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Many research projects and implementations related to QoS (Quality of Service) management technologies for providing stable network services have been undertaken. To perform QoS management, traffic classes are required. Generally, traffic is classified by setting priorities for packets, and the methods of setting those priorities include passive admission control in which clients set priorities and active admission control in which network edge routers check traffic and set priorities. To perform active admission control, identification information such as a TCP or UDP header must be inspected. However, with existing security protocols such as ESP (Encapsulating Security Payload) or SSH (Secure Shell), these kinds of information cannot be inspected because they are encrypted. Based on the point of view that active admission control should be performed to simplify QoS management of IP networks, the authors of this paper propose a security protocol named ESPQ (ESP considered QoS) for constructing active admission controllable packets and investigate the stability of that protocol. They also perform implementation experiments to show that existing control equipment such as routers can perform QoS management of ESPQ packets. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 87(2): 47–58, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.10215

Evolution of software often decreases desired properties like readability and maintainability of the evolved code. The process of refactoring aims at increasing the same desired properties by restructuring the code. New paradigms like AOP allow aspect-oriented refactorings as counterparts of object-oriented refactoring with the same aim. However, it is not obvious to the user, when to use which paradigm for achieving certain goals. In this paper we present an approach of code clone classification, which advises the developer when to use a respective refactoring technique or concept.

Traditional supervised keyphrase extraction models depend on the features of labeled keyphrases while prevailing unsupervised models mainly rely on global structure of the word graph, with nodes representing candidate words and edges/links capturing the co-occurrence between words. However, the local context information of the word graph can not be exploited in existing unsupervised graph-based keyphrase extraction methods and integrating different types of information into a unified model is relatively unexplored. In this paper, we propose a new word embedding model specially for keyphrase extraction task, which can capture local context information and incorporate them with other types of crucial information into the low-dimensional word vector to help better extract keyphrases. Experimental results show that our method consistently outperforms 7 state-of-the-art unsupervised methods on three real datasets in Computer Science area for keyphrase extraction.

Large-scale, multi-point, multimedia conferencing applications designed to facilitate long-distance collaboration are enjoying growing popularity. Usually composed of real-time audio, video and shared-drawing applications, these collaborative environments help render the geographical location of collaborators irrelevant. To complement these existing collaborative applications, it would be useful to have the ability to distribute documents synchronously over the World Wide Web (WWW). One model for synchronized information dissemination within the Web is webcasting in which data are simultaneously distributed to multiple destinations. The WWW's traditional unicast client/server communication model suffers, however, when applied to webcasting; solutions which require many clients to simultaneously fetch data from the origin server using the client/server model will likely cause server and link overload.A number of webcasting solutions have been proposed. Many have limited scalability because they are based on unicast while others use multicast for more scalable data delivery but require server modification or have rigid architectures. We believe that successful webcasting solutions will provide scalable, reliable delivery yet still be compatible with the existing Web infrastructure.In this paper we describe a webcast design that improves upon previous designs by leveraging application level framing (ALF) design methodology. We build upon the Scalable Reliable Multicast (SRM) framework, which is based upon ALF, to create a custom protocol to meet webcast's scalability needs. We employ the protocol in an architecture consisting of two reusable components: a webcache component and a browser control component. We have implemented our design using the MASH multimedia application toolkit and a SRM protocol module called libsrm. We present the results of a simple performance evaluation and report on lessons learned while using MASH and libsrm.

The Linear Mixing Model (LMM) of hyperspectral images asumes that pixel spectra are affine combinations of basic spectral signatures, called endmembers, which are the vertices of a convex polytope covering the image data. Endmember induction algorithms (EIA) extract the endmembers from the image data, obtaining a precise spectral characterization of the image. The WM algorithm assumes that a set of Affine Independent vectors can be extracted from the rows and columns of dual Lattice Autoassociative Memories (LAAM) built on the image spectra. Indeed, the set of endmembers induced by this algorithm defines a convex polytope covering the hyperspectral image data. However, the number of induced endmembers obtained by this procedure is too high for practical purposes, besides they are highly correlated. In this paper, we apply a greedy sparsification algorithm aiming to select the minimal set of endmembers that explains the data in the image. We report results on a well known benchmark image.

The Brazilian Government has adhered to the Linked Open Government Data publication policy. Thus, promoting a more transparent and open administration, allowing greater participation of society, the strengthening of democracy, and combating corruption. All of these matters can be affected by how open data is published. Beyond the data itself, data provenance allows aggregate metadata such as when, how, and why the data were created and published. Given this scenario, we consider that the combination of data and its provenance enriches the traceability of the data exposing the methods and agents involved in its creation. This paper presents a technological solution in the context of Linked Open Government Data to enhance the public open government data publishing. It is delivered employing an information architecture that can provide the data provenance of public open government data using the PROV-DM and a graph database. In addition, we also present an implementation of the proposed information architecture for a public open linked data as a case study.

