Abstract
Deep neural networks (DNN) are used to analyze images, videos, signals and texts require a lot of memory and intensive computing power. For example, the very successful GPT4 model contains more than a few trillion parameters. Although such models are of great impact, but they have been used very little in real-world applications, including industrial Internet of Things, self-driving cars, algorithmic health monitoring for use in limited mobile or edge devices. The requirement to run large models on resource-constrained peripherals has led to significant research interest in compressing DNN models. Signal processing researchers have traditionally advocated data (image/video/audio) compression, and by the way, many of these techniques are used for DNN compression. For example, source coding is a basic technique that has been widely used to compress various DNN models. In this paper, we present our views on the use of signal processing methods for DNN model compression.

Abstract
This paper introduces Labadain-30k+, a monolingual dataset comprising 33.6k documents in Tetun, a low-resource language spoken in Timor-Leste. The dataset was acquired through web crawling and augmented with Wikipedia documents released by Wikimedia. Both sets of documents underwent thorough manual audits at the document level by native Tetun speakers, resulting in the construction of a Tetun text dataset well-suited for a variety of natural language processing and information retrieval tasks. This dataset was employed to conduct a comprehensive content analysis aimed at providing a nuanced understanding of document composition and the evolution of Tetun documents on the web. The analysis revealed that news articles constitute the predominant documents within the dataset, accounting for 89.87% of the total, followed by Wikipedia documents at 4.34%, and legal and governmental documents at 3.65%, among others. Notably, there was a substantial increase in the number of documents in 2020, indicating 11.75 percentage points rise in document quantity, compared to an average of 4.76 percentage points per year from 2001 to 2023. Moreover, the year 2017, marked by the increased popularity of online news in Tetun, served as a threshold for analyzing the evolution of document writing on the web pre- and post-2017, specifically regarding vocabulary usage. Surprisingly, this analysis showed a significant increase of 6.12 percentage points in the Tetun written adhering to the Tetun official standard. Additionally, the persistence of Portuguese loanwords in that trajectory remained evident, reflecting an increase of 5.09 percentage points. © 2024 ELRA Language Resource Association.

Abstract
The high level of precision and consistency required for pallet detection in industrial environments and logistics tasks is a critical challenge that has been the subject of extensive research. This paper proposes a system for detecting pallets and its pockets using the You Only Look Once (YOLO) v8 Open Neural Network Exchange (ONNX) model, followed by the segmentation of the pallet surface. On the basis of the system a pipeline built on the ROS Action Server whose structure promotes modularity and ease of implementation of heuristics. Additionally, is presented a comparison between the YOLOv5 and YOLOv8 models in the detection task, trained with a customised dataset from a factory environment. The results demonstrate that the pipeline can consistently perform pallet and pocket detection, even when tested in the laboratory and with successive 3D pallet segmentation. When comparing the models, YOLOv8 achieved higher average metric values, with YOLOv8m providing better detection performance in the laboratory setting.

Abstract
[No abstract available]

Abstract
Sustainable development policies trigger a shift in the global development paradigm by aligning economic, social, and ecological goals. Concurrently, the rapid surge in digitalization is transforming business processes and communications across all sectors and levels. As a result, the integration of e-business and e-governance becomes a critical component in achieving Sustainable Development Goals (SDGs). In this context, the aim of this article is to analyze the effects of digitalization, specifically e-governance and e-business, on the attainment of SDGs in European Union (EU) countries. The method used is a panel of corrected standard errors and feasible generalized least squares models to identify the impact and significance of e-governance and e-business on SDG achievement. The e-governance indicators considered by this study were found to significantly impact SDG achievement. Moreover, e-business indicators were also found to positively impact the attainment of SDGs, with some exceptions. The findings suggest that EU countries should continue to intensify digitalization across all sectors as it enhances the transparency accountability of all business processes and communications and increases trust in government services, which are the core drivers of achieving SDGs.

Abstract
Reliable operation of production lines without unscheduled disruptions is of paramount importance for ensuring the proper operation of automated working cells involving robotic systems. This article addresses the issue of preventing disruptions to an automotive production line that can arise from incorrect placement of aluminum car parts by a human operator in a feeding container with 4 indexing pins for each part. The detection of the misplaced parts is critical for avoiding collisions between the containers and a high pressure washing machine and also to avoid collisions between the parts and a robotic arm that is feeding parts to a air leakage inspection machine. The proposed inspection system relies on a 3D sensor for scanning the parts inside a container and then estimates the 6 DoF pose of the container followed by an analysis of the overlap percentage between each part reference point cloud and the 3D sensor data. When the overlap percentage is below a given threshold, the part is considered as misplaced and the operator is alerted to fix the part placement in the container. The deployment of the inspection system on an automotive production line for 22 weeks has shown promising results by avoiding 18 hours of disruptions, since it detected 407 containers having misplaced parts in 4524 inspections, from which 12 were false negatives, while no false positives were reported, which allowed the elimination of disruptions to the production line at the cost of manual reinspection of 0.27% of false negative containers by the operator.