Evaluating Large Language Model Performance on User and Language Defined Elements in Code

Large Language Models of code have seen significant jumps in performance recently. However, these jumps tend to accompany a notable and perhaps concerning increase in scale and costs. We contribute an evaluation of prediction performance with respect to model size by assessing the layer-wise progression for language and user-defined elements in...

A Cross-Lingual Evaluation of CodeGen's Performance in Code Completion

We present an investigation into the relationship between the average depth of the first correct prediction and the performance of CodeGen. This was done on a dataset comprised of code files comprised of C++, Go, Java, Julia, Kotlin, and Python. The analysis involved investigating the model's predictions at different layers using a Tuned Lens,...

Cross-lingual Performance of CodeGPT on the Code Completion Task

The development of contemporary source code auto-completion tools have significantly boosted productivity and efficiency of developers. In 2021, the GPT-2-based Transformer CodeGPT was developed to support code completion and text-to-code generation. Similarly to most code models however, CodeGPT was trained on a limited set of widely-used...

A Study on the Impact of Common Code Structures on CodeParrot’s Autocompletion Performance

In recent years, deep learning techniques, particularly transformer models, have demonstrated remarkable advancements in the accuracy and efficiency of language models. These models provide the foundation for many natural language processing tasks, including code completion. The effectiveness of code completion models has been the subject of a...

Using Human Workload to Adjust Agent Explanations in Human-agent Teamwork

Artificial intelligence systems assist humans in more and more cases. However, such systems' lack of explainability could lead to a bad performance of the teamwork, as humans might not cooperate with or trust systems with black-box algorithms opaque to them. This research attempts to improve the explainability of artificial intelligence systems...

The Impact of Tailoring Agent Explanations According to Human Performance on Human-AI Teamwork

Nowadays, artificial intelligence (AI) systems are embedded in many aspects of our lives more than ever before. Autonomous AI systems (agents) are aiding people in mundane daily tasks, even outperforming humans in several cases. However, agents still depend on humans in unexpected circumstances. Thus, the main goal of these agents has...

Tailoring User-Aware Agent Explanations to Properly Align Human Trust

Aligning human trust to correspond with an agent's trustworthiness is an essential collaborative element within Human-Agent Teaming (HAT). Misalignment of trust could cause sub-optimal usage of the agent. Trust can be influenced by providing explanations which clarify the agent's actions. However, research often approaches explanations...

Using pre-trained convolutional neural networks to predict maturity levels of strawberries

To reduce food waste, the strawberry harvesting process should be optimized. In the modern era, computer vision can provide huge amounts of help. This paper focuses on optimizing pre-trained convolutional neural networks (CNN) to determine the maturity level of strawberries on a 1-10 scale. Here, 1 means unripe and 10 means overripe. Maturity...

The Effect of Color Spaces and Spectra on a Strawberry Prediction Model

The purpose of this research is to reduce food waste by monitoring the ripening process of strawberries in order to optimize the harvesting time. To improve the moment of harvest, we need to know the ripeness of a strawberry. Using data from different color ranges and spaces we should be able to predict the ripeness of a strawberry on a 1-10...

Measuring the size of strawberries using binocular photos

To reduce food waste, it is important to know what strawberries to prioritise for harvesting. Size is an important quality attribute for strawberry. In order to know the size, the depth of the strawberry in the image must been known. To estimate the depth, stereovision gets utilized using binocular images. Since classic stereovision methods are...

Detecting strawberries using different Convolutional Neural Networks

This paper tries to combat the food waste of strawberries during the harvesting steps.<br/>An automatic pipeline must be established to combat this food waste.<br/>One of the steps needed in this pipeline is detecting strawberries in images.<br/>Therefore, this paper aims to find out which Convolutional Neural Network (CNN) can be best used to...

Mining Attack Strategy: Using Process Mining to extract attacker strategy from IDS alerts

Ever since the invention of the Internet, more and more computers are connected throughout the world. Though this has brought numerous new inventions used every day, like social media, e-commerce, and video conferencing, it also opens up new opportunities for cyber criminals. As the intrusion detection systems used to identify malicious behavior...

MalPaCa Feature Combination: Which packet header features and combination thereof are the most generalizable, private and easy to extract to cluster malicious behavior?

MalPaCa is an unsupervised clustering tool, which the main purpose is to cluster unidirectional network connections based on network behavior. The clustering is only based on non-intrusive (private) packet features such as transport and network header fields, and thus it has a strong potential use-case. This paper focuses on feature extraction...