A rule-based hybrid method for anomaly detection in online-social-network graphs

Detecting anomalies in the online social network is a significant task as it assists in revealing the useful and interesting information about the user behavior on the network. This paper proposes a rule-based hybrid method using graph theory, Fuzzy clustering and Fuzzy rules for modeling user relationships inherent in online-social-network and for identifying anomalies. Fuzzy C-Means clustering is used to cluster the data and Fuzzy inference engine is used to generate rules based on the cluster behavior. The proposed method is able to achieve improved accuracy for identifying anomalies in comparison to existing methods.

Using performance-based evaluation to close the loop between biological and robotic navigation

In this paper we describe the benefits of a performance-based approach to modeling biological systems for use in robotics. Specifically, we describe the RatSLAM system, a computational model of the navigation processes thought to drive navigation in a part of the rodent brain called the hippocampus. Unlike typical computational modeling approaches, which focus on biological fidelity, RatSLAM’s development cycle has been driven primarily by performance evaluation on robots navigating in a wide variety of challenging, real world environments. We briefly describe three seminal results, two in robotics and one in biology. In addition, we present current research on brain-inspired learning algorithms with the aim of enabling a robot to autonomously learn how best to use its sensor suite to navigate, without requiring any specific knowledge of the robot, sensor types or environment characteristics. Our aim is to drive discussion on the merits of practical, performance-focused implementations of biological models in robotics.

Use of simple pharmacokinetic modeling to characterize exposure of Australians to perfluorooctanoic acid and perfluorooctane sulfonic acid

Perflurooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) have been used for a variety of applications including fluoropolymer processing, fire-fighting foams and surface treatments since the 1950s. Both PFOS and PFOA are polyfluoroalkyl chemicals (PFCs), man-made compounds that are persistent in the environment and humans; some PFCs have shown adverse effects in laboratory animals. Here we describe the application of a simple one compartment pharmacokinetic model to estimate total intakes of PFOA and PFOS for the general population of urban areas on the east coast of Australia. Key parameters for this model include the elimination rate constants and the volume of distribution within the body. A volume of distribution was calibrated for PFOA to a value of 170ml/kgbw using data from two communities in the United States where the residents' serum concentrations could be assumed to result primarily from a known and characterized source, drinking water contaminated with PFOA by a single fluoropolymer manufacturing facility. For PFOS, a value of 230ml/kgbw was used, based on adjustment of the PFOA value. Applying measured Australian serum data to the model gave mean+/-standard deviation intake estimates of PFOA of 1.6+/-0.3ng/kgbw/day for males and females >12years of age combined based on samples collected in 2002-2003 and 1.3+/-0.2ng/kg bw/day based on samples collected in 2006-2007. Mean intakes of PFOS were 2.7+/-0.5ng/kgbw/day for males and females >12years of age combined based on samples collected in 2002-2003, and 2.4+/-0.5ng/kgbw/day for the 2006-2007 samples. ANOVA analysis was run for PFOA intake and demonstrated significant differences by age group (p=0.03), sex (p=0.001) and date of collection (p<0.001). Estimated intake rates were highest in those aged >60years, higher in males compared to females, and higher in 2002-2003 compared to 2006-2007. The same results were seen for PFOS intake with significant differences by age group (p<0.001), sex (p=0.001) and date of collection (p=0.016).

Mechanism‐based selection of a potent kallikrein‐related peptidase 7 inhibitor from a versatile library based on the sunflower trypsin inhibitor SFTI‐1

Potent and specific enzyme inhibition is a key goal in the development of therapeutic inhibitors targeting proteolytic activity. The backbone-cyclized peptide, Sunflower Trypsin Inhibitor (SFTI-1) affords a scaffold that can be engineered to achieve both these aims. SFTI-1's mechanism of inhibition is unusual in that it shows fast-on/slow-off kinetics driven by cleavage and religation of a scissile bond. This phenomenon was used to select a nanomolar inhibitor of kallikrein-related peptidase 7 (KLK7) from a versatile library of SFTI variants with diversity tailored to exploit distinctive surfaces present in the active site of serine proteases. Inhibitor selection was achieved through the use of size exclusion chromatography to separate protease/inhibitor complexes from unbound inhibitors followed by inhibitor identification according to molecular mass ascertained by mass spectrometry. This approach identified a single dominant inhibitor species with molecular weight of 1562.4 Da, which is consistent with the SFTI variant SFTI-WCTF. Once synthesized individually this inhibitor showed an IC50 of 173.9 ± 7.6 nM against chromogenic substrates and could block protein proteolysis. Molecular modeling analysis suggested that selection of SFTI-WCTF was driven by specific aromatic interactions and stabilized by an enhanced internal hydrogen bonding network. This approach provides a robust and rapid route to inhibitor selection and design.

