Miracle-dz/UniFAR-FTUs huggingface dataset:
A formal analysis of trust and distrust relationships in Shakespeare’s Othello

In settings where agents can be exploited, trust and reputation are key issues. As a consequence, within the multi-agent systems community, considerable research has gone into the development of computationally viable models of these concepts. In this paper, we present an abstract framework that allows agents to form coalitions with agents that they believe to be trustworthy. In contrast to many other models, we take the notion of distrust to be our key social concept. We use a graph theoretic model to capture the distrust relations within a society, and use this model to formulate several notions of mutually trusting coalitions. We investigate principled techniques for how the information present in our distrust model can be aggregated to produce individual measures of how trustworthy an agent is considered to be by a society. We extend the framework with trust relationships and introduce the notion of Trust-Coalitions (T-Coalitions) . We ﬁnally provide an analysis of the Shakespeare’s tragedy, Othello, using our abstract framework.
An Efficient Byzantine-Resilient Tuple Space

Open distributed systems are typically composed by an unknown number of processes running in heterogeneous hosts. Their communication often requires tolerance to temporary disconnections and security against malicious actions. Tuple spaces are a well-known coordination model for this kind of systems. They can support communication that is decoupled both in time and space. There are currently several implementations of distributed fault-tolerant tuple spaces but they are not Byzantine-resilient, i.e., they do not provide a correct service if some replicas are attacked and start to misbehave. This paper presents an efficient implementation of a linearizable Byzantine fault-tolerant Tuple Space (LBTS) that uses a novel Byzantine quorum systems replication technique in which most operations are implemented by quorum protocols while stronger operations are implemented by more expensive protocols based on consensus. LBTS is linearizable and wait-free, showing interesting performance gains when compared to a similar construction based on state machine replication.
Performance Tradeoffs for Networked Jump Observer-Based Fault Diagnosis

In this paper, we address the fault diagnosis problem for discrete-time multi-sensor systems over communication networks with measurement dropouts. We use the measurement outcomes to model the measurement reception scenarios. Based on this, we propose the use of a jump observer to diagnose multiple faults. We model the faults as slow time-varying signals and introduce this dynamic in the observer to estimate the faults and to generate a residual. The fault detection is assured by comparing the residual signal with a prescribed threshold. We design the jump observer, the residual and the threshold to attain disturbance attenuation, fault tracking and detection conditions and a given false alarm rate. The false alarm rate is upper bounded by means of Markov's inequality. We explore the tradeoffs between the minimum detectable faults, the false alarm rate and the response time to faults of the fault diagnoser. By imposing the disturbances and measurement noises to be Gaussian, we tighten the false alarm rate bound which improves the time needed to detect a fault. A numerical example is provided to illustrate the effectiveness of the theory developed in the paper.
Characterizing Web Spam Using Content and HTTP Session Analysis

Web spam research has been hampered by a lack of statistically significant collections. In this paper, we perform the first large-scale characterization of web spam using content and HTTP session analysis techniques on the Webb Spam Corpus ‐ a collection of about 350,000 web spam pages. Our content analysis results are consistent with the hypothesis that web spam pages are dierent from normal web pages, showing far more duplication of physical content and URL redirections. An analysis of session information collected during the crawling of the Webb Spam Corpus shows significant concentration of hosting IP addresses in two narrow ranges as well as significant overlaps among session header values. These findings suggest that content and HTTP session analysis may contribute a great deal towards future eorts to automatically distinguish web spam pages from normal web pages.
International Environmental Modelling and Software Society (iemss) 2010 International Congress on Environmental Modelling and Software Modelling for Environment's Sake, Fifth Biennial Meeting, Ottawa, Canada Fuzzy Logic Based Iedsss for Environmental Risk Assessment and Management

Environmental problems and the related adaptation strategies have grown in importance and complexity during the last years. The large amount of data and information that needs to be handled and integrated requires specific methodologies and tools. Several research and application activities are undergoing worldwide for the development of Decision Support Systems (DSSs) that allow management of multiple and different data in order to aid decision-making. In this paper the following DSSs using fuzzy models based Artificial Intelligence to address environmental problems will be presented. SYRIADE is a Spatial DSS for Regional Risk Assessment of degraded land supporting the inventory and assessment of contaminated sites and mining waste sites at regional scale. DESYRE addresses the main phases of contaminated sites' remediation process, e.g. analysis of social and economic benefits and constraints, site characterization, risk assessment, selection of best available technologies, analysis of residual risk and comparison of different remediation scenarios. MODELKEY is a GIS-based DSS that supports the EU Water Framework Directive (WFD) implementation by allowing the environmental quality evaluation of fluvial ecosystems and the prioritization of hot spots along the river basin. Finally, the CMCC DSS supports the identification and prioritization of climate change impacts and risks on coastal zones, in order to guide the definition of appropriate adaptations strategies. 1. INTRODUCTION There is a growing desire to develop effective and efficient computational methods and tools that facilitate environmental analysis, evaluation and problem solving. Environmental problems of interest may include concerns as apparently dissimilar as revitalization of contaminated land, evaluation of the impacts of climate change and effective management of inland and coastal waters. The approach to effective problem solving in both of these examples can involve the development of what are commonly called Decision Support Systems (DSSs). A DSS is a system for helping to choose among alternative actions. There are many DSS application areas, possible approaches to making decisions, and levels at which decisions can be supported. A DSS might support decision-making for a specific problem or type of problems. DSSs for environmental problems and, more specifically, for risk assessment and management, are always related to complex problems where physical status of environment
Charged track multiplicity in B meson decay

