Architectural Innovations for Efficient Denoising and Classification: A Manual vs. Neural Architecture Search Comparison

In this paper, we combine image denoising and classification, aiming to enhance human perception of noisy images captured by edge devices, like security cameras. Since edge devices have little computational power, we also optimize for efficiency by proposing a novel architecture that integrates the two tasks. Additionally, we alter a Neural...

Optimal Robust Decision Trees: A dynamic programming approach

Decision trees are integral to machine learning, with their robustness being a critical measure of effectiveness against adversarial data manipulations. Despite advancements in algorithms, current solutions are either optimal but lack scalability or scale well, but do not guarrantee optimality. This paper presents a novel adaptation of the...

Evaluating an RCPSP Implementation of Quantum Program Scheduling

The goal of quantum application scheduling is to enable the execution of applications on a quantum network. As the final step in the application scheduling process, program scheduling locally schedules execution of blocks of instructions on each node by defining so-called node schedules. In this thesis, we present a formal definition of program...

Extracting insights from black box neural networks for network-wide traffic predictions

Accurate and trustworthy short-term traffic prediction is crucial in the modern world for the comfort of drivers and decision-makers as it is used to improve the performance of traffic management systems, lessen congestion, increase safety, and shorten journey times. It is possible to discover useful information for network transportation...

Instruction scheduling for blind quantum computing

Quantum networks offer more capabilities than classical networks. For example, quantum networks can solve certain problems faster than classical networks and they can even solve problems which cannot be solved with classical networks. One well-known quantum network application is blind quantum computing (BQC). In a BQC application a client node...

Evaluating Stochastic Floating-Point Superoptimization with STOKE

The superoptimizer STOKE has previously been shown to be effective at optimizing programs containing floating-point numbers. The STOKE optimizer obtains these results by running a stochastic search over the set of all programs and selecting the best-optimized one. This study aims to find more clearly what floating-point programs STOKE optimizes...

Evaluating the egg Equality Saturation Superoptimizer

Superoptimization is the idea of creating the most optimal program possible from a given input program. Equality saturation is a method of superoptimization using rewrite rules and e-graphs. A rewrite rule defines a piece of code that can be rewritten as another part while keeping equal behavior. This is added to an e-graph, a data structure...

Evaluating STOKE

STOKE is one of the Superoptimizers which are programs that given a function and a set of instructions of a processor, traverse through a space of programs that compute a given function and try to find the optimal usually in terms of execution speed or size of the binary. Authors of STOKE make some extraordinary claims. They suggest that it is...

Evaluating Souper: A Synthesizing Superoptimizer

Modern compilers exploit syntax \&amp; semantics to optimize input programs.<br/>Often such optimization rules are arduous to get right and the output is not guaranteed to be globally optimal.<br/>Superoptimizers take a different approach to this problem by traversing the program space.<br/>This study focuses on Souper, a synthesizing...

Domain-Knowledge-Driven Explainable Product Quality Prediction: Using prior knowledge to improve explanations of quality prediction models

Explainable artificial intelligence has in recent years allowed us to investigate how many machine learning methods are creating its predictions. This is especially useful in scenarios where the goal is not to predict a variable, but to explain what influences that variable. However, the methods that have been created thus far do not focus on...

Validation of the Hypotension Prediction Index on the Intensive Care Patient Population

A low blood pressure (hypotension, as mean arterial pressure &lt; 65 mmHg) in patients on the intensive care unit (ICU) is associated with adverse outcomes and death. Proactive treatment with the use of a predictive alarm could improve patient outcome. The Hypotension Prediction Index (HPI) is a machinelearning algorithm that uses arterial blood...

The application of a Fuzzy Adaptive Learning Control Network in cost estimates for road bridges

In general, costs are an important aspect long before the construction of a civil project starts. In the conceptual phase, which is the first phase of a civil construction project, little cost information is available, and the cost estimate is at that stage of project development less accurate than when the project is almost completed. When a...

Extending A* to solve multi-agent pathfinding problems with waypoints

In the field of cooperative multi-agent pathfinding (MAPF) the optimal set of non-conflicting paths must be found for a set of agents in a graph. The addition of waypoints to this problem (MAPFW) gives rise to the possibility of more complex applications, such as in vehicle routing, aviation, computer games or robotics. Yet no algorithms have...