DUTCH : teaching method-based design

All design classes followed a systematic design approach, that, in an abstract way, can be characterized by figure 1. This approach is based on our design approach [1] that we labeled DUTCH (design for users and tasks, from concepts to handles).Consequently, each course starts with collecting, modeling, and analyzing an existing situation. The next step is the development of a vision on a future domain world where new technology and / or new representations have been implemented. This second step is the first tentative global design that will be represented in scenarios or prototypes and can be assessed. This second design model is based on both the client’s requirements and technological possibilities and challenges. In an iterative way multiple instantiations of detail design may follow, that each can be assessed and evaluated again...

Facilitating query decomposition in query language modeling by association rule mining using multiple sliding windows

This paper presents a novel framework to further advance the recent trend of using query decomposition and high-order term relationships in query language modeling, which takes into account terms implicitly associated with different subsets of query terms. Existing approaches, most remarkably the language model based on the Information Flow method are however unable to capture multiple levels of associations and also suffer from a high computational overhead. In this paper, we propose to compute association rules from pseudo feedback documents that are segmented into variable length chunks via multiple sliding windows of different sizes. Extensive experiments have been conducted on various TREC collections and our approach significantly outperforms a baseline Query Likelihood language model, the Relevance Model and the Information Flow model.

Physical activity and determinants of physical activity in obese and non-obese children

OBJECTIVE To compare the physical activity (PA) patterns and the hypothesized psychosocial and environmental determinants of PA in an ethnically diverse sample of obese and non-obese middle school children. DESIGN Cross-sectional study. SUBJECTS One-hundred and thirty-three non-obese and 54 obese sixth grade children (mean age of 11.4 +/-0.6). Obesity status determined using the age-, race- and gender-specific 95th percentile for BMI from NHANES-1. MEASUREMENTS Objective measurements were collected of PA over a 7-day period using the CSA 7164 accelerometer: total daily counts; daily moderate (3-5.9 METs) physical activity (MPA); daily vigorous physical activity (greater than or equal to 6 METs; VPA); and weekly number of 5, 10 and 20 min bouts of moderate-to-vigorous physical activity (greater than or equal to 3 METs, MVPA). Self-report measures were collected of PA self-efficacy; social influences regarding PA, beliefs about PA outcomes; perceived PA levels of parents and peers, access to sporting and/or fitness equipment at home, involvement in community-based PA organizations; participation in community sports teams; and hours spent watching television or playing video games. RESULTS Compared to their non-obese counterparts, obese children exhibited significantly lower daily accumulations of total counts, MPA and VPA as well as significantly fewer 5, 10 and 20 min bouts of MVPA. Obese children reported significantly lower levels of PA self-efficacy, were involved in significantly fewer community organizations promoting PA and were significantly less likely to report their father or male guardian as physically active. CONCLUSIONS The results are consistent with the hypothesis that physical inactivity is an important contributing factor in the maintenance of childhood obesity. Interventions to promote PA in obese children should endeavor to boost self-efficacy perceptions regarding exercise, increase awareness of, and access to, community PA outlets, and increase parental modeling of PA.

Tensor-based item recommendation using probabilistic ranking in social tagging systems

A common problem with the use of tensor modeling in generating quality recommendations for large datasets is scalability. In this paper, we propose the Tensor-based Recommendation using Probabilistic Ranking method that generates the reconstructed tensor using block-striped parallel matrix multiplication and then probabilistically calculates the preferences of user to rank the recommended items. Empirical analysis on two real-world datasets shows that the proposed method is scalable for large tensor datasets and is able to outperform the benchmarking methods in terms of accuracy.

A particle based model to simulate microscale morphological changes of plant tissues during drying

Fundamental understanding on microscopic physical changes of plant materials is vital to optimize product quality and processing techniques, particularly in food engineering. Although grid-based numerical modelling can assist in this regard, it becomes quite challenging to overcome the inherited complexities of these biological materials especially when such materials undergo critical processing conditions such as drying, where the cellular structure undergoes extreme deformations. In this context, a meshfree particle based model was developed which is fundamentally capable of handling extreme deformations of plant tissues during drying. The model is built by coupling a particle based meshfree technique: Smoothed Particle Hydrodynamics (SPH) and a Discrete Element Method (DEM). Plant cells were initiated as hexagons and aggregated to form a tissue which also accounts for the characteristics of the middle lamella. In each cell, SPH was used to model cell protoplasm and DEM was used to model the cell wall. Drying was incorporated by varying the moisture content, the turgor pressure, and cell wall contraction effects. Compared to the state of the art grid-based microscale plant tissue drying models, the proposed model can be used to simulate tissues under excessive moisture content reductions incorporating cell wall wrinkling. Also, compared to the state of the art SPH-DEM tissue models, the proposed model better replicates real tissues and the cell-cell interactions used ensure efficient computations. Model predictions showed good agreement both qualitatively and quantitatively with experimental findings on dried plant tissues. The proposed modelling approach is fundamentally flexible to study different cellular structures for their microscale morphological changes at dehydration.