We have used the CLEO II detector to study the multiplicity of charged particles in the decays of B mesons produced at the {upsilon}(4S) resonance. Using a sample of 1.5x10{sup 6} B meson pairs, we find the mean inclusive charged particle multiplicity to be 10.71{+-}0.02{sub -0.15}{sup +0.21} for the decay of the pair. This corresponds to a mean multiplicity of 5.36{+-}0.01{sub -0.08}{sup +0.11} for a single B meson. Using the same data sample, we have also extracted the mean multiplicities in semileptonic and nonleptonic decays. We measure a mean of 7.82{+-}0.05{sub -0.19}{sup +0.21} charged particles per BB(bar sign) decay when both mesons decay semileptonically. When neither B meson decays semileptonically, we measure a mean charged particle multiplicity of 11.62{+-}0.04{sub -0.18}{sup +0.24} per BB(bar sign) pair. (c) 2000 The American Physical Society.
Overview of the Special Issue on Trust and Veracity of Information in Social Media

From a business and government point of view, there is an increasing need to interpret and act upon information from large-volume media, such as Twitter, Facebook and Web news. However, knowledge gathered from such online sources comes with a major caveat—it cannot always be trusted, nor is it always factual or of high quality. Rumors tend to spread rapidly through social networks, and their veracity is hard to establish in a timely fashion. For instance, during an earthquake in Chile, rumors spread through Twitter that a volcano became active and there was a tsunami warning in Valparaiso [Castillo et al. 2013]. Later, these reports were found to be false. Another example concerns “astroturf campaigns”—a malicious use of Twitter and other social media during election campaigns to provide fake support of a message or project by grassroots participants, while at the same time hiding the original campaign sponsors (usually elite groups or their lobbies). Researchers have identified numerous sources of untrusted content, and through them they found that several online communities interact with narratives stemming from conspiracy theories [Bessi et al. 2015]. A 2012
Deadlines and Distractions

We consider a task, demanding a sequence of efforts, that must be completed by a deadline. Effort is not contractible. Agents face shocks to their opportunity cost of time and are sometimes distracted from work. We show that agents who are often distracted may outperform agents who are distracted less often. The reason is that anticipation of distractions induces agents to start earlier for precautionary reasons. Principals can increase the probability of completion, and achieve higher profits, by strategically setting "tight" deadlines, provided that the deadlines can be extended with some positive probability.
A Recovery Conscious Framework for Fault Resilient Storage Systems

This paper presents a recovery-conscious framework for improving the fault resiliency and recovery efficiency of highly concurrent embedded storage software systems. Our framework consists of a three-tier architecture and a suite of recovery conscious techniques. In the top tier, we promote the fine-grained recovery at the task level by introducing recovery scopes to model recovery dependencies between tasks. At the middle tier we develop highly effective groupings of recovery scopes into recovery groups based on system and workload characteristics. We study how to distribute recovery scopes between recovery groups and schedule recovery groups effectively in a multi-core storage system through a careful tuning of recovery-efficiency sensitive parameters. At the bottom tier, advocate the use of recovery-conscious scheduling instead of performance oriented scheduling to provide high recovery efficiency without sacrificing system performance. An important question to address in this tier is under which combinations of resource pools and recovery groups, the recovery-conscious scheduling outperforms the performance oriented scheduling. Our techniques have been implemented on a real industry-standard storage system. Experimental results show that the right choice of recovery-sensitive parameters is critical and our techniques are effective, non-intrusive and can significantly boost system resilience while delivering high performance under a variety of system configurations.
On software architecture analysis of flexibility, Complexity of changes: Size isn't everything