This demo paper presents a new sensor focusing on the measurement of real time foot plantar pressure distribution. IEE S.A. releases a single insole-solution for daily usage in order to acquire information on the plantar load dispersion in a large range of shoe configurations. The new High-Dynamic Force-Sensing Resistor (HD-FSR) multi-cell smart insole sensor enables advanced real-time gait analysis applications.

This paper presents a novel shape descriptor to effectively and efficiently characterize the local image statistics. The proposed descriptor, termed contour covariance (CC), characterizes covariance features driven by a moving point on the shape contour at multiple scales. To calculate the covariance matrices, three basic features including texture, intensity and distance map, are extracted from the object image. Based on coefficients of the obtained covariance matrices, the proposed CC descriptor is compact yet informative, as well as invariant to rotation, translation and scale. The experimental results on two databases demonstrate the superiority and efficiency of the proposed method among the state-of-the-art methods for shape classification.

Hierarchical clustering is one of effective methods in creating a phylogenetic tree based on the distance matrix between DNA (deoxyribonucleic acid) sequences. One of the well-known methods to calculate the distance matrix is k-mer method. Generally, k-mer is more efficient than some distance matrix calculation techniques. The steps of k-mer method are started from creating k-mer sparse matrix, and followed by creating k-mer singular value vectors. The last step is computing the distance amongst vectors. In this paper, we analyze the sequences of MERS-CoV (Middle East Respiratory Syndrome - Coronavirus) DNA by implementing hierarchical clustering using k-mer sparse matrix in order to perform the phylogenetic analysis. Our results show that the ancestor of our MERS-CoV is coming from Egypt. Moreover, we found that the MERS-CoV infection that occurs in one country may not necessarily come from the same country of origin. This suggests that the process of MERS-CoV mutation might not only be influenced by geo...

A novel combination forecasting model is presented in this paper, which combines single ones based on machine learning. The model has been applied to the prediction of five cities' election in Taiwan with combining the exposure rate and the approval rate, which obtains good results. The exposure rate is the frequency of a candidate's appearances in the news and approval rate is the proportion of the positive information of a candidate. And the polarity of a review is predicted by sentiment classification based on machine learning techniques. A novel method of feature extraction is used in sentiment classification, which makes the classifier effectively assign the review a type of polarity. Meanwhile, this paper proposes a method of feature clustering and extending based on the synonym dictionary, which obviously reduces the dimension of feature vector and improve the F-score of sentiment classification.

The neural control system of a high speed monocular camera head for the tracking of real-world targets is presented in this paper. The tracking system consists of four subsystems: monocular camera head. adaptive image processing system for estimation of the momentary position of object, neural network predictor and PID-controller, controlling motors of the camera head. The designed neural network tracking system performs smooth pursuit of slow objects (50°) with a foveal error less than 0.7° and is able to track objects up to a maximum speed of 320° with foveal error less than 4.5°.

For the realization of future wireless networks, gigabit wireless technology which can achieve higher-than-1Gbps data transmission with extremely low transmit power is indispensable. We have been studying the distributed antenna network (DAN) and the frequency-domain wireless signal processing. In DAN, many antennas or clusters of antennas are spatially distributed over a service area and they are connected by means of optical fiber links with DAN signal processing center (SPC). A number of distributed antennas cooperatively serve mobile users using spatial multiplexing, diversity, array or relaying technique. In this paper, the recent research advances of gigabit DAN and its related wireless techniques are introduced.

In this paper, we present SoDa, an irradiance-based synthetic Solar Data generation tool to generate realistic sub-minute solar photovoltaic (PV) output power time series, that emulate the weather pattern for a certain geographical location. Our tool relies on the National Solar Radiation Database (NSRDB) to obtain irradiance and weather data patterns for the site. Irradiance is mapped onto a PV model estimate of a solar plant’s 30-min power output, based on the configuration of the panel. The working hypothesis to generate high-resolution (e.g. 1 second) solar data is that the conditional distribution of the time series of solar power output given the cloud density is the same for different locations. We therefore propose a stochastic model with a switching behavior due to different weather regimes as provided by the cloud type label in the NSRDB, and train our stochastic model parameters for the cloudy states on the high-resolution solar power measurements from a Phasor Measurement Unit (PMU). In the paper we introduce the stochastic model, and the methodology used for the training of its parameters. The numerical results show that our tool creates synthetic solar time series at high resolutions that are statistically representative of the measured solar power and illustrate how to make use of the tool to create synthetic data for arbitrary sites in the footprint covered by the NSRDB.

