Journal articles: 'Temporal rule'

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KORPIPÄÄ, PANU. "Visualizing constraint-based temporal association rules." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 5 (November 2001): 401–10. http://dx.doi.org/10.1017/s0890060401155034.

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When dealing with time continuous processes, the discovered association rules may change significantly over time. This often reflects a change in the process as well. Therefore, two questions arise: What kind of deviation occurs in the association rules over time, and how could these temporal rules be presented efficiently? To address this problem of representation, we propose a method of visualizing temporal association rules in a virtual model with interactive exploration. The presentation form is a three-dimensional correlation matrix, and the visualization methods used are brushing and glyphs. Interactive functions used for displaying rule attributes and exploring temporal rules are implemented by utilizing Virtual Reality Modeling Language v2 mechanisms. Furthermore, to give a direction of rule potential for the user, the rule statistical interestingness is evaluated on the basis of combining weighted characteristics of rule and rule matrix. A constraint-based association rule mining tool which creates the virtual model as an output is presented, including the most relevant experiences from the development of the tool. The applicability of the overall approach has been verified by using the developed tool for data mining on a hot strip mill of a steel plant.

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Mohd Khairudin, Nazli, Aida Mustapha, and Mohd Hanif Ahmad. "Effect of Temporal Relationships in Associative Rule Mining for Web Log Data." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/813983.

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The advent of web-based applications and services has created such diverse and voluminous web log data stored in web servers, proxy servers, client machines, or organizational databases. This paper attempts to investigate the effect of temporal attribute in relational rule mining for web log data. We incorporated the characteristics of time in the rule mining process and analysed the effect of various temporal parameters. The rules generated from temporal relational rule mining are then compared against the rules generated from the classical rule mining approach such as the Apriori and FP-Growth algorithms. The results showed that by incorporating the temporal attribute via time, the number of rules generated is subsequently smaller but is comparable in terms of quality.

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Cariñena, Purificación. "Fuzzy temporal association rules: combining temporal and quantitative data to increase rule expressiveness." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 4, no. 1 (December 23, 2013): 64–70. http://dx.doi.org/10.1002/widm.1116.

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Agarwal, Reshu, Mandeep Mittal, and Sarla Pareek. "Loss Profit Estimation Using Temporal Association Rule Mining." International Journal of Business Analytics 3, no. 1 (January 2016): 45–57. http://dx.doi.org/10.4018/ijban.2016010103.

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Temporal association rule mining is a data mining technique in which relationships between items which satisfy certain timing constraints can be discovered. This paper presents the concept of temporal association rules in order to solve the problem of classification of inventories by including time expressions into association rules. Firstly, loss profit of frequent items is calculated by using temporal association rule mining algorithm. Then, the frequent items in particular time-periods are ranked according to descending order of loss profits. The manager can easily recognize most profitable items with the help of ranking found in the paper. An example is illustrated to validate the results.

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Li, Zhi Gang, and Feng Li Yang. "The Generation of the Fuzzy Control Rules Based on Association Rules with Temporal Constraints." Applied Mechanics and Materials 385-386 (August 2013): 931–34. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.931.

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In the field of fuzzy control, the generation of fuzzy control rules has always been a problem, because the industrial data is generally expressed in the order of time ,so it strongly depends on the time, it does not take the factors of temporal constraints into account in the previous extracting rule process.This paper uses temporal constraint association rule ,and uses the data mining methods to generate temporal fuzzy control rules. The method is verified by using the MATLAB7.1 ,the simulation shows that the method can achieve good fuzzy control rules.

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Verma, Keshri, and O. P. Vyas. "Efficient calendar based temporal association rule." ACM SIGMOD Record 34, no. 3 (September 2005): 63–70. http://dx.doi.org/10.1145/1084805.1084818.

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Ormerod, Thomas C., Kenneth I. Manktelow, and Gregory V. Jones. "Reasoning with three Types of Conditional: Biases and Mental Models." Quarterly Journal of Experimental Psychology Section A 46, no. 4 (November 1993): 653–77. http://dx.doi.org/10.1080/14640749308401032.

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Two experiments are reported which compare conditional reasoning with three types of rule. These consist of two types of rule that have been widely studied previously, if p then q and p only if q, together with a third type, q if p. In both experiments, the p only if q type of rule yields a different pattern of performance from the two other types of rule. Experiment 1 is an abstract rule-evaluation task and demonstrates differential effects of temporal order and of suppositional bias. Experiment 2 investigates rule generation, rephrasing, and comparison, and demonstrates differential effects of temporal order and of thematic content. An analysis of the results is offered in terms of biases and mental models. Effects of rule form and context can be explained as reflecting the different sequences in which mental models are created for each rule form. However, it is necessary to consider the internal structure of individual mental models to account for effects arising from temporal ordering of rules.

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Dawson, Kim A. "Effects of Temporal Speed Instruction on Temporal Length Judgment." Perceptual and Motor Skills 78, no. 3 (June 1994): 737–38. http://dx.doi.org/10.1177/003151259407800310.

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Given the flexible use of an inverse rule relating temporal speed and temporal length in some psychological studies, an hypothesized effect of an instruction regarding temporal speed on temporal length judgment was tested. By subjects asked to compare the speeds (rates of passage) and then to estimate the two temporal lengths (durations) of two successive intervals, there was greater use of the inverse rule and increased accuracy in estimating the first of the two intervals. The results have important implications for effortful processing of temporal information.

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Malet-Karas, Aurore, Marion Noulhiane, and Valérie Doyère. "Dynamics of Spatio-Temporal Binding in Rats." Timing & Time Perception 7, no. 1 (January 11, 2019): 27–47. http://dx.doi.org/10.1163/22134468-20181124.

