Networked signal and information processing

The article reviews significant advances in networked signal and information processing, which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. As these interacting agents cooperate, new collective behaviors emerge from local decisions and actions. Moreover, and significantly, theory and applications show that networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions, while offering the potential for improved privacy, increasing resilience, and saving resources

High girth column-weight-two LDPC codes based on distance graphs

Copyright © 2007 G. Malema and M. Liebelt. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.LDPC codes of column weight of two are constructed from minimal distance graphs or cages. Distance graphs are used to represent LDPC code matrices such that graph vertices that represent rows and edges are columns. The conversion of a distance graph into matrix form produces an adjacency matrix with column weight of two and girth double that of the graph. The number of 1's in each row (row weight) is equal to the degree of the corresponding vertex. By constructing graphs with different vertex degrees, we can vary the rate of corresponding LDPC code matrices. Cage graphs are used as examples of distance graphs to design codes with different girths and rates. Performance of obtained codes depends on girth and structure of the corresponding distance graphs.Gabofetswe Malema and Michael Liebel

Evaluating and Aggregating Feature-based Model Explanations

A feature-based model explanation denotes how much each input feature contributes to a model's output for a given data point. As the number of proposed explanation functions grows, we lack quantitative evaluation criteria to help practitioners know when to use which explanation function. This paper proposes quantitative evaluation criteria for feature-based explanations: low sensitivity, high faithfulness, and low complexity. We devise a framework for aggregating explanation functions. We develop a procedure for learning an aggregate explanation function with lower complexity and then derive a new aggregate Shapley value explanation function that minimizes sensitivity

Explainable Machine Learning in Deployment

Explainable machine learning offers the potential to provide stakeholders with insights into model behavior by using various methods such as feature importance scores, counterfactual explanations, or influential training data. Yet there is little understanding of how organizations use these methods in practice. This study explores how organizations view and use explainability for stakeholder consumption. We find that, currently, the majority of deployments are not for end users affected by the model but rather for machine learning engineers, who use explainability to debug the model itself. There is thus a gap between explainability in practice and the goal of transparency, since explanations primarily serve internal stakeholders rather than external ones. Our study synthesizes the limitations of current explainability techniques that hamper their use for end users. To facilitate end user interaction, we develop a framework for establishing clear goals for explainability. We end by discussing concerns raised regarding explainability

Comparison of tensile strength of different carbon fabric reinforced epoxy composites

Carbon fabric/epoxy composites are materials used in aeronautical industry to manufacture several components as flaps, aileron, landing-gear doors and others. To evaluate these materials become important to know their mechanical properties, for example, the tensile strength. Tensile tests are usually performed in aeronautical industry to determinate tensile property data for material specifications, quality assurance and structural analysis. For this work, it was manufactured four different laminate families (F155/PW, F155/HS, F584/PW and F584/HS) using pre-impregnated materials (prepregs) based on F155TM and F584TM epoxy resins reinforced with carbon fiber fabric styles Plain Weave (PW) and Eight Harness Satin (8HS). The matrix F155TM code is an epoxy resin type DGEBA (diglycidil ether of bisphenol A) that contains a curing agent and the F584TM code is a modified epoxy resin type. The laminates were obtained by handing lay-up process following an appropriate curing cycle in autoclave. The samples were evaluated by tensile tests according to the ASTM D3039. The F584/PW laminates presented the highest values of tensile strength. However, the highest modulus results were determined for the 8HS composite laminates. The correlation of these results emphasizes the importance of the adequate combination of the polymeric matrix and the reinforcement arrangement in the structural composite manufacture. The microscopic analyses of the tested specimens show valid failure modes for composites used in aeronautical industry