In analysing big data for finite population inference, it is critical to adjust for the selection bias in the big data. In this paper, we propose two methods of reducing the selection bias associated with the big data sample. The first method uses a version of inverse sampling by incorporating auxiliary information from external sources, and the second one borrows the idea of data integration by combining the big data sample with an independent probability sample. Two simulation studies show that the proposed methods are unbiased and have better coverage rates than their alternatives. In addition, the proposed methods are easy to implement in practice.

Analog memory offers enormous potential to speed up computation in deep learning. We study the use of phase-change memory (PCM) as the resistive element in a crossbar array that allows the multiply-accumulate operation in deep neural networks to be performed in-memory. With this promise comes several challenges, including this paper’s main focus: the impact of conductance drift on deep neural network accuracy. Here we offer an overview of our recent work, including explanations of popular neural network architectures, along with a technique to compensate for drift ("slope correction") to allow in-memory computing with PCM during inference to reach software-equivalent deep learning baselines for a broad variety of important neural network workloads.

In recent years, multi-level steganography has become a new research direction in the network steganography. In order to ensure transmitting the key and the ciphertext covertly in multi-level steganography, a solution of key transmission is proposed in this paper. The proposed solution consists of 2 methods. The upper-level method is used to transmit the ciphertext. The lower-level is used to deliver the key covertly. The experimental results showed that this solution can realize synchronous transmission of the ciphertext and the key, which has a high steganographic bandwidth and a low steganographic cost, improving the security of network steganography.

An application of watermarking for automatic music monitoring of radio broadcasts is discussed. By embedding information into the music as a watermark before broadcasting it, it is possible to keep track of what music has been on the air at what time, and for how long. However, to effectively implement this application, the handling of content transitions is important, because the detection reliability deteriorates at the content boundaries. In this paper, a method of detecting content boundaries using overlapping detection windows is described. The most probable pattern of content transition is selected under the condition that detection results from multiple windows are available. The derived rules are represented using a finite state model, which is useful for detection in real time. Experimental results on FM radio broadcasts are also presented.

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.

Offshore and outsourced distributed software development models and processes are facing challenges, previously unknown, with respect to computing capacity, bandwidth, storage, security, complexity, reliability, and business uncertainty. Clouds promise to address these challenges by adopting recent advances in virtualization, parallel and distributed systems, utility computing, and software services. In this paper, we envision a cloud-based platform that addresses some of these core problems. We outline a generic cloud architecture, its design and our first implementation results for three cloud forms - a compute cloud, a storage cloud and a cloud-based software service- in the context of global distributed software development (GSD). Our ”compute cloud” provides computational services such as continuous code integration and a compile server farm, ”storage cloud” offers storage (block or file-based) services with an on-line virtual storage service, whereas the on-line virtual labs represent a useful cloud service. We note some of the use cases for clouds in GSD, the lessons learned with our prototypes and identify challenges that must be conquered before realizing the full business benefits. We believe that in the future, software practitioners will focus more on these cloud computing platforms and see clouds as a means to supporting a ecosystem of clients, developers and other key stakeholders.

MASON is a widely-used open-source agent-based simulation toolkit that has been in constant development since 2002. MASON’s architecture was cutting-edge for its time, but advances in computer technology now offer new opportunities for the ABM community to scale models and apply new modeling techniques. We are extending MASON to provide these opportunities in response to community feedback. In this paper we discuss MASON, its history and design, and how we plan to improve and extend it over the next several years. Based on user feedback will add distributed simulation, distributed GIS, optimization and sensitivity analysis tools, external language and development environment support, statistics facilities, collaborative archives, and educational tools.

The upsurge of comprehensive automation and digitization in the energy industry is accelerating, while making the energy business management more efficient, it also brings new hidden dangers to network security. The blockchain-based network security mechanism has the characteristics of decentralization, non-tampering, traceability, high credibility and multi-party consensus, and will become an important technical means to improve network security. Aiming at the main network security problems faced by the energy industry, this paper proposes a security protection solution based on blockchain technology from the aspects of identity authentication, data protection and security operation and maintenance, which provides a new technical idea for improving the network security protection ability of the energy industry.

The problem of error detection and correction (both transient and permanent) in linear digital state variable systems, a very large class of circuits used in digital signal processing and control, is considered. The case of single faulty modules (adders, multipliers, shifters, etc.) is studied, and general circuit data flow graphs (with and without fanout) that realize linear digital state variable systems are analyzed to determine how additional system states might be added to the data flow graph to achieve error detection and correction. It is seen that error detection and correction can be achieved by the addition of a relatively small amount of additional hardware which functions as the checking circuitry. Next, error detection under multiple faulty modules with and without fanout of the module outputs is studied. An analysis tool called the gain matrix is introduced. The problem of fault location and correction of single faults is discussed. Recursive as well as nonrecursive systems can be handled. >

Indoor localization has become the focus of several research studies in recent years. It is very prominent in a wide range of services, such as location-based services. The indoor localization system is a part of the Internet of Thing (IoT) framework, which provides real-time tracking and monitoring solution. This research study aims to provide an update on the recently proposed localization systems. In this paper, localization techniques and emerging technologies are discussed and highlighted their strength and weakness. Different proposed systems are discussed and evaluated based on accuracy, robustness, scalability, complexity, and cost. This survey provides the researcher’s guideline and an excellent platform to further their research in indoor localization.

