classified into a class with the large membership value. By computational experiments shows that Experiments result shows that the accuracy is improved by linear norm fuzzy based TSVM (LN-FTSVM)) over the conventional methods.

In this paper, the generalized detector (GD) constructed on the basis of the generalized approach to signal processing in noise is employed by synthetic aperture radar (SAR) system to define a target location. The GD is proposed to solve the common problem of target detection at the very low signal-to-noise-ratio (SNR) when the conventional detection algorithms are not able to do it. Simulation results demonstrate a GD performance superiority in comparison with the search for undefined maritime objects (SUMO) detector one.

Reviews from players regarding different characteristics of an esports game are one of the worthiest sources for the developers to enhance their services or adjust operating strategy. However, little research has been conducted on detecting esports players’ favorite topics dealing with topic modeling. Thus, this paper aims to use a data mining approach to analyze community data in the games domain available on Steam. We collected more than 1.2 million English reviews from four esports games up to August 2020 on Steam. Our contributions in this paper are: (i) we manually build a dataset by filtering out high-quality esports reviews, (ii) we then infer and group reviews into 3 groups with 19 topics, and (iii) we add more contributions to finding the emerging opinions of esports players towards the different topics of esports reviews, which might benefit further research on understanding esports reviews.

This paper studies the dynamic time-varying formation control of multi-agent systems. Aiming at the unmanned vehicle systems, based on the static time-varying formation in literature [18], a multi-agent dynamic time-varying formation control based on consistency is put forward. Additionally, the sufficient conditions for the agreement and the steps for solving the control parameters are given. The simulation is conducted to prove the effectiveness and robustness of the theoretical results.

Software frameworks are difficult for plugin developers to use, even when they are well designed and documented. Some of these difficulties stem from the many constraints that frameworks impose on plugin code. These constraints might restrict operations from being called on certain objects, or they might restrict how long an object is available. Additionally, the constraints are relative to the current context of the plugin, and they can involve multiple, interacting framework objects. This paper proposes a lightweight specification system and analysis to check plugins from a semantic perspective, rather than a purely structural view.

This paper proposes a novel approach to design a controller in discrete time for the class of uncertain nonlinear systems in the presence of magnitude constrains of control signal which are treated as the saturation nonlinearity. A associative law between reinforcement learning algorithm based on adaptive NRBF neural networks and the theory of robust control Ho is set up in a novel control structure, in which the proposed controller allows learning and control on-line to compensate multiple uncertain nonlinearities as well as minimizing both the H. tracking performance index function and the unknown nonlinear dynamic approximation errors. The novel theorem of robust stabilization of the closed-loop system is declared and proved. Simulation results verify the theoretical analysis.

From the point of view of a data warehouse system, collecting and receiving information from source systems is crucial for all subsequent business intelligence applications. Incoming information can generally be classified into two types: (1) the state-oriented data and (2) event-oriented data or transactional data, which contains information about the change performed by processes on the instances of information objects. On the way towards achieving the goal of a full-fledged active data warehouse it becomes more and more important to provide data with minimal latency. In this paper we focus on dimensional data which is provided by general data warehouse applications. The information transfer is performed via messages containing the change of information on the dimension instances. The proposed approach is able to validate the event-messages, reconstruct the complete history of the dimension and provide a well applicable ''comprehensive slowly changing dimension'' (cSCD) interface for queries on the historical and current state of the dimension. A description of the prototype implementation for this kind of an ''active integration'' in a data warehouse and a case study at T-Mobile conclude the paper.