Degradation modeling and monitoring of machines using operation-specific hidden Markov models

In this paper, a novel data-driven approach to monitoring of systems operating under variable operating conditions is described. The method is based on characterizing the degradation process via a set of operation-specific hidden Markov models (HMMs), whose hidden states represent the unobservable degradation states of the monitored system while its observable symbols represent the sensor readings. Using the HMM framework, modeling, identification and monitoring methods are detailed that allow one to identify a HMM of degradation for each operation from mixed-operation data and perform operation-specific monitoring of the system. Using a large data set provided by a major manufacturer, the new methods are applied to a semiconductor manufacturing process running multiple operations in a production environment.

Close-quarters Quadrotor flying for a pole inspection with position based visual servoing and high-speed vision

This paper presents a 100 Hz monocular position based visual servoing system to control a quadrotor flying in close proximity to vertical structures approximating a narrow, locally linear shape. Assuming the object boundaries are represented by parallel vertical lines in the image, detection and tracking is achieved using Plücker line representation and a line tracker. The visual information is fused with IMU data in an EKF framework to provide fast and accurate state estimation. A nested control design provides position and velocity control with respect to the object. Our approach is aimed at high performance on-board control for applications allowing only small error margins and without a motion capture system, as required for real world infrastructure inspection. Simulated and ground-truthed experimental results are presented.

A loop cancellation based active damping solution for constant power instability in vehicular power systems

In recent years, electric propulsion systems have increasingly been used in land, sea and air vehicles. The vehicular power systems are usually loaded with tightly regulated power electronic converters which tend to draw constant power. Since the constant power loads (CPLs) impose negative incremental resistance characteristics on the feeder system, they pose a potential threat to the stability of vehicular power systems. This effect becomes more significant in the presence of distribution lines between source and load in large vehicular power systems such as electric ships and more electric aircrafts. System transients such as sudden drop of converter side loads or increase of constant power requirement can cause complete system instability. Most of the existing research work focuses on the modeling and stabilization of DC vehicular power systems with CPLs. Only a few solutions are proposed to stabilize AC vehicular power systems with non-negligible distribution lines and CPLs. Therefore, this paper proposes a novel loop cancellation technique to eliminate constant power instability in AC vehicular power systems with a theoretically unbounded system stability region. Analysis is carried out on system stability with the proposed method and simulation results are presented to validate its effectiveness.

Virtual resistance based active damping solution for constant power instability in AC microgrids

With ever-increasing share of power electronic loads constant power instability is becoming a significant issue in microgrids, especially when they operate in the islanding mode. Transient conditions like resistive load-shedding or sudden increase of constant power loads (CPL) might destabilize the whole system. Modeling and stability analysis of AC microgrids with CPLs have already been discussed in literature. However, no effective solutions are provided to stabilize this kind of system. Therefore, this paper proposes a virtual resistance based active damping method to eliminate constant power instability in AC microgrids. Advantages and limitations of the proposed method are also discussed in detail. Simulation results are presented to validate the proposed active damping solution.

Agent-based modelling of aircraft boarding methods

We implemented six different boarding strategies (Wilma, Steffen, Reverse Pyramid, Random, Blocks and By letter) in order to investigate boarding times for Boeing 777 and Airbus 380 aircraft. We also introduce three new boarding methods to find the optimum boarding strategy. Our models explicitly simulate the behaviour of groups of people travelling together and we explicitly simulate the timing to store their luggage as part of the boarding process. Results from the simulation demonstrates the Reverse Pyramid method is the best boarding method for Boeing 777, and the Steffen method is the best boarding method for Airbus 380. For the new suggested boarding methods, aisle first boarding method is the best boarding strategy for Boeing 777 and row arrangement method is the best boarding strategy for Airbus 380. Overall best boarding strategy is aisle first boarding method for Boeing 777 and Steffen method for Airbus 380.

Exploring location-dependency in process modeling

Purpose – Context-awareness has emerged as an important principle in the design of flexible business processes. The goal of the research is to develop an approach to extend context-aware business process modeling toward location-awareness. The purpose of this paper is to identify and conceptualize location-dependencies in process modeling. Design/methodology/approach – This paper uses a pattern-based approach to identify location-dependency in process models. The authors design specifications for these patterns. The authors present illustrative examples and evaluate the identified patterns through a literature review of published process cases. Findings – This paper introduces location-awareness as a new perspective to extend context-awareness in BPM research, by introducing relevant location concepts such as location-awareness and location-dependencies. The authors identify five basic location-dependent control-flow patterns that can be captured in process models. And the authors identify location-dependencies in several existing case studies of business processes. Research limitations/implications – The authors focus exclusively on the control-flow perspective of process models. Further work needs to extend the research to address location-dependencies in process data or resources. Further empirical work is needed to explore determinants and consequences of the modeling of location-dependencies. Originality/value – As existing literature mostly focusses on the broad context of business process, location in process modeling still is treated as “second class citizen” in theory and in practice. This paper discusses the vital role of location-dependencies within business processes. The proposed five basic location-dependent control-flow patterns are novel and useful to explain location-dependency in business process models. They provide a conceptual basis for further exploration of location-awareness in the management of business processes.