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

What Shapes Undergraduate Students’ Satisfaction in Unstable Learning Contexts?

This paper investigates what determinants, and to what extent, they influence students’ satisfaction in unstable learning contexts. Using a national-scaled sample of Vietnamese HEIs with a sound theoretical background, we find that regardless of instabilities from external shocks, the key factors that shape students’ satisfaction are fixed by traditional norms (self-efficacy, infrastructure, lecturer) rather than occasional factors occurring from each event. We find in particular that self-efficacy is the most influential factor for students’ satisfaction and friendship is the most prominent element that enhances students’ self- efficacy. Overall, this paper enriched the literature on student satisfaction, especially during unstable contexts. Thus, it has important implications for educators and HEIs stakeholders in management planning in the time to come

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

Some algorithms to solve a bi-objectives problem for team selection

In real life, many problems are instances of combinatorial optimization. Cross-functional team selection is one of the typical issues. The decision-maker has to select solutions among (kh) solutions in the decision space, where k is the number of all candidates, and h is the number of members in the selected team. This paper is our continuing work since 2018; here, we introduce the completed version of the Min Distance to the Boundary model (MDSB) that allows access to both the "deep" and "wide" aspects of the selected team. The compromise programming approach enables decision-makers to ignore the parameters in the decision-making process. Instead, they point to the one scenario they expect. The aim of model construction focuses on finding the solution that matched the most to the expectation. We develop two algorithms: one is the genetic algorithm and another based on the philosophy of DC programming (DC) and its algorithm (DCA) to find the optimal solution. We also compared the introduced algorithms with the MIQP-CPLEX search algorithm to show their effectiveness

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%