Pipelined Algorithms to Detect Cheating in Long-Term Grid Computations

This paper studies pipelined algorithms for protecting distributed grid computations from cheating participants, who wish to be rewarded for tasks they receive but don't perform. We present improved cheater detection algorithms that utilize natural delays that exist in long-term grid computations. In particular, we partition the sequence of grid tasks into two interleaved sequences of task rounds, and we show how to use those rounds to devise the first general-purpose scheme that can catch all cheaters, even when cheaters collude. The main idea of this algorithm might at first seem counter-intuitive--we have the participants check each other's work. A naive implementation of this approach would, of course, be susceptible to collusion attacks, but we show that by, adapting efficient solutions to the parallel processor diagnosis problem, we can tolerate collusions of lazy cheaters, even if the number of such cheaters is a fraction of the total number of participants. We also include a simple economic analysis of cheaters in grid computations and a parameterization of the main deterrent that can be used against them--the probability of being caught.Comment: Expanded version with an additional figure; ISSN 0304-397

Investigation of a Complex Reaction Network: II. Kinetics, Mechanism and Parameter Estimation

Conventional Strategies for Discrimination of Intrinsic and Apparent Kinetics from Crushed- and Whole-Catalyst-Pellet Experimental Data, Respectively, Do Not Yield Satisfactory Results for the Reaction Network in the Manufacture of Α-Aminomethyl-2-Furanmethanol (Aminoalcohol) from Α-Nitromethyl-2-Furanmethanol (Nitroalcohol). Laboratory Trickle-Bed Reactor Tests in the Range of Concentration and Product Yield of Commercial Interest Are Utilized to Yield a Reasonable Set of Kinetic Parameters, Which Are Otherwise Unobtainable. This is Accomplished by Proposing a Reaction Network, a Plausible Mechanism, and Optimizing the Kinetic Parameters based on the Reactor-Model-Generated Performance Data to Fit Experimental Output Concentrations of All Species for the Entire Set of Experiments. a Complex Reaction Network for the Key Reactions in the System is Developed based on the Reaction Scheme in Part I. Fitting of Trickle-Bed Reactor Data to This Model is Attempted to Yield an Insight into the Actual Kinetics. the Results Show Promise of Obtaining an overall Network Kinetic Model, Even with the Limited Data Available

Investigation of a Complex Reaction Network: I. Experiments in a High-Pressure Trickle-Bed Reactor

A High-Pressure Trickle-Bed Reactor Was Used to Achieve High Productivity and Selectivity for the Manufacture of a Key Herbicide Intermediate (Α-Aminomethyl-2-Furanmethanol (Amino Alcohol, AA) from Α-Nitromethyl-2-Furanmethanol (Nitro Alcohol, NA). Raney Nickel Catalysts of Varying Activity Were Prescreened for Suitability in Trickle-Bed Operation. the Effect of Operating Parameters Such as Reactant Feed Concentration, Liquid Mass Velocity, and Temperature on the Yield of Amino Alcohol (AA) for RNi-A Are Discussed. the Superiority of Trickle-Bed Reactors over Others Such as Semibatch and Batch Slurry Systems is Demonstrated. the AA Yield Increases with Decrease in Feed Reactant Concentration and Liquid Mass Velocity, as Well as with Lowering of the Operating Temperature. a Maximum Product Yield of 90.1% Was Obtained at 8.3 Wt. % Feed Concentration of Nitroalcohol (NA), While at the Highest Feed Concentration of 40 Wt. % NA, the Maximum Product Yield Was 58%. the Volumetric Productivity of AA Was Significantly Higher at Higher Reactant Feed Concentrations, Even Though the Yield Was Lower under These Conditions. the Operating Temperature Was Also an Important Parameter, with a Lower Temperature Being Preferable for Trickle-Bed Experiments. Bed Dilution with Inert Fines Improved Catalyst Utilization and Increased the AA Yield and Productivity in the Laboratory-Scale Trickle-Bed Reactor

Learning by building: A visual modelling language for psychology students

Cognitive modelling involves building computational models of psychological theories in order to learn more about them, and is a major research area allied to psychology and artificial intelligence. The main problem is that few psychology students have previous programming experience. The course lecturer can avoid the problem by presenting the area only in general terms. This leaves the process of building and testing models, which is central to the methodology, an unknown. Alternatively, students can be introduced to one of the existing cognitive modelling languages, though this can easily be overwhelming, hindering rather than helping their understanding. Our solution was to design and build a programming language for the intended population. The result is Hank, a visual cognitive modelling language for the psychologist. Our informal analyses have investigated the effectiveness of Hank in its intended context of use, both as a paper and pencil exercise for individuals, and as a computer based project to be carried out in groups. The findings largely support the Hank design decisions, and illuminate many of the challenges inherent in designing a programming language for an educational purpose

