Reasoning Capabilities Research Articles

Recognition of the Driving Style in Vehicle Drivers.

This paper presents three different approaches to recognize driving style based on a hierarchical-model. Specifically, it proposes a hierarchical model for the recognition of the driving style for advanced driver-assistance systems (ADAS) for vehicles. This hierarchical model for the recognition of the style of the car driving considers three aspects: the driver emotions, the driver state, and finally, the driving style itself. In this way, the proposed hierarchical pattern is composed of three levels of descriptors/features, one to recognize the emotional states, another to recognize the driver state, and the last one to recognize the driving style. Each level has a set of descriptors, which can be sensed in a real context. Finally, the paper presents three driving style recognition algorithms based on different paradigms. One is based on fuzzy logic, another is based on chronicles (a temporal logic paradigm), and the last is based on an algorithm that uses the idea of the recognition process of the neocortex, called Ar2p (Algoritmo Recursivo de Reconocimiento de Patrones, for its acronym in Spanish). In the paper, these approaches are compared using real datasets, using different metrics of interest in the context of the Internet of the Things, in order to determine their capabilities of reasoning, adaptation, and the communication of information. In general, the initial results are encouraging, specifically in the cases of chronicles and Ar2p, which give the best results.

Integrating Deep Learning with Logic Fusion for Information Extraction

Information extraction (IE) aims to produce structured information from an input text, e.g., Named Entity Recognition and Relation Extraction. Various attempts have been proposed for IE via feature engineering or deep learning. However, most of them fail to associate the complex relationships inherent in the task itself, which has proven to be especially crucial. For example, the relation between 2 entities is highly dependent on their entity types. These dependencies can be regarded as complex constraints that can be efficiently expressed as logical rules. To combine such logic reasoning capabilities with learning capabilities of deep neural networks, we propose to integrate logical knowledge in the form of first-order logic into a deep learning system, which can be trained jointly in an end-to-end manner. The integrated framework is able to enhance neural outputs with knowledge regularization via logic rules, and at the same time update the weights of logic rules to comply with the characteristics of the training data. We demonstrate the effectiveness and generalization of the proposed model on multiple IE tasks.

Guest Editorial Special Issue on Privacy and Security in Distributed Edge Computing and Evolving IoT

Recent advances in artificial intelligence, edge computing, and big data have enabled extensive reasoning capabilities at the edge of the network. Edge servers are now capable of extracting meaningful intelligence from IoT nodes, which can benefit a very diverse set of IoT applications, including smart carrier and distribution networks (power, people, water, and food), smart agriculture and manufacturing, and healthcare and maintenance. Unfortunately, as the infrastructures become more intelligent, they also become more vulnerable to disruption due to cyberattacks and information leakage. Furthermore, the rich data gathering and analytics involved in driving the intelligent management substantially raise the stakes in terms of privacy violation of the people and organizations that it serves.

Effect of Religiosity and Moral Identity Internalization on Prosocial Behaviour

The main aim of the present research was to study the religiosity and moral identity internalization as effective variables of prosocial behaviour. Sample consisted of 400 emerging adults (females) in the age range of 18–24. All participants completed questionnaire measures, namely religiosity scale (Bhushan, 1970), moral identity inventory (Aquino & Reed, 2002, Journal of Personality and Social Psychology, 83, 1423–1440) and prosocial personality battery (Penner, Fritzsche, Craiger & Friefeld, 1995, Advances in Personality Assessment (vol. 10, pp. 147–184). Hillsdale, NJ: Lea). The data were subjected to 2 × 2 analysis of variance. The results of ANOVA clearly revealed that highly religious people are more responsible socially, exhibit higher empathic concern, are more able to take others perspective, have higher prosocial moral reasoning capabilities, are more helpful, altruistic and demonstrate a high level of other oriented empathy. High moral identity internalization leads to socially responsible behaviour, along with higher empathic concern and perspective-taking abilities. Individuals with high level of moral identity were found capable to use prosocial moral reasoning, and they also exhibited higher levels of other oriented empathy. Religiosity and moral identity internalization interacted with each other to produce a combined effect on mutual concern moral reasoning.