The functional model is one of the key development foundations to understand, anticipate and reflect products and technical systems, since the interplay of functions directly reflect the intended and their expected behaviors. Furthermore, with the help of functions, it is possible to implicitly describe a significant portion of the characteristics of the product under development. The development of a functional structure requires significant knowledge and experience; often engineers have a difficult task to describe the functionality of a product in a solution neutral form. During such development phases, questions arise about the possibility of exchanging functions between products, which is a nontrivial task. To explore the nature of exchanging functions, the authors conducted research about functional flows of two complex mechatronic products. Functional flows have shown the ability to establish relationships between functions of two products. The analysis reveals aspects of functional modeling in terms of the hierarchy of functions and their interrelationships. The paper considers an approach to build a common functional model, determining the structure of functional decomposition and determining the functional flow of a product. A clear decomposition and structure of product functions show the possibility of establishing links between products, in order to exchange functions. The constructed concept of the common functional model demonstrates its theoretical and practical use as a framework for building the individual functionality of a new product. This paper concludes with an outlook to future steps and directions to explore the potential for function exchanging.

The Data Warehouse projects confront different communities of practice to create one body of knowledge and help increase companies' competitive advantage. This paper gives an overview of communication problems on boundaries between participating communities. We have focused on the potential relation between the early creation of language communities of the involved communities and lowering data warehouse projects’ development costs. We provide theoretical foundations for ontology-based data warehouse development scheme proposed in this paper.

In this paper, we propose to tackle egocentric action recognition by suppressing background distractors and enhancing action-relevant interactions. The existing approaches usually utilize two independent branches to recognize egocentric actions, i.e., a verb branch and a noun branch. However, the mechanism to suppress distracting objects and exploit local human-object correlations is missing. To this end, we introduce two extra sources of information, i.e., the candidate objects' spatial location and their discriminative features, to enable concentration on the occurring interactions. We design a Symbiotic Attention withObject-centric featureAlignmentframework (SAOA) to provide meticulous reasoning between the actor and the environment. First, we introduce an object-centric feature alignment method to inject the local object features to the verb branch and noun branch. Second, we propose a symbiotic attention mechanism to encourage the mutual interaction between the two branches and select the most action-relevant candidates for classification. The framework benefits from the communication among the verb branch, the noun branch, and the local object information. Experiments based on different backbones and modalities demonstrate the effectiveness of our method. Notably, our framework achieves the state-of-the-art on the largest egocentric video dataset.

Recent years have witnessed a tremendous increasing of first-person videos captured by wearable devices. Such videos record information from different perspectives than the traditional third-person view, and thus show a wide range of potential usages. However, techniques for analyzing videos from different views can be fundamentally different, not to mention co-analyzing on both views to explore the shared information. In this paper, we take the challenge of cross-view video co-analysis and deliver a novel learning-based method. At the core of our method is the notion of "joint attention", indicating the shared attention regions that link the corresponding views, and eventually guide the shared representation learning across views. To this end, we propose a multi-branch deep network, which extracts cross-view joint attention and shared representation from static frames with spatial constraints, in a self-supervised and simultaneous manner. In addition, by incorporating the temporal transition model of the joint attention, we obtain spatial-temporal joint attention that can robustly capture the essential information extending through time. Our method outperforms the state-of-the-art on the standard cross-view video matching tasks on public datasets. Furthermore, we demonstrate how the learnt joint information can benefit various applications through a set of qualitative and quantitative experiments.

The popularity of egocentric cameras and their always-on nature has lead to the abundance of day long first-person videos. The highly redundant nature of these videos and extreme camera-shakes make them difficult to watch from beginning to end. These videos require efficient summarization tools for consumption. However, traditional summarization techniques developed for static surveillance videos or highly curated sports videos and movies are either not suitable or simply do not scale for such hours long videos in the wild. On the other hand, specialized summarization techniques developed for egocentric videos limit their focus to important objects and people. This paper presents a novel unsupervised reinforcement learning framework to summarize egocentric videos both in terms of length and the content. The proposed framework facilitates incorporating various prior preferences such as faces, places, or scene diversity and interactive user choice in terms of including or excluding the particular type of content. This approach can also be adapted to generate summaries of various lengths, making it possible to view even 1-minute summaries of one's entire day. When using the facial saliency-based reward, we show that our approach generates summaries focusing on social interactions, similar to the current state-of-the-art (SOTA). The quantitative comparisons on the benchmark Disney dataset show that our method achieves significant improvement in Relaxed F-Score (RFS) (29.60 compared to 19.21 from SOTA), BLEU score (0.68 compared to 0.67 from SOTA), Average Human Ranking (AHR), and unique events covered. Finally, we show that our technique can be applied to summarize traditional, short, hand-held videos as well, where we improve the SOTA F-score on benchmark SumMe and TVSum datasets from 41.4 to 46.40 and 57.6 to 58.3 respectively. We also provide a Pytorch implementation and a web demo at https://pravin74.github.io/Int-sum/index.html.

