An evaluation of approaches for enhancing inductive learning with a transductive view

Availability of enough labeled data is a challenge for most inductive learners who try to generalize based on limited labeled dataset. A traditional semi-supervised approach for the same problem attempts to approach it by methods such as wrapping multiple inductive learners on derived pseudo-labels, unsupervised feature extraction or suitable modification of the objective function. In this work, a simple approach is adopted whereby an inductive learner is enhanced by suitably enabling it with a transductive view of the data. The experiments, though conducted on a small dataset, successfully provide few insights i.e. transductive view benefits an inductive learner, a transductive view that considers both attribute and relations is more effective than one that considers either attributes or relations and graph convolution based embedding algorithms effectively captures the information from transductive views compared to popular knowledge embedding approaches.

Learning by Ignoring the Most Wrong

Imprecise probabilities (IP) are an increasingly popular way of reasoning about rational credence. However they are subject to an apparent failure to display convincing inductive learning. This paper demonstrates that a small modification to the update rule for IP allows us to overcome this problem, albeit at the cost of satisfying only a weaker concept of coherence.

Fostering Learner Autonomy through Cooperative and Collaborative Learning

Both cooperative and collaborative learning are learner-centered teaching approaches in English Language Teaching (ELT) to support active learning, shared learning, inductive learning, and autonomous learning. However, definitional and conceptual clarity of these two concepts did not receive as much attention as they deserve. As a result, these two terms are often confused, conflated or used interchangeably. This review paper critiqued the two terms by revealing their components, identifying their commonalities as well as variances, and explicating their theoretical bases and exploring their role in fostering learner autonomy. Searching and reviewing published literature were used to achieve the objectives of the study. The study perceived that cooperative learning is the educational technique that uses small groups of students guided by the teacher to benefit their individual and each other’s learning whereas collaborative learning is a philosophy of interaction of a learning group where people take responsibility for their own learning while recognizing their peers’ abilities and contributions. Both the approaches foster autonomous learning behavior where cooperative learning is considered the foundation stage for collaborative learning. This article will benefit current and future ELT practitioners and researchers of this emerging field of pedagogy by providing a clearer analyses of the terms and role in fostering learner autonomy.

Brief inductive learning but not explicit instruction of a new grammar can produce a Whorfian effect

It is known that brief training on new vocabulary and metaphors can shift how we represent concepts and categorize stimuli even when we are not using the language. But it remains unknown whether brief training on grammar can also produce such ‘Whorfian’ effects. Besides, previous studies have neglected how the way in which the language was learned might be a factor. To fill these gaps, Mandarin native speakers learned a new grammatical marker of transitivity through either inductive training or explicit instruction. In subsequent non-verbal matching task the inductively trained group based their judgments on the number of entities involved in motion events to a greater extent than controls naïve to the grammar, but the explicitly trained group did not, despite showing equivalent knowledge of the grammar in a grammaticality judgment task. We interpret the effects in terms of dynamic and unconscious top-down feedback from grammar to lower-level perceptual processes.

Digital Identification Algorithms for Primary Frequency Control in Unified Power System

The article studies and develops the methods for assessing the degree of participation of power plants in the general primary frequency control in a unified energy system (UES) of Russia based on time series analysis of frequency and power. To identify the processes under study, methods of associative search are proposed. The methods are based on process knowledgebase development, data mining, associative research, and inductive learning. Real-time identification models generated using these algorithms can be used in automatic control and decision support systems. Evaluation of the behavior of individual UES members enables timely prevention of abnormal and emergency situations. Methods for predictive diagnostics of generating equipment in terms of their readiness to participate in the primary frequency control are also proposed. In view of the non-stationarity of the load in electrical networks, the algorithms have been developed using wavelet analysis. Case studies are given showing the operating of the proposed methods.

