Rule-based Behavior Research Articles

NERO: NEural algorithmic reasoning for zeRO-day attack detection in the IoT: A hybrid approach

Anomaly detection approaches for network intrusion detection learn to identify deviations from normal behavior on a data-driven basis. However, current approaches strive to infer the degree of abnormality of out-of-distribution samples when these appertain to different zero-day attacks. Inspired by the successes of the neural algorithmic reasoning paradigm to leverage the generalization of rule-based behavior, this paper presents a deep learning strategy for solving zero-day network attack detection and categorization. Moreover, focusing on the particular scenario of the Internet of Things (IoT), the privacy preservation requirement may imply a low training data regime for any learning algorithm. To this respect, the presented framework uses metric-based meta-learning to achieve few-shot learning capabilities. The presented pipeline is called NERO, as it imports the encode-process-decode architecture from the NEural algorithmic reasoning blueprint to converge zeRO-day attack detection policies within constrained training data.

Technical Evaluation Requirements on Welding-Related Nonconformance Reports

Abstract During the manufacturing and installation of nuclear power equipment and components, welding-related nonconformance reports (NCRs) prepared by initiators for evaluation vary considerably in quality, influencing working efficiency and project progress. In accordance with the project practices, this article tries to determine the reasons for low-quality welding-related NCR content from three aspects: document flow process, skill levels of technicians, and nonconforming-item control procedure. The technical evaluation requirements on welding-related NCRs are put forward from the perspective of design evaluation in accordance with regulations, standards, design principles, professional knowledge, and engineering practices. According to human performance theory, these evaluation requirements can be used to guide the preparation and review of welding-related NCRs. The inherent logic behind these requirements is to help the initiators and reviewers of welding-related NCRs transform their knowledge-based behavior modes to rule-based behavior modes, thus greatly improving the content quality of NCRs.

Comparison between the WHO-CFICPS and the PRISMA classification of safety-related events in a radiation oncology department.

Describing Safety-Related Events (SREs) in a radiotherapy (RT) department and comparing WHO-CFICPS (World Health Organization's Conceptual Framework For The International Classification For Patient Safety) and PRISMA (Prevention and Recovery Information System for Monitoring and Analysis) methods for classifying SREs. From February 2017 to October 2020, two Quality Managers (QMs) randomly classified 1173 SREs using 13 incident types of WHO-CFICPS. The same two QMs, reclassified the same SREs according to 20 PRISMA incident codes. Statistical analysis was performed to assess the association between the 13 incident types of WHO-CFICPS and the 20 PRISMA codes. The chi-squared and post-hoc tests using adjusted standardized residuals were applied to detect the association between the two systems. There was a significant association between WHO-CFICPS incident types and PRISMA codes (P < 0.001). Ninety-two percent of all SREs were categorized using 4 of 13 WHO-CFICPS incident types including Clinical Process/Procedure (n = 448, 38.2%), Clinical Administration (n = 248, 21.1%), Documentation (n = 226, 19.2%) and Resources/Organizational Management (n = 15,613.3%). According to PRISMA classification, 14 of the 20 codes were used to describe the same SREs. PRISMA captured 41 Humans Skill Slips from 226 not better defined WHO-CFICPS Documentation Incidents, 38 Human Rule-based behaviour Qualification from not better defined 447 Clinical Process/Procedure and 40 Organization Management priority events from 156 not better defined WHO-CFICPS Resources/Organizational Management events (P < 0.001). Although there was a significant association between WHO-CFICPS and PRISMA, The PRISMA method provides a more detailed insight into SREs compared to WHO-CFICPS in a RT department.

Modeling and Simulation for Non-Motorized Vehicle Flow on Road Based on Modified Social Force Model

Non-motorized vehicles have become one of the most commonly used means of transportation for people due to their advantages of low carbon, environmental protection, convenience and safety. Frequent interaction among non-motorized vehicle users in the shared space will bring security risks to their movement. Therefore, it is necessary to adopt appropriate means to evaluate the traffic efficiency and safety of non-motorized vehicle users in the passage, and using a micro model to conduct simulation evaluation is one of the effective methods. However, some existing micro simulation models oversimplify the behavior of non-motorized vehicle users, and cannot reproduce the dynamic interaction process between them. This paper proposes a modified social force model to simulate the dynamic interaction behaviors between non-motorized vehicle users on the road. Based on the social force model, a new behavioral force is introduced to reflect the three dynamic interaction behaviors of non motor vehicle users, namely, free movement, following and overtaking. Non-motorized vehicle users choose which behavior is determined by the introduced decision model. In this way, the rule-based behavior decision model is combined with the force based method to simulate the movement of non-motorized vehicles on the road. The modified model is calibrated using 1534 non-motorized vehicle trajectories collected from a road in Xi’an, Shaanxi, China. The validity of the model is verified by analyzing the speed distribution and decision-making process of non-motorized vehicles, and comparing the simulation results of different models. The effects of the number of bicycles and the speed of electric vehicles on the flow of non-motorized vehicles are simulated and analyzed by using the calibrated model. The relevant results can provide a basis for urban management and road design.

