Personalized recommender systems integrating tags and item taxonomy

The social tags in Web 2.0 are becoming another important information source to profile users' interests and preferences to make personalized recommendations. To solve the problem of low information sharing caused by the free-style vocabulary of tags and the long tails of the distribution of tags and items, this paper proposes an approach to integrate the social tags given by users and the item taxonomy with standard vocabulary and hierarchical structure provided by experts to make personalized recommendations. The experimental results show that the proposed approach can effectively improve the information sharing and recommendation accuracy.

Catalytic cracking of tars derived from rice hull gasification over goethite and palygorskite

Palygorskite (P), goethite (G), and hydrothermally synthesized goethite (HG) were used as supports for Fe and Ni. The catalytic activity of these materials was investigated involving in P, G and HG (supported Fe and Ni) for catalytic decomposition of biomass tar derived from rice hull gasification. The materials were characterized by X-ray diffraction (XRD), X-ray fluorescence (XRF), and transmission electron microscopy (TEM) with an energy dispersive X-ray (EDS). The catalytic activity of P for removal of tar was significantly better than that of G and HG. However, the activity of G with 6 mass% Ni labeled as Ni6/G (tar conversion 94.6%), which was equal to Fe6Ni6/P (94.4%), was better than Ni6/P (64.4%) and Ni6/HG (46.7%). When the loading of Ni (mass%) was 6 mass% on G, tar conversion had the best value (94.6%) and yield of gaseous products reached 486.9, 167.8 and 22.2 mL/(g·tar) for H2, CO, CH4, respectively. The catalytic activity of goethite supported Ni was better in improving tar conversion and improving increased yield of H2, CO, CH4, which was attributed to the existence of Al/Fe substitution of goethite

High strong order methods for non-commutative stochastic ordinary differential equation systems and the Magnus formula

In recent years considerable attention has been paid to the numerical solution of stochastic ordinary differential equations (SODEs), as SODEs are often more appropriate than their deterministic counterparts in many modelling situations. However, unlike the deterministic case numerical methods for SODEs are considerably less sophisticated due to the difficulty in representing the (possibly large number of) random variable approximations to the stochastic integrals. Although Burrage and Burrage [High strong order explicit Runge-Kutta methods for stochastic ordinary differential equations, Applied Numerical Mathematics 22 (1996) 81-101] were able to construct strong local order 1.5 stochastic Runge-Kutta methods for certain cases, it is known that all extant stochastic Runge-Kutta methods suffer an order reduction down to strong order 0.5 if there is non-commutativity between the functions associated with the multiple Wiener processes. This order reduction down to that of the Euler-Maruyama method imposes severe difficulties in obtaining meaningful solutions in a reasonable time frame and this paper attempts to circumvent these difficulties by some new techniques. An additional difficulty in solving SODEs arises even in the Linear case since it is not possible to write the solution analytically in terms of matrix exponentials unless there is a commutativity property between the functions associated with the multiple Wiener processes. Thus in this present paper first the work of Magnus [On the exponential solution of differential equations for a linear operator, Communications on Pure and Applied Mathematics 7 (1954) 649-673] (applied to deterministic non-commutative Linear problems) will be applied to non-commutative linear SODEs and methods of strong order 1.5 for arbitrary, linear, non-commutative SODE systems will be constructed - hence giving an accurate approximation to the general linear problem. Secondly, for general nonlinear non-commutative systems with an arbitrary number (d) of Wiener processes it is shown that strong local order I Runge-Kutta methods with d + 1 stages can be constructed by evaluated a set of Lie brackets as well as the standard function evaluations. A method is then constructed which can be efficiently implemented in a parallel environment for this arbitrary number of Wiener processes. Finally some numerical results are presented which illustrate the efficacy of these approaches. (C) 1999 Elsevier Science B.V. All rights reserved.