The geophysical inversion with combining prior information is very important for resource exploration and studies of the Earth’s internal structure. Guided fuzzy C-means clustering inversion (FCM) is normally applied for the Tikhonov regularized inversion, but has the shortcoming of uniform model parameter shrinkage, leading to inaccuracy. In this paper, an improved guided fuzzy clustering algorithm is proposed by adding a fuzzy entropy term to the original guided FCM. This method not only enforces the discrete values to a high degree of approximation by guiding the recovered model to cluster tightly around the known petrophysical property values, but also calculates the distributed characteristics of the model parameter set. Based on this method, the shortcoming of uniform shrinkage of the original guided FCM clustering algorithm is improved, and more accurate inversion results are obtained, making the FCM method more efficient and broadly applicable. Furthermore, a new parameter search algorithm is proposed to accelerate the search speed. The results recovered by using this method with three kinds of theoretical gravity anomaly data show more accurate density anomalies compared with the results generated from the original guided FCM clustering inversion and greater efficiency in the parametric search process when using the new parameter search algorithm. The improved FCM clustering algorithm could enable more extensive and efficient use of gravity inversion.

The luminosity upgrade of the Large Hadron Collider (LHC) at CERN requires the installation of additional collimators in the dispersion suppressor regions of the accelerator. The upgrade foresees the installation of one additional collimator on either side of interaction point 7 (IP7) at the location of the existing main dipoles (MBs) that will be replaced by shorter and more powerful dipoles, and of one additional collimator on either side of IP2 at the location of existing empty cryostats. This paper describes the design and the construction status of the full-length prototype of the 11-T dipole magnet, which is needed for IP7. This magnet features a two-in-one structure, like the LHC MB, impregnated coils made of Nb3Sn conductor, an inner bore of 60 mm, and a magnetic length of about 5.3 m. Two 11-T magnets are needed to replace a 15-m long MB. A by-pass cryostat placed in between the two magnets allows creating a room temperature space for the additional collimators. The magnet is designed to provide the same integrated field as the MB at nominal field. However, due to the difference in transfer function at lower field, a correction by means of a trim current has been considered. A full-length prototype is currently under construction at CERN with the goal of developing the manufacturing and inspection procedures prior to launch the series production. For this, new tooling has been developed and optimized during the fabrication of fully representative practice coils. This paper describes the design of the magnet, the main manufacturing steps, and corresponding quality indicators, which will be used to monitor the series production. Finally, the production and installation schedule will be presented.

A novel uncertainty assessment methodology, based on a statistical non-parametric approach, is presented in this paper. It achieves quantification of code physical model uncertainty by making use of model performance information obtained from studies of appropriate separate-effect tests. Uncertainties are quantified in the form of estimated probability density functions (pdfs), calculated with a newly developed non-parametric estimator. The new estimator objectively predicts the probability distribution of the models error (its uncertainty) from databases reflecting the models accuracy on the basis of available experiments. The methodology is completed by applying a novel multi-dimensional clustering technique based on the comparison of model error samples with the Kruskall-Wallis test. This takes into account the fact that a models uncertainty depends on system conditions, since a best estimate code can give predictions for which the accuracy is affected by the regions of the physical space in which the experiments occur. The final result is an objective, rigorous and accurate manner of assigning uncertainty to coded models, i.e. the input information needed by code uncertainty propagation methodologies used for assessing the accuracy of best estimate codes in nuclear systems analysis. The new methodology has been applied to the quantification of the uncertainty in the RETRAN-3D void model and then used in the analysis of an independent separate-effect experiment. This has clearly demonstrated the basic feasibility of the approach, as well as its advantages in yielding narrower uncertainty bands in quantifying the codes accuracy for void fraction predictions.[All rights reserved Elsevier].