Dynamic tracking of sparse targets has been one of the important topics in array signal processing. Recently, compressed sensing (CS) approaches have been extensively investigated as a new tool for this problem using partial support information obtained by exploiting temporal redundancy. However, most of these approaches are formulated under single measurement vector compressed sensing (SMV-CS) framework, where the performance guarantees are only in a probabilistic manner. The main contribution of this paper is to allow deterministic tracking of time varying supports with multiple measurement vectors (MMV) by exploiting multi-sensor diversity. In particular, we show that a novel compressive MUSIC (CS-MUSIC) algorithm with optimized partial support selection not only allows removal of inaccurate portion of previous support estimation but also enables addition of newly emerged part of unknown support. Numerical results confirm the theory.

Asynchronous transfer mode (ATM) traffic emulators have been designed for demonstrating the performance of various protocols and services in ATM networks under user-defined traffic conditions. Analytical tools and traffic measurements show that specific traffic conditions can be emulated by using the superposition of various stochastic processes. This paper presents a generic method for implementing stochastic processes using dedicated hardware and describes how this method has been used for implementing a real-time traffic emulator of ATM virtual channels.

The purpose of this paper is threefold: first to look at all the classifications of capacity, second to define and analyze subjectively a particular type of capacity to be called "psychometric capacity" and third, to quantify this new concept of capacity in order that it can be measured and analyzed objectively. Capacity is classified in a matrix form, first by its micro and macro aspects and then by type; statutory, optimum and psychometric. Psychometric capacity is that amount of risk to be measured in terms of premium dollars written which an underwriter is willing to take in exchange for the return which he expects to earn from the premium. To measure psychometric capacity, an equation, the hyperbolic tangent function, is used. This function has characteristics which are similar to the utility curve of an individual who is averse to risk. The inputs for this function are obtained from three simple questions rather than a series of complicated questions as in the case of a utility index. The primary use of psychometric capacity is to devise a supply curve for an insurance company so that its operation can be simulated for planning and decision making purposes. In recent years many people associated with the insurance industry have become increasingly concerned with the "capacity problem." Now that profits for the industry are up, this problem has been downgraded in importance. However, several important aspects of this situation remain unsolved. This includes the availability of convenient workable definitions and standard measures for all types of capacity. The purpose of this paper is threefold: first to look at all the classifications of capacity, second to define and analyze subjectively a particular type of capacity to be called "psychometric capacity" and third, to quantify this new concept of capacity in order that it can be measured and analyzed objectively. In order to accomplish the first objective, it is convenient to express capacity in a matrix form. This allows the relationship between various classifications of capacity to come into a clear perspective. Robert A. Hershbarger, Ph.D., is Associate Professor of Finance in the University of Missouri-Columbia. From 1959-1966 he was Assistant Supervisor of Insurance at the University of Illinois and from 1966-1969 was Coordinator of Insurance at Northern Illinois University. Before going to his present position, Dr. Hershbarger was Assistant Professor of Finance and Insurance in the University of Rhode Island. This paper was presented at the 1973 Annual Meeting of ARIA. (51 ) This content downloaded from 157.55.39.136 on Thu, 19 May 2016 04:27:46 UTC All use subject to http://about.jstor.org/terms 52 The Journal of Risk and Insurance To facilitate this manner of presentation, capacity is first subdivided between its micro and macro aspects. Then the micro subdivision is further broken down into the capacity available for (1) single exposure unit, (2) the line of insurance and (3) the insurer. The macro side is divided between the industry's capacity and alien reinsurance. The types of capacity considered are psychometric, optimum and statutory. These are described in detail in the following sections. Matrix The interrelationships between all the components of capacity become clearer when put in matrix form.' The column headings outline underwriting capacity from the systems point of view, building from the basic exposure unit up to and including alien reinsurance. Figure 1 Capacity Matrix for the Insurance Industry

This paper will show how turbulence plays a leading role in the design of space launching systems. It presents several areas for which turbulent flows represent critical issues either for the reliability of the systems or for their operational performance. Turbulence is a leading physical phenomenon involved in both the external aerodynamics of space vehicles as well as in the operation of their propulsive systems. Physical issues concern, for instance, unsteady low-velocity and large-scale atmospheric aerodynamics, subsonic, trans-sonic and supersonic vehicle aerodynamics, re-entry aerothermodynamics, single- and multiple-phase mixing and combustion, aerodynamics and hydraulics of turbomachinery. The mastering of these complex phenomena is a critical challenge for the space industry if it is to propose, in the future, reliable and high-performance systems with ambitious low-cost objectives. This article was chosen from Selected Proceedings of the 1st French Seminar on Turbulence and Space Launchers (CNES...