General order conditions for stochastic Runge-Kutta methods for both commuting and non-commuting stochastic ordinary differential equation systems

In many modeling situations in which parameter values can only be estimated or are subject to noise, the appropriate mathematical representation is a stochastic ordinary differential equation (SODE). However, unlike the deterministic case in which there are suites of sophisticated numerical methods, numerical methods for SODEs are much less sophisticated. Until a recent paper by K. Burrage and P.M. Burrage (1996), the highest strong order of a stochastic Runge-Kutta method was one. But K. Burrage and P.M. Burrage (1996) showed that by including additional random variable terms representing approximations to the higher order Stratonovich (or Ito) integrals, higher order methods could be constructed. However, this analysis applied only to the one Wiener process case. In this paper, it will be shown that in the multiple Wiener process case all known stochastic Runge-Kutta methods can suffer a severe order reduction if there is non-commutativity between the functions associated with the Wiener processes. Importantly, however, it is also suggested how this order can be repaired if certain commutator operators are included in the Runge-Kutta formulation. (C) 1998 Elsevier Science B.V. and IMACS. All rights reserved.

Framework for evaluating collaborative intrusion detection systems

Securing IT infrastructures of our modern lives is a challenging task because of their increasing complexity, scale and agile nature. Monolithic approaches such as using stand-alone firewalls and IDS devices for protecting the perimeter cannot cope with complex malwares and multistep attacks. Collaborative security emerges as a promising approach. But, research results in collaborative security are not mature, yet, and they require continuous evaluation and testing. In this work, we present CIDE, a Collaborative Intrusion Detection Extension for the network security simulation platform ( NeSSi 2 ). Built-in functionalities include dynamic group formation based on node preferences, group-internal communication, group management and an approach for handling the infection process for malware-based attacks. The CIDE simulation environment provides functionalities for easy implementation of collaborating nodes in large-scale setups. We evaluate the group communication mechanism on the one hand and provide a case study and evaluate our collaborative security evaluation platform in a signature exchange scenario on the other.

Transmission system expansion planning considering combined operation of wind farms and energy storage systems

An energy storage system (ESS) can provide ancillary services such as frequency regulation and reserves, as well as smooth the fluctuations of wind power outputs, and hence improve the security and economics of the power system concerned. The combined operation of a wind farm and an ESS has become a widely accepted operating mode. Hence, it appears necessary to consider this operating mode in transmission system expansion planning, and this is an issue to be systematically addressed in this work. Firstly, the relationship between the cost of the NaS based ESS and its discharging cycle life is analyzed. A strategy for the combined operation of a wind farm and an ESS is next presented, so as to have a good compromise between the operating cost of the ESS and the smoothing effect of the fluctuation of wind power outputs. Then, a transmission system expansion planning model is developed with the sum of the transmission investment costs, the investment and operating costs of ESSs and the punishment cost of lost wind energy as the objective function to be minimized. An improved particle swarm optimization algorithm is employed to solve the developed planning model. Finally, the essential features of the developed model and adopted algorithm are demonstrated by 18-bus and 46-bus test systems.

Crowdsourcing information systems : definition, typology and design

Crowdsourcing has become a popular approach for capitalizing on the potential of large and open crowds of people external to the organization. While crowdsourcing as a phenomenon is studied in a variety of fields, research mostly focuses on isolated aspects and little is known about the integrated design of crowdsourcing efforts. We introduce a socio-technical systems perspective on crowdsourcing, which provides a deeper understanding of the components and relationships in crowdsourcing systems. By considering the function of crowdsourcing systems within their organizational context, we develop a typology of four distinct system archetypes. We analyze the characteristics of each type and derive a number of design requirements for the respective system components. The paper lays a foundation for IS-based crowdsourcing research, channels related academic work, and helps guiding the study and design of crowdsourcing information systems.

Influence of damping systems on building structures subject to seismic effects

This paper investigates the response of multi-storey structures under simulated earthquake loads with friction dampers, viscoelastic dampers and combined friction-viscoelastic damping devices strategically located within shear walls. Consequently, evaluations are made as to how the damping systems affect the seismic response of these structures with respect to deflections and accelerations. In particular, this paper concentrates on the effects of damper types, configurations and their locations within the cut-outs of shear walls. The initial stiffness of the cut out section of the shear wall is removed and replaced by the stiffness and damping of the device. Influence of parameters of damper properties such as stiffness, damping coefficient, location, configuration and size are studied and evaluated using results obtained under several different earthquake scenarios. Structural models with cut outs at different heights are treated in order to establish the effectiveness of the dampers and their optimal placement. This conceptual study has demonstrated the feasibility of mitigating the seismic response of building structures by using embedded dampers.

