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

Thread-local, step-local proof obligations for refinement of state-based concurrent systems

This paper presents a proof technique for proving refinements for general state-based models of concurrent systems that reduces proving forward simulations to thread-local, step-local proof obligations. Instances of this proof technique should be applicable to systems specified with ASM rules, B events, or Z operations. To exemplify the proof technique, we demonstrate it with a simple case study that verifies linearizability of a lock-free implementation of concurrent hash sets by showing that it refines an abstract concurrent system with atomic operations. Our theorem prover KIV translates programs to a set of transition rules and generates proof obligations according to the technique

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