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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.

Design of analog and digital electronic circuits is an integral part of the electrical engineering curriculum. Software tools, which enable design, simulation, measuring and circuit analysis as well as PCB (Printed Circuit Board) design, are called EDA (Electronic Design Automation). The paper describes the introduction of EDA software in the first semester of the professional study of electrical engineering at the Zagreb University of Applied Sciences within course Personal Computers in Electrical Engineering. Examples of laboratory exercises related to electrical circuit diagrams, use of component libraries, work in standard laboratory environment (power supplies, function generators, voltmeters, ammeters, oscilloscopes, etc.), circuit analysis in a DC and AC mode as well as in time and frequency domain are described. Focus is on the optimal implementation of EDA tool NI Multisim and its integration with the existing learning management system Moodle as a platform for exercise learning and knowledge assessment.

Location-based services are quickly becoming immensely popular. In addition to services based on users' current location, many potential services rely on users' location history, or their spatial-temporal provenance. Malicious users may lie about their spatial-temporal provenance without a carefully designed security system for users to prove their past locations. In this paper, we present the Spatial-Temporal provenance Assurance with Mutual Proofs (STAMP) scheme. STAMP is designed for ad-hoc mobile users generating location proofs for each other in a distributed setting. However, it can easily accommodate trusted mobile users and wireless access points. STAMP ensures the integrity and non-transferability of the location proofs and protects users' privacy. A semi-trusted Certification Authority is used to distribute cryptographic keys as well as guard users against collusion by a light-weight entropy-based trust evaluation approach. Our prototype implementation on the Android platform shows that STAMP is low-cost in terms of computational and storage resources. Extensive simulation experiments show that our entropy-based trust model is able to achieve high $(>0.9)$ collusion detection accuracy.

Current component interfaces, such as those defined by CORBA, are devoid of additional constraints that make helps in the task of composing software, therefore the dependent relations and interaction protocols among components are hidden within the implementations. The "implicity" reduces the composability and makes it difficult to reuse, validate and manage components. This paper presents a message based architectural model and an XML-message based Architecture Description Language (XADL). XADL promotes the description of dependent relations from the implementation level to the architectural level and enhances the interface specification by adding the sequencing, behavior and quality constraints. Furthermore, we formulate the notion of architectural mismatch and propose a checking algorithm to prevent systems from potential mismatches. The use of XML facilitates not only the specification descriptions but also the system implementations, and increases the openness of the whole system as well.

In this study a multi-objective problem considering uncertainty and flexibility of job sequence in an automated flexible job shop (AFJS) is considered using manufacturing simulation. The AFJS production system is considered as a complex problem due to automatic elements requiring planning and optimization. Several solution approaches are proposed lately in different categories of meta-heuristics, combinatorial optimization and mathematically originated methods. This paper provides the metamodel using simulation optimization approach based on multi-objective efficiency. The proposed metamodel includes different general techniques and swarm intelligent technique to reach the optimum solution of uncertain resource assignment and job sequences in an AFJS. In order to show the efficiency and productivity of the proposed approach, various experimental scenarios are considered. Results show the optimal resources assignment and optimal job sequence which cause efficiency and productivity maximization. The makespan, number of late jobs, total flow time and total weighted flow time minimization have been resulted in an automated flexible job shop too.

The increasing transistor scale integration poses, among others, the thermal-aware floorplanning problem consisting of how to place the hardware components in order to reduce overheating by dissipation. Because of the huge amount of feasible floorplans, most of the solutions found in the literature include an evolutionary algorithm for, either partially or completely, carrying out the task of floorplanning. Evolutionary algorithms usually have a bottleneck in the fitness evaluation. In the problem of thermal-aware floorplanning, the layout evaluation by the thermal model takes 99.5% of the computational time for the best floorplanning algorithm proposed so far. The contribution of this paper is to present a parallelization of this evaluation phase in a master-worker model to achieve a dramatic speed-up of the thermal-aware floorplanning process. Exhaustive experimentation was carried out over 3D integrated circuits, with 48 and 128 cores, outperforming previous published works. Copyright (c) 2012 John Wiley & Sons, Ltd.

Recently, Internet of Things (IoT) and multimedia are gaining popularity because of their usages in various applications. Numerous sensors and automated devices are generating huge volumes of data. Therefore, it is required to efficiently and effectively analyze this voluminous data. It can be achieved by using appropriate machine leaning techniques such as clustering. Among the clustering techniques, the k-means method/algorithm is one of the simplest, effective and commonly used methods. For making the cluster, it uses a measure of similarity/distance among the data observations. Nearby/similar data observations are placed within the same cluster whereas distant/dis-similar data observations are placed in other clusters. Hence, the similarity/distance metric plays a major role on the performance and accuracy of the k-means. Therefore, using an appropriate similarity/distance metric, the performance and accuracy of the k-means can be improved. K-means algorithm is majorly implemented using Euclidean distance metric. With the objective to explore the better and/or alternate similarity/distance metric(s) for k-means, a case study, based on empirical evaluation, of thirteen different similarity/distance metrics on six well-known datasets is performed and presented in this paper. By using the efficient and effective similarity / distance metrics, the performance and accuracy of the k-means algorithm can be improved which leads to formation of good clusters of various data observations or things or images etc. The results of the empirical study are analyzed and compared on the basis of widely used statistical clustering evaluation/validation measures. Based on the comparative results, these metrics are assigned with the ranks. Overall, the results demonstrate that Manhattan and Minkowski distance metrics gives better results for k-means algorithm.

