Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments

The scheduling problem in distributed data-intensive computing environments has become an active research topic due to the tremendous growth in grid and cloud computing environments. As an innovative distributed intelligent paradigm, swarm intelligence provides a novel approach to solving these potentially intractable problems. In this paper, we formulate the scheduling problem for work-flow applications with security constraints in distributed data-intensive computing environments and present a novel security constraint model. Several meta-heuristic adaptations to the particle swarm optimization algorithm are introduced to deal with the formulation of efficient schedules. A variable neighborhood particle swarm optimization algorithm is compared with a multi-start particle swarm optimization and multi-start genetic algorithm. Experimental results illustrate that population based meta-heuristics approaches usually provide a good balance between global exploration and local exploitation and their feasibility and effectiveness for scheduling work-flow applications. © 2010 Elsevier Inc. All rights reserved.

Composition Games for Distributed Systems: the EU Grant games

We analyze ways by which people decompose into groups in distributed systems. We are interested in systems in which an agent can increase its utility by connecting to other agents, but must also pay a cost that increases with the size of the sys- tem. The right balance is achieved by the right size group of agents. We formulate and analyze three intuitive and realistic games and show how simple changes in the protocol can dras- tically improve the price of anarchy of these games. In partic- ular, we identify two important properties for a low price of anarchy: agreement in joining the system, and the possibil- ity of appealing a rejection from a system. We show that the latter property is especially important if there are some pre- existing constraints regarding who may collaborate (or com- municate) with whom.

A sparse representation based fast detection method for surface defect detection of bottle caps

A practical machine-vision-based system is developed for fast detection of defects occurring on the surface of bottle caps. This system can be used to extract the circular region as the region of interests (ROI) from the surface of a bottle cap, and then use the circular region projection histogram (CRPH) as the matching features. We establish two dictionaries for the template and possible defect, respectively. Due to the requirements of high-speed production as well as detecting quality, a fast algorithm based on a sparse representation is proposed to speed up the searching. In the sparse representation, non-zero elements in the sparse factors indicate the defect's size and position. Experimental results in industrial trials show that the proposed method outperforms the orientation code method (OCM) and is able to produce promising results for detecting defects on the surface of bottle caps.

Tools for Finding Inconsistencies in Real-world Logic-based Systems

Currently there is extensive theoretical work on inconsistencies in logic-based systems. Recently, algorithms for identifying inconsistent clauses in a single conjunctive formula have demonstrated that practical application of this work is possible. However, these algorithms have not been extended for full knowledge base systems and have not been applied to real-world knowledge. To address these issues, we propose a new algorithm for finding the inconsistencies in a knowledge base using existing algorithms for finding inconsistent clauses in a formula. An implementation of this algorithm is then presented as an automated tool for finding inconsistencies in a knowledge base and measuring the inconsistency of formulae. Finally, we look at a case study of a network security rule set for exploit detection (QRadar) and suggest how these automated tools can be applied.

Behavioral Model Generation from Use Cases Based on Ontology Mapping and GRASP Patterns

This paper contributes a new approach for developing UML software designs from Natural Language (NL), making use of a meta-domain oriented ontology, well established software design principles and Natural Language Processing (NLP) tools. In the approach described here, banks of grammatical rules are used to assign event flows from essential use cases. A domain specific ontology is also constructed, permitting semantic mapping between the NL input and the modeled domain. Rules based on the widely-used General Responsibility Assignment Software Principles (GRASP) are then applied to derive behavioral models.

Isotretinoin therapy changes the expression of antimicrobial peptides in acne vulgaris

In acne vulgaris, antimicrobial peptides (AMPs) could play a dual role; i.e., protective by acting against Propionibacterium acnes, pro-inflammatory by acting as signalling molecules. The cutaneous expression of 15 different AMPs was investigated in acne patients; furthermore, the impact of isotretinoin therapy on AMP expression was analysed in skin biopsies from 13 patients with acne vulgaris taken before, during and after a 6-month treatment cycle with isotretinoin using quantitative real-time polymerase chain reaction. Cutaneous expression of the AMPs cathelicidin, human β-defensin-2 (HBD-2), lactoferrin, lysozyme, psoriasin (S100A7), koebnerisin (S100A15), and RNase 7 was upregulated in untreated acne vulgaris, whereas α-defensin-1 (HNP-1) was downregulated compared to controls. While relative expression levels of cathelicidin, HBD-2, lactoferrin, psoriasin (S100A7), and koebnerisin (S100A15) decreased during isotretinoin treatment, only those of cathelicidin and koebnerisin returned to normal after 6 months of isotretinoin therapy. The increased expression of lysozyme and RNase 7 remained unaffected by isotretinoin treatment. The levels of granulysin, RANTES (CCL5), perforin, CXCL9, substance P, chromogranin B, and dermcidin were not regulated in untreated acne patients and isotretinoin had no effect on these AMPs. In conclusion, the expression of various AMPs is altered in acne vulgaris. Isotretinoin therapy normalizes the cutaneous production of distinct AMPs while the expression of others is still increased in healing acne. Considering the antimicrobial and pro-inflammatory role of AMPs, these molecules could serve as specific targets for acne therapy and maintenance of clinical remission.