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Time and space are commonly approached as two distinct dimensions, and rarely combined together in a single task, preventing a comparison of their interaction. In this project, using a version of a timing task with a spatial component, we investigate the learning of a spatio-temporal rule in animals. To do so, rats were placed in front of a five-hole nose-poke wall in a Peak Interval (PI) procedure to obtain a reward, with two spatio-temporal combination rules associated with different to-be-timed cues and lighting contexts. We report that, after successful learning of the discriminative task, a single Pavlovian session was sufficient for the animals to learn a new spatio-temporal association. This was seen as evidence for a beneficial transfer to the new spatio-temporal rule, as compared to control animals that did not experience the new spatio-temporal association during the Pavlovian session. The benefit was observed until nine days later. The results are discussed within the framework of adaptation to a change of a complex associative rule involving interval timing processes.

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Kunzendorf, Robert G., Scott S. Lyman, Brenda Sousa, and Emily Hilly. "“Imageless” Spatial and Temporal Rules Can Be Tested and Refined by Constructing Vivid Visual and Auditory Images." Imagination, Cognition and Personality 32, no. 2 (October 2012): 115–50. http://dx.doi.org/10.2190/ic.32.2.c.

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In this computerized study, research participants completed both Marks' (1973) Vividness of Visual Imagery Questionnaire and Kunzendorf's (1979) Vividness of Auditory Imagery Questionnaire and, immediately thereafter, completed either a visuo-spatial rule-development exercise or an audio-temporal rule-development exercise. During the visuo-spatial exercise, participants were administered 20 four-alternative quizzes regarding the schematic rules of 3-point perspective (3PP) and, between quizzes, were instructed to figure out the rules by constructing visual images that serve to test their developing schema. During the audio-temporal exercise, participants were administered 24 two-alternative quizzes regarding artificial grammatical rules and, between quizzes, were instructed to figure out the rules by generating auditory images that serve to test their developing grammar. Across 83 participants in the visuo-spatial rule-development exercise, greater development of 3PP rules correlated significantly with greater vividness of visual imagery, but not auditory imagery. Conversely, across 94 participants in the audio-temporal rule-development exercise, greater development of grammatical rules correlated significantly with greater vividness of auditory imagery, but not visual imagery. In the historical context of null correlations between image vividness and problem-solving success, the current correlational findings provide modality-specific support for Kunzendorf and Reynold's (2004-2005) and Kunzendorf and Buker's (2008-2009) thesis that the vivid imager's construction of sensory images from imageless rules facilitates the testing and refinement of newly hypothesized rules, but does not facilitate the application of previously developed rules to new problems.

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Guillame-Bert, Mathieu, Artur Dubrawski, Donghan Wang, Marilyn Hravnak, Gilles Clermont, and Michael R. Pinsky. "Learning temporal rules to forecast instability in continuously monitored patients." Journal of the American Medical Informatics Association 24, no. 1 (June 6, 2016): 47–53. http://dx.doi.org/10.1093/jamia/ocw048.

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Inductive machine learning, and in particular extraction of association rules from data, has been successfully used in multiple application domains, such as market basket analysis, disease prognosis, fraud detection, and protein sequencing. The appeal of rule extraction techniques stems from their ability to handle intricate problems yet produce models based on rules that can be comprehended by humans, and are therefore more transparent. Human comprehension is a factor that may improve adoption and use of data-driven decision support systems clinically via face validity. In this work, we explore whether we can reliably and informatively forecast cardiorespiratory instability (CRI) in step-down unit (SDU) patients utilizing data from continuous monitoring of physiologic vital sign (VS) measurements. We use a temporal association rule extraction technique in conjunction with a rule fusion protocol to learn how to forecast CRI in continuously monitored patients. We detail our approach and present and discuss encouraging empirical results obtained using continuous multivariate VS data from the bedside monitors of 297 SDU patients spanning 29 346 hours (3.35 patient-years) of observation. We present example rules that have been learned from data to illustrate potential benefits of comprehensibility of the extracted models, and we analyze the empirical utility of each VS as a potential leading indicator of an impending CRI event.

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Mittal, Mandeep, Sarla Pareek, and Reshu Agarwal. "Ordering policy using temporal association rule mining." International Journal of Data Science 1, no. 2 (2015): 157. http://dx.doi.org/10.1504/ijds.2015.072419.

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Hu, Xiaohua, Il Yeol Song, Hyoil Han, Illhoi Yoo, Ann A. Prestrud, Murray F. Brennan, and Ari D. Brooks. "Temporal rule induction for clinical outcome analysis." International Journal of Business Intelligence and Data Mining 1, no. 1 (2005): 122. http://dx.doi.org/10.1504/ijbidm.2005.007322.

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Byon, Lu Na, and Jeong Hye Han. "Fast Algorithms for Temporal Association Rules in a Large Database." Key Engineering Materials 277-279 (January 2005): 287–92. http://dx.doi.org/10.4028/www.scientific.net/kem.277-279.287.

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As electronic commerce progresses, temporal association rules are developed by time to offer personalized services for customer’s interests. In this article, we propose a temporal association rule and its discovering algorithm with exponential smoothing filter in a large transaction database. Through experimental results, we confirmed that this is more precise and consumes a shorter running time than existing temporal association rules.

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Djajic, Sanja. "Temporal jurisdiction of international judicial and arbitral courts." Zbornik Matice srpske za drustvene nauke, no. 135 (2011): 211–30. http://dx.doi.org/10.2298/zmsdn1135011d.

