Deductive Tasks Research Articles

Semantic-Enriched Code Knowledge Graph to Reveal Unknowns in Smart Contract Code Reuse

Programmers who work with smart contract development often encounter challenges in reusing code from repositories. This is due to the presence of two unknowns that can lead to non-functional and functional failures. These unknowns are implicit collaborations between functions and subtle differences among similar functions. Current code mining methods can extract syntax and semantic knowledge (known knowledge), but they cannot uncover these unknowns due to a significant gap between the known and the unknown. To address this issue, we formulate knowledge acquisition as a knowledge deduction task and propose an analytic flow that uses the function clone as a bridge to gradually deduce the known knowledge into the problem-solving knowledge that can reveal the unknowns. This flow comprises five methods: clone detection, co-occurrence probability calculation, function usage frequency accumulation, description propagation, and control flow graph annotation. This provides a systematic and coherent approach to knowledge deduction. We then structure all of the knowledge into a semantic-enriched code Knowledge Graph (KG) and integrate this KG into two software engineering tasks: code recommendation and crowd-scaled coding practice checking. As a proof of concept, we apply our approach to 5,140 smart contract files available on Etherscan.io and confirm high accuracy of our KG construction steps. In our experiments, our code KG effectively improved code recommendation accuracy by 6% to 45%, increased diversity by 61% to 102%, and enhanced NDCG by 1% to 21%. Furthermore, compared to traditional analysis tools and the debugging-with-the-crowd method, our KG improved time efficiency by 30 to 380 seconds, vulnerability determination accuracy by 20% to 33%, and vulnerability fixing accuracy by 24% to 40% for novice developers who identified and fixed vulnerable smart contract functions.

Electrophysiological connectivity of logical deduction: Early cortical MEG study

Complex human reasoning involves minimal abilities to extract conclusions implied in the available information. These abilities are considered “deductive” because they exemplify certain abstract relations among propositions or probabilities called deductive arguments. However, the electrophysiological dynamics which supports such complex cognitive processes has not been addressed yet. In this work we consider typically deductive logico-probabilistically valid inferences and aim to verify or refute their electrophysiological functional connectivity differences from invalid inferences with the same content (same relational variables, same stimuli, same relevant and salient features). We recorded the brain electrophysiological activity of 20 participants (age = 20.35 ± 3.23) by means of an MEG system during two consecutive reasoning tasks: a search task (invalid condition) without any specific deductive rules to follow, and a logically valid deductive task (valid condition) with explicit deductive rules as instructions. We calculated the functional connectivity (FC) for each condition and conducted a seed-based analysis in a set of cortical regions of interest. Finally, we used a cluster-based permutation test to compare the differences between logically valid and invalid conditions in terms of FC. As a first novel result we found higher FC for valid condition in beta band between regions of interest and left prefrontal, temporal, parietal, and cingulate structures. FC analysis allows a second novel result which is the definition of a propositional network with operculo-cingular, parietal and medial nodes, specifically including disputed medial deductive “core” areas. The experiment discloses measurable cortical processes which do not depend on content but on truth-functional propositional operators. These experimental novelties may contribute to understand the cortical bases of deductive processes.

Two birds in the hand: Concurrent and switching cognitive flexibility in preschoolers

Cognitive flexibility, the ability to think of something in more than one way, has been studied in preschoolers from two different approaches. Within executive function research, most studies operationalize cognitive flexibility using sequential tasks in which children must think of a specific stimulus first in one way and then in another way (switching cognitive flexibility). In contrast, Piagetian multiplicative classification tasks also require cognitive flexibility by asking children to consider multiple dimensions of a single stimulus at the same time. Piagetian tasks are typically inductive and require abstraction skills. Although research with Piagetian tasks suggests that children are unable to consider multiple dimensions simultaneously until they reach the concrete operational stage, there is some evidence that preschoolers can coordinate two dimensions simultaneously in deductive tasks. In this study, we examined a deductive version of a matrix completion task in which preschoolers (N = 102) were asked to consider two dimensions of a given stimulus simultaneously. We show that 4- and 5-year-olds can succeed on this task and compare children’s performance on the matrix completion task with their performance on a widely used switching cognitive flexibility task, the Dimensional Change Card Sort. We discuss the implications of relating the two aspects of cognitive flexibility development.

