High-level Rules Research Articles

PIPER: A logic-driven deep contrastive optimization pipeline for event temporal reasoning

Event temporal relation extraction is an important task for information extraction. The existing methods usually rely on feature engineering and require post-process to achieve optimization, though inconsistent optimization may occur in the post-process module and main neural network due to their independence. Recently, a few works start to incorporate the temporal logic rules into the neural network and achieve joint optimization. However, these methods still suffer from two shortcomings: (1) Although the joint optimization is applied, the differences between rules are neglected in the unified design of rule losses and further the interpretability and flexibility of the design of model are reduced. (2) Because of lacking abundant syntactic connections between events and rule-match features, the performance of the model may be suppressed by the inefficient interaction in training between features and rules. To tackle these issues, this paper proposes PIPER, a logic-driven deep contrastive optimization pipeline for event temporal reasoning. Specifically, we apply joint optimization (including multi-stage and single-stage joint paradigms) by combining independent rule losses (i.e., flexibility) to make PIPER more interpretable. Also, by proposing a hierarchical graph distillation network to obtain more abundant syntactic information, the designed rule-match features can effectively aid in the interaction between low-level features and high-level rules during training. The final experiments on TB-Dense and MATRES demonstrate that the proposed model can achieve competitive performance compared with the recent advances.

When might we break the rules? A statistical analysis of aesthetics in photographs.

High-quality photographs often follow certain high-level rules well known to photographers, but some photographs intentionally break these rules. Doing so is usually a matter of artistry and intuition, and the conditions and patterns that allow for rule-breaks are often not well articulated by photographers. This article first applies statistical techniques to help find and evaluate rule-breaking photographs, and then from these photographs discover those patterns that justify their rule-breaking. With this approach, this article discovered some significant patterns that explain why some high-quality photographs successfully break the common photographic rules by positioning the subject in the center or the horizon in the vertical center. These patterns included reflections, leading lines, crossing objects, ambiguous lines, implied lines, thirds line subjects, and busy foregrounds for center horizon photographs, and symmetry, circular-shaped objects, thirds line elements, gestalt, framing, leading lines, and perspective lines for center subject photographs.

Learning and planning with logical automata

We introduce a method to learn policies from expert demonstrations that are interpretable and manipulable. We achieve interpretability by modeling the interactions between high-level actions as an automaton with connections to formal logic. We achieve manipulability by integrating this automaton into planning via Logical Value Iteration, so that changes to the automaton have predictable effects on the learned behavior. These qualities allow a human user to first understand what the model has learned, and then either correct the learned behavior or zero-shot generalize to new, similar tasks. Our inference method requires only low-level trajectories and a description of the environment in order to learn high-level rules. We achieve this by using a deep Bayesian nonparametric hierarchical model. We test our model on several domains of interest and also show results for a real-world implementation on a mobile robotic arm platform for lunchbox-packing and cabinet-opening tasks.

Towards Dynamically Crafted Affective Experiences Through Emotional Response Player Modelling

Designing adaptive games for individual emotional experiences is an elaborate task due to the necessary dedicated hardware, complex signal processing and unique emotional bonds each player may forge with the game. The research work I propose focuses on developing a novel affective interaction scheme where players’ emotional reactions are learned by a computational model that leverages this information to influence their future emotional states. To this end I aim at interpreting the causality relations between physiologically measured emotional response variations and their eliciting events. I then plan using these reactions to build each player’s individual emotional reaction model, which will guide the game engine’s logic on how to plan future game events, in order to elicit a target affective experience. Ultimately, I expect this technique to allow game designers to focus on defining high-level rules for generating their envisioned affective gameplay experience, rather than in manually tuning it in a repetitive and time-consuming fashion.

Guided Emotional State Regulation: Understanding and Shaping Players’ Affective Experiences in Digital Games

Designing adaptive games for individual emotional experiences is a tricky task, especially when detecting a player’s emotional state in real time requires physiological sensing hardware and signal processing software. There is currently a lack of software that can identify and learn how emotional states in games are triggered. To address this problem, we developed a system capable of understanding the fundamental relations between emotional responses and their eliciting events. We propose time-evolving Affective Reaction Models (ARM), which learn new affective reactions and manage conflicting ones. These models are then meant to provide information on how a set of predetermined game parameters (e.g., enemy and item spawning, music and lighting effects) should be adapted, to modulate the player’s emotional state. In this paper, we propose and describe a framework for modulating player emotions and the main components involved in regulating players’ affective experience. We expect our technique will allow game designers to focus on defining high-level rules for generating gameplay experiences instead of having to create and test different content for each player type.