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Author explores different temporal aspects of jurisdiction of International Court of Justice, European Court for Human Rights and international investment arbitrations. Temporal limitations are two-fold: non-retroactivity of international acts, on one hand, and ratione temporis conditions for each and every international forum, on the other. Despite differences courts tend to conceptualize common elements across the borders of different jurisdictional rules. The rule of non-retroactivity will find its application before different fora, but discrepancies will emerge with respect to concepts of continuous and composite acts which potentially may overcome temporal limitations. This article explores intertemporal rule and non-retroactivity within the meaning of Article 28 of the Vienna Convention on the Law of Treaties and Articles 13-15 of ILC Articles on State Responsibility.

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Yang, Dan, Zheng Tie Nie, and Fajun Yang. "Time-Aware CF and Temporal Association Rule-Based Personalized Hybrid Recommender System." Journal of Organizational and End User Computing 33, no. 3 (May 2021): 19–34. http://dx.doi.org/10.4018/joeuc.20210501.oa2.

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Most recommender systems usually combine several recommendation methods to enhance the recommendation accuracy. Collaborative filtering (CF) is a best-known personalized recommendation technique. While temporal association rule-based recommendation algorithm can discover users' latent interests with time-specific leveraging historical behavior data without domain knowledge. The concept-drifting and user interest-drifting are two key problems affecting the recommendation performance. Aiming at the above problems, a time-aware CF and temporal association rule-based personalized hybrid recommender system, TP-HR, is proposed. The proposed time-aware CF algorithm considers evolving features of users' historical feedback. And time-aware users' similar neighbors selecting measure and time-aware item rating prediction function are proposed to keep track of the dynamics of users' preferences. The proposed temporal association rule-based recommendation algorithm considers the time context of users' historical behaviors when mining effective temporal association rules. Experimental results on real datasets show the feasibility and performance improvement of the proposed hybrid recommender system compared to other baseline approaches.

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Güzel Kalayci, Elem, Sebastian Brandt, Diego Calvanese, Vladislav Ryzhikov, Guohui Xiao, and Michael Zakharyaschev. "Ontology–based access to temporal data with Ontop: A framework proposal." International Journal of Applied Mathematics and Computer Science 29, no. 1 (March 1, 2019): 17–30. http://dx.doi.org/10.2478/amcs-2019-0002.

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Abstract Predictive analysis gradually gains importance in industry. For instance, service engineers at Siemens diagnostic centres unveil hidden knowledge in huge amounts of historical sensor data and use it to improve the predictive systems analysing live data. Currently, the analysis is usually done using data-dependent rules that are specific to individual sensors and equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers. One solution to this problem is to employ ontology-based data access (OBDA), which provides a conceptual view of data via an ontology. However, classical OBDA systems do not support access to temporal data and reasoning over it. To address this issue, we propose a framework for temporal OBDA. In this framework, we use extended mapping languages to extract information about temporal events in the RDF format, classical ontology and rule languages to reflect static information, as well as a temporal rule language to describe events. We also propose a SPARQL-based query language for retrieving temporal information and, finally, an architecture of system implementation extending the state-of-the-art OBDA platform Ontop.

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Matthews, Stephen G., Mario A. Gongora, and Adrian A. Hopgood. "Evolutionary algorithms and fuzzy sets for discovering temporal rules." International Journal of Applied Mathematics and Computer Science 23, no. 4 (December 1, 2013): 855–68. http://dx.doi.org/10.2478/amcs-2013-0064.

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Abstract A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented into a dataset to analyse the method’s ability in a controlled experiment. It is shown that the method is capable of discovering temporal patterns, and the effect of Boolean itemset support on the efficacy of discovering temporal fuzzy association rules is presented.

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Mitchison, Graeme. "Removing Time Variation with the Anti-Hebbian Differential Synapse." Neural Computation 3, no. 3 (September 1991): 312–20. http://dx.doi.org/10.1162/neco.1991.3.3.312.

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I describe a local synaptic learning rule that can be used to remove the effects of certain types of systematic temporal variation in the inputs to a unit. According to this rule, changes in synaptic weight result from a conjunction of short-term temporal changes in the inputs and the output. Formally, This is like the differential rule proposed by Klopf (1986) and Kosko (1986), except for a change of sign, which gives it an anti-Hebbian character. By itself this rule is insufficient. A weight conservation condition is needed to prevent the weights from collapsing to zero, and some further constraint—implemented here by a biasing term—to select particular sets of weights from the subspace of those which give minimal variation. As an example, I show that this rule will generate center-surround receptive fields that remove temporally varying linear gradients from the inputs.

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Shigematsu, Yukifumi, and Gen Matsumoto. "Temporal learning rule and dynamic neural network model." Applied Mathematics and Computation 111, no. 2-3 (May 2000): 209–18. http://dx.doi.org/10.1016/s0096-3003(99)00164-2.

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Stone, James, and Alistair Bray. "A learning rule for extracting spatio-temporal invariances." Network: Computation in Neural Systems 6, no. 3 (January 1995): 429–36. http://dx.doi.org/10.1088/0954-898x_6_3_008.

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Agarwal, Reshu. "Opportunity cost estimation using temporal association rule mining." International Journal of Services Sciences 6, no. 3/4 (2017): 261. http://dx.doi.org/10.1504/ijssci.2017.091819.

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Agarwal, Reshu. "Opportunity cost estimation using temporal association rule mining." International Journal of Services Sciences 6, no. 3/4 (2017): 261. http://dx.doi.org/10.1504/ijssci.2017.10013059.