Origins of Dualism and Nondualism in the History of Religion and Spiritual Practice

In a previous article we proposed that spiritual traditions and practices emerged to counterbalance humans inherent “dualism”, or perceived separation from the world around us, by cultivating experiences of “subject-object nonduality” more commonly referred to as “unification with the divine” or “enlightenment”. This implies that religions (social and political institutions ostensibly based in spiritual practices) similarly aim towards overcoming the separation of self and other. However, many religions include dualistic elements. In particular, many religions incorporate “ethical dualism” in which certain individuals and groups are seen as essentially “good” or “bad”, a feature not seen in nondual traditions. Here, we explore this seeming paradox, highlighting an intriguing correspondence between the degree to which religions include dualistic or nondualistic elements and, respectively, the prevalence of conflict or cooperation as the organizing principle in their associated social context. We find major “dualistic” religions to be generally traceable to pastoral societies largely organized around intergroup conflict, whereas major “nondual religions” are generally traceable to societies in which large-scale cooperation and rule-based behavior was necessary for collective survival. Finally, we apply this pattern to the modern world, speculating that large-scale cooperation and rule-based behavior in modern society may be currently encouraging the renewal of nondual practices in modern social and political institutions that is indicated by the growing popularity of “spiritual but not religious” groups, and that this renewal of nondual practices may in turn set the frame for and reinforce behavior that will be necessary to address the historic challenges of our day like climate change and democratic backsliding.

Social Learning of Sustainability in a Pandemic—Changes to Sustainability Understandings, Attitudes, and Behaviors during the Global Pandemic in a Higher Education Setting

When people learn from each other and change their behavior accordingly, this is called social learning. COVID-19 not only taught us new habits to limit contagion, imposed restrictions also limited people’s everyday practices and behavior. Our study aims to analyze how (much) the pandemic may have incidentally fostered social learning of sustainability (SLS), representing a shift from rule-based behavior and forced behavior changes to more associative and potentially long-lasting sustainable behavior. To answer this question, we analyzed data from two mixed-method surveys with which we approached two customized samples in a higher education setting in Australia (n = 100) and Austria (n = 264). The findings show that in a higher education and, specifically, a university context, there are less sustainable practices evaluated as stable and “new normal” than assumed. Still, sustainability is more rule-related and less associative, predominantly in Australia. Nevertheless, a certain degree of awareness of what sustainable practices are can be observed mainly in Austria. As a broader implication, the study at hand leads to the conclusion that through COVID-19, sustainability at least became more tangible. Furthermore, universities as a specific institution have the potential to put sustainability higher on their agenda and take responsibility for social change. In the conclusion and outlook of the paper, limitations of the study as well as future research potential on social learning processes for sustainable development are presented and discussed.

Human Decision Time in Uncertain Binary Choice

Decision time, also known as choice reaction time, has been frequently discussed in the field of psychology. The Hick–Hyman Law (HHL) has been a fundamental model that has revealed the quantitative relationship between the mean choice reaction time of human and the information entropy of stimuli. However, the HHL is only focused on rule-based behavior in which rules for selecting response according to stimulus are certain and neglects to model the knowledge-based behavior in which choices are uncertain and influenced by human belief. In this article, we explored the decision time related to one basic knowledge-based behavior—uncertain binary choice, where selections of response are determined by human belief degrees but not by stimuli uncertainties. Two experiments were conducted: one for verifying the HHL and the other for uncertain binary choice. The former (experiment) demonstrated the effectiveness of the HHL, and the latter one indicated that there is an exponential relationship existing between decision time and entropy of belief degree in uncertain binary choice. Moreover, data obtained from both experiments showed that the disturbance term of decision time should not be seen as probabilistic as existing studies have assumed, which highlighted the necessity and advantage of uncertain regression analysis.

