Unobserved Behavior Research Articles

Behavioral model identification and classification of multi-component systems

Learning model from the execution traces has been considered in several domains such as traffic classification, malware detection, and software engineering since it distinguishes which processes actually executed through their traces. Behavioral model learning and classification (in terms of learned models) are taken into accounts to eliminate the shortages of models derived based on non-behavioral features and improve the resulting classifications. So far, no general method has been proposed to automatically derive behavioral models. To this aim, we assume that the models of applications can be abstractly defined in terms of how they execute their depending components, well-known in the domain. To automatically derive such models, we extend the passive automata learning by considering the behavior of the depending components in addition to the observed behaviors. The state merging algorithm of the learning process is equipped with a new equivalence relation which aggregate states modulo counter abstraction of symmetric reduction. To improve the generated model to cover unobserved behaviors, we leverage the technique of complex event processing to complete the model with the unseen interleaving of actions due to the concurrent execution of components. The derived models, specified by parametrized transition systems, can distinguish different executions of instances of each component by assigning a unique symbolic identifier to each instantiation and parameterizing actions with such identifiers. The learned models are used to distinguish the executions of applications in an interleaved execution trace of different systems. The detection procedure is more complicated for parametric models because of the need for relating the information of the trace to symbolic identifiers as the parameters. We utilize runtime verification techniques in a three-step novel approach so as to enhance the performance of the matching process for a trace. To illustrate the applicability of our approach, we have employed it for traffic classification in the network domain and then applied it on some real applications. To demonstrate the effectiveness of our approach in this domain, we compare it to related approaches in terms of their true positive rate, false positive rate, and test time. Our results indicate that our technique prevents including invalid traces so that unobserved behaviors are covered with an acceptable precision. • We extend the passive automata learning for multi-component applications. • The learning approach aggregates those states preserving the behavior of components. • We provide an automata-based classifier based on the runtime verification technique. • We compare our classifier with statistical ones for network applications.

Evaluation of Psychological Behavior Predictions Using SPSS Statistical Analysis

The goal of scientific psychology is to understand human behavior. Historically it has been about explaining behavior—that is, accurately describing its causal underpinnings—and predicting behavior—that is, accurately predicting previously unobserved behaviors. Psychologists understand what happens and why once understood, it’s when, why and how again we can make predictions about what might happen. Successfully predicting behavior, our actions whether we understand the underlying causes one of the best ways to know. Psychologists derived from previous studies Using knowledge, why, when and how Behaviors observed can be predicted to occur in the future. Psychologists can predict behavior patterns. behavior By predicting methods, psychologists are our To better understand the underlying causes of actions can Behavioral Intention – It influences a given behavior Refers to motivating factors, where behavior A strong intention to perform will result in the behavior being performed. Subjective norms – this is for most people’s behavior indicates a belief of approval or disapproval.

Optimal prediction of decisions and model selection in social dilemmas using block models

Advancing our understanding of human behavior hinges on the ability of theories to unveil the mechanisms underlying such behaviors. Measuring the ability of theories and models to predict unobserved behaviors provides a principled method to evaluate their merit and, thus, to help establish which mechanisms are most plausible. Here, we propose models and develop rigorous inference approaches to predict strategic decisions in dyadic social dilemmas. In particular, we use bipartite stochastic block models that incorporate information about the dilemmas faced by individuals. We show, combining these models with empirical data on strategic decisions in dyadic social dilemmas, that individual strategic decisions are to a large extent predictable, despite not being “rational.” The analysis of these models also allows us to conclude that: (i) individuals do not perceive games according their game-theoretical structure; (ii) individuals make decisions using combinations of multiple simple strategies, which our approach reveals naturally.

Standard map-like models for single and multiple walkers in an annular cavity

Recent experiments on walking droplets in an annular cavity showed the existence of complex dynamics including chaotically changing velocity. This article presents models, influenced by the kicked rotator/standard map, for both single and multiple droplets. The models are shown to achieve both qualitative and quantitative agreement with the experiments, and make predictions about heretofore unobserved behavior. Using dynamical systems techniques and bifurcation theory, the single droplet model is analyzed to prove dynamics suggested by the numerical simulations.

