Righting an injustice or American Taliban? the removal of Confederate statues

In recent years, several racial instances have occurred in the United States that have reinvigorated and demanded action concerning Confederate flags, statues and symbology. The Charleston massacre in 2015 prompted South Carolina to finally remove the Confederate battle flag from state grounds. The Charlottesville riots in 2017 accelerated the removal of Confederate statues from the public square. However, the controversy has broadened the discussion of how the Civil War monuments are to be viewed, especially in the public square. Many of the monuments were not built immediately following the Civil War, but later, during the era of Jim Crow and the disenfranchisement of African Americans during segregation in the South. Are they tributes to heroes or are they relics of a racist past that sought not to remember as much as to intimidate and bolster white supremacy? This work seeks to break up the eras of Confederate monument building and demonstrate that different monuments were built at different times (and are still being built). The monuments reflect other events in the country happening at the time, as well as the thinking of those who built them. This author hopes that these nuances will add to the general discussion and the usual three responses toward the statues of either taking them down to either destroy them, keep them, but add context, or place them in museums, cemeteries or private property. These nuances are important, possibly rendering all three as valid decisions. This author will use multiple lenses, including Union, Confederate, and African American lenses as interpreters for the various eras discussed. (Author abstract)Reif, A.W. (2018). Righting an injustice or American Taliban? the removal of Confederate statues. Retrieved from http://academicarchive.snhu.eduMaster ArtsHistoryCollege of Online and Continuing Educatio

Turning software engineers into machine learning engineers

A first challenge in teaching machine learning to software engineering and computer science students consists of changing the methodology from a constructive design-first perspective to an empirical one, focusing on proper experimental work. On the other hand, students nowadays can make significant progress using existing scripts and powerful (deep) learning frameworks -- focusing on established use cases such as vision tasks. To tackle problems in novel application domains, a clean methodological style is indispensable. Additionally, for deep learning, familiarity with gradient dynamics is crucial to understand deeper models. Consequently, we present three exercises that build upon each other to achieve these goals. These exercises are validated experimentally in a master's level course for software engineers

Jaco: an offline running privacy-aware voice assistant

With the recent advance in speech technology, smart voice assistants have been improved and are now used by many people. But often these assistants are running online as a cloud service and are not always known for a good protection of users' privacy. This paper presents the architecture of a novel voice assistant, called Jaco, with the following features: (a) It can run completely offline, even on low resource devices like a RaspberryPi. (b) Through a skill concept it can be easily extended. (c) The architectural focus is on protecting users' privacy, but without restricting capabilities for developers. (d) It supports multiple languages. (e) It is competitive with other voice assistant solutions. In this respect the assistant combines and extends the advantages of other approaches

Graph machine learning for assembly modeling

Assembly modeling refers to the design engineering process of composing assemblies (e.g., machines or machine components) from a common catalog of existing parts. There is a natural correspondence of assemblies to graphs which can be exploited for services based on graph machine learning such as part recommendation, clustering/taxonomy creation, or anomaly detection. However, this domain imposes particular challenges such as the treatment of unknown or new parts, ambiguously extracted edges, incomplete information about the design sequence, interaction with design engineers as users, to name a few. Along with open research questions, we present a novel data set

Finstreder: simple and fast spoken language understanding with finite state transducers using modern speech-to-text models

In Spoken Language Understanding (SLU) the task is to extract important information from audio commands, like the intent of what a user wants the system to do and special entities like locations or numbers. This paper presents a simple method for embedding intents and entities into Finite State Transducers, and, in combination with a pretrained general-purpose Speech-to-Text model, allows building SLU-models without any additional training. Building those models is very fast and only takes a few seconds. It is also completely language independent. With a comparison on different benchmarks it is shown that this method can outperform multiple other, more resource demanding SLU approaches

A recommendation system for CAD assembly modeling based on graph neural networks

In computer-aided design (CAD), software tools support design engineers during the modeling of assemblies, i.e., products that consist of multiple components. Selecting the right components is a cumbersome task for design engineers as they have to pick from a large number of possibilities. Therefore, we propose to analyze a data set of past assemblies composed of components from the same component catalog, represented as connected, undirected graphs of components, in order to suggest the next needed component. In terms of graph machine learning, we formulate this as graph classification problem where each class corresponds to a component ID from a catalog and the models are trained to predict the next required component. In addition to pretraining of component embeddings, we recursively decompose the graphs to obtain data instances in a self-supervised fashion without imposing any node insertion order. Our results indicate that models based on graph convolution networks and graph attention networks achieve high predictive performance, reducing the cognitive load of choosing among 2,000 and 3,000 components by recommending the ten most likely components with 82-92% accuracy, depending on the chosen catalog