Модель исследования социально-психологических особенностей студентов-курильщиков

The article considers the problem of tobacco smoking from the standpoint of system analy-sis of personality’s social relations. The proposed model for studying of the psychosocial characteristics of smokers includes: socializing factors, socialization problems, description of socialization and psychosocial characteristics of tobacco smokers. The modelled system represents a dynamically unfolding character both in its biographical perspective and in its immediate actuality. The subsystem component is the subjects’ socialization starting from the early childhood in their families in communication with their mothers. The degrees of addiction to smoking are viewed as necessary components for analysis of social relations of smokers in their young adulthood. The sample for the study consisted of 374 individuals. The research techniques were the following: 1) unfinished sentence method; 2) observation of the subjects’ behavior and the statements they make; 3) “My family” mini-essays; 4) “Psychosocial characteristics of youth” questionnaire survey. The above-mentioned tech-niques were used to study the causes of socialization problems in the respondents, as well as their attitudes to themselves and to their surroundings. Student’s t-test was used to reveal the distinctions between the control and experimental groups (p&lt;0.01; p&lt;0.05). Tobacco smoking should be viewed both as a violation of standard, rule-based behavior in one’s ear-ly adulthood and as one’s behavior striving to pave a constructive way into the future.

Modeling Self-Adapting and Multicultural Non-Player Characters in Artificial Worlds

Modeling the behavior of Non-Player Characters (NPCs) so that they can act properly in an artificial world is a difficult, yet essential task in the development of modern computer games, often requiring never- ending adjustments of handwritten rules in a way to enable NPCs to perform well in the game world. This pa- per introduces an evolutionary method relying on high-level rule-based behaviors, genetic algorithms and speciation. By interconnecting these three areas, we pro- pose a technique that has the potential to provide multicultural NPCs with the ability to adapt themselves to an artificial worl

Pragmatism and Political Practice: Language, Skills and Institutions

This article aims to show the importance of the pragmatic conception of language proposed by Wittgenstein in relation to his philosophy of action, as well as the possibilities it offers to political theory, by explaining the impact and influence that his theory had in some of his readers. This is particularly important for the notion of political representation, which emerges as a linguistic practice of rule-based behavior in terms of giving and asking for reasons. This idea will be reinforced by a reconstruction of Wittgenstein’s arguments applied to a social instance a work that is done by many of his readers and commentators, specifically Searle, Brandom and Mouffe. Such instance would be limited in this text, by making use of Rawls’ idea of public reason through the lens of this socially conceived Wittgenstein’s pragmatism. &#x0D;

Evaluating imitation and rule-based behaviors of eye contact and blinking using an android for conversation

In this paper, we investigate which approach to generate eye behaviors using an android robot makes what impressions on humans and clarify which are the important factors for attractive eye behaviors. Thus, we evaluate the human impression of eye behaviors displayed by an android robot while talking to a human by comparing the motions generated by the two approaches. In the first approach, we develop a method to imitate human eye behavior obtained from eye trackers. In the second approach, we control the eye direction, eye-contact duration, and eyeblinks based on the findings of human eye behavior in psychology and cognitive research. In the experiments, we asked male and female subjects to evaluate their impression by comparing the eye behaviors with an android that controls the eye-contact duration and eyeblinks by editing the imitation parameters or the rule-based behavior. In the experiments, we asked subjects to evaluate their impression of different eye behaviors displayed by an android. The eye behaviors were generated by modifying the imitation parameters or the rule-based behavior, which resulted in different eye-contact duration and eyeblink duration and timing. From the results, we found that (1) the imitation and rule-based behaviors showed no difference in terms of human-likeness, (2) the 3-second eye contact obtained better scores regardless of the imitation or rule-based eye behavior, (3) the subjects might regard the long eyeblinks as voluntary eyeblinks, with the intention to break eye contact, and (4) female subjects preferred short eyeblinks rather than long ones and considered that short eyeblinks might be one of the keys to making eye contact more suitable, in contrast to male subjects who preferred long eyeblinks.

A modified social force model for high-density through bicycle flow at mixed-traffic intersections