Improving Children’s Logical and Mathematical Performance via a Pragmatic Approach

Deductive and logical reasoning is a crucial topic for cognitive psychology and has largely been investigated in adults, concluding that humans are apparently irrational. Yet, from a pragmatic approach, the logical level of meaning is only one of possible communicative interpretations, and the least likely to be assigned if the intent of the task is not adequately transmitted. Indeed, new formulations of the mathematical tasks (syllogisms, selection task, class inclusion task, prolem solving), of greater relevance to the problem and to its aim, greatly improved adults’ logical performance. The current study tested whether pragmatic manipulations of task instructions influenced in a similar way children’s performance in deductive and logical tasks (Experiment 1) and in insight problems (Experiment 2). We found that, when task instructions were in accordance with the conversational rules of communication, 10-year-old children substantially improved their performance. We suggest that language use imposes constraints in terms of informativeness and relevance which are crucial in teaching logic and mathematics.

Toddlers’ understanding and use of verbal negation in inferential reasoning search tasks

Past research has demonstrated that young children and nonhuman animals are able to reason by elimination (“If not A, then B”) by relying on visual cues such as seeing that one container is empty. Other research has shown that young children can solve similar, simple inferential reasoning tasks where “emptiness” is conveyed verbally through negation (e.g., “The toy is not in the box”). However, it is unclear whether these tasks involved reasoning through the disjunctive syllogism, which requires the representation of logical negation (NOT A) and disjunction (A OR B) or simpler, nondeductive strategies. In Study 1, we extended this work by investigating whether 2-year-olds can infer the location of a toy in typical two-location elimination trials, when given both affirmative and negative sentences, and more complex three-location trials, when information about emptiness was conveyed verbally and visually. Younger 2-year-olds performed significantly better on the search task when hearing affirmative than negative sentences, whereas older 2-year-olds were equally successful with both types of sentences. Study 2 examined children’s ability to use verbal negation to solve a more complex deductive task involving disjunctive syllogism. Results showed that, in this linguistic version of the disjunctive reasoning task, both 2.5- and 3-year-olds made accurate inferences about the location of a reward, unlike prior (nonlinguistic) evidence that demonstrated this ability in 3-year-olds but not in younger children. We conclude that by the end of their second year of life, children have a robust understanding of negation which they can apply in abstract reasoning.

Thinking about social and nonsocial alternative possibilities in premature preschoolers

ABSTRACTIntroduction: Prematurely born preschoolers show developmental cognitive delay compared to full-term children. There are important neurological networks developing at preschool age related to perspective taking about the attribution of belief and to deduction with contrary-to-fact situations. Other deductive abilities may be completed during that period. Method: A group of very prematurely born children (N = 35) aged between 4 and 5 years was compared with a control group of children born at full term (N = 35). They completed different cognitive tasks that required making inferences about possible true facts and false facts, and about others’ beliefs. Results: Results showed that preterm children had more difficulties with false beliefs and counterfactual tasks than the controls but they did not differ in equivalent deductive tasks. Conclusions: We discuss the possible difficulties of preterm children when they first reach primary school age, not only with social perspective taking, but also with considering nonsocial contrary-to-fact alternatives. Prematurity is not a syndrome, but could be a risk condition. Therefore, these results are relevant in the field of differential diagnosis—in particular, for children with difficulties in perspective taking, a condition with which children born prematurely could share some characteristics.

OH, THE HUMANITY! THE CONTROL SIDE OF SYSTEM SECURITY

ABSTRACTIn 1951, Fitts observed that humans are better than machines at inductive reasoning while machines excel at deductive reasoning. Meanwhile, system security users' needs unrepresented in design, require high‐load deductive tasks such as unaided recall of passwords. The purpose of system security is to control access; therefore, security should be approached from the perspective of control theory. Intelligent control theory incorporates critical components of security and structures the diagnostic process in the form of a logical induction. A control theoretic perspective that purports to apply to system security must account for induction in the control loop and automation is often proposed for these solutions. But, there are no fully automated systems. Human users are components of a control process. Modern security systems are almost exclusively logical systems, have hybrid intelligence (hybridized from users and software) and exhibit Emergent Behaviors (EB) that cannot be known a priori. Emergence in complex systems is readily apparent in most failures and frequently labeled latent human error. Human‐system integration‐induced EB must be accounted for because as a source of major vulnerability. The only viable strategy is suggested by control theory and incorporates embedded simulation as a critical component. A human‐centered design perspective is required.