Publisher's version (útgefin grein), This paper is motivated by the notion that coupling systems allows for mitigating the failure of individual ones. We present a novel approach to determining asymptotic stability and robustness of a network consisting of coupled dynamical systems, where individual system dynamics are represented through polynomial or rational functions. The analysis relies on a local analysis; thus, making it computationally implementable. We present an efficient computational method that relies on semidefinite programming. Importantly, for cases where multiple equilibrium points exist, we show how to determine regions around an asymptotically stable equilibrium point that bounds solutions. These regions increase when systems are coupled as we observe when applying the presented analysis framework to a mathematical model of a continuous stirred tank reactor. Importantly, the presented work has implications to other fields as well., "Peer Reviewed"

Intrinsic isometric shape matching has become the standard approach for pose invariant correspondence estimation among deformable shapes. Most existing approaches assume global consistency, i.e., the metric structure of the whole manifold must not change significantly. While global isometric matching is well understood, only a few heuristic solutions are known for partial matching. Partial matching is particularly important for robustness to topological noise (incomplete data and contacts), which is a common problem in real-world 3D scanner data. In this paper, we introduce a new approach to partial, intrinsic isometric matching. Our method is based on the observation that isometries are fully determined by purely local information: a map of a single point and its tangent space fixes an isometry for both global and the partial maps. From this idea, we develop a new representation for partial isometric maps based on equivalence classes of correspondences between pairs of points and their tangent spaces. From this, we derive a local propagation algorithm that find such mappings efficiently. In contrast to previous heuristics based on RANSAC or expectation maximization, our method is based on a simple and sound theoretical model and fully deterministic. We apply our approach to register partial point clouds and compare it to the state-of-the-art methods, where we obtain significant improvements over global methods for real-world data and stronger guarantees than previous heuristic partial matching algorithms., 17 pages, 12 figures

Mobile Ad-Hoc Networks (MANETs) are autonomous and decentralized wireless systems. Mobile Ad hoc Network is a collection of mobile nodes in which the wireless links are frequently broken down due to mobility and dynamic infrastructure. Routing is a significant issue and challenge in ad hoc networks. Many Routing protocols have been proposed so far to improve the routing performance and reliability. This research paper describes the characteristics of ad hoc routing protocols Ad-hoc On Demand Distance Vector Routing (AODV), Optimized link State Routing (OLSR), Temporally Ordered Routing Algorithm (TORA), Dynamic Source Routing (DSR), Destination-Sequenced Distance-Vector Routing (DSDV) based on the performance metrics like packet delivery fraction, Average delay, Normalized Routing load, Throughput and Jitter under low mobility and low traffic network as well as under high mobility and high traffic network. Results show that AODV has maximum throughput under low traffic and DSDV has maximum throughput under high traffic. As network becomes dense OLSR, DSR and DSDV perform well in terms of Throughput than AODV and TORA. TORA performs well in dense networks in terms of packet delivery fraction but at the same time Normalized Routing load of TORA is maximum among all the protocols in both the networks. DSDV has least Normalized Routing load in both low and high traffic. OLSR and DSDV give the least Jitter and Average Delay in both networks.

This paper presents high performance speaker identification and verification systems based on Gaussian mixture speaker models: robust, statistically based representations of speaker identity. The identification system is a maximum likelihood classifier and the verification system is a likelihood ratio hypothesis tester using background speaker normalization. The systems are evaluated on four publically available speech databases: TIMIT, NTIMIT, Switchboard and YOHO. The different levels of degradations and variabilities found in these databases allow the examination of system performance for different task domains. Constraints on the speech range from vocabulary-dependent to extemporaneous and speech quality varies from near-ideal, clean speech to noisy, telephone speech. Closed set identification accuracies on the 630 speaker TIMIT and NTIMIT databases were 99.5% and 60.7%, respectively. On a 113 speaker population from the Switchboard database the identification accuracy was 82.8%. Global threshold equal error rates of 0.24%, 7.19%, 5.15% and 0.51% were obtained in verification experiments on the TIMIT, NTIMIT, Switchboard and YOHO databases, respectively.