Multihoming, the connection of a stub network through multiple Internet Service Providers (ISPs) to the Internet, has broadly been employed by enterprise networks as a sort of redundancy technique to augment the availability and reliability of their Internet access. Recently, with the emergence of Intelligent Route Control (IRC) products, IRC-capable multihomed networks dynamically select which ISPs' link to use for different destinations in their traffic in a smart way to bypass congested or long paths as well as Internet outages. This dynamic traffic switch between upstream ISPs is mostly driven by regular measurement of performance metrics such as delay, loss ratio, and available bandwidth of existing upstream paths. However, since IRC systems are commercial products, details of their technical implementation are not available yet. Having the incentive to delve into these systems deeply, in this paper, we employ traditional ant colony optimization (ACO) paradigm to study IRC systems in that domain. Specifically, we are interested in two major questions. Firstly, how much effectively does an ant based IRC system switch between upstream links in comparison to a commercial IRC system? Secondly, what are the realistic underlying performance metrics by which ants pick the path to a food source (destination network) in a multihomed colony? Through extensive simulations under different traffic load and link reliability scenarios, we observe that ants perform well in switching between available egress links. Moreover, delay of paths is not the only criterion by which ants select the path; instead, through their intuitive ACO paradigm, they tend to choose the path with a better performance in terms of both delay and loss ratio.

As a result of the increased switching complexity of many communications satellites, often transponders are available with a variety of configuration options offering flexibility in power, bandwidth, and coverage. Many of these configurations share resources that prevent them from being simultaneously active, making it a challenge to understand and manage the satellite capacity inventory. In this paper we define the logical channel configuration (LCC) as an unambiguous unit of the inventory and present a novel method of modelling the interdependencies between these LCCs using linear algebra. In particular, a projector matrix is defined which can be used to update the activation state of all LCCs when the state of one or more of these is changed. As a result, a number of valid LCC scenarios can be easily generated and evaluated for optimal use of the satellite resources. Copyright © 2005 John Wiley & Sons, Ltd.

An essential component of today's embedded system is an instruction-set processor running real-time software. All variations of these core components contain at least the minimum data-flow processing capabilities, while a certain class contain specialized units for highly data-intensive operations for Digital Signal Processing (DSP). For the required level of memory interaction, the parallel executing Address Calculation Unit (ACU) is often used to tune the architecture to the memory access characteristics of the application. The design of the ACU is performance critical. In today's typical design flow, this design task is somewhat driven by intuition as the transformation from application algorithm to architecture is complex and the exploration space is immense. Automatic utilities to aid the designer are essential; however, the key compilation techniques which map high-level language constructs onto addressing units have lagged far behind the emergence of these units. This paper presents a new retargetable approach and prototype tool for the analysis of array references and traversals for efficient use of ACUs. In addition to being an enhancement to existing compiler systems, the ArrSyn utility may be used as an aid to architecture exploration. A simple specification of the addressing resources and basic operations drives the available transformations and allows the designer to quickly evaluate the effects on speed and code size of his/her algorithm. Thus, the designer can tune the design of the ACU toward the application constraints. ArrSyn has been successfully used together with a C compiler developed for a VLIW architecture for an MPEG audio decoding application. The combination of these methods with the C compiler showed on average a 39% speedup and 29% code size reduction for a representative set of DSP benchmarks.

With the increasingly integration of distributed generator (DG) in active distribution network (ADN), the fault location problem is facing more challenges. This paper proposes a hierarchical fault location model for ADN, in which the upper-layer model is to determine the area location of the fault and the lower-layer model is to get the section location of the fault. To solve the hierarchical fault location model in an efficient way, a hybrid solving strategy combing the genetic algorithm and the integer linear programming method is applied. Case studies are carried out on a modified IEEE 33-bus distribution network, in which the DGs are of high penetration. The results show that when a single or multiple fault occurs in the ADN, the hierarchical fault location method can detect the fault location more accurately than the traditional single-layer fault location method. And the results also indicate that the model and algorithm used in this paper perform significantly better than the single model and single algorithm in terms of convergence speed, accuracy, and fault tolerance.

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.