• A modified social force model for high-density through bicycle flow is proposed. • A dynamic boundary model is established to present the dynamic dispersion extent. • Behavior force model with the decision process is developed to capture interactions. • Provide a simulation model to evaluate bicycle operation precisely at intersections. Mixed-traffic intersections with high-density bicycle traffic flow are one of the most common bottlenecks in the urban traffic network. Due to the shared space and frequent interactions among heterogeneous road users, through bicycle flow shows an obvious tendency of lateral dispersion, resulting in a reduction in traffic efficiency and safety at intersections. A precise evaluation of its impacts on intersection efficiency and safety is necessary, while the microscopic simulation model is a useful tool to fulfill the need. However, existing microscopic simulation models of through bicycles simplify cyclists’ behaviors and interactions with other road users, and cannot reproduce the lateral dispersion with dynamic variations accurately. To address the shortcomings, this paper proposes a modified social force model to simulate the through bicycle flow at mixed-traffic intersections. A dynamic boundary model and the behavior force model embedded with the decision process are introduced. The dynamic boundary model captures the dynamic characteristics of the lateral dispersion for different cycles under various traffic context. The behavior force model composes four interactive behaviors, i.e. freely moving, following, overtaking, and merging behavior, and rule-based behavior decision models are integrated with force-based approaches to represent interactions and bicycle dynamics. The proposed model has been calibrated based on 743 through bicycle trajectories at a signalized intersection in Shanghai, China. The simulation results indicate that the proposed model is more capable of reproducing the realistic motion features of through bicycles, and show higher simulation accuracy than the original social force model.

Corrigendum to “Combined dynamic multi-feature and rule-based behavior for accurate malware detection”

Corrigendum to “Combined dynamic multi-feature and rule-based behavior for accurate malware detection”

A framework for pervasive computing applications based on smart objects and end user development

Pervasive Computing (PerComp) research remains to this date technology-centric, requiring more focus on utilizing human and societal intelligence. To bridge this gap we motivate the need for a conceptual framework that provides a vision for enabling end-users to participate actively in the design of PerComp applications. The framework in particular provides guidance for the development of tools that allow end-users to configure their own smart environments. It emphasizes the benefits of using Smart Objects (SOs) as components of PerComp applications under a system engineering perspective and the benefits of using affordances as dynamic connectable capabilities under a user experience perspective. A generic application model is developed within the framework that manifests the concepts governing the structure and operation of applications composed by connecting SOs together and adhering to a rule-based behavior. A multi-layered software mediator is defined to provide a platform for the runtime support of such PerComp applications. Theoretical foundations for the key concepts adopted regarding the interaction design and system engineering perspectives are discussed and the results of a user evaluation study focusing on the perceived usability of the high-level conceptual models and the developed tools are reported.

Cognitive Radar for Classification

The article presents the cognitive radar architecture of Fraunhofer FHR based on a three-layer model of human cognitive performance. The approach is illustrated using examples for non-cooperative target identification and classification. On the skill based layer, a target-matched waveform design is presented and experimental results are shown. For the transition to the rule-based layer, convolutional neural networks are explained for the identification of air-targets and a novel auto-encoder for change detection is introduced. For rule-based behavior, a policy based decision making algorithm for NCTI waveform selection is explained, using CPOMDPs.

Combined dynamic multi-feature and rule-based behavior for accurate malware detection

Malware have become the scourge of the century, as they are continuously evolving and becoming more complex with increasing damages. Therefore, an adequate protection against such threats is vital. Behavior-based malware detection techniques have shown to be effective at overcoming the weaknesses of the signature-based ones. However, they are known for their high false alarms, which is still a very challenging problem. In this article, we address this shortcoming by proposing a rule-based behavioral malware detection system, which inherits the advantages of both signature and behavior-based approaches. We apply the proposed detection system on a combined set of three types of dynamic features, namely, (1) list of application programming interface calls; (2) application programming interface sequences; and (3) network traffic, which represents the IP addresses and domain names used by malware to connect to remote command-and-control servers. Feature selection and construction techniques, that is, term frequency–inverse document frequency and longest common subsequence, are performed on the three extracted features to generate new set of features, which are used to build behavioral Yet Another Recursive Acronym rules. The proposed malware detection approach is able to achieve an accuracy of 97.22% and a false positive rate of 4.69%.