The Impact of Deductive, Inductive, and L1-Based Consciousness-Raising Tasks on EFL Learners' Acquisition of the Request Speech Act

The necessity and importance of teaching pragmatics has come to light by many researchers (e.g. Rose & Kasper, 2001). Due to the consensus over the need to teach pragmatic competence, the main issue now centers on the question of how we should teach this competence in the most effective way. Consistent with this line of research, the present study aimed to investigate the effectiveness of deductive, inductive, and L1-based consciousness-raising instructional tasks on EFL learners' acquisition of the request speech act during a seven-week instruction period. The results obtained through a written DCT administered to 140 EFL learners indicated that instruction had a significantly positive effect on learners' acquisition of the request speech act. The comparison of the task types demonstrated that, all in all, the deductive task was the most effective one. Furthermore, the results showed that the learners were generally receptive to L1-based awareness-raising tasks and that these tasks were more effective than inductive tasks. This study suggests that consciousness-raising instructional tasks could be utilized in raising students’ sociopragmatic awareness and be applied in helping them develop their interlanguage pragmatics.

New normative standards of conditional reasoning and the dual-source model.

There has been a major shift in research on human reasoning toward Bayesian and probabilistic approaches, which has been called a new paradigm. The new paradigm sees most everyday and scientific reasoning as taking place in a context of uncertainty, and inference is from uncertain beliefs and not from arbitrary assumptions. In this manuscript we present an empirical test of normative standards in the new paradigm using a novel probabilized conditional reasoning task. Our results indicated that for everyday conditional with at least a weak causal connection between antecedent and consequent only the conditional probability of the consequent given antecedent contributes unique variance to predicting the probability of conditional, but not the probability of the conjunction, nor the probability of the material conditional. Regarding normative accounts of reasoning, we found significant evidence that participants' responses were confidence preserving (i.e., p-valid in the sense of Adams, 1998) for MP inferences, but not for MT inferences. Additionally, only for MP inferences and to a lesser degree for DA inferences did the rate of responses inside the coherence intervals defined by mental probability logic (Pfeifer and Kleiter, 2005, 2010) exceed chance levels. In contrast to the normative accounts, the dual-source model (Klauer et al., 2010) is a descriptive model. It posits that participants integrate their background knowledge (i.e., the type of information primary to the normative approaches) and their subjective probability that a conclusion is seen as warranted based on its logical form. Model fits showed that the dual-source model, which employed participants' responses to a deductive task with abstract contents to estimate the form-based component, provided as good an account of the data as a model that solely used data from the probabilized conditional reasoning task.

Automatic deduction in (dynamic) geometry: Loci computation

A symbolic tool based on open source software that provides robust algebraic methods to handle automatic deduction tasks for a dynamic geometry construction is presented. The prototype has been developed as two different worksheets for the open source computer algebra system Sage, corresponding to two different ways of coding a geometric construction, namely with the open source dynamic geometry system GeoGebra or using the common file format for dynamic geometry developed by the Intergeo project. Locus computation algorithms based on Automatic Deduction techniques are recalled and presented as basic for an efficient treatment of advanced methods in dynamic geometry. Moreover, an algorithm to eliminate extraneous parts in symbolically computed loci is discussed. The algorithm, based on a recent work on the Gröbner cover of parametric systems, identifies degenerate components and extraneous adherence points in loci, both natural byproducts of general polynomial algebraic methods. Several examples are shown in detail.