Adaptive reasoning and strategic competence through problem based learning model in middle school

Most textbooks that are provided by the government employ a lot of non-routine problems in their exercise practice. However, students are incapable of understanding the non-routine problems and they struggle in using their reasoning power which affects the overall achievement of mathematics. The purpose of this study is to identify the students’ ability in relation to adaptive reasoning and strategic competence through Problem Based Learning (PBL) model. This study involved six seventh graders in middle school. Data on students’ adaptive reasoning capabilities and strategic competencies are collected through tests which were conducted over five learning sessions with the PBL model. The research data was analyzed descriptively by comparing test result and interview with the indicator of adaptive reasoning and strategic competences ability. The results show that students’ adaptive reasoning ability can be initiated through learning by PBL model. Two out of the three strategic competence indicators are met by all students. The two indicators are understanding and presenting math problems to verbal forms and selecting the right formula, approach and/or method to solve the problem. Only one indicator of strategic competence is not achieved, which is checking the truth on the settlement of problems that have been obtained. This study provides an overview of the importance of training students to answer non-routine problems through learning. It is a challenge for teachers to familiarize students with systematic steps through various strategies that involve reasoning.

EUCISE-OWL: An ontology-based representation of the Common Information Sharing Environment (CISE) for the maritime domain

The timely and efficient cooperation across sectors and borders during maritime crises is paramount for the safety of human lives. Maritime monitoring authorities are now realizing the grave importance of cross-sector and cross-border information sharing. However, this cooperation is compromised by the diversity of existing systems and the vast volumes of heterogeneous data generated and exchanged during maritime operations. In order to address these challenges, the EU has been driving several initiatives, including several EU-funded projects, for facilitating information exchange across sectors and borders. A key outcome from these efforts is the Common Information Sharing Environment (CISE), which constitutes a collaborative initiative for promoting automated information sharing between maritime monitoring authorities. However, the adoption of CISE is substantially limited by its existing serialization as an XML Schema only, which facilitates information sharing and exchange to some extent, but fails to deliver the fundamental additional benefits provided by ontologies, like the richer semantics, enhanced semantic interoperability and semantic reasoning capabilities. Thus, this paper presents EUCISE-OWL, an ontology representation of the CISE data model that capitalizes on the benefits provided by ontologies and aims to encourage the adoption of CISE. EUCISE-OWL is an outcome from close collaboration in an EU-funded project with domain experts with extensive experience in deploying CISE in practice. The paper also presents a representative example for handling information exchange during a maritime crisis as well as performance results for specific querying tasks that can demonstrate and evaluate the use of the proposed ontology in practice.

Designing a Rapid Optical Response Trigger(RORT) for self-navigating and path explorer robot

Human beings are capable of making complex decisions with a limited number of sensors due to their rich logical reasoning capability. The decision-making process derives in combination with perceiving the environment and referring to previously known concepts. Similarly, machines can interact with the environment with large support of sensors in a number of ways and each of them has their advantage as well as limitations. In this work, systematic Optical Marker(OM) are used as control triggers as an efficient alternative of sensing the physical environment and rapid movement-control of Remotely Operating Vehicle(ROV). The quick response of OM is much more efficient in different lighting condition, cost-effective and less resource hungry. Sequential placement of OM as control triggers contain information about the physical environment which in turn provides a guided path to ROV for a kind of supervised path exploration. The device scans the optical marker and perceives that information about its physical surroundings. For self-navigation, the robot uses a mobile device camera as an optical precept and parse the OM code to process state-space representation. The path of navigation is explored by applying graph theoretic algorithmic approach for finding an optimal path. The episodic interaction design is experimented under different device and lighting variation. The proposed RORT algorithm for path exploration and compensating missing information proved to be robust enough to hop forward or retract to the point of origin without any external support of complex computations.