Flink: Semantic Web technology for the extraction and analysis of social networks

We present the Flink system for the extraction, aggregation and visualization of online social networks. Flink employs semantic technology for reasoning with personal information extracted from a number of electronic information sources including web pages, emails, publication archives and FOAF profiles. The acquired knowledge is used for the purposes of social network analysis and for generating a webbased presentation of the community. We demonstrate our novel method to social science based on electronic data using the example of the Semantic Web research community

Optimization and Evaluation of Antiparasitic Benzamidobenzoic Acids as Inhibitors of Kinetoplastid Hexokinase 1

Kinetoplastid-based infections are neglected diseases that represent a significant human health issue. Chemotherapeutic options are limited due to toxicity, parasite susceptibility, and poor patient compliance. In response, we studied a molecular-target-directed approach involving intervention of hexokinase activity—a pivotal enzyme in parasite metabolism. A benzamidobenzoic acid hit with modest biochemical inhibition of Trypanosoma brucei hexokinase 1 (TbHK1, IC50=9.1 μm), low mammalian cytotoxicity (IMR90 cells, EC50>25 μm), and no appreciable activity on whole bloodstream-form (BSF) parasites was optimized to afford a probe with improved TbHK1 potency and, significantly, efficacy against whole BSF parasites (TbHK1, IC50=0.28 μm; BSF, ED50=1.9 μm). Compounds in this series also inhibited the hexokinase enzyme from Leishmania major (LmHK1), albeit with less potency than toward TbHK1, suggesting that inhibition of the glycolytic pathway may be a promising opportunity to target multiple disease-causing trypanosomatid protozoa

Cross-syndrome comparison of real-world executive functioning and problem solving using a new problem-solving questionnaire

Background. Individuals with neurodevelopmental disorders like Williams syndrome and Down syndrome exhibit executive function impairments on experimental tasks (Lanfranchi, Jerman, Dal Pont, Alberti, & Vianello, 2010; Menghini, Addona, Costanzo, & Vicari, 2010), but the way that they use executive functioning for problem solving in everyday life has not hitherto been explored. The study aim is to understand cross-syndrome characteristics of everyday executive functioning and problem solving. Methods. Parents/carers of individuals with Williams syndrome (n=47) or Down syndrome (n=31) of a similar chronological age (m =17 years 4 months and 18 years respectively) as well as those of a group of younger typically developing children (n=34; m=8 years 3 months) completed two questionnaires: the Behavior Rating Inventory of Executive Function (BRIEF; Gioia, Isquith, Guy, & Kenworthy, 2000) and a novel Problem-Solving Questionnaire. Results. The rated likelihood of reaching a solution in a problem solving situation was lower for both syndromic groups than the typical group, and lower still for the Williams syndrome group than the Down syndrome group. The proportion of group members meeting the criterion for clinical significance on the BRIEF was also highest for the Williams syndrome group. While changing response, avoiding losing focus and maintaining perseverance were important for problem-solving success in all groups, asking for help and avoiding becoming emotional were also important for the Down syndrome and Williams syndrome groups respectively. Keeping possessions in order was a relative strength amongst BRIEF scales for the Down syndrome group. Conclusion. Results suggest that individuals with Down syndrome tend to use compensatory strategies for problem solving (asking for help and potentially, keeping items well ordered), while for individuals with Williams syndrome, emotional reactions disrupt their problem- solving skills. This paper highlights the importance of identifying syndrome-specific problem-solving strengths and difficulties to improve effective functioning in everyday life

Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP

During the 1980s Michie defined Machine Learning in terms of two orthogonal axes of performance: predictive accuracy and comprehensibility of generated hypotheses. Since predictive accuracy was readily measurable and comprehensibility not so, later definitions in the 1990s, such as Mitchell’s, tended to use a one-dimensional approach to Machine Learning based solely on predictive accuracy, ultimately favouring statistical over symbolic Machine Learning approaches. In this paper we provide a definition of comprehensibility of hypotheses which can be estimated using human participant trials. We present two sets of experiments testing human comprehensibility of logic programs. In the first experiment we test human comprehensibility with and without predicate invention. Results indicate comprehensibility is affected not only by the complexity of the presented program but also by the existence of anonymous predicate symbols. In the second experiment we directly test whether any state-of-the-art ILP systems are ultra-strong learners in Michie’s sense, and select the Metagol system for use in humans trials. Results show participants were not able to learn the relational concept on their own from a set of examples but they were able to apply the relational definition provided by the ILP system correctly. This implies the existence of a class of relational concepts which are hard to acquire for humans, though easy to understand given an abstract explanation. We believe improved understanding of this class could have potential relevance to contexts involving human learning, teaching and verbal interaction