Cerebral aneurysm is a cerebrovascular disorder caused by a weakness in the wall of an artery or vein, which causes a localized dilation or ballooning of the blood vessel. It is life-threatening; hence, an early and accurate diagnosis would be a great aid to medical professionals in making the correct choice of treatment. HemeLB is a massively parallel Lattice-Boltzmann simulation software, which is designed to provide the radiologist with estimates of flow rates, pressures, and shear stresses throughout the relevant vascular structures, intended to eventually permit greater precision in the choice of therapeutic intervention. However, in order to allow surgeries and doctors to view and visualize the results in real-time at medical environments, a cost-efficient, practical platform is needed. In this article, we have developed and evaluated a version of HemeLB on various heterogeneous system-on-chip platforms, allowing users to run HemeLB on a low-cost embedded platform and to visualize the simulation results in real-time. A comprehensive evaluation of implementation on the Zynq SoC and Jetson TX1 embedded graphic processing unit platforms are reported. The achieved results show that the proposed Jetson TX1 implementation outperforms the Zynq implementation by a factor of 19 in terms of site updates per second. 2007-2012 IEEE. Manuscript received February 19, 2019; revised September 20, 2019; accepted November 4, 2019. Date of publication December 2, 2019; date of current version June 3, 2020. This paper was supported by the National Priorities Research Program (NPRP) under Grant 5-792-2-328 from the Qatar National Research Fund (a member of Qatar Foundation). X. Zhai is partially supported by the UK Engineering and Physical Sciences Research Council under Grants EP/R02572X/1 and EP/P017487/1. (Corresponding author: Xiaojun Zhai.) X. Zhai is with the School of Computer Science and Electronic Engineering, University of Essex, CO4 3SQ Colchester, U.K. (e-mail: xzhai@essex.ac.uk). Scopus

User stories are descriptions of functionality that a software user needs. They play an important role in determining which software requirements and bug fixes should be handled and in what order. Developers elicit user stories through meetings with customers. But user story elicitation is complex, and involves many passes to accommodate shifting and unclear customer needs. The result is that developers must take detailed notes during meetings or risk missing important information. Ideally, developers would be freed of the need to take notes themselves, and instead speak naturally with their customers. This paper is a step towards that ideal. We present a technique for automatically extracting information relevant to user stories from recorded conversations between customers and developers. We perform a qualitative study to demonstrate that user story information exists in these conversations in a sufficient quantity to extract automatically. From this, we found that roughly 10.2% of these conversations contained user story information. Then, we test our technique in a quantitative study to determine the degree to which our technique can extract user story information. In our experiment, our process obtained about 70.8% precision and 18.3% recall on the information.

As the success of deep models has led to their deployment in all areas of computer vision, it is increasingly important to understand how these representations work and what they are capturing. In this paper, we shed light on deep spatiotemporal representations by visualizing the internal representation of models that have been trained to recognize actions in video. We visualize multiple two-stream architectures to show that local detectors for appearance and motion objects arise to form distributed representations for recognizing human actions. Key observations include the following. First, cross-stream fusion enables the learning of true spatiotemporal features rather than simply separate appearance and motion features. Second, the networks can learn local representations that are highly class specific, but also generic representations that can serve a range of classes. Third, throughout the hierarchy of the network, features become more abstract and show increasing invariance to aspects of the data that are unimportant to desired distinctions (e.g. motion patterns across various speeds). Fourth, visualizations can be used not only to shed light on learned representations, but also to reveal idiosyncrasies of training data and to explain failure cases of the system.

Recent renewed interest in computational writer identification has resulted in an increased number of publications. In relation to historical musicology its application has so far been limited. One of the obstacles seems to be that the clarity of the images from the scans available for computational analysis is often not sufficient. In this paper, the use of the Hinge feature is proposed to avoid segmentation and staff-line removal for effective feature extraction from low quality scans. The use of an auto encoder in Hinge feature space is suggested as an alternative to staff-line removal by image processing, and their performance is compared. The result of the experiment shows an accuracy of 87 % for the dataset containing 84 writers’ samples, and superiority of our segmentation and staff-line removal free approach. Practical analysis on Bach’s autograph manuscript of the Well-Tempered Clavier II (Additional MS. 35021 in the British Library, London) is also presented and the extensive applicability of our approach is demonstrated.

Contour filling is one of the most common problems in image and graphics processing. The executing speed of the contour filling is vital, especially for a real-time system. This paper studies the advantages and disadvantages of the conventional filling algorithms, analyzes their original ideas, and proposes a new filling algorithm. The new algorithm presents a very simple idea for finding seeds automatically, and limits the scan within the filling regions. Repeated experiments have proven that with the new algorithm, any complex inner and outer contours can be filled with higher speed.

Power-line interference can severely distort electrocardiographs (ECGs). Several techniques have been proposed to eliminate the power-line interference without affecting the ECGs. One of the well-established techniques is the Adaptive Noise Canceler (ANC). In this paper we propose an improvement to the ANC by eliminating the need to sample an external power-line reference. The proposed method is shown to be comparable in performance to the ANC. It is therefore possible to implement the proposed canceler in software without the need to change the acquisition hardware system as was previously needed by the ANC.

SUMMARY UWB (Ultra Wide-Band) pulse radar is a promising candidate for surveillance systems designed to prevent crimes and terror-related activities. The high-speed SEABED (Shape Estimation Algorithm based on BST and Extraction of Directly scattered waves) imaging algorithm, is used in the application of UWB pulse radar in fields that require realtime operations. The SEABED algorithm assumes that omni-directional antennas are scanned to observe the scattered electric field in each location. However, for surveillance systems, antenna scanning is impractical because it restricts the setting places of the devices. In this paper, movement of a body is used to replace antenna scanning. The instantaneous velocity of any given motion is an unknown variable that changes as a function of time. A pair of antennas is used to analyze delay time to estimate the unknown motion. We propose a new algorithm to estimate the shape of a human body using data obtained from a human body passing stationary antennas.

