Dynamic Tiling: A Model-Agnostic, Adaptive, Scalable, and Inference-Data-Centric Approach for Efficient and Accurate Small Object Detection

We introduce Dynamic Tiling, a model-agnostic, adaptive, and scalable approach for small object detection, anchored in our inference-data-centric philosophy. Dynamic Tiling starts with non-overlapping tiles for initial detections and utilizes dynamic overlapping rates along with a tile minimizer. This dual approach effectively resolves fragmented objects, improves detection accuracy, and minimizes computational overhead by reducing the number of forward passes through the object detection model. Adaptable to a variety of operational environments, our method negates the need for laborious recalibration. Additionally, our large-small filtering mechanism boosts the detection quality across a range of object sizes. Overall, Dynamic Tiling outperforms existing model-agnostic uniform cropping methods, setting new benchmarks for efficiency and accuracy

The Working Posture Controller: Automated Adaptation of the Work Piece Pose to Enable a Natural Working Posture

We present a novel approach to prevent awkward working posture by automatically assessing and optimising the work place for a given task. Our system is called the Working Posture Controller (WPC) and enables to accomplish tasks in a natural posture by adapting the pose of work piece to be processed. Unlike other approaches to prevent posture-related Musculo-skeletal Disorders (MSDs), our system is able to propose an immediate adjustment in the process neither requiring tedious manual planning nor expert knowledge. Additionally, the proposed solution is personalised to the anthropometry of the user. First experiments on a simulated height-adjustable platform reveal promising results

Adapting Ergonomic Assessments to Social Life Cycle Assessment

In Social Life Cycle Assessment (SLCA), the health and safety aspect of workers is usually evaluated by considering the numbers of injuries and accidents; however, the work related musculoskeletal disorders (MSDs), which dominate occupational diseases, are often neglected in SLCA since the effects do not occur immediately. Thus, the MSDs lead to increased working absences and compensation costs, and also reduced productivity of workers. To address the gap, applying ergonomic assessment is proposed since it identifies and quantifies the health risks at work based on a set of pre-defined criteria e.g. force, posture, repetition and duration, and provides the numeric results analyzing the physical load and their sources. In the study, the application of ergonomic assessment and its indicators in SLCA is displayed to screen risks and to further improve working place design

Secrecy performance enhancement for underlay cognitive radio networks employing cooperative multi-hop transmission with and without presence of hardware impairments

In this paper, we consider a cooperative multi-hop secured transmission protocol to underlay cognitive radio networks. In the proposed protocol, a secondary source attempts to transmit its data to a secondary destination with the assistance of multiple secondary relays. In addition, there exists a secondary eavesdropper who tries to overhear the source data. Under a maximum interference level required by a primary user, the secondary source and relay nodes must adjust their transmit power. We first formulate effective signal-to-interference-plus-noise ratio (SINR) as well as secrecy capacity under the constraints of the maximum transmit power, the interference threshold and the hardware impairment level. Furthermore, when the hardware impairment level is relaxed, we derive exact and asymptotic expressions of end-to-end secrecy outage probability over Rayleigh fading channels by using the recursive method. The derived expressions were verified by simulations, in which the proposed scheme outperformed the conventional multi-hop direct transmission protocol.Web of Science212art. no. 21

Architecture Parallel for the Renewable Energy System

This chapter present one possible evolution is the parallel topology on the high-voltage bus for the renewable energy system. The system is not connected to a chain of photovoltaic (PV) modules and the different sources renewable. This evolution retains all the advantages of this system, while increasing the level of discretization of the Maximum Power Point Tracker (MPPT). So it is no longer a chain of PV modules that works at its MPPT but each PV module. In addition, this greater discretization allows a finer control and monitoring of operation and a faster detection of defects. The main interest of parallel step-up voltage systems, in this case, lies in the fact that the use of relatively high DC voltages is possible in these architectures distributed

XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection

With the advancement of deep learning (DL) in various fields, there are many attempts to reveal software vulnerabilities by data-driven approach. Nonetheless, such existing works lack the effective representation that can retain the non-sequential semantic characteristics and contextual relationship of source code attributes. Hence, in this work, we propose XGV-BERT, a framework that combines the pre-trained CodeBERT model and Graph Neural Network (GCN) to detect software vulnerabilities. By jointly training the CodeBERT and GCN modules within XGV-BERT, the proposed model leverages the advantages of large-scale pre-training, harnessing vast raw data, and transfer learning by learning representations for training data through graph convolution. The research results demonstrate that the XGV-BERT method significantly improves vulnerability detection accuracy compared to two existing methods such as VulDeePecker and SySeVR. For the VulDeePecker dataset, XGV-BERT achieves an impressive F1-score of 97.5%, significantly outperforming VulDeePecker, which achieved an F1-score of 78.3%. Again, with the SySeVR dataset, XGV-BERT achieves an F1-score of 95.5%, surpassing the results of SySeVR with an F1-score of 83.5%

VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models

The VNHSGE (VietNamese High School Graduation Examination) dataset, developed exclusively for evaluating large language models (LLMs), is introduced in this article. The dataset, which covers nine subjects, was generated from the Vietnamese National High School Graduation Examination and comparable tests. 300 literary essays have been included, and there are over 19,000 multiple-choice questions on a range of topics. The dataset assesses LLMs in multitasking situations such as question answering, text generation, reading comprehension, visual question answering, and more by including both textual data and accompanying images. Using ChatGPT and BingChat, we evaluated LLMs on the VNHSGE dataset and contrasted their performance with that of Vietnamese students to see how well they performed. The results show that ChatGPT and BingChat both perform at a human level in a number of areas, including literature, English, history, geography, and civics education. They still have space to grow, though, especially in the areas of mathematics, physics, chemistry, and biology. The VNHSGE dataset seeks to provide an adequate benchmark for assessing the abilities of LLMs with its wide-ranging coverage and variety of activities. We intend to promote future developments in the creation of LLMs by making this dataset available to the scientific community, especially in resolving LLMs' limits in disciplines involving mathematics and the natural sciences.Comment: 74 pages, 44 figure