Analysis and Design of Computational News Angles

A key skill for a journalist is the ability to assess the newsworthiness of an event or situation. To this purpose journalists often rely on news angles, conceptual criteria that are used both i) to assess whether something is newsworthy and also ii) to shape the structure of the resulting news item. As journalism becomes increasingly computer-supported, and more and more sources of potentially newsworthy data become available in real time, it makes sense to try and equip journalistic software tools with operational versions of news angles, so that, when searching this vast data space, these tools can both identify effectively the events most relevant to the target audience, and also link them to appropriate news angles. In this paper we analyse the notion of news angle and, in particular, we i) introduce a formal framework and data schema for representing news angles and related concepts and ii) carry out a preliminary analysis and characterization of a number of commonly used news angles, both in terms of our formal model and also in terms of the computational reasoning capabilities that are needed to apply them effectively to real-world scenarios. This study provides a stepping stone towards our ultimate goal of realizing a solution capable of exploiting a library of news angles to identify potentially newsworthy events in a large journalistic data space.

Temporal Reasoning via Audio Question Answering

Multimodal question answering tasks can be used as proxy tasks to study systems that can perceive and reason about the world. Answering questions about different types of input modalities stresses different aspects of reasoning such as visual reasoning, reading comprehension, story understanding, or navigation. In this article, we use the task of Audio Question Answering (AQA) to study the temporal reasoning abilities of machine learning models. To this end, we introduce the Diagnostic Audio Question Answering (DAQA) dataset comprising audio sequences of natural sound events and programmatically generated questions and answers that probe various aspects of temporal reasoning. We adapt several recent state-of-the-art methods for visual question answering to the AQA task, and use DAQA to demonstrate that they perform poorly on questions that require in-depth temporal reasoning. Finally, we propose a new model, Multiple Auxiliary Controllers for Linear Modulation (MALiMo) that extends the recent Feature-wise Linear Modulation (FiLM) model and significantly improves its temporal reasoning capabilities. We envisage DAQA to foster research on AQA and temporal reasoning and MALiMo a step towards models for AQA.

Utilisation and Evaluation of Cooperative Case-Based Teaching for Integration of Microbiology and Pharmacology in Veterinary Education

PurposeIntegrating basic sciences with clinical disciplines while fostering clinical reasoning capabilities is difficult. We investigated the utilisation of diagnostic specimens and, a cooperative, case-based learning and teaching model to integrate principles of antimicrobial drug pharmacology and microbiology in the fifth year of a veterinary course. MethodsIn small groups, students were assigned diagnostic specimens from which they isolated and identified clinically relevant microorganisms and then performed antimicrobial susceptibility tests based on a review of pharmacology, microbiology and pathophysiology. Results were recorded and analysed followed by a student-led integrative tutorial. Learning outcomes were assessed via individually written reports discussing the disease process, interpretation of diagnostic results and, recommendations and rationales for therapeutic interventions. ResultsThis approach yielded high quality student reports that conformed to antimicrobial prescription guidelines with consistently high summative assessment scores. Mean scores for the final report in this learning activity were: 82 ± 12%, 80 ± 12% and 80 ± 11% for 2015, 2016 and 2017 cohorts respectively; over the same time period, 98 ± 1% of students indicated that these learning activities facilitated the development of confidence, professional knowledge and skills. DiscussionThis was a consistent approach for integrating principles of veterinary pharmacology and microbiology in clinical disciplines. These data illustrate the benefit of a systematic application of a cooperative, case-based learning and teaching model in integrating pre-clinical and clinical disciplines in a bachelor of veterinary science course.

Enhanced graph-based fault diagnostic system for nuclear power plants

Scheduled maintenance and condition-based online monitoring are among the focal points of recent research to enhance nuclear plant safety. One of the most effective ways to monitor plant conditions is by implementing a full-scope, plant-wide fault diagnostic system. However, most of the proposed diagnostic techniques are perceived as unreliable by operators because they lack an explanation module, their implementation is complex, and their decision/inference path is unclear. Graphical formalism has been considered for fault diagnosis because of its clear decision and inference modules, and its ability to display the complex causal relationships between plant variables and reveal the propagation path used for fault localization in complex systems. However, in a graph-based approach, decision-making is slow because of rule explosion. In this paper, we present an enhanced signed directed graph that utilizes qualitative trend evaluation and a granular computing algorithm to improve the decision speed and increase the resolution of the graphical method. We integrate the attribute reduction capability of granular computing with the causal/fault propagation reasoning capability of the signed directed graph and comprehensive rules in a decision table to diagnose faults in a nuclear power plant. Qualitative trend analysis is used to solve the problems of fault diagnostic threshold selection and signed directed graph node state determination. The similarity reasoning and detection ability of the granular computing algorithm ensure a compact decision table and improve the decision result. The performance of the proposed enhanced system was evaluated on selected faults of the Chinese Fuqing 2 nuclear reactor. The proposed method offers improved diagnostic speed and efficient data processing. In addition, the result shows a considerable reduction in false positives, indicating that the method provides a reliable diagnostic system to support further intervention by operators.