Next Point-of-interest (POI) recommendation has been recognized as an important technique in location-based services, and existing methods aim to utilize sequential models to return meaningful recommendation results. But these models fail to fully consider the phenomenon of user interest drift, i.e. a user tends to have different preferences when she is in out-of-town areas, resulting in sub-optimal results accordingly. To achieve more accurate next POI recommendation for out-of-town users, an adaptive attentional deep neural model HOPE is proposed in this paper for modeling user’s out-of-town dynamic preferences precisely. Aside from hometown preferences of a user, it captures the long and short-term preferences of the user in out-of-town areas using “Asymmetric-SVD” and “TC-SeqRec” respectively. In addition, toward the data sparsity problem of out-of-town preference modeling, a region-based pattern discovery method is further adopted to capture all visitor’s crowd preferences of this area, enabling out-of-town preferences of cold start users to be captured reasonably. In addition, we adaptively fuse all above factors according to the contextual information by adaptive attention, which incorporates temporal gating to balance the importance of the long-term and short-term preferences in a reasonable and explainable way. At last, we evaluate the HOPE with baseline sequential models for POI recommendation on two real datasets, and the results demonstrate that our proposed solution outperforms the state-of-art models significantly.

Re documentation and design recovery are two important areas of reverse engineering. Detection of recurring organizations of classes and communicating objects, called Software Patterns, supports this process [1]. Many approaches to detect Software Patterns which have been published in the past years suffer from the problems of necessity of reference library, performance and language compatibility. This paper presents a model to solve those problems in software pattern detection. The proposed model solves the problem of necessity of reference library by detecting software patterns using Formal Concept Analysis (FCA). The proposed model solves the problem of performance by using the most efficient algorithm CMCG (Concept-Matrix based Concepts Generation) for the construction of concept lattice, which is the core data structure of FCA [3]. The proposed model solves the problem of language compatibility by using the language independent Meta model called MOOSE for taking the input information. The validity of this model was proved in theory and by experiment.

Unsupervised manifold learning has become accepted as an important tool for reducing dimensionality of a dataset by finding its meaningful low-dimensional representation lying on an unknown nonlinear subspace. Most manifold learning methods only embed an existing dataset but do not provide an explicit mapping function for novel out-of-sample data, thereby potentially resulting in an ineffective tool for classification purposes, particularly for iterative methods, such as active learning. To address this issue, out-of-sample extension methods have been introduced to generalize an existing embedding of new samples. In this paper, a novel out-of-sample method is introduced by utilizing high dimensional model representation (HDMR) as a nonlinear multivariate regression with the Tikhonov regularizer for unsupervised manifold learning algorithms. The proposed method was extensively analyzed using illustrative datasets sampled from known manifolds. Several experiments with 3D synthetic datasets and face recognition datasets were also conducted, and the performance of the proposed method was compared to several well-known out-of-sample methods. The results obtained with locally linear embedding (LLE), Laplacian Eigenmaps (LE), and t-distributed stochastic neighbor embedding (t-SNE) showed that the proposed method achieves competitive even better performance than the other out-of-sample methods.

SLAM (Simultaneous Localization and Mapping) of robot is the key to achieve autonomous control of robot, and also a significant topic in the field of mobile robotics. Aiming at 3D modeling of indoor complex environment, this paper presents a fast three-dimensional simultaneous location and mapping (SLAM) method for mobile robots. On the basis of RGB-D SLAM algorithm, the open-source software combining the RGB-D sensor like Kinect with the wheeled mobile robot is used to obtain the odometry data, and then the information of their location is matched through the image feature extraction and in the end the map is constructed. Finally, the feasibility and effectiveness of the proposed method are verified by experiments in indoor environment.

Region covariance matrices (RCMs) as feature descriptors have been developed due to the advantages of low dimensionality, being scale and illumination independent. How to define a feature mapping vector for the RCMs construction of strong discriminating ability is still an open issue. In this paper, there is a focus on finding a more efficient feature mapping vector for RCMs as palmprint descriptors based on Gabor magnitude and phase (GMP) information. Specially, Gabor magnitude (GM) features of each palmprint image approximate a lognormal distribution. For palmprint recognition, the logarithmic transformation of GM proves to be important for the discriminating ability of corresponding RCMs. All experiments are performed on the public Hong Kong Polytechnic University (PolyU) Palmprint Database of 7752 images. The results demonstrate the efficiency of our proposed method, and also show that adding pixel locations and intensity component to the feature mapping vector has a negative effect on palmprint recognition performance for our proposed Log_GMP based RCM method.

with the development of large scale wireless networks, there has been short comings and limitations in traditional network topology management systems. In this paper, an adaptive algorithm is proposed to maintain topology of hybrid wireless superstore network by considering the transactions and individual network load. The adaptations include to choose the best network connection for the response, and to perform network Connection switching when network situation changes. At the same time, in terms of the design for topology management systems, aiming at intelligence, real-time, the study makes a step-by-step argument and research on the overall topology management scheme. Architecture for the adaptive topology management of hybrid wireless networking resources is available to user’s mobile device. Simulation results describes that the new scheme has outperformed the original topology management and it is simpler than the original rate borrowing scheme.