Forensic taphonomy: Scavenger-induced scattering patterns in the temperate southwestern Cape, South Africa — A first look

The effect of vertebrate scavenging can drastically alter the rate of decomposition and cause skeletal scatter, which hinders human forensic recovery and identification. Patterns of scavenging, disarticulation and scatter in a forensic context are specific to different environments with no known data for South Africa. A better understanding of these patterns can increase the chances of full body recovery and improve identification of human remains. In this preliminary study, the effect of wild vertebrate scavenging on skeletal scatter was examined using a porcine model in the forensically significant thicketed Cape Flats Dune Strandveld habitat. This area is a densely populated part of Cape Town, which suffers from poor socioeconomic conditions and a high murder rate. Ethics was granted for the use of three small (∼20kg) domestic pig (Sus scrofa domesticus) carcasses as proxies for human decomposition. They were deployed in Delft, Cape Town, and observed by motion-activated cameras to record wild scavenger activity. One pig served as a control and was caged to prevent vertebrate but not invertebrate access; the other two served as experimental treatments. Scatter was recorded every second day by marking the location of skeletal elements and measuring the distance and angle from the centre of each deposition site. No scattering was observed in the control, but notable scattering patterns were observed in the experimental pigs due to Cape grey mongoose (Galerella pulverulenta) scavenging, the only vertebrate scavenger species observed. No clear pattern of scatter distance over time was followed. Scatter began in the skeletonisation phase (day 25–30), resulting in a maximum distance of 12.67m and scatter/search area of 504.32m2. Mongoose-induced skeletal scatter followed a distinct pattern of movement into dense undergrowth, a previously unobserved behaviour and a key finding of this study. These results provide baseline data for sub-adult human scatter, or scatter of smaller components of an adult human skeleton, as demonstrated in the forensic case example provided. Knowledge is provided on locally relevant decomposition patterns and informs search methods for improved human skeletal recovery in forensic cases. There is scope for expansion of this study, with an investigation of seasonal effects, the interaction between invertebrate and vertebrate activity, as well as, the effect of clothing on scavenger access.

Suing for Peace in the War Against Mentalism.

The antimentalists' war against mentalism has not vanquished it. To examine why, we focus on two theses-mind as causal and internal-and three standard attacks against mentalism as defined by both theses: 1) mentalism implies dualism; 2) mind is unobservable, which hinders its scientific study; and 3) mentalism is impractical. These salients fail because: 1) if the mind is causal and internal, it must be material; 2) the observable/unobservable distinction is too problematic, with antimentalists equivocal about where to draw that line, with some even embracing publicly unobservable behavior as causally relevant; and 3) mentalism has not been demonstrated to be less practical than antimentalism. For the war on mentalism to succeed, stronger attacks must be devised, both scientific and philosophical. We contemplate some possibilities, while expressing doubts as to the wisdom of continuing the war. Peace may be better than war, and the resulting intellectual commerce may be good for both sides.

A relative identification method for reactive systems

This paper proposes a method to build an identified model from an observed sequence of input/output vectors and a model of knowledge on the system to identify. This method comprises two steps. The first one starts by checking whether the knowledge model is compatible with the observed sequence; when this is the case, the identified model is built automatically. From this result, the observed and unobserved behaviors of the knowledge model are then explicitly determined, in a second step.

Automated Partitioning of Concurrent Discrete-Event Systems for Distributed Behavioral Identification

The aim of behavioral identification of discrete-event systems is to build, from a sequence of observed inputs/outputs events, an understandable model that exhibits both the direct relations between inputs and outputs events (i.e., the observable behavior of the system) and the internal state evolutions (i.e., the unobservable behavior). Since parallelism hinders the construction of monolithic models, distributed identification builds instead the models of subsystems. This paper proposes an automated partitioning of the system and optimal regarding the readability of the identified distributed models, thus fitting reverse-engineering purposes. To solve the optimization problem, a first solution is extracted from the observable behavior; then additional solutions are computed by agglomerative clustering. The approach is applied to a benchmark, resulting in an adequate functional partition. Note to Practitioners —Identification is an approach to obtain the models of an existing closed-loop discrete-event system from an observed I/O sequence, discovering both sequential and concurrent processes within the same system. The result is an understandable and compact model that approximates the observed behavior and can be used for reverse engineering. To get better insight on the behavior of the system, splitting it into subsystems and studying distributed models might be easier. Besides, identifying smaller subsystems reduces the computational cost. The main contribution of this paper is therefore a partitioning approach for distributed DES identification. Only the knowledge of the I/Os and the observed I/O sequence are required to provide a partitioning adapted to reverse engineering and compact distributed models, in reasonable computation times.