Dynamic Edge Access System in IoT Environment

Edge computing has enabled extensive reasoning capabilities at the edge of the network. Edge nodes have direct access to edge sources (e.g., sensors, devices) to read and process relevant data, while each sensor/device manufacturer uses their bespoke protocols in the Internet of Things (IoT) environment, hence, addressing compatibility problems to ensure seamless access has become a cumbersome task. A conventional solution is to save all sensor/device protocols in an edge node, however, which brings higher redundancy and hardware expenditure. Hence, we propose a dynamic edge access system (DEA) built on a collaborative IoT architecture. It solves the compatibility issues with low redundancy by customizing access programs. Besides, embedded programmable logic controllers are designed as edge nodes and have been applied in around 78 000 base stations. After implementing DEA, the analysis illustrates that the average improvement of every station’s protocol utilization is 96.3% and 66.6%, while the memory footprint is decreased to 4.5% and 33.3% compared with the conventional solution and global dynamic reconfiguration method.

Learning visual features for relational CBIR

Recent works in deep-learning research highlighted remarkable relational reasoning capabilities of some carefully designed architectures. In this work, we employ a relationship-aware deep learning model to extract compact visual features used relational image descriptors. In particular, we are interested in relational content-based image retrieval (R-CBIR), a task consisting in finding images containing similar inter-object relationships. Inspired by the relation networks (RN) employed in relational visual question answering (R-VQA), we present novel architectures to explicitly capture relational information from images in the form of network activations that can be subsequently extracted and used as visual features. We describe a two-stage relation network module (2S-RN), trained on the R-VQA task, able to collect non-aggregated visual features. Then, we propose the aggregated visual features relation network (AVF-RN) module that is able to produce better relationship-aware features by learning the aggregation directly inside the network. We employ an R-CBIR ground-truth built by exploiting scene-graphs similarities available in the CLEVR dataset in order to rank images in a relational fashion. Experiments show that features extracted from our 2S-RN model provide an improved retrieval performance with respect to standard non-relational methods. Moreover, we demonstrate that the features extracted from the novel AVF-RN can further improve the performance measured on the R-CBIR task, reaching the state-of-the-art on the proposed dataset.

Symbolic Analysis of Maude Theories with Narval

AbstractConcurrent functional languages that are endowed with symbolic reasoning capabilities such as Maude offer a high-level, elegant, and efficient approach to programming and analyzing complex, highly nondeterministic software systems. Maude’s symbolic capabilities are based on equational unification and narrowing in rewrite theories, and provide Maude with advanced logic programming capabilities such as unification modulo user-definable equational theories and symbolic reachability analysis in rewrite theories. Intricate computing problems may be effectively and naturally solved in Maude thanks to the synergy of these recently developed symbolic capabilities and classical Maude features, such as: (i) rich type structures with sorts (types), subsorts, and overloading; (ii) equational rewriting modulo various combinations of axioms such as associativity, commutativity, and identity; and (iii) classical reachability analysis in rewrite theories. However, the combination of all of these features may hinder the understanding of Maude symbolic computations for non-experienced developers. The purpose of this article is to describe how programming and analysis of Maude rewrite theories can be made easier by providing a sophisticated graphical tool called Narval that supports the fine-grained inspection of Maude symbolic computations.