In brief

PAGE 1288 Researchers from the College of Automotive Engineering, Seoul, have developed a driver model for use in driver-assisted system technologies. The model is based on a generative adversarial network (GAN) and performs better than a rule-based model when replicating human driving behaviour. PAGE 1265 Chinese researchers present a novel symmetrical antenna design using a chessboard grid hollow substrate (CGHS). Scattering performance of the circularly polarised antenna is enhanced and the device operates between 7 and 20 GHz. Reduction of radar cross section (RCS) at the operating bandwidth is realised by the CGHS. Comparison of different scenarios comparing human, GAN and rule-based behaviour. PAGE 1279 Fei Zhou and colleagues present an anisotropy-based image smoothing method, designed to remove unwanted texture from images. The method was found to compare well with recent similar methodologies. In particular, the methodology presented here did a better job of preserving image edges compared to other methods. Structure of the proposed antenna. PAGE 1303 A collaboration of Chinese researchers presents an AlGaN/GaN ultra-thin barrier hybrid-anode-diode (UTB-HAD) which has been tailored to allow zero-bias microwave detection. Computer simulations found the optimal thickness of the AlGaN barrier to be ∼5 nm. A high-voltage sensitivity of 2.7 mV/μW at zero bias was found. Illustration of structural edge (solid) and textural edge (dashed) parts of an image. PAGE 1297 Dr Hidekazu Tsuchida presents a new modulation and detection scheme for frequency modulated continuous wave light detection and ranging (FMCW-LiDAR) systems. The proposed scheme is immune to nonlinear chirp and relative accuracies below ±2 mm/s and ±1 mm for velocity and distance measurements, respectively, are reported. Ultra-thin barrier hybrid-anode-diode (UTB-HAD) device proposed by the group. Experimental schematic for velocity and distance measurements.

Visualizing distances as a function of speed: Design and evaluation of a distance-speedometer

This paper contributes to the research on transportation and human factors by designing and evaluating an ecological human-machine interface for speed and distance control. In the future, traffic management will determine optima for speed and safety distances. Wirelessly broadcasted, the question remains how to present this information to drivers in a way that is easily understood and intuitively followed. To this end, a human-machine interface, called distance-speedometer, was first developed according to the principles of ecological interface design and then evaluated in a driving simulator study with forty-nine participants. It presents augmented distances derived from speed in a head-up display. Using a within-subject design, the distance-speedometer and standard speedometer were compared against each other in two scenarios (car-following & sign-following). Each scenario triggered several speed changes. When reacting to speed signs, driving performance did not differ between the two interfaces. However, with the distance-speedometer, visual workload was reduced as more secondary tasks could be completed. When reacting to speed changes of a car ahead, the engagement in the secondary task did not differ significantly between the two interfaces. However, the variance of speed deviations was significantly lower with the distance-speedometer. Results can be explained by the ability to process the distance-speedometer in a skill-based manner, whereas the normal speedometer demands rule-based behaviour to control the speed. With regard to traffic management, the distance-speedometer might be used to unconsciously influence the driver’s choice of speed in order to increase traffic safety and efficiency.

Human behaviour with automated driving systems: a quantitative framework for meaningful human control

Automated driving systems (ADS) with partial automation are currently available for the consumer. They are potentially beneficial to traffic flow, fuel consumption, and safety, but human behaviour whilst driving with ADS is poorly understood. Human behaviour is currently expected to lead to dangerous circumstances as ADS could place human drivers ‘out-of-the-loop’ or cause other types of adverse behavioural adaptation. This article introduces the concept of ‘meaningful human control’ to better address the challenges raised by ADS, and presents a new framework of human control over ADS by means of literature-based categorisation. Using standards set by European authorities for driver skills and road rules, this framework offers a unique, quantified perspective into the effects of ADS on human behaviour. One main result is a rapid and inconsistent decrease in required skill- and rule-based behaviour mismatching with the increasing amount of required knowledge-based behaviour. Furthermore, the development of higher levels of automation currently requires different human behaviour than feasible, as a mismatch between supply and demand in terms of behaviour arises. Implications, discrepancies and emerging mismatches this framework elicits are discussed, and recommendations towards future design strategies and research opportunities are made to provide a meaningful transition of human control over ADS.

Novel representations that support rule-based categorization are acquired on-the-fly during category learning.

Humans learn categorization rules that are aligned with separable dimensions through a rule-based learning system, which makes learning faster and easier to generalize than categorization rules that require integration of information from different dimensions. Recent research suggests that learning to categorize objects along a completely novel dimension changes its perceptual representation, making it more separable and discriminable. Here, we asked whether such newly learned dimensions could support rule-based category learning. One group received extensive categorization training and a second group did not receive such training. Later, both groups were trained in a task that made use of the category-relevant dimension, and then tested in an analogical transfer task (Experiment 1) and a button-switch interference task (Experiment 2). We expected that only the group with extensive pre-training (with well-learned dimensional representations) would show evidence of rule-based behavior in these tasks. Surprisingly, both groups performed as expected from rule-based learning. A third experiment tested whether a single session (less than 1 h) of training in a categorization task would facilitate learning in a task requiring executive function. There was a substantial learning advantage for a group with brief pre-training with the relevant dimension. We hypothesize that extensive experience with separable dimensions is not required for rule-based category learning; rather, the rule-based system may learn representations "on the fly" that allow rule application. We discuss what kind of neurocomputational model might explain these data best.