Non-intrusive continuous user behaviour analysis using computerized systems

There are different ways to authenticate humans, which is an essential prerequisite for access control. The authentication process can be subdivided into three categories that rely on something someone i) knows (e.g. password), and/or ii) has (e.g. smart card), and/or iii) is (biometric features). Besides classical attacks on password solutions and the risk that identity-related objects can be stolen, traditional biometric solutions have their own disadvantages such as the requirement of expensive devices, risk of stolen bio-templates etc. Moreover, existing approaches provide the authentication process usually performed only once initially. Non-intrusive and continuous monitoring of user activities emerges as promising solution in hardening authentication process: iii-2) how so. behaves. In recent years various keystroke dynamic behavior-based approaches were published that are able to authenticate humans based on their typing behavior. The majority focuses on so-called static text approaches, where users are requested to type a previously defined text. Relatively few techniques are based on free text approaches that allow a transparent monitoring of user activities and provide continuous verification. Unfortunately only few solutions are deployable in application environments under realistic conditions. Unsolved problems are for instance scalability problems, high response times and error rates. The aim of this work is the development of behavioral-based verification solutions. Our main requirement is to deploy these solutions under realistic conditions within existing environments in order to enable a transparent and free text based continuous verification of active users with low error rates and response times.

Effects of rice husk biochar and sugar-mill by-products on methane consumption from two different soils

Methane (CH4) is an important greenhouse gas with a global warming potential (GWP) 25 times greater than carbon dioxide (CO2) that can be produced or consumed in soils depending on environmental conditions and other factors. Biochar application to soils has been shown to reduce CH4 emissions and to increase CH4 consumption. However, the effects of rice husk biochar (RB) have not been thoroughly investigated. Two 60-day laboratory incubation experiments were conducted to investigate the effects of amending two soil types with RB, raw mill mud (MM) and composted mill mud (CM) on soil CH4 consumption and emissions. Soil cores incubated in 1 L glass jars and gas samples were analysed for CH4 using gas chromatography. Average CH4 consumption rates varied from -0.06 to -0.68 g CH4-C( )/ha/d in sandy loam soil and -0.59 to -1.00 g CH4-C/ha/d in clay soil. Application of RB resulted in CH4 uptake of -0.52 to -0.55 g CH4-C/ha/d in sandy loam and -0.76 to -0.91 g CH4-C/ha/d in clay soil. Addition of MM showed low CH4 emissions or consumption at 60% water-filled pore space (WFPS) in both soils. However, at high water contents (>75% WFPS) the application of MM produced high rates of CH4 emissions which were significantly suppressed when RB was added. Cumulative emissions of the MM treatment produced 108.9 g CH4-C/ha at 75% WFPS and 11 459.3 g CH4-C/ha at 90% WFPS in sandy loam soil over a period of 60 days. RB can increase CH4 uptake under low soil water content (SWC) and decrease CH4 emissions under anaerobic conditions. CM expressed more potential to reduce CH4 emissions than those of MM.

Archival analysis of enterprise resource planning systems : the current state and future directions

Research on Enterprise Resource Planning (ERP) Systems is becoming a well-established research theme in Information Systems (IS) research. Enterprise Resource Planning Systems, given its unique differentiations with other IS applications, have provided an interesting backdrop to test and re-test some of the key and fundamental concepts in IS. While some researchers have tested well-established concepts of technology acceptance, system usage and system success in the context of ERP Systems, others have researched how new paradigms like cloud computing and social media integrate with ERP Systems. Moreover, ERP Systems provided the context for cross disciplinary research such as knowledge management, project management and business process management research. Almost after two-decades since its inception in IS research, this paper provides a critique of 198 papers published on ERP Systems since 2006-2012. We observe patterns on ES research, provide comparisons to past studies and provide future research directions.