Purpose: In Micro-CT systems based on optical coupling detectors, the defects of scintillator or CCD-camera would lead to heavy artifacts in reconstructed CT images. Meanwhile, different detector units usually suffer from inhomogeneous response, which also leads to artifacts in the CT images. Detector shifting is a simple and efficient method to remove the artifacts due to inhomogeneous responses of detector units. However, it does not work well for heavy artifacts due to defects in scintillator or CCD. The purpose of this paper is to develop a data preprocessing method to reduce both kinds of artifacts. Methods: A hybrid method which involves detector random shifting and data inpainting is proposed to correct the projection data, so as to suppress the artifacts in the reconstructed CT images. The defects in scintillator or CCD-camera lead to data lost in some areas of the projection data. The Criminisi algorithm is employed to recover the lost data. By detector random shifting, the location of the lost data in one view might be shifted away in adjacent views. This feature is utilized to design the search window, such that the best match patch shall be searched across adjacent views. By this way, the best match patches should really enjoy high similarity. As a result, the heavy artifacts due to defects of scintillator or CCD-camera should be suppressed. Furthermore, a multiscale tessellation method is proposed to locate the defects and similarity patches, which makes the Criminisi algorithm very fast. Results: The authors tested the proposed method on both simulated projection data and real projection data. Experiments show that the proposed method could correct the bad data in the projections quite well. Compared to other popular methods, such as linear interpolation, wavelet combining Fourier transform, and TV-inpainting, experimental results suggest that the CT images reconstructed from the preprocessed data sets by our method is significantly better in quality. Conclusions: They have proposed a hybrid method for projection data preprocessing which fits well to typical Micro-CT systems. The hybrid method could suppress the ring artifacts in the reconstructed CT images efficiently, while the spatial resolution is not reduced even with a critical eye.

To improve the intelligent image recognition abilities of edge devices, a parallel-optimization-based framework called POWER is introduced in this paper. With FPGA (Field-Programmable Gate Array) as its hardware module, POWER provides well extensibility and flexible customization capability for developing intelligent firmware suitable for different types of edge devices in various scenarios. Through an actual case study, we design and implement a firmware prototype following the specification of POWER and explore its performance improvement using parallel optimization. Our experimental results show that the firmware prototype we implement exhibits good performance and is applicable to substation inspection robots, which also validate the effectiveness of our POWER framework in designing edge intelligent firmware modules indirectly.

The dynamic programming is an important procedure for speech understanding and image recognition. In this paper, the application of dynamic programming to recognize the image basing on its contour is recalled, then the parallelism of this process is analyzed and exploited. Some parallel dynamic programming algorithms for evaluating the smallest value of the minimum intersegment distance path in a data table are designed in detail on several kinds or the specialized parallel computer, such as 2D Systolic Array, ID Systolic Array and an associative computer of the SIMD type. Their computation complexities are respectively estimated as O(k), O(k) and O(kw), while the cost O(mn) is obtained on serial computer, where k=max(2n,m), m is the number of segments of a contour to be recognized, n gives the number of segments of the sample contour and w is the bit-width of the used associative fields.

Network coding has been applied successfully in peer-to-peer systems to shorten the distribution time, particularly in extreme conditions where peers in the network are clustered and separated by physical links with limited bandwidth. In this paper, we focus more closely on the use of source coding, i.e. encoding is done only at the source, as an alternative to network coding to facilitate content distribution under such limited bandwidth configuration. We observe analytically and experimentally that, in this specific case, with appropriately chosen expansion factors, source coding can have comparable performance to network coding in terms of distribution time, yet consuming much less computational resources than the latter approach does.

Ontologies have been designed for the purpose of representing a domain of knowledge in a way that makes it understandable by machines and humans. Ontologies have been used in many areas in computer sciences such as knowledge engineering, semantic web, and information retrieval. Religious studies is one of the areas were ontologies are in demand. In this paper, we present the design of domain ontology about prophets and messengers in Islam from trustworthy resources: the Quran and Hadith. The ontology can be used to better understanding all life aspects of prophets and messengers in Islam. The classes, relations and properties of this special domain ontology were gathered and integrated to help researchers to identify and explore knowledge about prophets in Islam such as their lives, books and teachings, the nature of the messages they brought to their nations and tribes and much more.