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Recer, G. M., W. U. Blanckenhorn, J. A. Newman, E. M. Tuttle, M. L. Withiam, and T. Caraco. "Temporal resource variability and the habitat-matching rule." Evolutionary Ecology 1, no. 4 (October 1987): 363–78. http://dx.doi.org/10.1007/bf02071559.

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Wałęga, Przemysław Andrzej, Mark Kaminski, and Bernardo Cuenca Grau. "Reasoning over Streaming Data in Metric Temporal Datalog." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3092–99. http://dx.doi.org/10.1609/aaai.v33i01.33013092.

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We study stream reasoning in datalogMTL—an extension of Datalog with metric temporal operators. We propose a sound and complete stream reasoning algorithm that is applicable to a fragment datalogMTLFP of datalogMTL, in which propagation of derived information towards past time points is precluded. Memory consumption in our algorithm depends both on the properties of the rule set and the input data stream; in particular, it depends on the distances between timestamps occurring in data. This is undesirable since these distances can be very small, in which case the algorithm may require large amounts of memory. To address this issue, we propose a second algorithm, where the size of the required memory becomes independent on the timestamps in the data at the expense of disallowing punctual intervals in the rule set. Finally, we provide tight bounds to the data complexity of standard query answering in datalogMTLFP without punctual intervals in rules, which yield a new PSPACE lower bound to the data complexity of the full datalogMTL.

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Lam, Vitus S. W. "A Framework-Driven Comparison of Automata-Based Tools for Identifying Business Rule Conflicts." International Journal of Software Engineering and Knowledge Engineering 29, no. 03 (March 2019): 433–55. http://dx.doi.org/10.1142/s0218194019500190.

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Drawing on business rules for constructing business process models by a constraint-driven methodology is a distinct characteristic of declarative process modeling. Given the intricacies of business rules, there is a pragmatic need to conduct conflict-free assessments for business rules in an automatic manner. In this paper, business rules are stated in terms of restricted English by harnessing a group of predefined business rule templates. With linear temporal logic that serves as a semantic foundation for the business rule templates, a pair of business rules represented as a linear temporal logic specification is translated into an associated Büchi automaton via LTL2BA, LTL3BA and ltl2tgba. A Büchi automaton that accepts the empty language signifies that the two business rules are in conflict with each other. The suitability of the formal framework and the three automated tools is evaluated by an industry-level case study.

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Shigematsu, Yukifumi, Hiroshi Okamoto, Kazuhisa Ichikawa, and Gen Matsumoto. "Temporal Event Association and Output-Dependent Learning: A Proposed Scheme of Neural Molecular Connections." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 4 (August 20, 1999): 234–44. http://dx.doi.org/10.20965/jaciii.1999.p0234.

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We introduce a model of temporal-event-associated and output-dependent learning rule, genetically acquired and expressed in a single neuron. This is essentially indispensable for the brain to acquire algorithms, how to process its self-selected information, by itself. This proposed learning rule is revised-Hebbian with a synaptic history trace to correlate one temporal event to others. Temporal events are memorized to be expressed at the synaptic site of inputs and in the form of the asymmetric neural strength corrections associated with temporal events. This learning algorithm has an advantage to associate one temporal event with others, resulting in the neuron with predictability but also makes recalling flexible. Re ’ calling is, according to this learning, independent of timing, supposed to be crucial in learning. Underlying molecular mechanisms for our proposed learning rule are discussed and we identify three important factors: 1) the back-propagating action potentials experimentally observed a single neuron play a crucial role for outputdependent learning, 2) temporally associated, nonlinear couplings are modeled at molecular levels with glutamate receptors, voltage-dependent channels, intracellular calcium concentration, protein kinases and phosphatase, and 3) intracellular concentration of inositol-tri-phosphate [IP3] is the memory substrate of synaptic history.

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Ullman, Michael T., Suzanne Corkin, Marie Coppola, Gregory Hickok, John H. Growdon, Walter J. Koroshetz, and Steven Pinker. "A Neural Dissociation within Language: Evidence that the Mental Dictionary Is Part of Declarative Memory, and that Grammatical Rules Are Processed by the Procedural System." Journal of Cognitive Neuroscience 9, no. 2 (March 1997): 266–76. http://dx.doi.org/10.1162/jocn.1997.9.2.266.

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Language comprises a lexicon for storing words and a grammar for generating rule-governed forms. Evidence is presented that the lexicon is part of a temporal-parietalhnedial-temporal “declarative memory” system and that granlmatical rules are processed by a frontamasal-ganglia “procedural” system. Patients produced past tenses of regular and novel verbs (looked and plagged), which require an -ed-suffixation rule, and irregular verbs (dug), which are retrieved from memory. Word-finding difficulties in posterior aphasia, and the general declarative memory impairment in Alzheimer's disease, led to more errors with irregular than regular and novel verbs. Grammatical difficulties in anterior aphasia, and the general impairment of procedures in Parkinson's disease, led to the opposite pattern. In contrast to the Parkinson's patients, who showed sup pressed motor activity and rule use, Huntington's disease patients showed excess motor activity and rule use, underscoring a role for the basal ganglia in grammatical processing.

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Wennekers, Thomas, and Nihat Ay. "Finite State Automata Resulting from Temporal Information Maximization and a Temporal Learning Rule." Neural Computation 17, no. 10 (October 1, 2005): 2258–90. http://dx.doi.org/10.1162/0899766054615671.