The decisions made in a ~stem 's so~'are architecture have a major impact on the qua/ity of the resu/ting ~stem. The goa/ of softwore architecture ana/ysis is to assess whether these decisions /ead to the required qua/ity. Dur research concentrates on sofiware architecture ana/ysis of administrative ~stems. In this paper, we focus on the qua/ity attribute jlexibility, because it is considered to be an important property for this c/ass of ~stems. In our opinion, existing methods are not suJJicient for ana/yzing this qua/ity attribute within this domain, because theyoffer /it tie support for identifying scenarios that are rea/ly hord to rea/ize. In addition, they do not take into account a/l factors that cause comp/exity of changes for ~stems within this domain. Therefore, we have dejined an a/temalive method that does, which is presented in this paper.
Invasive Cane Toads: Social Facilitation Depends upon an Individual’s Personality

Individual variation in behavioural traits (including responses to social cues) may influence the success of invasive populations. We studied the relationship between sociality and personality in invasive cane toads (Rhinella marina) from a recently established population in tropical Australia. In our field experiments, we manipulated social cues (the presence of a feeding conspecific) near a food source. We captured and compared toads that only approached feeding sites where another toad was already present, with conspecifics that approached unoccupied feeding sites. Subsequent laboratory trials showed correlated personality differences (behavioural syndromes) between these two groups of toads. For example, toads that approached already-occupied rather than unoccupied feeding sites in the field, took longer to emerge from a shelter-site in standardized trials, suggesting these individuals are ‘shy’ (whereas toads that approached unoccupied feeding stations tended to be ‘bold’). Manipulating hunger levels did not abolish this difference. In feeding trials, a bold toad typically outcompeted a shy toad under conditions of low prey availability, but the outcome was reversed when multiple prey items were present. Thus, both personality types may be favored under different circumstances. This invasive population of toads contains individuals that exhibit a range of personalities, hinting at the existence of a wide range of social dynamics in taxa traditionally considered to be asocial.
A Qualitative Analysis Algorithm and Its

We promote a qualitative analysis algorithm to intelligently identify the ammonia, ethanol and their mixture. This work based on an electronic nose with a set of specific gas senor array building in a temperature controlled mini-cavity. A BP neural network has been trained for identification the samples inputs which preprocessed by principal component analysis (PCA) or linear discriminant analysis (LDA) method for dimension reduction. Results showed that ammonia, ethanol and different proportions of their mixture can be identified accurately. And the LDA performed better on dimension reduction in this case. 
Effective recruitment and retention of older adults in physical activity research: PALS study.

OBJECTIVES To develop strategies to recruit and retain inactive older adults into a physical activity program. METHODS Names of 7378 older adults were obtained from 60 neighborhoods. Then, 6401 potential subjects were matched to telephone numbers and phoned. Subjects meeting the screening criteria were invited to join the program (n = 4209). Walk leaders and social support were used to enhance retention. RESULTS Five hundred seventy-three subjects were recruited (260 intervention and 313 control). The respective participation rate was 12.6% (260/2056) and 14.5% (313/2153), with low attrition of 31.9% (83/260) and 24.6% (77/313). CONCLUSION Effective recruitment and retention strategies were identified.
Polynomial Methods and Convex Optimization for theControl of Input Constrained Systems

A polynomial approach is pursued for locally stabilizing discrete-time linear systems subject to input constraints. Using the Youla-Ku cera parametrization and geometric properties of polyhedra and ellipsoids, the problem of simultaneously deriving a stabilizing controller and the corresponding stability region is cast into a standard LMI problem. The approach is illustrated by a numerical example underlining the use of powerful CACSD tools such as the Polynomial Toolbox, Lmitool or Simulink for Matlab.
Aging, Globalization and Inequality : The New Critical Gerontology

This book is a major reassessment of work in the field of critical gerontology, providing a comprehensive survey of issues by a team of contributors drawn from Europe and North America. The book focuses on the variety of ways in which age and ageing are socially constructed, and the extent to which growing old is being transformed through processes associated with globalisation. The collection offers a range of alternative views and visions about the nature of social ageing, making a major contribution to theory-building within the discipline of gerontology. The different sections of the book give an overview of the key issues and concerns underlying the development of critical gerontology. These include: first, the impact of globalisation and of multinational organizations and agencies on the lives of older people; second, the factors contributing to the "social construction" of later life; and third, issues associated with diversity and inequality in old age, arising through the effects of cumulative advantage and disadvantage over the life course. These different themes are analysed using a variety of theoretical perspectives drawn from sociology, social policy, political science, and social anthropology. "Aging, Globalization and Inequality" brings together key contributors to critical perspectives on aging and is unique in the range of themes and concerns covered in a single volume. The study moves forward an important area of debate in studies of aging, and thus provides the basis for a new type of critical gerontology relevant to the twenty-first century.
The dynamic emotion recognition system based on functional connectivity of brain regions