Using Wikidata and Metaphactory to Underpin an Integrated Flora of Canada

We are using Wikidata and Metaphactory to build an Integrated Flora of Canada (IFC). IFC will be integrated in two senses: First, it will draw on multiple existing flora (e.g. Flora of North America, Flora of Manitoba, etc.) for content. Second, it will be a portal to related resources such as annotations, specimens, literature, and sequence data. Background We had success using Semantic Media Wiki (SMW) as the platform for an on-line representation of the Flora of North America (FNA). We used Charaparser (Cui 2012) to extract plant structures (e.g. “stem”), characters (e.g. “external texture”), and character values (e.g. “glabrous”) from the semi-structured FNA treatments. We then loaded this data into SMW, which allows us to query for taxa based on their character traits, and enables a broad range of exploratory analysis, both for purposes of hypothesis generation, and also to provide support for or against specific scientific hypotheses. Migrating to Wikidata/Wikibase We decided to explore a migration from SMW to Wikibase for three main reasons: simplified workflow; triple level provenance; and sustainability. Simplified workflow: Our workflow for our FNA-based portal includes Natural Language Processing (NLP) of coarse-grained XML to get the fine-grained XML, transforming this XML for input into SMW, and a custom SMW skin for displaying the data. We consider the coarse-grained XML to be canonical. When it changes (because we find an error, or we improve our NLP), we have to re-run the transformation, and re-load the data, which is time-consuming. Ideally, our presentation would be based on API calls to the data itself, eliminating the need to transform and re-load after every change. Provenance: Wikidata's provenance model supports having multiple, conflicting assertions for the same character trait, which is something that inevitably happens when floristic data is integrated. Sustainability: Wikidata has strong support from the Wikimedia Foundation, while SMW is increasingly seen as a legacy system. Wikibase vs. Wikidata Wikidata, however, is not a suitable home for the Integrated Flora of Canada. It is built upon a relatively small number of community curated properties, while we have ~4500 properties for the Asteraceae family alone. The model we want to pursue is to use Wikidata for a small group of core properties (e.g. accepted name, parent taxon, etc.), and to use our own instance of Wikibase for the much larger number of specialized morphological properties (e.g. adaxial leaf colour, leaf external texture, etc.) Essentially, we will be running our own Wikidata, over which we would exercise full control. Miller (2018) decribes deploying this curation model in another domain. Metaphactory Metaphactory is a suite of middleware and front-end interfaces for authoring, managing, and querying knowledge graphs, including mechanisms for faceted search and geospatial visualizations. It is also the software (together with Blazegraph) behind the Wikidata Query Service. Metaphactory provides us with a SPARQL endpoint; a templating mechanism that allows each taxonomic treatment to be rendered via a collection of SPARQL queries; reasoning capabilities (via an underlying graph database) that permit the organization of over 42,000 morphological properties; and a variety of search and discovery tools. There are a number of ways in which Wikidata and Metaphactory can work together, and we are still exploring questions such as: Will provenance be managed via named graphs, or via the Wikidata snak model?; How will data flow between the two platforms? Etc. We will report on our findings to date, and invite collaboration with related Wikimedia-based projects.

Practical reasoning using values: an argumentative approach based on a hierarchy of values

Values are at the heart of human decision-making. They are used to decide whether something or some state of affairs is good or not, and they are also used to address the moral dilemma of the right thing to do under given circumstances. Both uses are present in several everyday situations, from the design of a public policy to the negotiation of employee benefit packages. Both uses of values are specially relevant when one intends to design or validate that artificial intelligent systems behave in a morally correct way. In real life, the choice of policy components or the agreed upon benefit package are processes that involve argumentation. Likewise, the design and deployment of value-driven artificial entities may be well served by embedding practical reasoning capabilities in these entities or using argumentation for their design and certification processes. In this paper, we propose a formal framework to support the choice of actions of a value-driven agent and arrange them into plans that reflect the agent’s preferences. The framework is based on defeasible argumentation. It presumes that agent values are partially ordered in a hierarchy that is used to resolve conflicts between incommensurable values.