Designing a new robust on-line secondary path modeling technique for feedforward active noise control systems

Several approaches have been introduced in the literature for active noise control (ANC) systems. Since the filtered-x least-mean-square (FxLMS) algorithm appears to be the best choice as a controller filter, researchers tend to improve performance of ANC systems by enhancing and modifying this algorithm. This paper proposes a new version of the FxLMS algorithm, as a first novelty. In many ANC applications, an on-line secondary path modeling method using white noise as a training signal is required to ensure convergence of the system. As a second novelty, this paper proposes a new approach for on-line secondary path modeling on the basis of a new variable-step-size (VSS) LMS algorithm in feed forward ANC systems. The proposed algorithm is designed so that the noise injection is stopped at the optimum point when the modeling accuracy is sufficient. In this approach, a sudden change in the secondary path during operation makes the algorithm reactivate injection of the white noise to re-adjust the secondary path estimate. Comparative simulation results shown in this paper indicate the effectiveness of the proposed approach in reducing both narrow-band and broad-band noise. In addition, the proposed ANC system is robust against sudden changes of the secondary path model.

An efficient online secondary path estimation for feedback active noise control systems

In practical cases for active noise control (ANC), the secondary path has usually a time varying behavior. For these cases, an online secondary path modeling method that uses a white noise as a training signal is required to ensure convergence of the system. The modeling accuracy and the convergence rate are increased when a white noise with a larger variance is used. However, the larger variance increases the residual noise, which decreases performance of the system and additionally causes instability problem to feedback structures. A sudden change in the secondary path leads to divergence of the online secondary path modeling filter. To overcome these problems, this paper proposes a new approach for online secondary path modeling in feedback ANC systems. The proposed algorithm uses the advantages of white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the algorithm and to prevent the instability effect of the white noise. In this approach, instead of continuous injection of the white noise, a sudden change in secondary path during the operation makes the algorithm to reactivate injection of the white noise to correct the secondary path estimation. In addition, the proposed method models the secondary path without the need of using off-line estimation of the secondary path. Considering the above features increases the convergence rate and modeling accuracy, which results in a high system performance. Computer simulation results shown in this paper indicate effectiveness of the proposed method.

Adaptive metering algorithm for electronic commercial vehicle preclearance systems

This paper presents an adaptive metering algorithm for enhancing the electronic screening (e-screening) operation at truck weight stations. This algorithm uses a feedback control mechanism to control the level of truck vehicles entering the weight station. The basic operation of the algorithm allows more trucks to be inspected when the weight station is underutilized by adjusting the weight threshold lower. Alternatively, the algorithm restricts the number of trucks to inspect when the station is overutilized to prevent queue spillover. The proposed control concept is demonstrated and evaluated in a simulation environment. The simulation results demonstrate the considerable benefits of the proposed algorithm in improving overweight enforcement with minimal negative impacts on nonoverweighed trucks. The test results also reveal that the effectiveness of the algorithm improves with higher truck participation rates in the e-screening program.

Evaluating the effectiveness of heat warning systems : a systematic review of epidemiological evidence

Objectives To review the existing research on the effectiveness of heat warning systems (HWSs) in saving lives and reducing harm. Methods A systematic search of major databases was conducted, using “heat, heatwave, high temperature, hot temperature, OR hot climate” AND “warning system”. Results Fifteen articles were retrieved. Six studies asserted that fewer people died of excessive heat after HWS implementation. HWS was associated with reduction in ambulance use. One study estimated the benefits of HWS to be 468millionforsaving117livescomparedto210,000 costs of running the system. Eight studies showed that mere availability of HWS did not lead to behavioral changes. Perceived threat of heat dangers to self/others was the main factor related to heeding warnings and taking proper actions. However, costs and barriers associated with taking protective actions, such as costs of running air conditioners, were of significant concern particularly to the poor. Conclusions Research in this area is limited. Prospective designs applying health behavior theories should establish whether HWS can produce the health benefits they are purported to achieve by identifying the target vulnerable groups.