Deep Semi-Supervised Image Classification Algorithms: a Survey

Semi-supervised learning is a branch of machine learning focused on improving the performance of models when the labeled data is scarce, but there is access to large number of unlabeled examples. Over the past five years there has been a remarkable progress in designing algorithms which are able to get reasonable image classification accuracy having access to the labels for only 0.1% of the samples. In this survey, we describe most of the recently proposed deep semi-supervised learning algorithms for image classification and identify the main trends of research in the field. Next, we compare several components of the algorithms, discuss the challenges of reproducing the results in this area, and highlight recently proposed applications of the methods originally developed for semi-supervised learning.

Designing anti-predator training to maximize learning and efficacy assessments

Despite the growing need to use conservation breeding and translocations in species’ recovery, many attempts to reintroduce animals to the wild fail due to predation post-release. Released animals often lack appropriate behaviours for survival, including anti-predator responses. Anti-predator training—a method for encouraging animals to exhibit wariness and defensive responses to predators—has been used to help address this challenge with varying degrees of success. The efficacy of anti-predator training hinges on animals learning to recognize and respond to predators, but learning is rarely assessed, or interventions miss key experimental controls to document learning. An accurate measure of learning serves as a diagnostic tool for improving training if it otherwise fails to reduce predation. Here we present an experimental framework for designing anti-predator training that incorporates suitable controls to infer predator-specific learning and illustrate their use with the critically endangered Hawaiian crow, ‘alalā (Corvus hawaiiensis). We conducted anti-predator training within a conservation breeding facility to increase anti-predator behaviour towards a natural predator, the Hawaiian hawk, ‘io (Buteo solitaries). In addition to running live-predator training trials, we included two control groups, aimed at determining if responses could otherwise be due to accumulated stress and agitation, or to generalized increases in fear of movement. We found that without these control groups we may have wrongly concluded that predator-specific learning occurred. Additionally, despite generations in human care that can erode anti-predator responses, ‘alalā showed unexpectedly high levels of predatory wariness during baseline assessments. We discuss the implications of a learning-focused approach to training for managing endangered species that require improved behavioural competence for dealing with predatory threats, and the importance of understanding learning mechanisms in diagnosing behavioural problems.

Approaches to teaching in higher education: the perspective of network analysis using the revised approaches to teaching inventory

Over time, the academics’ approaches to teaching (i.e., content- or learning-focused approach) were intensively studied. Traditionally, studies estimated the shared variance between the items that describe a behavioral pattern (i.e., the psychometric approach), defined as a learning- or content-focused approach to teaching. In this study, we used a different perspective (i.e., network analysis) to investigate academics’ approaches to teaching. We aimed to bring in new insights regarding the interactions between the elements that define academics’ approaches to teaching. We used the Revised Approaches to Teaching Inventory to collect responses from 705 academics (63.97% female) from six Romanian universities. The main results indicated that academics’ conceptions about the subject matter are central to their preferences concerning the adoption of a content-focused or a learning-focused approach to teaching. The estimated network is stable across different sub-samples defined by the academic disciplines, class size, academics’ gender, and teaching experience. We highlighted the implications of these findings for research and teaching practice in higher education. Also, several recommendations for developing pedagogical training programs for academics were suggested. In particular, this study brings valuable insights for addressing academics’ conception about the subject matter and suggests that this could be a new topic for pedagogical training programs dedicated to university teachers.

SpikePropamine: Differentiable Plasticity in Spiking Neural Networks

The adaptive changes in synaptic efficacy that occur between spiking neurons have been demonstrated to play a critical role in learning for biological neural networks. Despite this source of inspiration, many learning focused applications using Spiking Neural Networks (SNNs) retain static synaptic connections, preventing additional learning after the initial training period. Here, we introduce a framework for simultaneously learning the underlying fixed-weights and the rules governing the dynamics of synaptic plasticity and neuromodulated synaptic plasticity in SNNs through gradient descent. We further demonstrate the capabilities of this framework on a series of challenging benchmarks, learning the parameters of several plasticity rules including BCM, Oja's, and their respective set of neuromodulatory variants. The experimental results display that SNNs augmented with differentiable plasticity are sufficient for solving a set of challenging temporal learning tasks that a traditional SNN fails to solve, even in the presence of significant noise. These networks are also shown to be capable of producing locomotion on a high-dimensional robotic learning task, where near-minimal degradation in performance is observed in the presence of novel conditions not seen during the initial training period.

EFFECT OF KNOWLEDGE MANAGEMENT ON ORGANIZATION PERFORMANCE

Purpose: The underlying rationale for learning organizations is that in circumstances of quick change just those that are adaptable, versatile and gainful will exceed expectations. For this to happen, it is contended, associations need to find how to tap individuals' responsibility and ability to learn at all levels. The general objective of the study was to evaluate effect of knowledge management on organization performance. Methodology: The paper used a desk study review methodology where relevant empirical literature was reviewed to identify main themes and to extract knowledge gaps. Findings: The study concludes that knowledge protection had the greatest effect on the performance of microfinance organizations, followed by knowledge acquisition, then knowledge conversion while knowledge application had the least effect to the performance. The study found out that the organizations have impressed attributes of knowledge applications such as individuals, organization culture, and identity, policies and documents in their organization which had resulted in improved performance. However, routines and systems were found to have less influence on the performance of these organizations. Recommendations: There is a need for managers also need to take advantage of the technological capability to support knowledge application processes. In particular, organizations should use technology to map the location of specific types of knowledge, thereby facilitating the application and sharing of knowledge. Technology also should be connected to encourage individuals in different areas to take in as a gathering from a solitary or numerous assets and a single or various focuses in time. Thusly, social and specialized infrastructural components can supplement each other and meet up to improve learning focused procedures.