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We extend Linkser's Infomax principle for feedforward neural networks to a measure for stochastic interdependence that captures spatial and temporal signal properties in recurrent systems. This measure, stochastic interaction, quantifies the Kullback-Leibler divergence of a Markov chain from a product of split chains for the single unit processes. For unconstrained Markov chains, the maximization of stochastic interaction, also called Temporal Infomax, has been previously shown to result in almost deterministic dynamics. This letter considers Temporal Infomax on constrained Markov chains, where some of the units are clamped to prescribed stochastic processes providing input to the system. Temporal Infomax in that case leads to finite state automata, either completely deterministic or weakly nondeterministic. Transitions between internal states of these systems are almost perfectly predictable given the complete current state and the input, but the activity of each single unit alone is virtually random. The results are demonstrated by means of computer simulations and confirmed analytically. It is furthermore shown numerically that Temporal Infomax leads to a high information flow from the input to internal units and that a simple temporal learning rule can approximately achieve the optimization of temporal interaction. We relate these results to experimental data concerning the correlation dynamics and functional connectivities observed in multiple electrode recordings.

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Wennekers, Thomas, and Nihat Ay. "A temporal learning rule in recurrent systems supports high spatio-temporal stochastic interactions." Neurocomputing 69, no. 10-12 (June 2006): 1199–202. http://dx.doi.org/10.1016/j.neucom.2005.12.075.

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Siebra, Clauirton Albuquerque, Ramon N. Santos, and Natasha C. Q. Lino. "A Self-Adjusting Approach for Temporal Dropout Prediction of E-Learning Students." International Journal of Distance Education Technologies 18, no. 2 (April 2020): 19–33. http://dx.doi.org/10.4018/ijdet.2020040102.

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This work proposes a dropout prediction approach that is able to self-adjust their outcomes at any moment of a degree program timeline. To that end, a rule-based classification technique was used to identify courses, grade thresholds and other attributes that have a high influence on the dropout behavior. This approach, which is generic so that it can be applied to any distance learning degree program, returns different rules that indicate how the predictions are adjusted along with academic terms. Experiments were carried out using four rule-based classification algorithms: JRip, OneR, PART and Ridor. The outcomes show that this approach presents better accuracy according to the progress of students, mainly when the JRip and PART algorithms are used. Furthermore, the use of this method enabled the generation of rules that stress the factors that mainly affect the dropout phenomenon at different degree moments.

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Lieberman, Matthew D., Grace Y. Chang, Joan Chiao, Susan Y. Bookheimer, and Barbara J. Knowlton. "An Event-related fMRI Study of Artificial Grammar Learning in a Balanced Chunk Strength Design." Journal of Cognitive Neuroscience 16, no. 3 (April 2004): 427–38. http://dx.doi.org/10.1162/089892904322926764.

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Artificial grammar learning (Reber, 1967) is a form of implicit learning in which cognitive, rather than motor, implicit learning has been found. After viewing a series of letter strings formed according to a finite state rule system, people are able to classify new letter strings as to whether or not they are formed according to these grammatical rules despite little conscious insight into the rule structure. Previous research has shown that these classification judgments are based on knowledge of abstract rules as well as superficial similarity (“chunk strength”) to training strings. Here we used event-related fMRI to identify neural regions involved in using both sources of information as test stimuli were designed to unconfound chunk strength from rule use. Using functional connectivity analyses, the extent to which the sources of information are complementary or competitive was also assessed. Activation in the right caudate was associated with rule adherence, whereas medial temporal lobe activations were associated with chunk strength. Additionally, functional connectivity analyses revealed caudate and medial temporal lobe activations to be strongly negatively correlated (r = −88) with one another during the performance of this task.

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Rao, Rajesh P. N., and Terrence J. Sejnowski. "Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning." Neural Computation 13, no. 10 (October 1, 2001): 2221–37. http://dx.doi.org/10.1162/089976601750541787.

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A spike-timing-dependent Hebbian mechanism governs the plasticity of recurrent excitatory synapses in the neocortex: synapses that are activated a few milliseconds before a postsynaptic spike are potentiated, while those that are activated a few milliseconds after are depressed. We show that such a mechanism can implement a form of temporal difference learning for prediction of input sequences. Using a biophysical model of a cortical neuron, we show that a temporal difference rule used in conjunction with dendritic backpropagating action potentials reproduces the temporally asymmetric window of Hebbian plasticity observed physiologically. Furthermore, the size and shape of the window vary with the distance of the synapse from the soma. Using a simple example, we show how a spike-timing-based temporal difference learning rule can allow a network of neocortical neurons to predict an input a few milliseconds before the input's expected arrival.

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Duan, Xuliang, Bing Guo, Yan Shen, Yuncheng Shen, Xiangqian Dong, and Hong Zhang. "Research on Data Currency Rule and Quality Evaluation." Information Technology and Control 50, no. 2 (June 17, 2021): 247–63. http://dx.doi.org/10.5755/j01.itc.50.2.25583.

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Data currency is a temporal reference of data, it reflects the degree to which the data is current with the worldit models. Currency rule is a formal rule extracted from the data set and reflecting the currency order of thedata tuples, it can be used for both data repairing and currency quality evaluation. Based on the research of datacurrency repairing, the basic form of currency rule is extended, and parallel rule extraction and update algorithmsare proposed to meet the requirement of running on dynamic data sets. Besides, four data currency qualityevaluation models are proposed and verified by experiments. The performance test show that the efficiencyof parallel algorithms is significantly improved, the rules compliance mean(CM2) model based on extendedcurrency rule has the highest average precision. The extended currency rules not only improve the efficiencyand adaptability, but also provide more valuable features for data quality evaluation.