In this paper, solutions for precise maneuvering of an autonomous small (e.g., 350-class) Unmanned Aerial Vehicles (UAVs) are designed and implemented from smart modifications of non expensive mass market technologies. The considered class of vehicles suffers from light load, and, therefore, only a limited amount of sensors and computing devices can be installed on-board. Then, to make the prototype capable of moving autonomously along a fixed trajectory, a &ldquo, cyber-pilot&rdquo, able on demand to replace the human operator, has been implemented on an embedded control board. This cyber-pilot overrides the commands thanks to a custom hardware signal mixer. The drone is able to localize itself in the environment without ground assistance by using a camera possibly mounted on a 3 Degrees Of Freedom (DOF) gimbal suspension. A computer vision system elaborates the video stream pointing out land markers with known absolute position and orientation. This information is fused with accelerations from a 6-DOF Inertial Measurement Unit (IMU) to generate a &ldquo, virtual sensor&rdquo, which provides refined estimates of the pose, the absolute position, the speed and the angular velocities of the drone. Due to the importance of this sensor, several fusion strategies have been investigated. The resulting data are, finally, fed to a control algorithm featuring a number of uncoupled digital PID controllers which work to bring to zero the displacement from the desired trajectory.

Summary Multiples are real reflections from the underground interface, and if internal multiples can be effectively used, it can assist in illuminating shadowed areas that cannot be reached by primary., which is of great research value for the exploration and development of complex structural reservoirs. The single internal multiples imaging method is difficult to extract remove the illusion. Therefore, it is of great significance to study the joint imaging method of primary and internal multiples. In this paper, the feasibility of the joint imaging of primary and internal multiples is firstly verified. Then, the problem of opposite polarity between primary and internal multiples imaging is studied, and the problem of inconsistent polarity is well solved, so that the energy at the imaging interface is effectively compensated.

The paper presents the study on European global navigation satellite system (GNSS): Galileo system which is a European infrastructure of spatial information being implemented by European Community and European Space Agency mainly. GSSF is conceived as a simulations environment that reproduces the functional and performance behavior of the Galileo system to support the entire Galileo programmer lifecycle. In this paper, Simulation results with prime focus on UERE, DOP, NSP, Visibility Analyses and Integrity Analysis of the Galileo system.

Focal boost to dominant intraprostatic lesions (DILs) has recently been proposed for prostate radiation therapy. Accurate and fast delineation of the prostate and DILs is thus required during treatment planning. In this paper, we develop a learning-based method using positron emission tomography (PET)/computed tomography (CT) images to automatically segment the prostate and its DILs. To enable end-to-end segmentation, a deep learning-based method, called cascaded regional-Net, is utilized. The first network, referred to as dual attention network, is used to segment the prostate via extracting comprehensive features from both PET and CT images. A second network, referred to as mask scoring regional convolutional neural network (MSR-CNN), is used to segment the DILs from the PET and CT within the prostate region. Scoring strategy is used to diminish the misclassification of the DILs. For DIL segmentation, the proposed cascaded regional-Net uses two steps to remove normal tissue regions, with the first step cropping images based on prostate segmentation and the second step using MSR-CNN to further locate the DILs. The binary masks of DILs and prostates of testing patients are generated on the PET/CT images by the trained model. For evaluation, we retrospectively investigated 49 prostate cancer patients with PET/CT images acquired. The prostate and DILs of each patient were contoured by radiation oncologists and set as the ground truths and targets. We used five-fold cross-validation and a hold-out test to train and evaluate our method. The mean surface distance and DSC values were 0.666 ± 0.696 mm and 0.932 ± 0.059 for the prostate and 0.814 ± 1.002 mm and 0.801 ± 0.178 for the DILs among all 49 patients. The proposed method has shown promise for facilitating prostate and DIL delineation for DIL focal boost prostate radiation therapy.

In this paper, a new configuration for a multi-terminal offshore wind farms based VSC-HVDC and large scale photovoltaic system using modified unified adaptive linear neuron (Adaline) algorithm is presented. In the offshore station, the permanent magnet synchronous generator (PMSG)-based wind turbines are tied to the bus via back-to-back converters and a step-up transformer. The large scale photovoltaic power is connected to the dc bus between the offshore voltage source converters (VSC) and onshore VSC-HVDC stations via a modular multilevel converter (MMC). The Power management and the synchronization between two buses are achieved by controlling the two VSC converters. The performance of the topology proposed is tested using Matlab/Simulink during permanent operation mode and in the presence of DC fault.