During a user's transactions over the Internet, there are risks of unauthorised third party transactions carried out on the user's account, using the man-in-the-middle (MITM) attack and Trojan horse. In this paper, we propose a new interactive input method of sensitive information such as credit-card numbers and account numbers against these attacks. The proposing method relatively decides the input value that a user inputs using GUI with two or more cursors which move to different directions simultaneously. The user inputs the information based on a shared secret between the user and a server beforehand, and moves one of the cursors from the shared secret to the input value, and the server changes the cursors' position and asks a question about the value which the users cursors points at. The server can decide the user's input value though the response for the question. This method is strong and does not give any hint about which cursor is used t to the attacker unless both the user and the server expose the shared value and the input value.

This paper presents a general and systematic approach for geometric error modeling of machine tools due to the geometric errors arising from manufacturing and assembly. The approach can be implemented in three steps: (1) development of a linear map between the pose error twist and source errors within machine tool kinematic chains using homogeneous transformation matrix method; (2) formulation of a linear map between the pose error twist and the error intensities of a machine tool; (3) combination of these two models for error separation. The merit of this approach lies in that it enables the source errors affecting the compensatable and uncompensatable pose accuracy of the machine tool to be explicitly separated, thereby providing designers and/or field engineers with an informative guideline for the accuracy improvement by suitable measures, i.e. component tolerancing in design, manufacturing and assembly processes, and error compensation. Two typical multi-axis machine tools are taken as examples to illustrate the generality and effectiveness of this approach.

Today is the era of intelligence in machines. Through Artificial Intelligence, machines have started to mimic various human traits today. One instance of this is the artificial intelligence conversational entities, also called chat bots, which are computer programs capable to carry out near-natural conversation with people. Chatbots are important because they are one of the most prolific examples of Human Computer Interaction (HCI). These programs are mainly serving as personal assistants these days. We programmed two working chat bots using two different programming languages, C++ and AIML, to study their construction and design practices thoroughly and to devise the further possible improvements in such kind of programs. The paper focuses a discussion about Artificial Intelligence via chat bots, their framework design, capabilities, utilization and future scope.

Renewable energy power prediction is crucial to economic dispatch and reliable operation of power systems. This paper proposes a wind power forecasting approach based on the Extreme Gradient Boosting (XGBoost) algorithm. XGBoost is not only an effective feature selection method but also an accurate forecasting approach. In order to avoid excessive manual interventions for hyperparameter tuning, the Tree-Structured Parzen Estimator (TPE) model is presented to optimize the hyperparameters of XGBoost. This forecasting strategy has been tested in a real wind farm in Spain, compared with Persistence and Support Vector Regression (SVR). The results show that the XGBoost algorithm has higher accuracy and is a novel effective approach for very short-term wind power prediction.

This paper describes adoption of Domain Driven Design and Feature Driven Development paradigms for decision support systems, using credit risk evaluation DSS as a case study. Possible development scenarios using both of these methodologies are discussed, using transformations from previously described development framework. It is concluded that these techniques might be adopted for development of complex DSS.

Single chip processor performance has improved dramatically since the inception of the four-bit microprocessor in 1971. This is due in part to technological advances, (i.e., faster devices and greater device density), but also because of the adoption of architectural approaches well suited to the opportunities and limitations of VLSI. The most appropriate are those that effectively reduce off-chip memory accesses and admit to a regular pipelined implementation. The over-riding goal of pipelining is to achieve “single cycle execution”, i.e., instructions appear to execute in a single processor cycle. Today’s RISC processors are close to realizing this goal, and the next generation will reduce the cycles per instruction even further. In this paper, we will review the design issues and the proposed architectures for high performance VLSI processors.

In this paper, a robotic user interface (RUI) as an information management system is described. There have been numerous studies regarding 2D- or 3D-based GUIs that attempt to overcome the disadvantages of a hierarchal structure in information handling. Here, the GUI system is extended to RUI in a technique that enhances its capacity to store and recall by exploiting a spatial/functional association between semantic categories and body parts. A robot resembling a bear was implemented for the evaluation of this association. Experimental results revealed that information can be clustered on a specific body part with statistical significance. Information storage and retrieval becomes easier because there is no need for maintenance or memorization of a location in which specific information is stored. In addition, the fact that a robotic interface can be used in parallel with an existing desktop-based system enhances work efficiency.