MSGCN: Multi-Subgraph Based Heterogeneous Graph Convolution Network Embedding

Heterogeneous graph embedding has become a hot topic in network embedding in recent years and has been widely used in lots of practical scenarios. However, most of the existing heterogeneous graph embedding methods cannot make full use of all the auxiliary information. So we proposed a new method called Multi-Subgraph based Graph Convolution Network (MSGCN), which uses topology information, semantic information, and node feature information to learn node embedding vector. In MSGCN, the graph is firstly decomposed into multiple subgraphs according to the type of edges. Then convolution operation is adopted for each subgraph to obtain the node representations of each subgraph. Finally, the node representations are obtained by aggregating the representation vectors of nodes in each subgraph. Furthermore, we discussed the application of MSGCN with respect to a transductive learning task and inductive learning task, respectively. A node sampling method for inductive learning tasks to obtain representations of new nodes is proposed. This sampling method uses the attention mechanism to find important nodes and then assigns different weights to different nodes during aggregation. We conducted an experiment on three datasets. The experimental results indicate that our MSGCN outperforms the state-of-the-art methods in multi-class node classification tasks.

Learning to Call Bullsh*t via Induction: Categorization Training Improves Critical Thinking Performance

Participants performed a categorization training task, where each trial presented an example scenario in which an individual makes a claim based on an observation, and participants marked which fallacy or bias, if any, the individual in the scenario was committing. In two studies, we measure the effect of this training task on critical thinking, measured using an open-ended critical thinking assessment, both pre- and post-training. In Study 1, we pilot these materials in an online college course across a full academic semester and observe credible improvements in critical thinking performance. In Study 2, we conduct a pre-registered randomized controlled experiment using online research participants and observe credible improvements in critical thinking relative to no training, and relative to comparable learning activities focused on conventional curricular content. We infer that the categorization training task facilitated inductive learning of patterns of biased and flawed reasoning, which improved participants’ ability to detect and identify such patterns in the delayed open-ended critical thinking assessment. Such categorization training shows promise as an effective and practical method for improving learners’ resistance to online disinformation.

Brain Storm Optimization Algorithm for Data Classification

With the increasing of the number of dimensions or variables in the search space, the inductive learning of fuzzy rule classifier will be influenced by the generation and optimization of rules. Thus, the extensibility and accuracy of fuzzy systems will be affected. In this paper, the brain storm optimization algorithm was used. A new fuzzy system was designed by modifying the rules definition process in traditional fuzzy system. In the derivation of rules, the exponential model was introduced to improve the traditional brain storming algorithm. On the basis, this new fuzzy system was used for the research on data classification. The experimental results show that this new fuzzy system can improve the accuracy of data classification.

UNDERSTANDING LOW ACADEMIC ACHIEVEMENT BASED ON PASS AND ERICKSONIAN HUMANISTIC THERAPY

Academic underachievement is a burning problem far from being solved. This study evaluated the efficacy of a humanistic psychotherapy intervention program based on planning, attention, successive and simultaneous (PASS) inductive learning, and indirect metaphorical Ericksonian communication grounded in the neuroscientific knowledge of human behavior. The rational neuroscientific foundations are explained throughout the discussion, highlighting the interaction cognition-emotion. The sample was 600 subjects classified as low achievers, very low achievers, and behavioral-psychosomatic dysfunctional low achievers. The mean age was 13.93 (SD = 1.56; range 12-17), 29.5% women. A normal control group of 172 subjects was selected (mean age, 13.88; SD = 1.75;range 12-17; 49.4% women). ANOVA and stepwise regression analysis were performed. No PASS deficit explains the low achievers. A dysfunctional emotional reason is suggested. A lower simultaneous PASS appears related to very low achievers. A lower planning PASS and the "N" pattern appear related to behavioral-psychosomatic low achievers. The "N" pattern is a suggestive marker of emotional dysfunction. After 6 months of intervention, 55% of very low achievers, 85% of low achievers, and 80% of behavioral-psychosomatic participants did not satisfy the criterion of an underachiever. More studies are required to contribute to the accumulative understanding of scientific phenomena, and so investigate replication.