Choice of Information to Discuss

Members of 43 three-person groups completed a deductive logic task. Each member was provided with unique clues and information indicating the utility of each clue. Objective validity varied because some clues were essential to problem solution and others were not useful. Social validity was manipulated by informing participants that some clues would likely be useful and that other clues were not likely to be useful. Clues were discussed more frequently if either objective validity or social validity was high, but the effects were not additive. Clues were discussed less frequently in the low objective validity–low social validity condition than in all other conditions. Alignment of both types of validity facilitated the group’s ability to focus on relevant information.

Automatic Deduction in Dynamic Geometry using Sage

We present a symbolic tool that provides robust algebraic methods to handle automatic deduction tasks for a dynamic geometry construction. The main prototype has been developed as two different worksheets for the open source computer algebra system Sage, corresponding to two different ways of coding a geometric construction. In one worksheet, diagrams constructed with the open source dynamic geometry system GeoGebra are accepted. In this worksheet, Groebner bases are used to either compute the equation of a geometric locus in the case of a locus construction or to determine the truth of a general geometric statement included in the GeoGebra construction as a boolean variable. In the second worksheet, locus constructions coded using the common file format for dynamic geometry developed by the Intergeo project are accepted for computation. The prototype and several examples are provided for testing. Moreover, a third Sage worksheet is presented in which a novel algorithm to eliminate extraneous parts in symbolically computed loci has been implemented. The algorithm, based on a recent work on the Groebner cover of parametric systems, identifies degenerate components and extraneous adherence points in loci, both natural byproducts of general polynomial algebraic methods. Detailed examples are discussed.

Making sense of theory of mind and paranoia: The psychometric properties and reasoning requirements of a false belief sequencing task

Introduction. This study used Item–Response Theory (IRT) to model the psychometric properties of a false belief picture sequencing task. Consistent with the mental time travel hypothesis of paranoia, we anticipated that performance on this deductive theory of mind (ToM) task would not be associated with the presence of persecutory delusions but would be related to other clinical, cognitive, and demographic factors. Method. A large (N=237) and diverse clinical and nonclinical sample differing in levels of depression and paranoid ideation performed 2 ToM tasks: the false belief sequencing task and a ToM stories task that was used to assess the validity of the false belief sequencing task as a measure of ToM. Results. A unidimensional IRT model was found to fit the data well. Latent ToM ability as measured by the false belief sequencing task was negatively related with age and positively with IQ. In contrast to the ToM stories measure, there was no association between clinical diagnosis or symptoms and false belief picture sequencing after controlling for age and IQ. Conclusions. In line with mental time travel hypothesis of paranoia (Corcoran, 2010), performance on this deductive nonverbal ToM task is not related to the presence of paranoid symptoms. This measure is best suited for assessing ToM functioning where participants’ performance falls just short of the average latent ToM ability. Furthermore, it is sensitive to the effects of increasing age and decreasing IQ.

An analysis of safe driving using concurrent tasks: effects of making decisions from the traffic sign information and performing verbal versus spatial-imagery tasks

Driving requires performing different subtasks concurrently that can interfere. Specifically, two driving sub-tasks are analysed in this work: Participants judge manoeuvres regulated by traffic signs (turn left or turn right...

Conditional and syllogistic deductive tasks dissociate functionally during premise integration.

Deduction allows us to draw consequences from previous knowledge. Deductive reasoning can be applied to several types of problem, for example, conditional, syllogistic, and relational. It has been assumed that the same cognitive operations underlie solutions to them all; however, this hypothesis remains to be tested empirically. We used event-related fMRI, in the same group of subjects, to compare reasoning-related activity associated with conditional and syllogistic deductive problems. Furthermore, we assessed reasoning-related activity for the two main stages of deduction, namely encoding of premises and their integration. Encoding syllogistic premises for reasoning was associated with activation of BA 44/45 more than encoding them for literal recall. During integration, left fronto-lateral cortex (BA 44/45, 6) and basal ganglia activated with both conditional and syllogistic reasoning. Besides that, integration of syllogistic problems additionally was associated with activation of left parietal (BA 7) and left ventro-lateral frontal cortex (BA 47). This difference suggests a dissociation between conditional and syllogistic reasoning at the integration stage. Our finding indicates that the integration of conditional and syllogistic reasoning is carried out by means of different, but partly overlapping, sets of anatomical regions and by inference, cognitive processes. The involvement of BA 44/45 during both encoding (syllogisms) and premise integration (syllogisms and conditionals) suggests a central role in deductive reasoning for syntactic manipulations and formal/linguistic representations.