This paper presents an approach to teaching SolidWorks, Matlab and Arduino using self-paced modules. Each module consists of self-guided lessons scheduled over a consecutive four-day period of 26-28 contact hours. The class takes place in a teaching computer laboratory with about 30 seats, with a basic Arduino electronics kit provided on each computer station. The instructor and teaching assistants provide brief demonstrations through the day and supports students individually as they complete lessons at their own pace. Student performance and feedback suggests this is an effective method for teaching SolidWorks, Matlab and Arduino.

Software Product Line (SPL) Engineering analyzes and applies various existing software modules from different systems in a domain for software mass customization. Feature models are often used to represent all of the product configurations in an SPL in terms of features. A main challenge in SPLs is how to select a good feature set to achieve customer requirements, subject to the constraints of feature model as well as the resource constraints. To solve it, this paper presents an approach to formalize the feature selection problem using 0-1 programming. Moreover we document the problem formalization in canonical form. Our approach makes it possible that most existing techniques for 0-1 programming can be used directly to deal with the feature selection problem in SPLs, which provides a new perspective to solve the problem and improve the efficiency.

Forming effective coalitions is a major research challenge in AI and multi-agent systems (MAS). Coalition Structure generation (CSG) involves partitioning a set of agents into coalitions so that social surplus (the sum of the rewards of all coalitions) is maximized. A partition is called a Coalition Structure (CS). In traditional works, the value of a coalition is given by a black box function called a characteristic function. In this paper, we propose a novel formalization of CSG, i.e., we assume the value of a characteristic function is given by an optimal solution of a distributed constraint optimization problem (DCOP) among the agents of a coalition. A DCOP is a popular approach for modeling cooperative agents, since it is quite general and can formalize various application problems in MAS. At first glance, one might assume that the computational costs required in this approach would be too expensive, since we need to solve an NP-hard problem just to obtain the value of a single coalition. To optimally solve a CSG, we might need to solve n-th power of 2 DCOP problem instances, where n is the number of agents. However, quite surprisingly, we show that an approximation algorithm, whose computational cost is about the same as solving just one DCOP, can find a CS with quality guarantees. More specifically, we develop an algorithm with parameter k that can find a CS whose social surplus is at least max(k/(w*+1), 2k/n) of the optimal CS, where w* is the tree width of a constraint graph. When k=1, the complexity of this algorithm is about the same as solving just one DCOP. These results illustrate that the locality of interactions among agents, which is explicitly modeled in the DCOP formalization, is quite useful in developing an efficient CSG algorithm with quality guarantees.

Wireless sensor networks usually adopt a tree structure for routing, where each node sends and forwards messages to its parent. However, lifetime maximization with tree routing structure is NP-hard, and all algorithms attempting to find the optimal solution run in exponential time unless $$P=\mathrm{NP}$$ . This paper revisits the problem of non-tree routing structure, where a node can send different messages to different neighbors. Though lifetime maximization with non-tree routing can be solved in polynomial time, the existing method transforms it into a series of maximum flow problems, which are either complicated or with high running time. This paper proposes an algorithm with O(mn) running time, where m is the number of edges and n is the number of nodes. The heart of the algorithm is a method to find a routing path from any node to the sink in O(m) time without disconnecting existing routing paths. The proposed algorithm is also suitable for distributed implementation. When a node fails, each influenced node can establish a new routing path in O(m) time. Simulations are conducted to compare the optimal lifetimes of tree structure and non-tree structure on random networks. The results verify the effectiveness of the proposed algorithm.

Network-based signature generation (NSG) has been proposed as a way to automatically and quickly generate accurate signatures for worms, especially polymorphic worms. In this paper, we propose a new NSG system-PolyTree, to defend against polymorphic worms. We observe that signatures from worms and their variants are relevant and a tree structure can properly reflect their familial resemblance. Hence, in contrast to an isolated view of generated signatures in previous approaches, PolyTree organizes signatures extracted from worm samples into a tree structure, called signature tree, based on the formally defined "more specific” relation of simplified regular expression signatures. PolyTree is composed of two components, signature tree generator and signature selector. The signature tree generator implements an incremental signature tree generation algorithm from worm sample clustering, up-to-date signature refinement to efficient tree construction. The incremental signature tree construction gives insight on how the worm variants evolve over time and allows signature refinement upon a new worm sample arrival. The signature selector chooses a set of signatures for worm detection from a benign traffic pool and the current signature tree constructed by the signature tree generator. Experiments show that PolyTree cannot only generate accurate signatures for polymorphic worms with noise, but these signatures are well organized in the signature tree to reflect the inherent relations of worms and their variants.