An investigation into an aerodynamic optimization benchmark case that exhibits multimodality is presented using a global optimization approach. The recently suggested case 6 of the AIAA Aerodynamic Design Optimization Discussion Group involves the drag minimization of a rectangular NACA0012 wing subject to lift and root bending moment, as well as other geometric constraints. In this paper, optimization of that case using a state-of-the-art constrained population-based global optimization framework is presented. Shape control is achieved via a hierarchical application of the radial basis function domainelement approach. Three different fidelity of optimization cases are performed to investigate the multimodality of various aspects of the full problem, with four independent runs of each case being performed. When considering chord optimization, the optimizer converges to two independently identifiable minima with very similar performance. When adding dihedral and sweep variation, further multimodality is introduced, though this multimodality is reasonably well defined, with minima including forward and rearward sweep, and an upward and downward winglet. Introducing thickness variables leads to a highly multimodal problem due to the coupling between the chord and thickness variables. Thetheoretical minimum drag for this case is 24.7 counts, which a number of the runs get close to achieving.

There are thousands of old black and white movies that are the cultural heritage of nations. These films are quite often seriously degraded. This is a problem of significant importance especially in Poland, where most of cinematic heritage was damaged during and after World War II. There is a wide spectrum of defects of different kinds and various complexity, which is a serious challenge for image processing scientists. In this paper a systematic methodology for solving these difficult problems is proposed. It contains an analysis of most common defects and introduces their taxonomy. The most important part of the work is devoted to the detection and removal of degradations. For this purpose different tools of image processing are applied, especially based on mathematical morphology. Considering the diversity and complexity of the defects one can easily observe that there is no uniform methodology that could be successfully applied to all degradation types. Unfortunately it does not seem to be possible to detect and remove all of them completely automatically. Therefore the whole system for semi-automatic treatment (with limited human interaction) is proposed.

2.5-D integration is a promising technique to significantly reduce the interconnection delay and thus bring advancement to VLSI technology. New CAD tools and approaches are desired by 2.5-D IC circuit design. This paper designed a novel representation multi-layer-BSG and proposed an effective algorithm based on this structure. Quick strategy for solution evaluation and phrase-based strategy for simulated annealing engine are used to reduce the runtime complexity and to improve the performance. Compared with other representations, our algorithm are experimentally proved to be a better choice for 2.5-D floorplanning problem.

As mobile devices are becoming more ubiquitous, it becomes important to continuously verify the identity of the user during all interactions rather than just at login time. This paper investigates the effectiveness of methods for fully-automatic face recognition in solving the Active Authentication (AA) problem for smartphones. We report the results of face authentication using videos recorded by the front camera. The videos were acquired while the users were performing a number of tasks under three different ambient conditions to capture the type of variations caused by the 'mobility' of the devices. An inspection of these videos reveal a combination of favorable and challenging properties unique to smartphone face videos. In addition to variations caused by the mobility of the device, other challenges in the dataset include occlusion, occasional pose changes, blur and face/fiducial points localization errors. We evaluate still image and image set-based authentication algorithms using intensity features extracted around fiducial points. The recognition rates drop dramatically when enrollment and test videos come from different sessions. We will make the dataset and the computed features publicly available1 to help the design of algorithms that are more robust to variations due to factors mentioned above.

The main contributions of this paper are twofold. On the one hand, the twin Diffie-Hellman (twin DH) problem proposed by Cash, Kiltz and Shoup is extended to the n-Diffie-Hellman (n-DH) problem for an arbitrary integer n, and this new problem is shown to be at least as hard as the ordinary DH problem. Like the twin DH problem, the n-DH problem remains hard even in the presence of a decision oracle that recognizes solution to the problem. On the other hand, observe that the double-size key in the Cash et al. twin DH based encryption scheme can be replaced by two separated keys each for one entity, that results in a 2-party encryption scheme which holds the same security feature as the original scheme but removes the key redundancy. This idea is further extended to an n-party case, which is also known as n-out-of-n encryption. As examples, a variant of ElGamal encryption and a variant of Boneh-Franklin IBE have been presented; both of them have proved to be CCA secure under the computational DH assumption and the computational bilinear Diffie-Hellman (BDH) assumption respectively, in the random oracle model. The two schemes are efficient, due partially to the size of their ciphertext, which is independent to the value n.

Similar documents arise in software and business domains. Examples are user guides for different versions of a software product, contracts between vendors and clients, or legal documents. The usual practice is to capture common document formats and contents in templates that must be manually customized to a new context — often a slow, tedious, and error-prone process. We propose a method based on a proven approach developed for software reuse that simplifies and automates routine tasks involved in creating and updating families of similar documents. Our Document Management Environment (DME) provides functions to create templates capable of higher levels of document contents reuse than templates supported by word processors such as MS Word. DME allows users to designate any arbitrary document part as a template's variation point that can be customized to produce a specific document. DME automates document production by syncing inter-dependent customizations occurring at different variation points. The paper describes two “proof of concept” implementations of DME as Word add-in: The first one uses Content Control mechanism and is specific to MS Word. The second one is based on ART (Adaptive Reuse Technique), a general text manipulation method and tool, and can be used to manage similar documents in any editor that provides an access to the internal representation of documents.