Multi-resolution Tensor Learning for Large-Scale Spatial Data

High-dimensional tensor models are notoriously computationally expensive to train. We present a meta-learning algorithm, MMT, that can significantly speed up the process for spatial tensor models. MMT leverages the property that spatial data can be viewed at multiple resolutions, which are related by coarsening and finegraining from one resolution to another. Using this property, MMT learns a tensor model by starting from a coarse resolution and iteratively increasing the model complexity. In order to not "over-train" on coarse resolution models, we investigate an information-theoretic fine-graining criterion to decide when to transition into higher-resolution models. We provide both theoretical and empirical evidence for the advantages of this approach. When applied to two real-world large-scale spatial datasets for basketball player and animal behavior modeling, our approach demonstrate 3 key benefits: 1) it efficiently captures higher-order interactions (i.e., tensor latent factors), 2) it is orders of magnitude faster than fixed resolution learning and scales to very fine-grained spatial resolutions, and 3) it reliably yields accurate and interpretable models

Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited

Since the late 1990s predicate invention has been under-explored within inductive logic programming due to difficulties in formulating efficient search mechanisms. However, a recent paper demonstrated that both predicate invention and the learning of recursion can be efficiently implemented for regular and context-free grammars, by way of metalogical substitutions with respect to a modified Prolog meta-interpreter which acts as the learning engine. New predicate symbols are introduced as constants representing existentially quantified higher-order variables. The approach demonstrates that predicate invention can be treated as a form of higher-order logical reasoning. In this paper we generalise the approach of meta-interpretive learning (MIL) to that of learning higher-order dyadic datalog programs. We show that with an infinite signature the higher-order dyadic datalog class H2 2 has universal Turing expressivity though H2 2 is decidable given a finite signature. Additionally we show that Knuth–Bendix ordering of the hypothesis space together with logarithmic clause bounding allows our MIL implementation MetagolD to PAC-learn minimal cardinality H2 2 definitions. This result is consistent with our experiments which indicate that MetagolD efficiently learns compact H2 2 definitions involving predicate invention for learning robotic strategies, the East–West train challenge and NELL. Additionally higher-order concepts were learned in the NELL language learning domain. The Metagol code and datasets described in this paper have been made publicly available on a website to allow reproduction of results in this paper

The Influence of the Jigsaw Model Based on Higher Order Thinking Skills on Students 21st Century Skills : Meta-Analysis

The purpose of the study was to determine the effect of jigsaw learning model based on Higher Order Thinking Skills on students' 21st century thinking skills. This type of research is quantitative research with a meta-analysis approach. The data sources in this study came from 10 national and international journals published in 2018-2023. The process of searching for data sources through google scholar, Eric and Wiley databases. The inclusion criteria in this study are a) research must have experimental and control classes, b) research comes from Scopus and SINTA indexed journals or proceedings, c) research has a relationship with the Higher Order Thinking Skills-based jigsaw learning model towards students' 21st century skills, d) research must be published in the last 5 years range 2018-2023; e) articles must have a value (t), (r), (f); f) sample size> 20 students. Data analysis in this meta-analysis with the help of JSAP 0.8.5 application.  From this meta-analysis, it can be concluded that there is a significant effect of jigsaw learning model based on Higher Order Thinking Skills on students' 21st century thinking skills (Z = 1.431; p < 0.001; 95% CI [0.612; 0.942]. This effect is criterion high (rRE = 0.845). This finding explains the learning model of jigsaw learning model based on Higher Order Thinking Skills provides a high influence on students' 21st century thinking skills

DiCE: The Infinitely Differentiable Monte-Carlo Estimator

The score function estimator is widely used for estimating gradients of stochastic objectives in stochastic computation graphs (SCG), eg, in reinforcement learning and meta-learning. While deriving the first-order gradient estimators by differentiating a surrogate loss (SL) objective is computationally and conceptually simple, using the same approach for higher-order derivatives is more challenging. Firstly, analytically deriving and implementing such estimators is laborious and not compliant with automatic differentiation. Secondly, repeatedly applying SL to construct new objectives for each order derivative involves increasingly cumbersome graph manipulations. Lastly, to match the first-order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for estimators of higher-order derivatives. To address all these shortcomings in a unified way, we introduce DiCE, which provides a single objective that can be differentiated repeatedly, generating correct estimators of derivatives of any order in SCGs. Unlike SL, DiCE relies on automatic differentiation for performing the requisite graph manipulations. We verify the correctness of DiCE both through a proof and numerical evaluation of the DiCE derivative estimates. We also use DiCE to propose and evaluate a novel approach for multi-agent learning. Our code is available at https://www.github.com/alshedivat/lola

Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited

Since the late 1990s predicate invention has been under-explored within inductive logic programming due to difficulties in formulating efficient search mechanisms. However, a recent paper demonstrated that both predicate invention and the learning of recursion can be efficiently implemented for regular and context-free grammars, by way of metalogical substitutions with respect to a modified Prolog meta-interpreter which acts as the learning engine. New predicate symbols are introduced as constants representing existentially quantified higher-order variables. The approach demonstrates that predicate invention can be treated as a form of higher-order logical reasoning. In this paper we generalise the approach of meta-interpretive learning (MIL) to that of learning higher-order dyadic datalog programs. We show that with an infinite signature the higher-order dyadic datalog class$$H^2_2$$H22has universal Turing expressivity though$$H^2_2$$H22is decidable given a finite signature. Additionally we show that Knuth–Bendix ordering of the hypothesis space together with logarithmic clause bounding allows our MIL implementation Metagol$$_{D}$$Dto PAC-learn minimal cardinality$$H^2_2$$H22definitions. This result is consistent with our experiments which indicate that Metagol$$_{D}$$Defficiently learns compact$$H^2_2$$H22definitions involving predicate invention for learning robotic strategies, the East–West train challenge and NELL. Additionally higher-order concepts were learned in the NELL language learning domain. The Metagol code and datasets described in this paper have been made publicly available on a website to allow reproduction of results in this paper

Logic Programs as Declarative and Procedural Bias in Inductive Logic Programming

Machine Learning is necessary for the development of Artificial Intelligence, as pointed out by Turing in his 1950 article ``Computing Machinery and Intelligence''. It is in the same article that Turing suggested the use of computational logic and background knowledge for learning. This thesis follows a logic-based machine learning approach called Inductive Logic Programming (ILP), which is advantageous over other machine learning approaches in terms of relational learning and utilising background knowledge. ILP uses logic programs as a uniform representation for hypothesis, background knowledge and examples, but its declarative bias is usually encoded using metalogical statements. This thesis advocates the use of logic programs to represent declarative and procedural bias, which results in a framework of single-language representation. We show in this thesis that using a logic program called the top theory as declarative bias leads to a sound and complete multi-clause learning system MC-TopLog. It overcomes the entailment-incompleteness of Progol, thus outperforms Progol in terms of predictive accuracies on learning grammars and strategies for playing Nim game. MC-TopLog has been applied to two real-world applications funded by Syngenta, which is an agriculture company. A higher-order extension on top theories results in meta-interpreters, which allow the introduction of new predicate symbols. Thus the resulting ILP system Metagol can do predicate invention, which is an intrinsically higher-order logic operation. Metagol also leverages the procedural semantic of Prolog to encode procedural bias, so that it can outperform both its ASP version and ILP systems without an equivalent procedural bias in terms of efficiency and accuracy. This is demonstrated by the experiments on learning Regular, Context-free and Natural grammars. Metagol is also applied to non-grammar learning tasks involving recursion and predicate invention, such as learning a definition of staircases and robot strategy learning. Both MC-TopLog and Metagol are based on a $\top$-directed framework, which is different from other multi-clause learning systems based on Inverse Entailment, such as CF-Induction, XHAIL and IMPARO. Compared to another $\top$-directed multi-clause learning system TAL, Metagol allows the explicit form of higher-order assumption to be encoded in the form of meta-rules.Open Acces

DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkAndroid malware has continued to grow in volume and complexity posing significant threats to the security of mobile devices and the services they enable. This has prompted increasing interest in employing machine learning to improve Android malware detection. In this paper, we present a novel classifier fusion approach based on a multilevel architecture that enables effective combination of machine learning algorithms for improved accuracy. The framework (called DroidFusion), generates a model by training base classifiers at a lower level and then applies a set of ranking-based algorithms on their predictive accuracies at the higher level in order to derive a final classifier. The induced multilevel DroidFusion model can then be utilized as an improved accuracy predictor for Android malware detection. We present experimental results on four separate datasets to demonstrate the effectiveness of our proposed approach. Furthermore, we demonstrate that the DroidFusion method can also effectively enable the fusion of ensemble learning algorithms for improved accuracy. Finally, we show that the prediction accuracy of DroidFusion, despite only utilizing a computational approach in the higher level, can outperform stacked generalization, a well-known classifier fusion method that employs a meta-classifier approach in its higher level

Enhancing pre-service teachers' diagnostic competence in Physics misconceptions at public universities in Tanzania