The paper provides an introduction into the area of inductive formation of knowledge bases. It presents traditional definitions of main problems in this area and highlights the current topical questions including the interpretability of the results. For solving of current problems in defined area the method of inductive formation of easily interpretable medical diagnostic knowledge bases is suggested. It includes the new definitions of classification and clustering problems for dependence models with parameters and the learning algorithm (solving mentioned problems in their new definitions) developed for the practically useful and easily interpretable mathematical dependence model with parameters which is a near real-life ontology of medical diagnostics (defined by a system of logical relationships with parameters). Also it includes the software package InForMedKB (INductive FORmation of MEDical Knowledge Bases) which implements above mentioned learning algorithm. InForMedKB allows to create training sets (consisting of clinical histories from various therapeutic areas) and to use them for inductive formation of medical diagnostic knowledge bases. These knowledge bases are presented in form accepted in the medical literature and contain descriptions of diseases (from specified therapeutic areas) as well as an explanation of these knowledge bases based on descriptions of clinical histories from used training sets. The formal representation of medical knowledge bases enables their usage for intelligent systems for medical diagnostics.

CERN, the European Laboratory for Particle Physics, is one of the largest scientific research laboratories in the world. Most of the research work carried out in the Laboratory is not only strongly dependent upon the large particle accelerators in operation, but it also relies heavily on the use of computer systems of which several hundreds, of various types, are presently installed. During the last 20 years, CERN has played a leading role as the focus for development of packages and software libraries to solve problems related to High Energy Physics (HEP). The results of the integration of resources from many different Laboratories can be expressed in several millions lines of code written at CERN during this period of time, used at CERN and distributed to collaborating Laboratories. Nowadays, this role of software developer is considered very important by the entire HEP community. This paper reports the development of a large application package. The paper is divided into two parts. Firstly, a large software package where graphics play a key role (PAW—Physics Analysis Workstation) is taken as a case study, and its origin and main characteristics are presented in detail. PAW is essentially an interactive system which includes many different software tools, strongly oriented towards data analysis and data presentation. Secondly, the criteria and rules to be obeyed for the development of a large package in the environment of a large international collaboration are outlined. Then, the development process of the package is described and some of the problems encountered and the solutions adopted are discussed.

Agent integrity verification and fault-tolerance are the two prevalent methods among the solutions to the Problem of Malicious Hosts in Mobile agent system. Agent integrity verification enables the owner of the agent to detect upon its return whether a visited host has maliciously altered the state of the agent based on agent integrity verification [6]. A known drawback of such method is that it cannot detect the tampering of agent immediately, and the tampering can be detected only when the agent returned. Agent fault-tolerance is one method that achieves agent fault-tolerance in migration itineraries by agent replication and majority voting [11]. The drawback of such method is that the agent replication and majority voting can produce many agent replicas in every agent migration step, which may cost significant resource and time. Aiming at those drawbacks, the paper incorporates the two methods, and presents a novel agent migration fault-tolerance model based on integrity verification, which can defend mobile agent against malicious hosts in migration itineraries effectively. The novel agent fault-tolerance model cannot only realize the fault-tolerant execution, but also reduce the complexity and resource cost of agent migration communication.

When I began in 1895 to apply the methods of artificial colour-blindness to test the colour-sensations of other people, I cast about in various directions for means of intensifying the effect while reducing the brilliancy of the light used to produce the necessary fatigue. The results obtained, though not immediately available for my purpose, were in several cases of sufficient interest to warrant their publication.

Most distributed data mining algorithms can efficiently manage and mine complete data from distributed resources. However, for an incomplete data some modifications are required in order to perform distributed data mining techniques and maintaining the privacy of the sensitive information to provide pretty good results of data mining. Classification is important tasks of data mining aimed at discovering knowledge and classify new instances. SVM is classified as one of the most important algorithm used for classification problems in several various spheres. In this paper, we proposed a new distributed privacy-preserving protocol with multiple imputations of missing or incomplete data. More so, a multiple imputations based on multivariate imputation by chained equations is used for missing data and Paillier cryptosystem for maintaining the privacy of the participants. Finally we constructed a global SVM model by introducing a third party (semi-honest approach) over vertical partition data based in Gram matrix without revealing the privacy of the data and used to classify new instances. The performance evolution of the proposed protocol was investigated while using accuracy metric on the distributed and centralized data. Results of our experiments reveal that the accuracy is the same as centralized data and achieve better results with imputed data while compared with omitted data. The performance of distributed data on our protocol achieves better processing time compared with centralized data.

When used as an interface in the context of Ambient Assisted Living (AAL), a social robot should not just provide a task-oriented support. It should also try to establish a social empathic relation with the user. To this aim, it is crucial to endow the robot with the capability of recognizing the user's affective state and reason on it for triggering the most appropriate communicative behavior. In this paper we describe how such an affective reasoning has been implemented in the NAO robot for simulating empathic behaviors in the context of AAL. In particular, the robot is able to recognize the emotion of the user by analyzing communicative signals extracted from speech and facial expressions. The recognized emotion allows triggering the robot's affective state and, consequently, the most appropriate empathic behavior. The robot's empathic behaviors have been evaluated both by experts in communication and through a user study aimed at assessing the perception and interpretation of empathy by elderly users. Results are quite satisfactory and encourage us to further extend the social and affective capabilities of the robot.

