Swallow: Resource and Tag Recommender System Based on Heat Diffusion Algorithm in Social Annotation Systems

Social annotation systems (SAS) allow users to annotate different online resources with keywords (tags). These systems help users in finding, organizing, and retrieving online resources to significantly provide collaborative semantic data to be potentially applied by recommender systems. Previous studies on SAS had been worked on tag recommendation. Recently, SAS‐based resource recommendation has received more attention by scholars. In the most of such systems, with respect to annotated tags, searched resources are recommended to user, and their recent behavior and click‐through is not taken into account. In the current study, to be able to design and implement a more precise recommender system, because of previous users' tagging data and users' current click‐through, it was attempted to work on the both resource (such as web pages, research papers, etc.) and tag recommendation problem. Moreover, by applying heat diffusion algorithm during the recommendation process, more diverse options would present to the user. After extracting data, such as users, tags, resources, and relations between them, the recommender system so called “Swallow” creates a graph‐based pattern from system log files. Eventually, following the active user path and observing heat conduction on the created pattern, user further goals are anticipated and recommended to him. Test results on SAS data set demonstrate that the proposed algorithm has improved the accuracy of former recommendation algorithms.     

Memory-adaptive high utility sequential pattern mining over data streams

High utility sequential pattern (HUSP) mining has emerged as an important topic in data mining. A number of studies have been conducted on mining HUSPs, but they are mainly intended for non-streaming data and thus do not take data stream characteristics into consideration. Streaming data are fast changing, continuously generated unbounded in quantity. Such data can easily exhaust computer resources (e.g., memory) unless a proper resource-aware mining is performed. In this study, we explore the fundamental problem of how limited memory can be best utilized to produce high quality HUSPs over a data stream. We design an approximation algorithm, called MAHUSP, that employs memory adaptive mechanisms to use a bounded portion of memory, in order to efficiently discover HUSPs over data streams. An efficient tree structure, called MAS-Tree, is proposed to store potential HUSPs over a data stream. MAHUSP guarantees that all HUSPs are discovered in certain circumstances. Our experimental study shows that our algorithm can not only discover HUSPs over data streams efficiently, but also adapt to memory allocation with limited sacrifices in the quality of discovered HUSPs. Furthermore, in order to show the effectiveness and efficiency of MAHUSP in real-life applications, we apply our proposed algorithm to a web clickstream dataset obtained from a Canadian news portal to showcase users’ reading behavior, and to a real biosequence database to identify disease-related gene regulation sequential patterns. The results show that MAHUSP effectively discovers useful and meaningful patterns in both cases.     

Perfect forward secrecy in VoIP networks through design a lightweight and secure authenticated communication scheme

With the growth of the internet, development of IP based services has increased. Voice over IP (VoIP) technology is one of the services which works based on the internet and packet switching networks and uses this structure to transfer the multimedia data e.g. voices and images. Recently, Chaudhry et al., Zhang et al. and Nikooghadam et al. have presented three authentication and key agreement protocols, separately. However, in this paper, it is proved that the presented protocols by Chaudhry et al. and also Nikooghadam et al. do not provide the perfect forward secrecy, and the presented protocol by Zhang et al. not only is vulnerable to replay attack, and known session-specific temporary information attack, but also does not provide user anonymity, re-registration and revocation, and violation of fast error detection. Therefore, a secure and efficient two-factor authentication and key agreement protocol is presented. The security analysis proves that our proposed protocol is secure against various attacks. Furthermore, security of proposed scheme is formally analyzed using BAN logic and simulated by means of the AVISPA tool. The simulation results demonstrate security of presented protocol against active and passive attacks. The communication and computation cost of the proposed scheme is compared with previously proposed authentication schemes and results confirm superiority of the proposed scheme.

     

A Comparative Study of Non-traditional Methods for Vehicle Crashworthiness and NVH Optimization

In this paper, metamodeling and five well-known metaheuristic optimization algorithms were used to reduce the weight and improve crash and NVH attributes of a vehicle simultaneously. A high-fidelity full vehicle model is used to analyze peak acceleration, intrusion and component’s internal-energy under Full-Frontal, Offset-Frontal, and Side crash scenarios as well as vehicle natural frequencies. The radial basis functions method is used to approximate the structural responses. A nonlinear surrogate-based mass minimization was formulated and solved by five different optimization algorithms under crash-vibration constraints. The performance of these algorithms is investigated and discussed.     

Globally stable and high-performance Internet congestion control through a computational inspiration from nature

There are many reasons to worry that the current congestion control schemes in the Internet may be reaching its limits. Since the nature is a source of excellent solutions for complex problems, this work attempts to solve the congestion control problem, by adopting some biological principles and mechanisms. The current work proposes that the congestion problem in the Internet can be addressed through an inspiration from the population control tactics in nature. Toward this idea, each flow (W ) in the networ...     