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Oh, Kabsuk, and Kaoru Hirota. "Support System for Multimedia Information Data Acquisition Based on Fuzzy Inference with a Fuzzy Shift." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 5 (September 20, 2000): 387–94. http://dx.doi.org/10.20965/jaciii.2000.p0387.

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Support system construction for multimedia information data acquisition based on fuzzy inference with a concept of fuzzy shift is proposed, where the multimedia means the five senses. Observed information from the outside world is characterized by VAGOT (visual, acoustic, gustatory, olfactory, and tactile) time series data. Here, multimedia information centers on image and sound are represented by membership functions. Fuzzy rules based on visual and acoustic information are used to identify the appropriate time interval on multimedia input data. The proposed system is constructed by rule construction, data acquisition, a rule base, and a support system. When an instruction of the user has temporal vagueness, rule base is updated by the support system based on fuzzy shift. The method is applied to an experiment related to the decision of a temporal boundary subset of vehicles and results verify the feasibility of the proposed method.

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Yan Hai, and Xiuli Li. "A General Temporal Association Rule Frequent Itemsets Mining Algorithm." International Journal of Advancements in Computing Technology 3, no. 11 (December 31, 2011): 63–71. http://dx.doi.org/10.4156/ijact.vol3.issue11.9.

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DeLima, Pedro G., and Gary G. Yen. "Multiple objective evolutionary algorithm for temporal linguistic rule extraction." ISA Transactions 44, no. 2 (April 2005): 315–27. http://dx.doi.org/10.1016/s0019-0578(07)60184-0.

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Gerstner, Wulfram, Richard Kempter, J. Leo van Hemmen, and Hermann Wagner. "A neuronal learning rule for sub-millisecond temporal coding." Nature 383, no. 6595 (September 1996): 76–78. http://dx.doi.org/10.1038/383076a0.

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Shalini, Sheel, and Kanhaiya Lal. "Mining Changes in Temporal Patterns in Latest Time Window for Knowledge Discovery." Journal of Information & Knowledge Management 18, no. 03 (September 2019): 1950028. http://dx.doi.org/10.1142/s021964921950028x.

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Temporal Association Rule mining uncovers time integrated associations in a transactional database. However, in an environment where database is regularly updated, maintenance of rules is a challenging process. Earlier algorithms suggested for maintaining frequent patterns either suffered from the problem of repeated scanning or the problem of larger storage space. Therefore, this paper proposes an algorithm “Probabilistic Incremental Temporal Association Rule Mining (PITARM)” that uncovers the changed behaviour in an updated database to maintain the rules efficiently. The proposed algorithm defines two support measures to identify itemsets expected to be frequent in the successive segment in advance. It reduces unnecessary scanning of itemsets in the entire database through three-fold verification and avoids generating redundant supersets and power sets from infrequent itemsets. Implementation of pruning technique in incremental mining is a novel approach that makes it better than earlier incremental mining algorithms and consequently reduces search space to a great extent. It scans the entire database only once, thus reducing execution time. Experimental results confirm that it is an enhancement over earlier algorithms.

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Bunge, Silvia A., Itamar Kahn, Jonathan D. Wallis, Earl K. Miller, and Anthony D. Wagner. "Neural Circuits Subserving the Retrieval and Maintenance of Abstract Rules." Journal of Neurophysiology 90, no. 5 (November 2003): 3419–28. http://dx.doi.org/10.1152/jn.00910.2002.

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Behavior is often governed by abstract rules or instructions for behavior that can be abstracted from one context and applied to another. Prefrontal cortex (PFC) is thought to be important for representing rules, although the contributions of ventrolateral (VLPFC) and dorsolateral (DLPFC) regions remain under-specified. In the present study, event-related fMRI was used to examine abstract rule representation in humans. Prior to scanning, subjects learned to associate unfamiliar shapes and nonwords with particular rules. During each fMRI trial, presentation of one of these cues was followed by a delay and then by sample and probe stimuli. Match and non-match rules required subjects to indicate whether or not the sample and probe matched; go rules required subjects to make a response that was not contingent on the sample/probe relation. Left VLPFC, parietal cortex, and pre-SMA exhibited sensitivity to rule type during the cue and delay periods. Delay-period activation in these regions, but not DLPFC, was greater when subjects had to maintain response contingencies (match, non-match) relative to when the cue signaled a specific response (go). In contrast, left middle temporal cortex exhibited rule sensitivity during the cue but not delay period. These results support the hypothesis that VLPFC interacts with temporal cortex to retrieve semantic information associated with a cue and with parietal cortex to retrieve and maintain relevant response contingencies across delays. Future investigations of cross-regional interactions will enable full assessment of this account. Collectively, these results demonstrate that multiple, neurally separable processes are recruited during abstract rule representation.

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Pan, Xiaoyi, Boyu Chen, Heng Weng, Yongyi Gong, and Yingying Qu. "Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning Approach." JMIR Medical Informatics 8, no. 7 (July 27, 2020): e17652. http://dx.doi.org/10.2196/17652.

