The role of information systems in human resource management

Persistency of the organization, competitive advantage and realization of extra profit, in contemporary environment, are directly connected with balance of the resources available to the firm. One of the key issues of successful business is human resource management and that process is under great influence of modern information technology. Human Resources Information Systems (HRIS) are systems used to collect, record, store, analyze and retrieve data concerning an organization’s human resources, but it is not merely reduction of administrative procedures. The importance of HRIS system is multifaceted, ranging from operational assistance in collecting, storing and preparing data for reports, simplifying and accelerating the processes and controlling the available data, reducing labour costs for HR departments, and providing timely and diverse information to the management of the company, based on which it is possible to make quality strategic decisions related to human capital. The aim of this paper is to highlight the importance of HRIS and to give a comprehensive insight of the subject. Special focus in the paper will be on companies in Serbia, which have started to apply this concept, but in most situations not widely, but just partially. They must be aware that positive results can be expected only if this subject is approached in the right wayHuman resource management, information systems, HRIS, ERP

BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images

In cryo-electron microscopy (EM), molecular structures are determined from large numbers of projection images of individual particles. To harness the full power of this single-molecule information, we use the Bayesian inference of EM (BioEM) formalism. By ranking structural models using posterior probabilities calculated for individual images, BioEM in principle addresses the challenge of working with highly dynamic or heterogeneous systems not easily handled in traditional EM reconstruction. However, the calculation of these posteriors for large numbers of particles and models is computationally demanding. Here we present highly parallelized, GPU-accelerated computer software that performs this task efficiently. Our flexible formulation employs CUDA, OpenMP, and MPI parallelization combined with both CPU and GPU computing. The resulting BioEM software scales nearly ideally both on pure CPU and on CPU+GPU architectures, thus enabling Bayesian analysis of tens of thousands of images in a reasonable time. The general mathematical framework and robust algorithms are not limited to cryo-electron microscopy but can be generalized for electron tomography and other imaging experiments

Implementation of a perception system for autonomous vehicles using a detection-segmentation network in SoC FPGA

Perception and control systems for autonomous vehicles are an active area of scientific and industrial research. These solutions should be characterised by high efficiency in recognising obstacles and other environmental elements in different road conditions, real-time capability, and energy efficiency. Achieving such functionality requires an appropriate algorithm and a suitable computing platform. In this paper, we have used the MultiTaskV3 detection-segmentation network as the basis for a perception system that can perform both functionalities within a single architecture. It was appropriately trained, quantised, and implemented on the AMD Xilinx Kria KV260 Vision AI embedded platform. By using this device, it was possible to parallelise and accelerate the computations. Furthermore, the whole system consumes relatively little power compared to a CPU-based implementation (an average of 5 watts, compared to the minimum of 55 watts for weaker CPUs, and the small size (119mm x 140mm x 36mm) of the platform allows it to be used in devices where the amount of space available is limited. It also achieves an accuracy higher than 97% of the mAP (mean average precision) for object detection and above 90% of the mIoU (mean intersection over union) for image segmentation. The article also details the design of the Mecanum wheel vehicle, which was used to test the proposed solution in a mock-up city.Comment: The paper was accepted for the 19th International Symposium on Applied Reconfigurable Computing - ARC 2023, Cottbus - German