Why epistemic justice matters in and for education

The paper considers the importance of epistemic justice in democratic life, and the significance of education as a key space to foster the relevant epistemic capabilities. Epistemic in/justice offers resources to think about conditions of possibility (what Amartya Sen calls ‘conversion factors’), given that societies train our sensibilities in ways which are flawed and prejudiced. It is proposed further that Amartya Sen’s emphasis on public reasoning is central to epistemic justice. Using the space of education to make the argument, core ideas in the capability approach are first outlined. Epistemic justice is described, and the claim is then advanced that Miranda Fricker’s ‘epistemic contribution capability’ is generative in education settings for developing democratic and public reasoning capabilities. To be fully involved in learning and development and fair-achieved outcomes in formal education, students would need opportunities to develop their epistemic capability of being able both to receive information and to make interpretive contributions to the common pool of knowledge, understanding, and practical deliberation. Conditions of respect, recognition and equal moral worth would be required so that all students should have access to the capability and to have their contributions taken up as integral to their flourishing. Thus, in universities and schools, epistemic virtues should be educated, trained, developed and scaffolded pedagogically, including cultivating emotions. The paper then considers the challenge of free speech and what this may demand of us educationally.

A general approach to reasoning with probabilities

We propose a general scheme for adding probabilistic reasoning capabilities to a wide variety of knowledge representation formalisms and we study its properties. Syntactically, we consider adding probabilities to the formulas of a given base logic. Semantically, we define a probability distribution over the subsets of a knowledge base by taking the probabilities of the formulas into account accordingly. This gives rise to a probabilistic entailment relation that can be used for uncertain reasoning. Our approach is a generalisation of many concrete probabilistic enrichments of existing approaches, such as ProbLog (an approach to probabilistic logic programming) and the constellation approach to abstract argumentation. We analyse general properties of our approach and provide some insights into novel instantiations that have not been investigated yet.

PENINGKATAN KEMAMPUAN PENALARAN SISWA MELALUI MODEL PROBLEM BASED LEARNING (PBL) DITINJAU DARI KEMAMPUAN AKADEMIK SISWA DI SMA

The research was to analyze the improvement of students' reasoning students through the model Problem Based Learning (PBL) in terms of academic ability in high school. This research is a quasi experimental. This research was designed using a factorial design experiment. The study population was all students of class XI IPA at SMAN 1 Darma in the 2014/2015 school year as much as 5 classes. Sampling by using cluster random sampling technique. The research sample of grade XI IPA 1 and XI IPA 3 as an experimental class and XI IPA 2 as the control class. The collection of data with the test description, method of observation (assessment of performance capability of reasoning, observation sheets PBL, and self assessment) and documentation. Test the hypothesis by using parametric statistical tests in this case to test the hypothesis using the t test improvement was obtained p <0.05 is 0.044 <0.05, which means there is increasing students' reasoning skills and two-way ANOVA test to determine the application of PBL models include: (1 ) There are differences in reasoning abilities of students who apply models that do not apply the PBL and PBL models (p <0,05; 0,000 <0,05). (2) There is no difference in reasoning abilities of students based on academic ability (p> 0,05; 0,203> 0,05). (3) There is no interaction between the model PBL and academic skills with reasoning (p> 0,05; 0,981> 0,05).

Fast Analog Transmission for High-Mobility Wireless Data Acquisition in Edge Learning

By implementing machine learning at the network edge, edge learning trains models by leveraging rich data distributed at edge devices (e.g., smartphones and sensors) and in return endow on them capabilities of seeing, listening, and reasoning. In edge learning, the need of high-mobility wireless data acquisition arises in scenarios where edge devices (or even servers) are mounted on the ground or aerial vehicles. In this paper, we present a novel solution, called fast analog transmission (FAT), for high- mobility data acquisition in edge-learning systems, which has several key features. First, FAT incurs low-latency. Specifically, FAT requires no source-and-channel coding and no channel training via the proposed technique of Grassmann analog encoding (GAE) that encodes data samples into subspace matrices. Second, FAT supports spatial multiplexing by directly transmitting analog vector data over an antenna array. Third, FAT can be seamlessly integrated with edge learning (i.e., training of a classifier model in this work). In particular, by applying a Grassmannian-classification algorithm from computer vision, the received GAE encoded data can be directly applied to training the model without decoding and conversion. This design is found by simulation to outperform conventional schemes in learning accuracy due to its robustness against data distortion induced by fast fading.