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Background Temporal information frequently exists in the representation of the disease progress, prescription, medication, surgery progress, or discharge summary in narrative clinical text. The accurate extraction and normalization of temporal expressions can positively boost the analysis and understanding of narrative clinical texts to promote clinical research and practice. Objective The goal of the study was to propose a novel approach for extracting and normalizing temporal expressions from Chinese narrative clinical text. Methods TNorm, a rule-based and pattern learning-based approach, has been developed for automatic temporal expression extraction and normalization from unstructured Chinese clinical text data. TNorm consists of three stages: extraction, classification, and normalization. It applies a set of heuristic rules and automatically generated patterns for temporal expression identification and extraction of clinical texts. Then, it collects the features of extracted temporal expressions for temporal type prediction and classification by using machine learning algorithms. Finally, the features are combined with the rule-based and a pattern learning-based approach to normalize the extracted temporal expressions. Results The evaluation dataset is a set of narrative clinical texts in Chinese containing 1459 discharge summaries of a domestic Grade A Class 3 hospital. The results show that TNorm, combined with temporal expressions extraction and temporal types prediction, achieves a precision of 0.8491, a recall of 0.8328, and a F1 score of 0.8409 in temporal expressions normalization. Conclusions This study illustrates an automatic approach, TNorm, that extracts and normalizes temporal expression from Chinese narrative clinical texts. TNorm was evaluated on the basis of discharge summary data, and results demonstrate its effectiveness on temporal expression normalization.

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APT, KRZYSZTOF R., and ERIC MONFROY. "Constraint programming viewed as rule-based programming." Theory and Practice of Logic Programming 1, no. 6 (November 2001): 713–50. http://dx.doi.org/10.1017/s1471068401000072.

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We study here a natural situation when constraint programming can be entirely reduced to rule-based programming. To this end we explain first how one can compute on constraint satisfaction problems using rules represented by simple first-order formulas. Then we consider constraint satisfaction problems that are based on predefined, explicitly given constraints. To solve them we first derive rules from these explicitly given constraints and limit the computation process to a repeated application of these rules, combined with labeling. We consider two types of rule here. The first type, that we call equality rules, leads to a new notion of local consistency, called rule consistency that turns out to be weaker than arc consistency for constraints of arbitrary arity (called hyper-arc consistency in Marriott & Stuckey (1998)). For Boolean constraints rule consistency coincides with the closure under the well-known propagation rules for Boolean constraints. The second type of rules, that we call membership rules, yields a rule-based characterization of arc consistency. To show feasibility of this rule-based approach to constraint programming, we show how both types of rules can be automatically generated, as CHR rules of Frühwirth (1995). This yields an implementation of this approach to programming by means of constraint logic programming. We illustrate the usefulness of this approach to constraint programming by discussing various examples, including Boolean constraints, two typical examples of many valued logics, constraints dealing with Waltz's language for describing polyhedral scenes, and Allen's qualitative approach to temporal logic.

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Bilqisth, Shona Chayy, and Khabib Mustofa. "Determination of Temporal Association Rules Pattern Using Apriori Algorithm." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 14, no. 2 (April 30, 2020): 159. http://dx.doi.org/10.22146/ijccs.51747.

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A supermarket must have good business plan in order to meet customer desires. One way that can be done to meet customer desires is to find out the pattern of shopping purchases resulting from processing sales transaction data. Data processing produces information related to the function of the association between items of goods temporarily. Association rules functions in data mining.Association rule is one of the data mining techniques used to find patterns in combination of transaction data. Apriori algorithm can be used to find association rules. Apriori algorithm is used to find frequent itemset candidates who meet the support count. Frequent itemset that meets the support count is then processed using the temporal association rules method. The function of temporal association rules is as a time limitation in displaying the results of frequent itemsets and association rules. This study aims to produce rules from transaction data, apriori algorithm is used to form temporal association rules. The final results of this research are strong rules, they are rules that always appear in 3 years at certain time intervals with limitation on support and confidence, so that the rules can be used for business plan layout recommendations in Maharani Supermarket Demak.

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Khoshahval, S., M. Farnaghi, and M. Taleai. "SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 395–99. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-395-2017.

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Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user’s visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users’ behaviour in a system and can be utilized in various location-based applications.

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Eiselt, Anne-Kathrin, and Andreas Nieder. "Rule Activity Related to Spatial and Numerical Magnitudes: Comparison of Prefrontal, Premotor, and Cingulate Motor Cortices." Journal of Cognitive Neuroscience 26, no. 5 (May 2014): 1000–1012. http://dx.doi.org/10.1162/jocn_a_00545.

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In everyday situations, quantitative rules, such as “greater than/less than,” need to be applied to a multitude of magnitude comparisons, be they sensory, spatial, temporal, or numerical. We have previously shown that rules applied to different magnitudes are encoded in the lateral PFC. To investigate if and how other frontal lobe areas also contribute to the encoding of quantitative rules applied to multiple magnitudes, we trained monkeys to switch between “greater than/less than” rules applied to either line lengths (spatial magnitudes) or dot numerosities (discrete numerical magnitudes). We recorded single-cell activity from the dorsal premotor cortex (dPMC) and cingulate motor cortex (CMA) and compared it with PFC activity. We found the largest proportion of quantitative rule-selective cells in PFC (24% of randomly selected cells), whereas neurons in dPMC and CMA rarely encoded the rule (6% of the cells). In addition, rule selectivity of individual cells was highest in PFC neurons compared with dPMC and CMA neurons. Rule-selective neurons that simultaneously represented the “greater than/less than” rules applied to line lengths and numerosities (“rule generalists”) were exclusively present in PFC. In dPMC and CMA, however, neurons primarily encoded rules applied to only one of the two magnitude types (“rule specialists”). Our data suggest a special involvement of PFC in representing quantitative rules at an abstract level, both in terms of the proportion of neurons engaged and the coding capacities.

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Ohe, K., and Y. Kawazoe. "An Ontology-based Mediator of Clinical Information for Decision Support Systems." Methods of Information in Medicine 47, no. 06 (2008): 549–59. http://dx.doi.org/10.3414/me9126.