Mental representations of obligatory and prohibitory traffic signs

Every traffic sign conveys a single proposition about traffic conditions. Drivers must integrate this proposition with their goals and other known facts to decide on an appropriate action in what amounts to a deduction task. For example, imagine that you are driving a car and you want to turn right at an intersection but there is a no right turn sign. You have to assess the options and make a decision (reach a conclusion). Theoretical accounts from research about reasoning can be applied to how we understand traffic signs and make inferences from them. This study includes two experiments that analysed how people decide whether a situation is allowed or not, taking into account the information provided by one sign – obligatory or prohibitory – or two signs – two obligatory or two prohibitory signs. The number of signs, that is, the complexity of the driving scene, was a key factor in this study. The two experiments differ in the presentation format of the two-sign experimental condition. In the first experiment, the signs were presented as two isolated signs. In the second experiment, the two signs were embedded in one. Equivalent results were found whether two isolated signs or two embedded signs were shown. When an obligatory sign was used at the junction, faster responses were obtained for allowed manoeuvres than for those not allowed. When a prohibitory sign was used, the results were the opposite. However, the advantage of prohibitory signs in the not-allowed situation was not replicated when people had to collect information from two prohibitory signs. Results showed that people integrate two obligatory sign messages – informing where one can go – more easily than two prohibitory sign messages – informing where not to go. Therefore, it could be said that each traffic sign elicits an internal mental model elaboration in which forbidden actions are explicitly labelled by means of attached “mental footnotes” indicating the epistemic status of “prohibitory information.”

Distributed Intelligent Systems

The first edition of the International Symposium on Intelligent and Distributed Computing —IDC'2007, took place on October 18–20, 2007 in Craiova, Romania. According to these who participated, Fall was beautiful in Craiova—as can be seen on the photograph below. While the Symposium itself covered a large spectrum of issues involved in Intelligent Computing, for this Special Issue we have selected these that match the specific focus of the SCPE. Therefore, out of 33 papers published in Symposium Proceedings we have selected the following six. In Studying SVM Method's Scalability Using Text Documents, D. Morariu, M. VinA£an, and L. VinA£an consider application of the SVN method to the case of automatic classification of large sets of text documents. They pay particular attention to the scalability of the approach and propose a method that allows working with very large data sets without increasing exponentially the training time and without significantly decreasing the classification accuracy. Next, in Learning Objects' Architecture and Indexing in WELSA Adaptive Educational System, E. Popescu, C. BAƒdicAƒ, and P. Trigano propose ways of organizing learning material in an adaptive educational hypermedia system. They are particularly interested in use of instructional metadata to facilitate detection of student learning style and application of adaptation techniques. Implementation of the proposed approach in the WELSA system is presented and discussed. Paper A Link-cluster Route Discovery Protocol for Ad Hoc Networks, by D. Bein, A. K. Datta and S. Yellenki introduces a route discovery algorithm for MANETs. This algorithm attempts at minimizing number of clusterheads and gateway nodes to avoid storing redundant data. Furthermore, it adapts to arbitrary movement of nodes, and joining and/or leaving of existent nodes. Focus of the next contribution entitled The Dilemma of Trust: a Social Network Based Approach, by V. Carchiolo, A. Longheu, M. Malgeri, G. Mangioni, and V. Nicosia studies how social networks can be usefully exploited in addressing the question of trust. In their approach, authors try to reproduce real-life behavior of humans when they establish and maintain trust relationships and utilize this approach to build an effective and efficient model for trust management. Next, in APP: Agent Planning Package, S. TosiA‡, M. RadovanoviA‡ and M. IvanoviA‡ introduce APP, a domain independent Agent Planning Package, which facilitates creation of intelligent agents. Within the APP, agents can generate plans for their operating environment, as well as execute them and supervise the execution. Finally, in Knowledge Processing for Web Search—an Integrated Model and Experiments, P. Gurský, T. Hor­vath, J. Jirasek, R. Novotný, J. Pribolova, V. Vanekova and P. Vojtas propose a model of a middleware system enabling personalized web search for users with different preferences. Their approach integrates inductive and deductive tasks as well as fuzzy sets and logic to find user preferences and consequently best objects. The proposed model was implemented and results of experiments are discussed. Hoping that the above summarized collection of papers will be of interest to SCPE readers we would also like to invite everyone to the second edition of the IDC Symposium, which will take place on September 18-20, 2008, in Catania, Italy ( http://idc08.diit.unict.it/ ). Maria Ganzha and Marcin Paprzycki, IBS PAN, Warszawa