An architecture framework for an adaptive extensible processor

To improve the performance of embedded processors, an effective technique is collapsing critical computation subgraphs as application-specific instruction set extensions and executing them on custom functional units. The problem with this approach is the immense cost and the long times required to design a new processor for each application. As a solution to this issue, we propose an adaptive extensible processor in which custom instructions (CIs) are generated and added after chip-fabrication. To support this feature, custom functional units are replaced by a reconfigurable matrix of functional units (FUs). A systematic quantitative approach is used for determining the appropriate structure of the reconfigurable functional unit (RFU). We also introduce an integrated framework for generating mappable CIs on the RFU. Using this architecture, performance is improved by up to 1.33, with an average improvement of 1.16, compared to a 4-issue in-order RISC processor. By partitioning the configuration memory, detecting similar/subset CIs and merging small CIs, the size of the configuration memory is reduced by 40%.     

Using hash tables to manage the time-storage complexity in a point location problem: Application to explicit model predictive control

The online computational burden of linear model predictive control (MPC) can be moved offline by using multi-parametric programming, so-called explicit MPC. The solution to the explicit MPC problem is a piecewise affine (PWA) state feedback function defined over a polyhedral subdivision of the set of feasible states. The online evaluation of such a control law needs to determine the polyhedral region in which the current state lies. This procedure is called point location; its computational complexity is challenging, and determines the minimum possible sampling time of the system. A new flexible algorithm is proposed which enables the designer to trade off between time and storage complexities. Utilizing the concept of hash tables and the associated hash functions, the proposed method solves an aggregated point location problem that overcomes prohibitive complexity growth with the number of polyhedral regions, while the storage–processing trade-off can be optimized via scaling parameters. The flexibility and power of this approach is supported by several numerical examples.     

Hyper-chaotic Feeded GA (HFGA): a reversible optimization technique for robust and sensitive image encryption

In recent years, due to their straightforward structure and efficiency, the chaos-based cryptographic algorithms have become a good candidate for image encryption. However, they still suffer from many weaknesses, such as insensitivity to the plain image, weak key streams, small key space, non-resistance to some attacks and failure to meet some security criteria. For this purpose in this paper, a novel hybrid image encryption algorithm named Hyper-chaotic Feeded GA (HFGA) is proposed to fill the gaps in two stages; initial encryption by using a hyper-chaotic system, and then outputs reinforcement by employing a customized Genetic Algorithm (GA). By applying an innovative technique, called gene-labelling, the proposed algorithm not only optimizes the preliminary encrypted images in terms of security criteria but also allows the legal receiver to easily and securely decrypt the optimized cipher image. In fact, in the first stage, besides unpredictable random sequences generated by a hyper-chaotic system, a new sensitive diffusion function is proposed which makes the algorithm resistant to differential attacks. In the second stage, the generated cipher images, which are labeled in a special way, will be used as the initial population of a GA which enhances randomness of the cipher images. The results of several experiments and statistical analysis show that the proposed image encryption scheme provides an efficient and secure way for fast image encrypting as well as providing robustness against some well-known statistical attacks.     

Predicting potential deadlocks in multithreaded programs

In a multithreaded program, competition of threads for shared resources raises the deadlock possibility, which narrows the system liveness. Because such errors appear in specific schedules of concurrent executions of threads, runtime verification of threads behavior is a significant concern. In this study, we extended our previous approach for prediction of runtime behavior of threads may lead to an impasse. Such a prediction is of importance because of the nondeterministic manner of competing threads. The prediction process tries to forecast future behavior of threads based on their observed behavior. To this end, we map observed behavior of threads into time‐series data sets and use statistical and artificial intelligence methods for forecasting subsequent members of the sets as future behavior of the threads. The deadlock prediction is carried out based on probing the allocation graph obtained from actual and predicted allocation of resources to threads. In our approach, we use an artificial neural network (ANN) because ANNs enjoy the applicable performance and flexibility in predicting complex behavior. Using three case studies, we contrasted results of the current and our previous approaches to demonstrate results. Copyright © 2015 John Wiley & Sons, Ltd.     

Analytical solution of mixed electromagnetic/pressure driven gaseous flows in microchannels

The influences of wall-slip/jump conditions on the fluid flow and heat transfer for hydrodynamically and thermally fully developed electrically conducting gaseous flow subject to an electromagnetic field inside a parallel plate microchannel with constant heat flux at walls are studied under the assumptions of a low-magnetic Reynolds number. The governing equations are non-dimensionalized and then analytical solutions are derived for the friction and the heat transfer coefficients. The fluid flow and the heat transfer characteristics obtained in the analytical solutions are discussed in detail for different parameters such as the Knudsen, Hartmann, and Brinkman numbers. The velocity profiles verify that even with a constant Knudsen number, applying a stronger electromagnetic field gives rise to an increase in the slip velocity. The results also reveal that on increasing the Hartmann number, the heat transfer rate as well as the friction factor is enhanced, whereas it tends to suppress the movement of the fluid. Further, it is found that the Nusselt and the Poiseuille numbers are less sensitive to the electromagnetic field effects with increase in rarefaction.