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Summary Objective: We have been developing a decision support system that uses electronic clinical data and provides alerts to clinicians. However, the inference rules for such a system are difficult to write in terms of representing domain concepts and temporal reasoning. To address this problem, we have developed an ontologybased mediator of clinical information for the decision support system. Methods: Our approach consists of three steps: 1) development of an ontology-based mediator that represents domain concepts and temporal information; 2) mapping of clinical data to corresponding concepts in the mediator; 3) temporal abstraction that creates high-level, interval-based concepts from time-stamped clinical data. As a result, we can write a concept-based rule expression that is available for use in domain concepts and interval-based temporal information. The proposed approach was applied to a prototype of clinical alert system, and the rules for adverse drug events were executed on data gathered over a 3-month period. Results: The system generated 615 alerts. 346 cases (56%) were considered appropriate and 269 cases (44%) were inappropriate. Of the false alerts, 192 cases were due to data inaccuracy and 77 cases were due to insufficiency of the temporal abstraction. Conclusion: Our approach enabled to represent a concept-based rule expression that was available for the prototype of a clinical alert system. We believe our approach will contribute to narrow the gaps of information model between domain concepts and clinical data repositories.

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Muratore, Paolo, Cristiano Capone, and Pier Stanislao Paolucci. "Target spike patterns enable efficient and biologically plausible learning for complex temporal tasks." PLOS ONE 16, no. 2 (February 16, 2021): e0247014. http://dx.doi.org/10.1371/journal.pone.0247014.

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Recurrent spiking neural networks (RSNN) in the brain learn to perform a wide range of perceptual, cognitive and motor tasks very efficiently in terms of energy consumption and their training requires very few examples. This motivates the search for biologically inspired learning rules for RSNNs, aiming to improve our understanding of brain computation and the efficiency of artificial intelligence. Several spiking models and learning rules have been proposed, but it remains a challenge to design RSNNs whose learning relies on biologically plausible mechanisms and are capable of solving complex temporal tasks. In this paper, we derive a learning rule, local to the synapse, from a simple mathematical principle, the maximization of the likelihood for the network to solve a specific task. We propose a novel target-based learning scheme in which the learning rule derived from likelihood maximization is used to mimic a specific spatio-temporal spike pattern that encodes the solution to complex temporal tasks. This method makes the learning extremely rapid and precise, outperforming state of the art algorithms for RSNNs. While error-based approaches, (e.g. e-prop) trial after trial optimize the internal sequence of spikes in order to progressively minimize the MSE we assume that a signal randomly projected from an external origin (e.g. from other brain areas) directly defines the target sequence. This facilitates the learning procedure since the network is trained from the beginning to reproduce the desired internal sequence. We propose two versions of our learning rule: spike-dependent and voltage-dependent. We find that the latter provides remarkable benefits in terms of learning speed and robustness to noise. We demonstrate the capacity of our model to tackle several problems like learning multidimensional trajectories and solving the classical temporal XOR benchmark. Finally, we show that an online approximation of the gradient ascent, in addition to guaranteeing complete locality in time and space, allows learning after very few presentations of the target output. Our model can be applied to different types of biological neurons. The analytically derived plasticity learning rule is specific to each neuron model and can produce a theoretical prediction for experimental validation.

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Wang, Chen, Kai Du, Yin Li Jin, and Ling Yun He. "Han Ning Highway Traffic Accident Spatio-Temporal Analysis." Applied Mechanics and Materials 135-136 (October 2011): 560–64. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.560.

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Collecting the traffic accidents data of Han Ning highway in 2008, 2009 and 2010 three years, through two aspects of time and space to do statistical analysis and data mining of the traffic accidents of this highway. The result of the analysis is that accident rates have a certain relevance to time and space. From the time perspective, holidays and vacation days are accident high-risk days. From the space perspective, accident rate in the ascent and in the downhill are higher than on the straight road, cars go straight have a higher accident rate than in the corners, traffic accident rate in single-km single-lane tunnel is higher than outside the tunnel. Through the time and space distribution rule of the traffic accident, educating traffic participants follow the time and space distribution rule to restrict their behavior. Educating traffic managers obey the time and space distribution rule of traffic accident to adopt targeted management measures and engineering measures. Both of these two aspects have very important significance in reducing traffic accidents.

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Agarwal, Reshu. "Ordering Policy and Inventory Classification Using Temporal Association Rule Mining." International Journal of Productivity Management and Assessment Technologies 6, no. 1 (January 2018): 37–49. http://dx.doi.org/10.4018/ijpmat.2018010103.

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A modified framework that applies temporal association rule mining to inventory management is proposed in this article. The ordering policy of frequent items is determined and inventory is classified based on loss rule. This helps inventory managers to determine optimum order quantity of frequent items together with the most profitable item in each time-span. An example is illustrated to validate the results.

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Agarwal, Reshu. "Modified Ranking With Temporal Association Rule Mining in Supply Chains." International Journal of Service Science, Management, Engineering, and Technology 11, no. 4 (October 2020): 58–71. http://dx.doi.org/10.4018/ijssmet.2020100104.

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This article deals with data mining applications for the supply chain inventory management. ABC classification is usually used for inventory items classification because the number of inventory items is so large that it is not computationally feasible to set stock and service control guidelines for each individual item. Moreover, in ABC classification, the inter-relationship between items is not considered. But practically, the sale of one item could affect the sale of other items (cross selling effect). Hence, within time-periods, the inventories should be classified. In this article, a modified approach is proposed considering both time-periods and cross-selling effect to rank inventory items. A numerical example and an empirical study with a data set are used to evaluate the proposed approach. It is illustrated that by using this modified approach, the ranking of items may get affected resulting in higher profit.

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Journal articles on the topic 'Temporal rule'

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