Securing SOME/IP for In-Vehicle Service Protection

Although high-speed in-vehicle networks are being increasingly adopted by the industry to support emerging use cases, previous research already demonstrated that car hacking is a real threat. This paper formalizes a novel framework proposed to provide improved security to the emerging SOME/IP middleware, without introducing at the same time limitations in the communication patterns available. Most notably, the entire traffic matrix is designed to be configured using simple high-level rules, clearly stating who can talk to whom according to the service abstraction adopted by SOME/IP. Three incremental security levels are made available, accounting for different services being associated with different requirements. The core security protocol, encompassing a session establishment phase followed by the transmission of secured SOME/IP messages, has been formally verified, to prove its correctness in terms of authentication and secrecy properties. Performance-wise, in-depth experimental evaluations conducted with an extended version of vsomeip confirmed the introduction of quite limited penalties compared to the bare unsecured implementation.

Electricity Regulation in the European Union: Uniform, Differentiated or Experimentalist?

This paper examines six major policy issues within EU electricity regulation, a case study chosen both for its intrinsic importance and because its interdependence, politicization and volatility make it a plausible candidate for uniform, differentiated, and experimentalist regulation. It finds that although EU electricity regulation is characterized by an ambition to uniformity due to the especially strong interdependence of electricity systems, no matter how uniform EU rules might be, they always leave some space for diversity. Contrary to differentiated integration expectations, divisions of member states into separate groups of “ins” and “outs” governed by different rules played virtually no role, despite the high interdependence and entrenched politicization characterizing the European electricity sector. Instead, in line with experimentalist governance expectations, prompted by environmental volatility and aware of persistent diversity, policy actors often made provisions to monitor and learn from such lower-level diversity in order to review and revise higher-level rules. Nonetheless, the use of lower-level diversity in experimentalist processes actually led to the generation of gradually more uniform rules which, accompanied by rapid and frequent revision of these rules based on review of implementation experience, suggests that experimentalist governance may be undertaking a metamorphosis in some sectors toward synchronic uniformity and diachronic revisability. Finally, in all the policy issues studied substantive EU rules applied also to countries beyond the Union, as posited by external differentiated integration; in most of them, moreover, non-member state actors also participated in the processes for creating and revising such rules, as described in extended (experimentalist) governance.

Protecting In-Vehicle Services: Security-Enabled SOME/IP Middleware

With every generation, vehicles are becoming smarter and more oriented toward information and communications technology (ICT). However, computerization is posing unforeseen challenges in a sector for which the first goal must be safety: car hacking has been shown to be a real threat. This article presents a novel mechanism to provide improved security for applications executed in the vehicle based on the principle of defining exactly who can talk to whom. The proposed security framework targets Ethernet-based com - munications and is tightly integrated within the emerging Scalable service-Oriented MiddlewarE over IP (SOME/IP) middleware. No complex configurations are needed: simple high-level rules, clearly stating the communications allowed, are the only element required to enable the security features. The designed solution has been implemented as a proof of concept (PoC) inside the vsomeip stack to evaluate the validity of the approach proposed: experimental measurements confirm that the additional overhead introduced in end-to-end communication is negligible.

Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People.

This paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context. The contribution also aims to analyse the usage of the models for the transfer of knowledge when different sensors with different settings contribute to the noise levels. Finally, based on the authors’ experience, a framework proposal for creating valuable and aggregated knowledge is depicted.

Three novel fuzzy logic concepts applied to reshoring decision-making

This paper investigates the possibility of increasing the interpretability of fuzzy rules and reducing the complexity when designing fuzzy rules. To achieve this, three novel fuzzy logic concepts (i.e., relative linguistic labels, high-level rules and linguistic variable weights) were conceived and implemented in a fuzzy logic system for reshoring decision-making. The introduced concepts increase the interpretability of fuzzy rules and reduce the complexity when designing fuzzy rules while still providing accurate results.

Multicellular Models Bridging Intracellular Signaling and Gene Transcription to Population Dynamics

Cell signaling and gene transcription occur at faster time scales compared to cellular death, division, and evolution. Bridging these multiscale events in a model is computationally challenging. We introduce a framework for the systematic development of multiscale cell population models. Using message passing interface (MPI) parallelism, the framework creates a population model from a single-cell biochemical network model. It launches parallel simulations on a single-cell model and treats each stand-alone parallel process as a cell object. MPI mediates cell-to-cell and cell-to-environment communications in a server-client fashion. In the framework, model-specific higher level rules link the intracellular molecular events to cellular functions, such as death, division, or phenotype change. Cell death is implemented by terminating a parallel process, while cell division is carried out by creating a new process (daughter cell) from an existing one (mother cell). We first demonstrate these capabilities by creating two simple example models. In one model, we consider a relatively simple scenario where cells can evolve independently. In the other model, we consider interdependency among the cells, where cellular communication determines their collective behavior and evolution under a temporally evolving growth condition. We then demonstrate the framework’s capability by simulating a full-scale model of bacterial quorum sensing, where the dynamics of a population of bacterial cells is dictated by the intercellular communications in a time-evolving growth environment.