Editorial—Causality, Unintended Consequences and Deducing Shared Causes

Despite warnings against inferring causality from observed correlations or statistical dependence, some articles do. Observed correlation is neither necessary nor sufficient to infer causality as defined by the term's everyday usage. For example, a deterministic causal process creates pseudorandom numbers; yet, we observe no correlation between the numbers. Child height correlates with spelling ability because age causes both. Moreover, order is problematic—we hear train whistles before observing trains, yet trains cause whistles. Scientific methods specifically prohibit inferring causal theories from specific observations (i.e., effects) because, in part, many credible causes are perfectly consistent with available observations. Moreover, actions inferred from effects have more unintended consequences than actions based on sound deductive causal theories because causal theories predict multiple effects. However, an often overlooked but key feature of these theories is that we describe the cause with more variables than the effect. Consequently, inductive processes might appear deductive as the number of effects increases relative to the number of potential causes. For example, in real criminal trials, jurors judge whether sufficient evidence exists to infer guilt. In contrast, determining guilt in criminal mystery novels is deductive because the number of clues (i.e., effects) is large relative to the number of potential suspects (i.e., causes). We can make inferential tasks resemble deductive tasks by increasing the number of effects (i.e., variables) relative to the number of potential causes and seeking a shared cause for all observed effects. Moreover, under some conditions, the method of seeking shared causes might approach deductive reasoning when the number of causes is strictly limited. At least, the resulting number of possible causal theories is far less than the number generated from repeated observations of a single effect (i.e., variable).

Conditional reasoning and the Wason selection task: Biconditional interpretation instead of reasoning bias

Two experiments were conducted to show that the IF … THEN … rules used in the different versions of Wason's (1966) selection task are not psychologically—though they are logically—equivalent. Some of these rules are considered by the participants as strict logical conditionals, whereas others are interpreted as expressing a biconditional relationship. A deductive task was used jointly with the selection task to show that the original abstract rule is quite ambiguous in this respect, contrary to deontic rules: the typical “error” made by most people may indeed be explained by the fact that they consider the abstract rule as a biconditional. Thus, there is no proper error or bias in the selection task as it is still argued, but a differential interpretation of the rule. The need for taking into account a pragmatic component in the process of reasoning is illustrated by the experiments.

Reconnecting interpretation to reasoning through individual differences

Computational theories of mind assume that participants interpret information and then reason from those interpretations. Research on interpretation in deductive reasoning has claimed to show that subjects' interpretation of single syllogistic premises in an "immediate inference" task is radically different from their interpretation of pairs of the same premises in syllogistic reasoning tasks (Newstead, 1989, 1995; Roberts, Newstead, & Griggs, 2001). Narrow appeal to particular Gricean implicatures in this work fails to bridge the gap. Grice's theory taken as a broad framework for credulous discourse processing in which participants construct speakers' "intended models" of discourses can reconcile these results, purchasing continuity of interpretation through variety of logical treatments. We present exploratory experimental data on immediate inference and subsequent syllogistic reasoning. Systematic patterns of interpretation driven by two factors (whether the subject's model of the discourse is credulous, and their degree of reliance on information packaging) are shown to transcend particular quantifier inferences and to drive systematic differences in subjects' subsequent syllogistic reasoning. We conclude that most participants do not understand deductive tasks as experimenters intend, and just as there is no single logical model of reasoning, so there is no reason to expect a single "fundamental human reasoning mechanism".