Emotion perception similar to thinking, learning and remembering is consequent of complicated brain processes which are related to specific biological metabolism. Different human's emotional states are recognizable by measuring and interpreting of human physiological signals. Bio-sensors possess a number of advantages against other emotion recognition methods as they are relatively more consistent across cultures and nations. Emotions have a serious effect on driving. Human beings in negative and sometimes positive emotional states can be distracted which will increase the risk of driving. This paper presents an EEG-based emotion recognition system. Mutual information and magnitude squared coherence are applied to investigate the interconnectivity between 8 scalp regions. A study was performed to collect 8 channels of EEG data from 26 healthy right-handed subjects in experiencing 4 emotional states while exposed to audio-visual emotional stimuli. After feature extraction, 5-fold cross-validation was then performed using the KNN and SVM classifier. The results showed existence of different kind of functional brain connectivity in different emotional states.
On the impact of network address translation on locality-aware P2P live streaming systems

More that 70% of all Internet hosts are behind network address translation (NAT) devices. In this paper, we study the impact of the NAT devices on application layer traffic optimization (ALTO) schemes. NAT brings forth difficulty if not impossible for peers to connect to one another, which may lead sever performance penalty of P2P applications. Most locality-aware P2P systems only consider network location such as autonomous system (AS) number. In this paper, we first demonstrated that peers may be expelled from the system with a high probability in P2P live streaming systems because NAT devices degrade the peer's capacity for contributing upload bandwidth. We proposed a simple model to compute the theoretical upper and lower bounds for the number of displaced peers. We also propose a simple peer selection algorithm with the NAT consideration in mind to build a non-random peer list that reduces transit traffic, increases peer connectivity and minimizes the playback start-up time for ALTO schemes. We conducted a ns2 simulation study to evaluate the performance of the proposed algorithm. Based on multiple simulation experiments, we demonstrated that the proposed algorithm decreases the number of expelled peers while it reduces the start-up delay of peers and reduces the inter-domain traffic.
Isotropy conditions for lattice Boltzmann schemes. Application to D2Q9

In this paper, we recall the linear version of the lattice Boltzmann schemes in the framework proposed by d'Humi\'eres. According to the equivalent equations we introduce a definition for a scheme to be isotropic at some order. This definition is chosen such that the equivalent equations are preserved by orthogonal transformations of the frame. The property of isotropy can be read through a group operation and then implies a sequence of relations on relaxation times and equilibrium states that characterizes a lattice Boltzmann scheme. We propose a method to select the parameters of the scheme according to the desired order of isotropy. Applying it to the D2Q9 scheme yields the classical constraints for the first and second orders and some non classical for the third and fourth orders.
Systematic pruning of Volterra series using Wiener G-functionals for power amplifier and predistorter modeling

The Volterra series is a well-established technique for modeling nonlinear dynamic system. Their wide adoption in predicting the behavior of Radio Frequency Power Amplifiers has been significantly hindered by the large number of coefficients involved especially when they exhibit high order of nonlinearity and strong memory effects. In this paper, we present a new pruning approach based on Wiener G-functionals which is sought for the selection of reduced number of dominant kernels in the Volterra series.
Regulation of the JNK3 Signaling Pathway during Islet Isolation: JNK3 and c-fos as New Markers of Islet Quality for Transplantation

Stress conditions generated throughout pancreatic islet processing initiate the activation of pro-inflammatory pathways and beta-cell destruction. Our goal is to identify relevant and preferably beta-specific markers to assess the activation of beta-cell stress and apoptotic mechanisms, and therefore the general quality of the islet preparation prior to transplantation. Protein expression and activation were analyzed by Western blotting and kinase assays. ATP measurements were performed by a luminescence-based assay. Oxygen consumption rate (OCR) was measured based on standard protocols using fiber optic sensors. Total RNA was used for gene expression analyzes. Our results indicate that pancreas digestion initiates a potent stress response in the islets by activating two stress kinases, c-Jun N-terminal Kinase (JNK) and p38. JNK1 protein levels remained unchanged between different islet preparations and following culture. In contrast, levels of JNK3 increased after islet culture, but varied markedly, with a subset of preparations bearing low JNK3 expression. The observed changes in JNK3 protein content strongly correlated with OCR measurements as determined by the Spearman's rank correlation coefficient rho in the matching islet samples, while inversely correlating with c-fos mRNA expression . In conclusion, pancreas digestion recruits JNK and p38 kinases that are known to participate to beta-cell apoptosis. Concomitantly, the islet isolation alters JNK3 and c-fos expression, both strongly correlating with OCR. Thus, a comparative analysis of JNK3 and c-fos expression before and after culture may provide for novel markers to assess islet quality prior to transplantation. JNK3 has the advantage over all other proposed markers to be islet-specific, and thus to provide for a marker independent of non-beta cell contamination.