Intelligent Computing for Sustainable Energy and Environment (ICSEE 2012)

No Abstract available

Laughter Type Recognition from Whole Body Motion

Despite the importance of laughter in social interactions it remains little studied in affective computing. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received almost no attention. The aim of this study is twofold: first an investigation into observers' perception of laughter states (hilarious, social, awkward, fake, and non-laughter) based on body movements alone, through their categorization of avatars animated with natural and acted motion capture data. Significant differences in torso and limb movements were found between animations perceived as containing laughter and those perceived as nonlaughter. Hilarious laughter also differed from social laughter in the amount of bending of the spine, the amount of shoulder rotation and the amount of hand movement. The body movement features indicative of laughter differed between sitting and standing avatar postures. Based on the positive findings in this perceptual study, the second aim is to investigate the possibility of automatically predicting the distributions of observer's ratings for the laughter states. The findings show that the automated laughter recognition rates approach human rating levels, with the Random Forest method yielding the best performance.

Sea-level changes in Iceland and the influence of the North Atlantic Oscillation during the last half millennium

We present a new, diatom-based sea-level reconstruction for Iceland spanning the last -500 years, and investigate the possible mechanisms driving the sea-level changes. A sea-level reconstruction from near the Icelandic low pressure system is important as it can improve understanding of ocean-atmosphere forcing on North Atlantic sea-level variability over multi-decadal to centennial timescales. Our reconstruction is from Viarhólmi salt marsh in Snæfellsnes in western Iceland, a site from where we previously obtained a 2000-yr record based upon less precise sea-level indicators (salt-marsh foraminifera). The 20th century part of our record is corroborated by tide-gauge data from Reykjavik. Overall, the new reconstruction shows ca0.6m rise of relative sea level during the last four centuries, of which ca0.2m occurred during the 20th century. Low-amplitude and high-frequency sea-level variability is super-imposed on the pre-industrial long-term rising trend of 0.65m per 1000 years. Most of the relative sea-level rise occurred in three distinct periods: AD 1620-1650, AD 1780-1850 and AD 1950-2000, with maximum rates of ~3±2mm/yr during the latter two of these periods. Maximum rates were achieved at the end of large shifts (from negative to positive) of the winter North Atlantic Oscillation (NAO) Index as reconstructed from proxy data. Instrumental data demonstrate that a strong and sustained positive NAO (a deep Icelandic Low) generates setup on the west coast of Iceland resulting in rising sea levels. There is no strong evidence that the periods of rapid sea-level rise were caused by ocean mass changes, glacial isostatic adjustment or regional steric change. We suggest that wind forcing plays an important role in causing regional-scale coastal sea-level variability in the North Atlantic, not only on (multi-)annual timescales, but also on multi-decadal to centennial timescales.

On the Complexity of Universal Leader Election

Electing a leader is a fundamental task in distributed computing. In its implicit version, only the leader must know who is the elected leader. This article focuses on studying the message and time complexity of randomized implicit leader election in synchronous distributed networks. Surprisingly, the most "obvious" complexity bounds have not been proven for randomized algorithms. In particular, the seemingly obvious lower bounds of Ω(m) messages, where m is the number of edges in the network, and Ω(D) time, where D is the network diameter, are nontrivial to show for randomized (Monte Carlo) algorithms. (Recent results, showing that even Ω(n), where n is the number of nodes in the network, is not a lower bound on the messages in complete networks, make the above bounds somewhat less obvious). To the best of our knowledge, these basic lower bounds have not been established even for deterministic algorithms, except for the restricted case of comparison algorithms, where it was also required that nodes may not wake up spontaneously and that D and n were not known. We establish these fundamental lower bounds in this article for the general case, even for randomized Monte Carlo algorithms. Our lower bounds are universal in the sense that they hold for all universal algorithms (namely, algorithms that work for all graphs), apply to every D, m, and n, and hold even if D, m, and n are known, all the nodes wake up simultaneously, and the algorithms can make any use of node's identities. To show that these bounds are tight, we present an O(m) messages algorithm. An O(D) time leader election algorithm is known. A slight adaptation of our lower bound technique gives rise to an Ω(m) message lower bound for randomized broadcast algorithms. 