Magnetic resonance imaging of disease progression and resolution in a transgenic mouse model of pulmonary fibrosis.

Pulmonary fibrosis contributes to morbidity and mortality in a range of diseases, and there are no approved therapies for reversing its progression. To understand the mechanisms underlying pulmonary fibrosis and assess potential therapies, mouse models are central to basic and translational research. Unfortunately, metrics commonly used to assess murine pulmonary fibrosis require animals to be grouped and euthanized, increasing experimental difficulty and cost. We examined the ability of magnetic resonance imaging (MRI) to noninvasively assess lung fibrosis progression and resolution in a doxycycline (Dox) regulatable, transgenic mouse model that overexpresses transforming growth factor-α (TGF-α) under control of a lung-epithelial-specific promoter. During 7 wk of Dox treatment, fibrotic lesions were readily observed as high-signal tissue. Mean weighted signal and percent signal volume were found to be the most robust MRI-derived measures of fibrosis, and these metrics correlated significantly with pleural thickness, histology scores, and hydroxyproline content (R = 0.75-0.89). When applied longitudinally, percent high signal volume increased by 1.5% wk-1 (P < 0.001) and mean weighted signal increased at a rate of 0.0065 wk-1 (P = 0.0062). Following Dox treatment, lesions partially resolved, with percent high signal volume decreasing by -3.2% wk-1 (P = 0.0034) and weighted mean signal decreasing at -0.015 wk-1 (P = 0.0028). Additionally, longitudinal MRI revealed dynamic remodeling in a subset of lesions, a previously unobserved behavior in this model. These results demonstrate MRI can noninvasively assess experimental lung fibrosis progression and resolution and provide unique insights into its pathobiology.

Anomalous behavior in temporal evolution of ripple wavelength under medium energy Ar+-ion bombardment on Si: A case of initial wavelength selection

We have studied the early stage dynamics of ripple patterns on Si surfaces, in the fluence range of 1–3 × 1018 ions cm−2, as induced by medium energy Ar+-ion irradiation at room temperature. Under our experimental conditions, the ripple evolution is found to be in the linear regime, while a clear decreasing trend in the ripple wavelength is observed up to a certain time (fluence). Numerical simulations of a continuum model of ion-sputtered surfaces suggest that this anomalous behavior is due to the relaxation of the surface features of the experimental pristine surface during the initial stage of pattern formation. The observation of this hitherto unobserved behavior of the ripple wavelength seems to have been enabled by the use of medium energy ions, where the ripple wavelengths are found to be order(s) of magnitude larger than those at lower ion energies.

CytoASP: a Cytoscape app for qualitative consistency reasoning, prediction and repair in biological networks.

BackgroundQualitative reasoning frameworks, such as the Sign Consistency Model (SCM), enable modelling regulatory networks to check whether observed behaviour can be explained or if unobserved behaviour can be predicted. The BioASP software collection offers ideal tools for such analyses. Additionally, the Cytoscape platform can offer extensive functionality and visualisation capabilities. However, specialist programming knowledge is required to use BioASP and no methods exist to integrate both of these software platforms effectively.ResultsWe report the implementation of CytoASP, an app that allows the use of BioASP for influence graph consistency checking, prediction and repair operations through Cytoscape. While offering inherent benefits over traditional approaches using BioASP, it provides additional advantages such as customised visualisation of predictions and repairs, as well as the ability to analyse multiple networks in parallel, exploiting multi-core architecture. We demonstrate its usage in a case study of a yeast genetic network, and highlight its capabilities in reasoning over regulatory networks.ConclusionWe have presented a user-friendly Cytoscape app for the analysis of regulatory networks using BioASP. It allows easy integration of qualitative modelling, combining the functionality of BioASP with the visualisation and processing capability in Cytoscape, and thereby greatly simplifying qualitative network modelling, promoting its use in relevant projects.