The Naturality of Natural Deduction

Developing a suggestion by Russell, Prawitz showed how the usual natural deduction inference rules for disjunction, conjunction and absurdity can be derived using those for implication and the second order quantifier in propositional intuitionistic second order logic NI $$^2$$ . It is however well known that the translation does not preserve the relations of identity among derivations induced by the permutative conversions and immediate expansions for the definable connectives, at least when the equational theory of NI $$^2$$ is assumed to consist only of $$\beta $$ - and $$\eta $$ -equations. On the basis of the categorial interpretation of NI $$^2$$ , we introduce a new class of equations expressing what in categorial terms is a naturality condition satisfied by the transformations interpreting NI $$^2$$ -derivations. We show that the Russell–Prawitz translation does preserve identity of proof with respect to the enriched system by highlighting the fact that naturality corresponds to a generalized permutation principle. Finally we sketch how these results could be used to investigate the properties of connectives definable in the framework of higher-level rules.

Executive Compensation and Environmental Performance: From a Perspective of External Governance Environment of Listed Companies

In order to deeply understand the impacts of corporate executive incentives on environmental performance, based on the principal-agent theory and legitimacy mechanism, this paper uses the PSM method to eliminate the interference of endogenous factors and empirically tests the correlation between executive compensation of listed companies and environmental performance under other unchanged conditions based on the sample of 166 listed companies on the Shanghai and Shenzhen Stock Exchanges from 2009 to 2012. The results show that the relationship between executive compensation and environmental performance is an inverted U” shape, and the external governance environment has a differentiated impact on executive compensation and environmental performance at different levels. In addition, the research results also suggest that executive compensation is an important factor affecting corporate environmental performance. It not only provides an empirical evidence that when the company takes the action of executive performance assessment to make sure the compensation, the environmental performance level can be assumed to be one of the important evaluation indexes, but also promotes decision-making reference for the governments to create external governance environment with low-degree government intervention and high-level rule of law, and transform into the service-oriented governments step by step as well as strengthen environment law enforcement.

How private meta-governance helps standard-setting partnerships deliver

PurposeThis paper aims to analyze the relevance of private meta-governance for multi-stakeholder partnerships. The authors assume that meta-governance, defined as higher level rules that shall guide partnerships’ governance activities, could build on and institutionalize lessons learned about partnerships’ success conditions and, in doing so, may render partnerships’ work more effective in the future.Design/methodology/approachThe research paper investigates a case of non-state meta-governance in the standard-setting arena. It explores how actors assess the interaction of the meta-governance efforts of the International Social and Environmental Accreditation and Labelling (ISEAL) Alliance (mainly in the form of their three Codes of Good Practice) and the Alliance for Water Stewardship’s efforts when setting and implementing their International Water Stewardship Standard. For this, a combination of research methods is applied: a literature review for deriving propositions on success conditions; document analysis, participatory observation and semi-structured interviews for gathering empirical evidence on the interaction between meta-governance and the partnership’s work.FindingsRespondents praise the benefits of ISEAL’s enabling meta-governance measures to strengthen their standard, structures and processes, as well as from ensuring activities, as these also improve their internal governance system while simultaneously providing credibility. In this context, they confirm the relevance of three success factors mentioned in the literature on voluntary standards: an inclusive process, a locally adapted design of the standard and institutionalized compliance management.Practical implicationsInstead of reinventing the wheel with every new multi-stakeholder partnership, meta-governance frameworks should be used to enable partnership staff and members, policymakers and stakeholders to learn from experience.Originality/valueThe authors’ analysis generates unique insights into perceptions of partnerships’ staff and stakeholders regarding lessons learned and private meta-governance. The present study on these actors’ perspectives provides a starting-point for further research on how meta-governance could help institutionalize success factors to scale-up and improve the impact of standard-setting partnerships.

A Semantic Web Approach to Simplifying Trigger-Action Programming in the IoT

End-user programming environments for the IoT such as IFTTT rely on a multitude of low-level trigger-action rules that categorize devices and services by technology or brand. EUPont is a Semantic Web ontology that enables users to meet their needs with fewer, higher-level rules that can be adapted to different contextual situations and as-yet-unknown IoT devices and services.