The combination of Java applications as graphical user interfaces and the Matlab-Simulink environment as the simulation engine seems to be a good solution for taking a particular control lab to distant students. In this paper, it is presented a new kind of simulation environment for qualitative teaching on concepts of Control Engineering by means of a suitable configuration of experiments by the tutor or teacher.

The learning behavior of the agent is a challenging and interesting issue in an unknown environment. Reinforcement learning obtain the developed strategy through exploration and interaction with the environment, and the characteristic of online learning make it as an important branch of machine learning research. In this paper, we summarize the current research of the reinforcement learning applications and hotspots. Firstly, the principle, structure and the main classic algorithms of the reinforcement learning are introduced. Secondly, according to the recent research results, we introduce four main applications of reinforcement learning, namely robot, unmanned aerial vehicle, multi-agent and intelligent traffic. Finally, the research hotspots and the development direction of the reinforcement learning are introduced, which conclude the partial perception, hierarchical reinforcement learning, combination with other artificial intelligence technologies and game theory.

Detecting roadside safety attributes and distances in point cloud data is a challenging task. The major problems are accurate detection of attributes and attribute centers for calculating safety distance among attributes. In this paper, we propose a point cloud classification framework for roadside safety attributes detection. In addition, we propose an object center approximation technique for distance calculation that has been integrated into the proposed framework. The proposed framework has been evaluated on large real-world point cloud data, and the experimental results are promising. The framework achieved 100% object-wise accuracy on detecting poles and trees, while the overall point-wise accuracy on detecting all seven attributes was 86%.

We report on the design and evaluation of the initial results of operation of a prototype of an advanced system for maritime security. The system is autonomous and is designed to remain in the ocean for extended periods up to two months. It is based on the Bottom Stationing Ocean Profiler (BSOP), an un-tethered, autonomous platform that stations itself on the sea floor and ascends to the surface at specific time intervals or, potentially, when triggered by certain events such as recognizable acoustic signals, collected and analyzed on board. The surface operations of the system include optical data acquisition, image data analysis, communication with the ground station, and retrieval based functionality. The system is designed to take video and imagery of the surrounding ocean surface and analyze it for the presence of ships, thus, potentially enabling automatic detection and tracking of marine vehicles as they transit in the vicinity of the platform. The system transmits the data to the ground control via bi-directional RF satellite link and can have its mission parameters reprogrammed during the deployment. The described unit is low cost, easy to deploy and recover, and does not reveal itself to the potential targets. The paper describes the system hardware, architecture, algorithms for visual ship detection and tracking.

Visual lifelogging is the process of automatically recording images and other sensor data for the purpose of aiding memory recall. Such lifelogs are usually created using wearable cameras. Given the vast amount of images that are maintained in a visual lifelog, it is a significant challenge for users to deconstruct a sizeable collection of images into meaningful events. In this paper, random matrix theory (RMT) is applied to a cross-correlation matrix C, constructed using SenseCam lifelog data streams to identify such events. The analysis reveals a number of eigenvalues that deviate from the spectrum suggested by RMT. The components of the deviating eigenvectors are found to correspond to "distinct significant events" in the visual lifelogs. Finally, the cross-correlation matrix C is cleaned by separating the noisy part from the non-noisy part. Overall, the RMT technique is shown to be useful to detect major events in SenseCam images.