A knowledge of the distance between the two eyes being required in dealing with the problems of binocular vision, the instruments described in this paper may be of interest to physiologists. All those methods which involve measurement of the distance between the pupils (or other external parts of the eyes) are liable to errors, for the following reasons:— 1. It is difficult to eliminate parallax between the scale or index of the measuring instrument and the eyes. 2. The distance between the pupils is affected by the convergence of the optical axes, and still more by the abnormal direction of either. 3. It is difficult to ensure the eyes being kept still during the process of measuring.

Background Third-generation single molecule sequencing technologies can sequence long reads, which is advancing the frontiers of genomics research. However, their high error rates prohibit accurate and efficient downstream analysis. This difficulty has motivated the development of many long read error correction tools, which tackle this problem through sampling redundancy and/or leveraging accurate short reads of the same biological samples. Existing studies to asses these tools use simulated data sets, and are not sufficiently comprehensive in the range of software covered or diversity of evaluation measures used. Results In this paper, we present a categorization and review of long read error correction methods, and provide a comprehensive evaluation of the corresponding long read error correction tools. Leveraging recent real sequencing data, we establish benchmark data sets and set up evaluation criteria for a comparative assessment which includes quality of error correction as well as run-time and memory usage. We study how trimming and long read sequencing depth affect error correction in terms of length distribution and genome coverage post-correction, and the impact of error correction performance on an important application of long reads, genome assembly. We provide guidelines for practitioners for choosing among the available error correction tools and identify directions for future research. Conclusions Despite the high error rate of long reads, the state-of-the-art correction tools can achieve high correction quality. When short reads are available, the best hybrid methods outperform non-hybrid methods in terms of correction quality and computing resource usage. When choosing tools for use, practitioners are suggested to be careful with a few correction tools that discard reads, and check the effect of error correction tools on downstream analysis. Our evaluation code is available as open-source at https://github.com/haowenz/LRECE.

I have elsewhere shown* that, in experiments on the isolated mammalian heart, it is perfectly possible to keep the exsected heart in cold Locke’s solution at ordinary room temperatures for hours and still to obtain powerful and regular contractions of the heart when, after this procedure, it is subsequently perfused with warm oxygenated Locke’s solution in the usual way. Similar observations have been made on other contractile tissues and will be dealt with in another communication. In the meantime, it is sufficient to point out that those observations open an easy way to experiments on a certain number of isolated human tissues, removed for surgical reasons, by which experiments certain questions can be answered which cannot readily, if at all, be decided in any other way.

The account commonly accepted of the manner in which the auriculo-ventricular values of the mammalian heart are operated relies chiefly upon the action of eddies and currents. As the result of recent work it appears possible that a muscular mechanism may be involved also. Such a mechanism is referred to in the following communication. It consists of a prolongation downwards of the muscular fibres of the auricular wall in such a manner as to produce a sheet of longitudinally coursing fibres which enter the base of the valve flaps and run a variable distance in their auricular portions.

As a sunrise and national supporting industry, the current specialty of computer science and technology is in an awkward situation. Many students cannot get offer, but enterprises want to employ thousands of talents. The reason is not difficult to find out that abilities of undergraduates cannot meet industrial demands. So reform of computer science and technology education is imperative. Industry-linked and practice-oriented cultivating mode need be established. Responding to Chinese "excellent engineer education plan", the cultivating scheme of excellent software engineer is proposed. The scenario details are also discussed in this paper, such as cultivating objective, curricula setting and practice education system. And the scheme is being performed.

This paper describes the ANSI C/C++ computer program dsoa, which implements an algorithm for the approximate solution of dynamics system optimization problems. The algorithm is a direct method that can be applied to the optimization of dynamic systems described by index-1 differential-algebraic equations (DAEs). The types of problems considered include optimal control problems and parameter identification problems. The numerical techniques are employed to transform the dynamic system optimization problem into a parameter optimization problem by: (i) parameterizing the control input as piecewise constant on a fixed mesh, and (ii) approximating the DAEs using a linearly implicit Runge-Kutta method. The resultant nonlinear programming (NLP) problem is solved via a sequential quadratic programming technique. The program dsoa is evaluated using 83 nontrivial optimal control problems that have appeared in the literature. Here we compare the performance of the algorithm using two different NLP problem solvers, and two techniques for computing the derivatives of the functions that define the problem. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper is presented an approach to achieving on-line modification of nominal biped gait without recomputing entire dynamics when steady motion is performed. Straight, dynamically balanced walk was used as a nominal gait, and applied modifications were speed-up and slow-down walk and turning left and right. It is shown that the disturbances caused by these modifications jeopardize dynamic stability, but they can be simply compensated to enable walk continuation.

This paper overviews some massively parallel topographic cellular computational approaches proposed for contour localization and tracking. When implemented on a focal plane cellular array microprocessor, these algorithms offer real-time object contour localization and tracking—even at very high frame rates. Three specific methods (Constrained Wave Computing, Pixel Level Snakes and Moving Patch Method) will be described and compared along with their associated hardware–software architectures. Computational complexity, implementation, and performance related issues are discussed on a common platform (ACE-BOX with the ACEx CNN-UM chips). In conclusion, a novel architecture is proposed incorporating the best solutions learned from this comparative study. Copyright © 2006 John Wiley & Sons, Ltd.