MODELLING OF INDOOR ENVIRONMENTS USING LINDENMAYER SYSTEMS

Abstract. Documentation of the “as-built” state of building interiors has gained a lot of interest in the recent years. Various data acquisition methods exist, e.g. the extraction from photographed evacuation plans using image processing or, most prominently, indoor mobile laser scanning. Due to clutter or data gaps as well as errors during data acquisition and processing, automatic reconstruction of CAD/BIM-like models from these data sources is not a trivial task. Thus it is often tried to support reconstruction by general rules for the perpendicularity and parallelism which are predominant in man-made structures. Indoor environments of large, public buildings, however, often also follow higher-level rules like symmetry and repetition of e.g. room sizes and corridor widths. In the context of reconstruction of city city elements (e.g. street networks) or building elements (e.g. fac¸ade layouts), formal grammars have been put to use. In this paper, we describe the use of Lindenmayer systems - which originally have been developed for the computer-based modelling of plant growth - to model and reproduce the layout of indoor environments in 2D.

A new approach for managing Android permissions: learning users\u2019 preferences

Today, permissions management solutions on mobile devices employ Identity Based Access Control (IBAC) models. If this approach was suitable when people had only a few games (like Snake or Tetris) installed on their mobile phones, the current situation is different. A survey from Google in 2013 showed that, on average, french users have installed 32 applications on their Android smartphones. As a result, these users must manage hundreds of permissions to protect their privacy. Scalability of IBAC is a well-known issue and many more advanced access control models have introduced abstractions to cope with this problem. However, such models are more complex to handle by non-technical users. Thus, we present a permission management system for Android devices that (1) learns users’ privacy preferences with a novel learning algorithm, (2) proposes them abstract authorization rules, and (3) provides advanced features to manage these high-level rules. Our learning algorithm is compared to two other well-known approaches to show its efficiency. Finally, we prove this whole approach is more efficient than current permission management system by comparing it to Privacy Guard Manager.

The Effect of Feedback and Operational Experience on Children's Rule Learning.

This study aimed to examine the relative effect of feedback and operational experience on children’s rule learning in a balance scale task, in which 88 children under the age of 7 years were asked to judge the state of equilibrium under four conditions. In the Control condition, children were required to observe the scale and predict which side would tilt down or keep balance, without feedback on the correctness of their answer. In the Operation (Op) condition, children were required to place the weights on the scale just like the experimenter did before they made predictions. In the Feedback (Fe) condition, feedback was provided for each prediction, but children were not allowed to operate the scale. In the Op-Fe condition, children could operate the scale and they were provided feedback for each prediction. The results showed that, (1) children in Control condition merely adopted the lowest level of rule, the Weight Rule; (2) when they were either given feedback or the opportunity to operate the scale, they used a higher level rule, such as the Distance Rule, more frequently; and feedback was more effective than the operational experience was in promoting rule learning; (3) when they were allowed to operate the scale, and were simultaneously provided feedback, rule learning increased markedly, suggesting that feedback-based operation is the most efficient method for facilitating children’s rule learning.

An Evaluation of Rule-Based Decision Support Methods in Norway 2005–2014: Practical Implications for Avalanche Education

The rate of recreational avalanche accidents has increased internationally in recent years. The purpose of this study was to (1) investigate how many historical (2005-2014) fatal avalanche accidents in Norway could have been prevented by using the Elementary Reduction Method (ERM) and the Obvious Clues Method (OCM/ALPTRUTh) and (2) discuss practical implications for decision making in avalanche terrain and avalanche education. We investigated 30 fatal slab avalanche accidents, of which 93% and 100% could have been prevented by applying the ERM and OCM (3 or more positive clues), respectively. Although experts are able to use 'higher level rules' as a result of their experience, novices are not. Thus, we conclude methods such as the ERM and OCM could help novices develop expertise. Such decision-making aids are practical tools to help novices recognize critical conditions and to facilitate their ability to acquire expertise that goes beyond the use of simple checklists.

MeTA

Physicians and health care organizations always collect large amounts of data during patient care. These large and high-dimensional datasets are usually characterized by an inherent sparseness. Hence, analyzing these datasets to figure out interesting and hidden knowledge is a challenging task. This article proposes a new data mining framework based on generalized association rules to discover multiple-level correlations among patient data. Specifically, correlations among prescribed examinations, drugs, and patient profiles are discovered and analyzed at different abstraction levels. The rule extraction process is driven by a taxonomy to generalize examinations and drugs into their corresponding categories. To ease the manual inspection of the result, a worthwhile subset of rules (i.e., nonredundant generalized rules) is considered. Furthermore, rules are classified according to the involved data features (medical treatments or patient profiles) and then explored in a top-down fashion: from the small subset of high-level rules, a drill-down is performed to target more specific rules. The experiments, performed on a real diabetic patient dataset, demonstrate the effectiveness of the proposed approach in discovering interesting rule groups at different abstraction levels.