Ensuring long processor lifetimes by limiting failuresdue to wear-out related hard errors is a critical requirementfor all microprocessor manufacturers. We observethat continuous device scaling and increasing temperaturesare making lifetime reliability targets even harder to meet.However, current methodologies for qualifying lifetime reliabilityare overly conservative since they assume worst-caseoperating conditions. This paper makes the case thatthe continued use of such methodologies will significantlyand unnecessarily constrain performance. Instead, lifetimereliability awareness at the microarchitectural design stagecan mitigate this problem, by designing processors that dynamicallyadapt in response to the observed usage to meeta reliability target.We make two specific contributions. First, we describean architecture-level model and its implementation, calledRAMP, that can dynamically track lifetime reliability, respondingto changes in application behavior. RAMP isbased on state-of-the-art device models for different wear-outmechanisms. Second, we propose dynamic reliabilitymanagement (DRM) - a technique where the processorcan respond to changing application behavior to maintainits lifetime reliability target. In contrast to currentworst-case behavior based reliability qualification methodologies,DRM allows processors to be qualified for reliabilityat lower (but more likely) operating points than theworst case. Using RAMP, we show that this can save costand/or improve performance, that dynamic voltage scalingis an effective response technique for DRM, and that dynamicthermal management neither subsumes nor is sub-sumedby DRM.

There are a number of methods for classification of mobile apps, but most of them rely on a fixed set of app categories and text descriptions associated with the apps. Often, one may need to classify apps into a different taxonomy and might have limited app usage data for the purpose. In this paper, we present an app classification system that uses object detection and recognition in images associated with apps, along with text based metadata of the apps, to generate a more accurate classification for a given app according to a given taxonomy. Our image based approach can, in principle, complement any existing text based approach for app classification. We train a fast RCNN to learn the coordinates of bounding boxes in an app image for effective object detection, as well as labels for the objects. We then use the detected objects in the app images in an ensemble with a text based system that uses a hierarchical supervised active learning pipeline based on uncertainty sampling for generating the training samples for a classifier. Using the ensemble, we are able to obtain better classification accuracy than if either of the text or image systems are used on their own.

Optical Burst-switched (OBS) is a promising switching technology and expected to support the future Internet backbone with dramatically increasing bandwidth demand. In an OBS network, burst contention causes burst loss due to bufferless nature of OBS core network. This kind of burst loss will interact with the above TCP layer. In this paper, we study the impact of this interaction on TCP fairness. We find significant unfairness among TCP flows that share the OBS core network, i.e. one flow obtains higher throughput while any others with much lower throughputs. The cause is the phenomenon called "the bigger eats the smaller (BES)", in which a TCP flow with higher rate occasionally will "see" less burst contentions and increase its rate further, while a TCP with lower rate will see more burst contentions and decrease its rate continually. Discuss a simple model to explain BES and verify that a continuous sequence of bursts will enhance BES. Then observe that offset time will be a good choice to control TCP fairness by a curve of unfairness control with offset time adjustment. Finally an adaptive offset time scheduling (AOS) algorithm is proposed. AOS assigns burst offset time value adaptive to the rate of TCP flow. The simulation results show that the fairness can be significantly improved by our AOS scheme.

In nature, many species became extinct as they could not adapt quickly enough to their environment. They were simply not fit enough to adapt to more and more challenging circumstances. Similar things happen when algorithms are too static to cope with particular challenges of their "environment", be it the workload, the machine, or the user requirements. In this regard, in this paper we explore the well-researched and fascinating family of adaptive indexing algorithms. Classical adaptive indexes solely adapt the indexedness of the data to the workload. However, we will learn that so far we have overlooked a second higher level of adaptivity, namely the one of the indexing algorithm itself. We will coin this second level of adaptivity meta-adaptivity. Based on a careful experimental analysis, we will develop an adaptive index, which realizes meta-adaptivity by (1) generalizing the way reorganization is performed, (2) reacting to the evolving indexedness and varying reorganization effort, and (3) defusing skewed distributions in the input data. As we will demonstrate, this allows us to emulate the characteristics of a large set of specialized adaptive indexing algorithms. In an extensive experimental study we will show that our meta-adaptive index is extremely fit in a variety of environments and outperforms a large amount of specialized adaptive indexes under various query access patterns and key distributions.