Hétérogénéité individuelle et dépendance d'état : De George Biddel Airy à James Joseph Heckman

In this paper I examine the evolution of an idea: that differences between statistical relationships and the observations on which they are based, or disturbances, may have meaning and structure and that such meaning and structure must be taken into account despite the fact that they arise from unobservables. When working with micro data, principal unobservables arise from individual heterogeneity which may occur in part from path dependence. My disturbing conclusion is that we cannot separate individual unobserved behavior due to past experience from other types of unobserved heterogeneity. This means, in particular, that we cannot obtain economically meaningful estimates of distributed lags or other models of path dependence without making ad hoc assumptions having no economic content.

Estimating the local effect of weather on field crop production with unobserved producer behavior: a bioeconomic modeling framework

The role of weather in crop production at field is central to understanding the impact of climate change on agriculture and its implications for food security. In this study, we developed a bioeconomic modeling framework for estimating the field effect of weather on crop production at the regional scale with unobserved producer behavior. We took a systematic perspective for model development, explicitly considering crop production as a coupled human–nature system dominated by management adapted to local environmental and economic conditions. We drew on economics to characterize producer management behavior and crop yield consistent with the process of field production. We integrated scientific findings on plant growth and production economics to parameterize the yield function of crop that can be statistically estimated with observed data. An empirical application of our approach to spring wheat production found spatially heterogeneous effect of weather and climate change impact. Our modeling approach can be applied to different crops or regions to develop locally specific understandings of the management adjusted, production effect of weather and climate change impact, with implications on cropping system resilience and for adaptation.

(In)definiteness-driven typology of arbitrary items

This article explores a typology of impersonal constructions [1] (constructions with arbitrary interpretations or arbs) in Russian, German, and English based on their semantic and discourse-pragmatic properties.(1)They speak English in America.The goal of this work is twofold. First, I want to introduce a meaning-based typological distinction into the realm of impersonals. I will argue that some arbs are semantically definite, while others are variable, drawing attention to previously unobserved behaviour of different arbs with respect to adverbial quantification. This divergent behaviour points to the inadequacy of any semantic analysis that treats the two types of arbs in the same fashion. I then point out further differences between the arbs (and non-arbitrary NPs) that emerge in their interaction with topic structure and discourse anaphora, which further corroborate the separation between the two types of arbs. Secondly, I want to use the study of arbs to explore the architecture of the semantics–pragmatics interface. I conclude that the behaviour of arbs forces a division of labour between semantics and pragmatics in the calculations pertaining to reference and referent-tracking.

Constructing “Behavioral” Comparison Groups

It is well known that the differences-in-differences (DD) estimator is based on the assumption that in the absence of treatment, the average outcomes for the treated group and the control group will follow a common trend over time. That can be problematic, especially when the selection for the treatment is influenced by the individual's unobserved behavior correlating with the medical utilization. The aim of this study was to develop an index for controlling a patient's unobserved heterogeneous response to reform, in order to improve the comparability of treatment assignment. This study showed that a DD estimator of the reform effects can be decomposed into effects induced by moral hazard and by changes in health risk within the same treated/untreated group. This article also presented evidence that the constructed index of the price elasticity of the adjusted clinical group has good statistical properties for identifying the impact of reform.

Formalizing Informality: The Stakeholder Politics of Clawbacks

Stakeholder management focuses on mutualism and competition within cohorts of stakeholders, but conflicts also occur between stakeholders who are connected to a firm at different times. This paper describes the process through which inter-cohort stakeholder conflicts arise: unobserved behavior leads to inaccurate valuations of organizational activities and earlier stakeholders benefit at the expense of later cohorts. Latency in the observation of impropriety motivates dysfunction by compensating actions that are inconsistent with the long-term interests of the firm or its stakeholders. One solution is to accept that historical calculations of risk and reward will often be inaccurate, and to settle accounts later by taking back ill-gotten gains. This is the practical spirit of the legal innovation known as clawbacks, found in bankruptcy proceedings and in compensation contracts. Relying upon these innovations to theorize an alternative system of governance, this paper considers how an economy of traceable ...