The later developments of Mendelian analysis have been in the main an attempt to elucidate the scope and nature of Segregation. Mendel proved the existence of characters determined by integral or unit factors. Their integrity is maintained by segregation, the capacity, namely, to separate unimpaired after combination with their opposites. Our first aim has been to discover specifically what characters behave in this way, whether there is any limit to the scope of segregation, or any characters or classes of characters which are determined by elements unable to segregate simply. The second object has been to decide the time and place in the various lifecycles at which segregation occurs. It is with the latter problem that I propose to deal more particularly in this Lecture, but a brief consideration of the range of characters, subject to segregation, is appropriate.

In a paper on the subject of the Quantum Theory of Vision, issued in the ‘Philosophical Magazine’ (February, 1921), I dwelt on the view that the sensation of light is in every case stimulated by the action of photo-electrons set free in the retina. Further, the energy of the photo-electron being pro­portional to the frequency of the light, the strength of the stimulus produced is the all-sufficient origin of colour sensations. That colour is entirely a cerebral phenomenon is evident. Light, visible and invisible, consists of a uniformly graduated series of wave motions or energies. There is nothing to distinguish one part of the spectrum from another save the difference of wave-length or frequency. But objects in Nature react differently towards these waves: absorbing some, reflecting others, and so the selective effect of natural objects towards light has discovered to the organism a means of improving on mono-chromatic vision: a means of distinguishing objects by their selective absorption and reflection. Our colour sensations were deve­loped solely for this purpose and solely under the influence of the light reflected by natural objects. Hence, a limited number of fundamental sensa­tions being the simplest, if not indeed the only, way of securing the desired end, we would expect that these sensations would be developed so as most effectively to interpret the frequencies met with among natural objects reflecting solar light. The evolutionary development of three highly deve­loped colour sensations according to the extreme and mean regions of the spectrum is the result. Colour sensations, i. e. (white), red, green, and blue were evolved, whereby the whole gamut of the spectrum can be dealt with. In my former paper I restricted my reference to colour vision to this evolutionary aspect of the matter. In the present paper I have something to add respecting the retinal apparatus developed to subserve the end which Nature had in view.

A trend in the consumer electronics market is the demand for new applications that have a lot of similarities to older applications but the new ones impose more challenging and special-purpose performance requirements. In the digital signal processing (DSP) industry, this clearly reflects a transition from the design regime of general DSP to the application-specific DSP. A key in effecting this transition is the engineering capability to make the design specification "match" the application, before detailed design starts. In this paper, we present an effective simulation tool to assist in the generation of accurate design specifications. It models the application by a parameter-driven conditional data-flow graph (CDFG) and the hardware (HW) architecture by a configurable HW graph. The simulator takes the application CDFG and HW graph as the input and carries the simulation at a low level to catch the detailed HW activities. Experimental results show that our tool is not only able to accurately estimate system performance, but also helps to identify performance bottlenecks and to find the optimal software (SW) implementation solution for video applications.

How do we assure that a scientific result is sound, that no fraud has been committed and we do guarantee scientific progress in this new era of big scientific data? In order to guarantee reproducibility, especially in the biomedical sciences one need to give access to third parties to analytical datasets (either raw or processed), software used, code developed by the researchers, a very detailed description of the methodology as well as all the relevant metadata. We want to argue that that sharing data is mandatory in order for science to progress. Each published paper should not be seen as an untouchable revealed truth, but as an opportunity of dialogue, confrontation and verification. This can be done only if all raw data are shared and each researcher has the right tools to reproduce an experiment.

This paper presents WiMAX technology (worldwide interoperability for microwave access) that aims to provide business and consumer wireless broadband services on the scale of the metropolitan area network (MAN). WiMAX can be used in disaster recovery scenes where the wired networks have broken down. In recent hurricane disasters, WiMAX networks were installed to help in recovery missions. Similarly, WiMAX can be used as backup links for broken wired links. The certification working group has developed a WiMAX product certification program, which aims to ensure interoperability between WiMAX equipment from vendors worldwide. The certification process also considers interoperability with the high performance radio metropolitan area network (HiperMAN), the European Telecommunications Standards Institute's MAN standard

This paper reports on an action research case study. The project involved nine classroom teachers using the action research process to develop an integrated curriculum algorithm and software classification system. The project also aimed to empower individual teachers with the knowledge, skill and confidence to understand, improve and transform their teaching procedures and practices with respect to the use of computers in primary classrooms. The discussion focuses on the development of the algorithm and the selection of a suite of software for the investigation of integrated curriculum topics. Also discussed is the impact of this type of instructional strategy on student and teacher motivation and performance. Also the lessons learnt in regard to software development, professional development, and the effectiveness of action research as a change agent.

If the following remarks venture to devise for reflex inhibition yet another schema additional to the several existent, they do so with full recognition of the value and service of schemes already (4, 13, 14, 17, 22, 42) in the field. A complex problem, however, may be helped by offer of alternative suggestions. Dealing with the well-known schema originating from Keith Lucas (42), and developed with such notable advantage to the subject by Adrian and Forbes, Adrian himself has very recently (4) summarised the present position of the problem and main views obtaining on it. Here, therefore, it may be permitted to proceed without further preface to the scheme which it is sought to offer for consideration.