The Multiple travelling salesman problem (MTSP) is a typical computationally complex combinatorial optimization problem, which is an extension of the famous travelling salesman problem (TSP) classified as NP-Hard. The MTSP can be generalized to a wide variety of routing and scheduling problems. The paper proposes a novel metaheuristic called Elephant Herding Optimization (EHO) to solve the MTSP. The EHO algorithm is bio-inspired from the natural herding behavior of elephant groups, which has proved its efficiency in solving the classic TSP and continued optimization problems. So, to extend the application of this algorithm, The paper makes the attempt to show how the EHO algorithm can be applied to the MTSP by testing some random benchmark instances of TSPLIB. The obtained results shows the performance of the proposed method.

This paper presents a new approach for an efficient decimator architecture for cascaded sigma-delta (/spl Sigma//spl Delta/) modulators. The special aspect of the presented structure is the effort to integrate the recombination logic for cascaded modulators in general and additionally a gain error correction for continuous time (CT) modulators in the first stage of the decimator. The design of an appropriate filter topology is derived, analyzed, and verified by simulations. The proposed structure is compared to a conventional implementation, thereby improving efficiency by decades.

Searching for information about a specific person is a frequent online activity. In most cases, users are aided in the search process by queries containing a name in Web search engines. Typically, Web search engines provide just a few accurate results associated with a name-containing query. Most existing solutions for suggesting synonyms in online search are based on pattern matching and phonetic encoding, however very often, the performance of such solutions is less than optimal. In this paper, we propose SpokenName2Vec, a novel and generic algorithm which addresses the synonym suggestion problem by utilizing automated speech generation, and deep learning to produce novel spoken name embeddings. These embeddings capture the way people pronounce names in a particular language and accent. Utilizing a name’s pronunciation can help detect names that sound alike, but are written differently. We demonstrated the proposed approach on a large-scale dataset with more than 250,000 forenames and surnames and evaluated it on two ground truth datasets containing 7400 forenames and 25,000 surnames (including their verified synonyms). The performance of SpokenName2Vec was found superior to the 10 other algorithms evaluated, including phonetic encoding, string similarity, and machine learning algorithms. The results obtained emphasize the potential of spoken name embeddings for improved synonym suggestion.

Vehicles may move at high speed in vehicular ad-hoc networks (VANETs) and the network topology changes arbitrarily as the nodes move. Vehicles may not find suitable resources because sharing information could quickly become obsolete or irrelevant resources. This paper proposes an efficient geo-aware Peer-to-Peer (P2P) search mechanism to enable resource discovery and sharing in VANETs. We propose discovery and sharing schemes based on the virtual backbone architecture, and then limit the data exchanging region to reduce unnecessary message transmissions. Because the resource node changes very fast, the coordinator must be set to maintain the local node of geographic information in the network. In this scheme, coordinators are established to store information and manage local resource information. In order to ensure dissemination performance, it must guarantee that vehicles possess enough resource pieces to allow efficient bartering. Furthermore, three kinds of piece selection mechanisms, including Nearest-First, Rarest-First, and In-Order are proposed to download resource pieces from other vehicles and to improve the dissemination efficiency. Experiment results demonstrate that our searching and sharing schemes are efficient and outperforms other schemes in terms of satisfactory ratio and download time.

For a purpose of improvement of project management, we should determine the success right or wrong of project objectively and quantitatively. On the other hand, a decision of success right or wrong of software development projects has been evaluated subjectively by a questionnaire for stakeholders concerned. Furthermore, the study of definition of project success right or wrong and quantitative decision techniques is not found. Recently, gathering of the attribute data about software development projects in Japan has been performed by the IPA/SEC (Software Engineering Centre of the Information-Technology Promotion Agency Japan). Therefore, we developed the diagnostic technique to judge the success right or wrong on a project by a discriminant analysis based on the actual data of IPA/SECs in the precedent study. Also, we tried to develop the diagnostic technique of success right or wrong of project based on the “Success degree estimation mode” in this study. In this paper, we would like to propose the result of comparison between the effectiveness of these diagnostic techniques of the project.