A Rare Use of a Shark's Fin? Pectoral Fin Scooping of a Sharksucker from The Flank Area of a Female Blacktip Shark

Sharks react in a variety of ways when sharksuckers attach to their bodies. Here, an unobserved behavior is de- scribed in which a blacktip shark, Carcharhinus limbatus, uses a pectoral fin in a scoop-like manner to specifically target the removal of a sharksucker, Echeneis naucrates, in the flank area. This coordinated use of the pectoral fin indicates an established bout in the shark's repertoire to remove sharksuckers. The anatomical flexibility and coordination of the fin is discussed; likewise, the potential origin and commonality within other species of the same family based on the rise of sharksuckers in the Eocene-Oligocene period. Special emphasis is placed on the value of a single observation of animal behavior in the field.

Automatically Generating Test Cases for Specification Mining

Dynamic specification mining observes program executions to infer models of normal program behavior. What makes us believe that we have seen sufficiently many executions? The TAUTOKO (“Tautoko” is the Maori word for “enhance, enrich.”) typestate miner generates test cases that cover previously unobserved behavior, systematically extending the execution space, and enriching the specification. To our knowledge, this is the first combination of systematic test case generation and typestate mining-a combination with clear benefits: On a sample of 800 defects seeded into six Java subjects, a static typestate verifier fed with enriched models would report significantly more true positives and significantly fewer false positives than the initial models.

An empirical movement model for sixgill sharks in Puget Sound: Combining observed and unobserved behavior

Abstract Understanding the movement of animals is fundamental to population and community ecology. Historically, it has been difficult to quantify movement patterns of most fishes, but technological advances in acoustic telemetry have increased our abilities to monitor their movement. In this study, we combined small-scale active acoustic tracking with large-scale passive acoustic monitoring to develop an empirical movement model for sixgill sharks in Puget Sound, WA, USA. We began by testing whether a correlated random walk model described the daily movement of sixgills; however, the model failed to capture home-ranging behavior. We added this behavior and used the resultant model (a biased random walk model) to determine whether daily movement patterns are able to explain large-scale seasonal movement. The daily model did not explain the larger-scale patterns of movement observed in the passive monitoring data. In order to create the large-scale patterns, sixgills must have performed behaviors (large, fast directed movements) that were unobserved during small-scale active tracking. In addition, seasonal shifts in location were not captured by the daily model. We added these ‘unobserved’ behaviors to the model and were able to capture large-scale seasonal movement of sixgill sharks over 150 days. The development of empirical models of movement allows researchers to develop hypotheses and test mechanisms responsible for a species movement behavior and spatial distribution. This knowledge will increase our ability to successfully manage species of concern.

Dynamic Slicing Research of UML Statechart Specifications

This paper extends the well-known technique of dynamic slicing to Statechart specifications of reactive systems. Statechart language extends state machines along hierarchy, concurrency and communication – resulting in a compact visual notation that allows engineers to structure and modularize system descriptions. Dynamic slicing is well known in the domain of sequential transformational programs and has been found to be useful in understanding, analysis and verification. The classical definition of dynamic slicing is unsuitable for Statechart specifications. In this paper, we firstly formally define a formal semantics model -- observable semantics, which is very suitable for dynamic slicing, because that it only describes outside observable behavior and conceals unobservable behavior of Statechart specifications, and it fully captures the run-time dependence relation among the state transitions in the Statechart specification. Then we propose a new notion of dynamic slicing that, in our opinion, is more natural for Statechart specifications. We formally define notions of dynamic slicing criterion, dynamic slice and minimal dynamic slice, and we also explain how to produce valid dynamic slicing criterion and propose a simple and practical approximation algorithm for minimal dynamic slice generation using observable semantics as an intermediate representation.