A Spiking Neuromorphic Architecture Using Gated-RRAM for Associative Memory

This work reports a spiking neuromorphic architecture for associative memory simulated in a SPICE environment using recently reported gated-RRAM (resistive random-access memory) devices as synapses alongside neurons based on complementary metal-oxide semiconductors (CMOSs). The network utilizes a Verilog A model to capture the behavior of the gated-RRAM devices within the architecture. The model uses parameters obtained from experimental gated-RRAM devices that were fabricated and tested in this work. Using these devices in tandem with CMOS neuron circuitry, our results indicate that the proposed architecture can learn an association in real time and retrieve the learned association when incomplete information is provided. These results show the promise for gated-RRAM devices for associative memory tasks within a spiking neuromorphic architecture framework.

Optimization of the Use of TOGAF ADM in the Design of Information Systems for Islamic Boarding Schools

Pesantren is an educational institution that stands traditionally where students live in one place to live with each other and study under the guidance of a teacher who is also known as a Kiai. For its implementation, many things must be managed by a boarding school, starting from student registration, student placement, student learning, and evaluation of student learning at the boarding school. So far, there is no adequate system for all of these administrative needs. The purpose of this study is to build an information system framework for Islamic boarding schools using the TOGAF FRAMEWORK. Miftahul Huda Islamic Boarding School Pekanbaru City uses the TOGAF-ADM methodology as the standard tool used. The use of TOGAF can bring a consistent enterprise architecture, based on stakeholder requirements, and bring some considerations. . In designing this blueprint, it will rely on the work steps of the TOGAF ADM Framework, in which this enterprise architecture framework is divided into (four) categories, namely: business architecture, data, applications, and technology.

Federated Learning in Edge Computing: A Systematic Survey

Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services closer to data sources. EC combined with Deep Learning (DL) is a promising technology and is widely used in several applications. However, in conventional DL architectures with EC enabled, data producers must frequently send and share data with third parties, edge or cloud servers, to train their models. This architecture is often impractical due to the high bandwidth requirements, legalization, and privacy vulnerabilities. The Federated Learning (FL) concept has recently emerged as a promising solution for mitigating the problems of unwanted bandwidth loss, data privacy, and legalization. FL can co-train models across distributed clients, such as mobile phones, automobiles, hospitals, and more, through a centralized server, while maintaining data localization. FL can therefore be viewed as a stimulating factor in the EC paradigm as it enables collaborative learning and model optimization. Although the existing surveys have taken into account applications of FL in EC environments, there has not been any systematic survey discussing FL implementation and challenges in the EC paradigm. This paper aims to provide a systematic survey of the literature on the implementation of FL in EC environments with a taxonomy to identify advanced solutions and other open problems. In this survey, we review the fundamentals of EC and FL, then we review the existing related works in FL in EC. Furthermore, we describe the protocols, architecture, framework, and hardware requirements for FL implementation in the EC environment. Moreover, we discuss the applications, challenges, and related existing solutions in the edge FL. Finally, we detail two relevant case studies of applying FL in EC, and we identify open issues and potential directions for future research. We believe this survey will help researchers better understand the connection between FL and EC enabling technologies and concepts.

Enacting a Raciolinguistic Perspective for the “New Mainstream” in Literacy Classrooms

This chapter contributes to the conversation on reconceptualizing literacy teacher education through exploring the fallout of neoliberal sensibilities on U.S. schools. It continues to describe the new mainstream to be primarily racialized bi/multilingual students that defy the mono-mainstream assumption. The chapter then defines the mono-mainstream assumption that surreptitiously pervades educational systems with its deleterious effects on students. To combat this, the author explores how literacy teachers can enact a language architecture framework as an extension of a raciolinguistic perspective with practical classroom examples, including the terms used to describe students, their languages and literacies, how to negotiate hegemonic systems of accountability, specific pedagogical practices, and continued teacher reflexivity.