Teachers require to diagnose students’ learning needs in order to plan and carryout effective lessons, a process similar to medical doctors diagnosing their patients before treatment. While it is crucial to enhance diagnostic competence in teachers, an issue remains about how we can best improve this competence among undergraduate pre-service teachers. In the teaching and learning process of science in middle or high schools, misconceptions can hinder learning of new Physics ideas if teachers do not detect and correct them in time. The current research carried out a meta-analysis of 22 empirical studies aimed at fostering diagnostic competences through intervention in teacher and medical education, summarized the findings, revealed the overall effect size, and examined the moderating factors. Following the results of the meta-analysis, we designed an experimental study to investigate the effects of problem solving and example-based learning instructional approaches on enhancing pre-service teachers’ diagnostic competence in Physics misconceptions. The meta-analysis revealed a positive medium mean effect size (g = 0.37) of interventions on fostering the development of diagnostic competences among undergraduate students in both domains. The moderator analysis suggests that an instructional approach is a significant moderator when we apply problem solving during the learning phase of an intervention. The experimental study revealed that both problem solving and example-based learning significantly enhanced pre-service teachers’ diagnostic competence in form of conceptual knowledge, but not the procedural knowledge. Problem solving instructional approach was more effective than example-based learning on enhancing diagnostic competence. The pre-service teachers’ diagnostic competence in the form of conceptual and procedural knowledge positively correlated with germane cognitive load, while it negatively correlated with intrinsic and extraneous cognitive loads. Example-based learning instructional approach significantly influenced both intrinsic and extraneous cognitive loads when compared with problem solving. Cognitive load did not significantly mediate the effect of the instructional approaches on diagnostic competences, and a rating scale questionnaire differentiated between the three types of cognitive load, but did not clearly discriminate between intrinsic and extraneous cognitive loads. The meta-analysis findings imply that learning to diagnose various aspects through problem solving is an effective means of advancing undergraduate students’ diagnostic competences. Learners’ prior diagnostic knowledge seems to be a covariate on enhancing diagnostic competences through interventions. An experimental study findings also imply that the problem solving instructional approach can enhance pre-service teacher’s diagnostic competence in identifying pupil’s Physics misconceptions better than example-based learning. In practice, the current research supports the assumption that integrating diagnostic practices into the Physics-methods course curriculum during undergraduate training programs can improve pre-service Physics teachers’ formative assessment skills. Some limitations can be accounted for by the findings in both studies. With respect to the meta-analysis, the restrictions of robust variance estimation method when estimating meta-regressions especially for moderator analyses could have limited the findings due to imbalances of level of some categorical moderator variables. This could have then affected the degrees of freedom and hence the power for moderation effect. In the experimental study, the random errors that might occur due to extraneous variable (e.g. individual ability) that could have affected the outcome measures rather than intervention treatment, and the assessment of pre-service teachers’ diagnostic knowledge through a same knowledge test could have also limited the findings. In conclusion, the meta-analysis supports the development of diagnostic competence through interventions (with a medium effect size), and indicates that problem solving is the best instructional approach. The meta-analysis also seems to point out the fact that example-based learning instructional approach may better fit learners with lower prior knowledge, whereas, problem solving may better fit learners with higher levels of prior knowledge. With respect to the experimental study, undergraduate pre-service teachers seem to learn abstract concepts and ideas about the diagnosis process better through problem solving than example-based learning. Both instructional approaches seem to facilitate the diagnostic competence effectively, if we consider the germane cognitive load high, while keeping the intrinsic and cognitive load to a minimum. The current research further emphasizes the need for a similar meta-analysis to include more studies and alternative moderators (e.g. types of feedback, prompts, and so on), and an experimental study to compare the effects of problem solving and example-based learning on diagnostic competences with immediate and delayed post testing

Twenty-first century learning, technology, and the impact on student engagement

xiv, 371 leaves ; 29 cmThe purpose of this study was to investigate the relationship between twenty-first century instructional methods and student learning experiences. To do so, a typical and representative group of eight students was selected for qualitative interviews which ascertained student perception of their engagement in a typical New Media class. The study determined the perceived impact of a "student-centered instructional approach to video creation" on levels of student engagement in order to understand the nature of engagement and how they moved towards higher levels of independent learning. Transcripts of these interviews were used to identify a thematic structure of student perceptions of their engagement in a classroom where a "student-centered instruction approach to video creation" was used. Lastly, using the teacher's professional reflections, notes, and anecdotal reports from the class, students' stories of engagement were created to illustrate each unique journey toward self-engaged independence from the teacher's perspective. The results of this data pointed to three meta-themes. Meta-theme 1: Positive Relationships and Affective Climate, Meta-theme 2: Personalized, Student-centered Supported Independence, and Meta-theme 3: Accelerated Lift and Independent Learning