Traditional methods of image restoration use only one observed image for processing. In this paper, we propose methods for image restoration using several distorted images obtained with a microscanning imaging system. We assume that the observed images contain an original image degraded either by additive or by multiplicative interferences. Additionally, the images are corrupted with the additive noise of a receiver sensor. Using a set of observed images, image restoration is carried out by solving a system of equations that is derived from optimization of an objective function. Since the proposed restoration methods possess a high computational complexity, a fast algorithm is proposed. Computer simulation results obtained with the proposed methods are analyzed in terms of restoration accuracy, tolerance to the additive input noise, and robustness to sensors’position errors.

In my former Paper, I have given the changes which have happened in the situation of six double stars. When the causes of these observed changes in the double star Castor were investigated, I had recourse to the most authentic observations I could find, of the motions in right-ascension and polar distance of this star. But the Tables which have been lately published, in the last volume of the observations made by the Astronomer Royal at Greenwich, give us now the proper motions of 36 principal stars, of which α Geminorum is one; and, as the motion of this star, especially in north polar distance, is very different from what it has been supposed in my former examination, it will be necessary to review the arguments which have been used, in order to ascertain what will be the result of this new motion. We shall here again follow the order of the paragraphs of the former Paper, and denote those which treat of the same motions, with the same letters, that they may be readily compared.

The attention of physiologists has been very much with­ drawn, for the last half century, from the consideration of the different states of the circulating and secreted fluids, in conse­quence of the opinion that the nervous and fibrous or mus­cular systems can afford satisfactory interpretations of the phenomena of living beings; and on account of the disgust produced by the visionary properties and groundless hypo­theses, originating in the humoural doctrines of Galen. But late experiments have manifested, that various things taken into the stomach can be made at pleasure to produce consider­able effects, by impregnating sensibly the blood and urine, as well as the milk, sweat, and perhaps saliva. Further; the fine experiments of Professor Colman have shewn, that the conta­gious glanders may be excited in the ass by the transfusion of the blood of a glandered horse, and the matter from the nose of the glandered ass can produce this disease in the horse or the ass. Hence I apprehend it is reasonable to expect, that the farther investigation of the properties of the animal fluids will afford gratifying instruction to the Researcher in natural science, and important practical information to the Physician. On the present occasion, I desire the honour of communi­cating the knowledge I may have acquired, by investigating the properties of expectorated matter secreted by the bron­chial membrane. The appearances of this substance serve to regulate the judgment of the Physician concerning several dis­eases of the lungs; but especially of that of pulmonary tubercles which yearly destroys 120,000 to 140,000 subjects of the United Kingdom. It is fit that I remark, that I do not notice in this paper the ingenious experiments of several learned Chemists, because by so doing I should be led into a detail of too great extent for my design.

In this Paper I shall offer to the Society a continuation of those researches, the details of which have been already honoured with a place in their Transactions; and I trust, that the experiments and observations which I have to communicate, will be found to elucidate some important but obscure parts of chemical philosophy. In the last Paper, which I had the honour of presenting to this Body, I have given an account of a number of experiments made with a view of decomposing the fluoric acid: the most probable inference, from my results, was that the pure liquid fluoric acid consists of hydrogen united to a substance, which, from its strong powers of combination, has not as yet been procured in a separate form, but which is detached from hydrogen by metals, and which, in union with the basis of the boracic acid and silica, forms the fluo-boric and silica ted fluoric gases.

The observations of the satellites of the Georgian planet, of which an account is given in this paper, are of such a nature that, in order to judge of them properly, and to make them useful to those who would continue them, it will be necessary to enter into some particulars relating to the telescopic powers required for critically viewing such difficult objects. The great distance of the Georgian planet renders an attempt to investigate the movements of its satellites a very arduous undertaking; for their light, having to traverse a space of such vast extent before it can reach us, is so enfeebled, and their apparent diameter so diminished, that an instrument, to be prepared for viewing them, must be armed with the double power of magnifying and of penetrating into space.