An interesting fundamental problem is whether both upper bounds (messages and time) can be reached simultaneously in the randomized setting for all graphs. The answer is known to be negative in the deterministic setting. We answer this problem partially by presenting a randomized algorithm that matches both complexities in some cases. This already separates (for some cases) randomized algorithms from deterministic ones. As first steps towards the general case, we present several universal leader election algorithms with bounds that tradeoff messages versus time. We view our results as a step towards understanding the complexity of universal leader election in distributed networks.

A systematic review investigating the effectiveness of Complementary and Alternative Medicine (CAM) for the management of low back and/or pelvic pain (LBPP) in pregnancy

Aim: To evaluate and summarize the current evidence on the effectiveness of complementary and alternative medicine for the management of low back pain and/or pelvic pain in pregnancy.

Background: International research demonstrates that 25-30% of women use complementary and alternative medicine to manage low back and pelvic pain in pregnancy without robust evidence demonstrating its effectiveness.

Design: A systematic review of randomized controlled trials to determine the effectiveness of complementary and alternative medicine for low back and/or pelvic pain in pregnancy.

Data Sources: Cochrane library (1898-2013), PubMed (1996-2013), MEDLINE (1946-2013), AMED (1985-2013), Embase (1974-2013), Cinahl (1937-2013), Index to Thesis (1716-2013) and Ethos (1914-2013).

Review Methods: Selected studies were written in English, randomized controlled trials, a group 1 or 2 therapy and reported pain reduction as an outcome measure. Study quality was reviewed using Risk of Bias and evidence strength the Cochrane Grading of Recommendations and Development Evaluation Tool.

Results: Eight studies were selected for full review. Two acupuncture studies with low risk of bias showed both clinically important changes and statistically significant results. There was evidence of effectiveness for osteopathy and chiropractic. However, osteopathy and chiropractic studies scored high for risk of bias. Strength of the evidence across studies was very low.

Conclusion: There is limited evidence supporting the use of general CAM for managing pregnancy-related low back and/or pelvic pain. However, the restricted availability of high-quality studies, combined with the very low evidence strength, makes it impossible to make evidence-based recommendations for practice.

Accumulation of the PX domain mutant Frank-ter Haar syndrome protein Tks4 in aggresomes

BACKGROUND: Cells deploy quality control mechanisms to remove damaged or misfolded proteins. Recently, we have reported that a mutation (R43W) in the Frank-ter Haar syndrome protein Tks4 resulted in aberrant intracellular localization.

RESULTS: Here we demonstrate that the accumulation of Tks4(R43W) depends on the intact microtubule network. Detergent-insoluble Tks4 mutant colocalizes with the centrosome and its aggregate is encaged by the intermediate filament protein vimentin. Both the microtubule inhibitor nocodazole and the histone deacetylase inhibitor Trichostatin A inhibit markedly the aggresome formation in cells expressing Tks4(R43W). Finally, pretreatment of cells with the proteasome inhibitor MG132 markedly increases the level of aggresomes formed by Tks4(R43W). Furthermore, two additional mutant Tks4 proteins (Tks4(1-48) or Tks4(1-341)) have been investigated. Whereas the shorter Tks4 mutant, Tks4(1-48), shows no expression at all, the longer Tks4 truncation mutant accumulates in the nuclei of the cells.

CONCLUSIONS: Our results suggest that misfolded Frank-ter Haar syndrome protein Tks4(R43W) is transported via the microtubule system to the aggresomes. Lack of expression of Tks4(1-48) or aberrant intracellular expressions of Tks4(R43W) and Tks4(1-341) strongly suggest that these mutations result in dysfunctional proteins which are not capable of operating properly, leading to the development of FTHS.

Two-Stage Orthogonal Least Squares Methods for Neural Network Construction

A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. Forward selection methods are popular fast subset selection approaches. However, they may only produce suboptimal models and can be trapped into a local minimum. More recently, a two-stage fast recursive algorithm (TSFRA) combining forward selection and backward model refinement has been proposed to improve the compactness and generalization performance of the model. This paper proposes unified two-stage orthogonal least squares methods instead of the fast recursive-based methods. In contrast to the TSFRA, this paper derives a new simplified relationship between the forward and the backward stages to avoid repetitive computations using the inherent orthogonal properties of the least squares methods. Furthermore, a new term exchanging scheme for backward model refinement is introduced to reduce computational demand. Finally, given the error reduction ratio criterion, effective and efficient forward and backward subset selection procedures are proposed. Extensive examples are presented to demonstrate the improved model compactness constructed by the proposed technique in comparison with some popular methods.