82 research outputs found

    The investigation of the relationship between the organization strategies, human resource management policies, attitudes and behaviors of employees and the organizational performance of Ansar Bank branches in Tehran city

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    The purpose of the present research was to investigate the relationship between the organization strategies, human resource management policies, attitudes and behaviors of employees and organizational performance of Ansar Bank branches in Tehran city. The research population is the staffs of Ansar bank branches in Tehran city, and 278 persons were selected through census method. Data collection tool was the researcher made questionnaire which its formal and substantive validity was confirmed by three experts in this field and its reliability was confirmed by Cronbach alpha. The results showed that there is a direct and significant relationship (p<0.05) between the organization strategies, human resource management policies, the staffs’ behavior and organizational performance of Ansar bank branches in Tehran city ; there is a direct and significant relationship (p<0.05) between the organization strategies, human resource, human resource policies of Ansar bank branches in Tehran city; there is a direct and significant relationship (p <0.05) between the attitudes and behaviors of employees of Ansar Bank branches in Tehran city. Keywords: Organization strategies, human resource management policies, staffs’ attitudes, staffs’ behavior, organizational performanc

    Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation

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    Dense depth estimation is essential to scene-understanding for autonomous driving. However, recent self-supervised approaches on monocular videos suffer from scale-inconsistency across long sequences. Utilizing data from the ubiquitously copresent global positioning systems (GPS), we tackle this challenge by proposing a dynamically-weighted GPS-to-Scale (g2s) loss to complement the appearance-based losses. We emphasize that the GPS is needed only during the multimodal training, and not at inference. The relative distance between frames captured through the GPS provides a scale signal that is independent of the camera setup and scene distribution, resulting in richer learned feature representations. Through extensive evaluation on multiple datasets, we demonstrate scale-consistent and -aware depth estimation during inference, improving the performance even when training with low-frequency GPS data.Comment: Accepted at 2021 IEEE International Conference on Robotics and Automation (ICRA

    The Relationship between Knowledge Transfer and Competitiveness in “SMES” with Emphasis on Absorptive Capacity and Combinative Capabilities

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    In order to improve SMES’ competitiveness, introduction of Knowledge into all aspects of production process and management levels is essential. The question is how the knowledge can be transfer into firms? The purpose of this study is to examine the role of knowledge transfer in Firm’s competitiveness. Firms’ need to manage resources flow effectively to be able to survive and to grow in competitive business environment. How can they do this? Over the last decade, the knowledge- based view has rapidly seized a prominent role in strategy research. The knowledge – based view explains that tacit knowledge is the critical component of the value that a firm adds to input , and that a firm’s ability to transfer this tacit knowledge is the essential source of sustained competitive advantage. Firms which have a good absorptive capacity and combinative capabilities are able to compete effectively. Absorptive capacity and combinative capability are main aspect of knowledge - transfer which has captured the attention of numerous studies in recent years. Large firms have possibilities to invest a large amount of money into R&D and to monopolize the knowledge which they have explored and then to exploit it, but the questions are: What about SMES? Are they able to explore and to exploit new knowledge? What are the advantages of K-T in SMES’ competitiveness? With consideration of SMES’ expansion in developed and developing countries, growth and survival of them depend on K-T in these firms and its relationship with firms’ competitiveness. When firms interact with external constituents, be they suppliers or customers, they seek to acquire and/or maintain access to knowledge that otherwise would not efficiently available. Based on the literature review a theoretical model of Small and medium enterprises (SME’S) competitiveness relating to that knowledge transfer is a function of absorptive capacity and combinative capability that characterize the competitiveness. Small and medium enterprises (SMEs) are assumed to play a key role in social and economic development. The theoretical model that was developed in this study predicted that knowledge transfer is a function of absorptive capacity and combinative capability that characterize the SMEs’ competitiveness. Absorptive capacity refers to the capability to understand and use new knowledge. Results from this study indicate that two dimensions of absorptive capacity, available complementary knowledge and prior related experience, are both important antecedents of knowledge transfer. Combinative capability refers to a firm’s capacity to combine and recombine existing knowledge. The theoretical model predicted that this capacity is a function of the opportunity, motivation, and ability to share knowledge. Key words: Competitiveness; Firm; Tacit; Strategy; Absorptive; Combinative; Knowledge; SMES; Capability; Capacity; Motivatio

    Adversarial Attacks on Monocular Pose Estimation

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    Advances in deep learning have resulted in steady progress in computer vision with improved accuracy on tasks such as object detection and semantic segmentation. Nevertheless, deep neural networks are vulnerable to adversarial attacks, thus presenting a challenge in reliable deployment. Two of the prominent tasks in 3D scene-understanding for robotics and advanced drive assistance systems are monocular depth and pose estimation, often learned together in an unsupervised manner. While studies evaluating the impact of adversarial attacks on monocular depth estimation exist, a systematic demonstration and analysis of adversarial perturbations against pose estimation are lacking. We show how additive imperceptible perturbations can not only change predictions to increase the trajectory drift but also catastrophically alter its geometry. We also study the relation between adversarial perturbations targeting monocular depth and pose estimation networks, as well as the transferability of perturbations to other networks with different architectures and losses. Our experiments show how the generated perturbations lead to notable errors in relative rotation and translation predictions and elucidate vulnerabilities of the networks.Comment: Accepted at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022

    Monocular Vision based Crowdsourced 3D Traffic Sign Positioning with Unknown Camera Intrinsics and Distortion Coefficients

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    Autonomous vehicles and driver assistance systems utilize maps of 3D semantic landmarks for improved decision making. However, scaling the mapping process as well as regularly updating such maps come with a huge cost. Crowdsourced mapping of these landmarks such as traffic sign positions provides an appealing alternative. The state-of-the-art approaches to crowdsourced mapping use ground truth camera parameters, which may not always be known or may change over time. In this work, we demonstrate an approach to computing 3D traffic sign positions without knowing the camera focal lengths, principal point, and distortion coefficients a priori. We validate our proposed approach on a public dataset of traffic signs in KITTI. Using only a monocular color camera and GPS, we achieve an average single journey relative and absolute positioning accuracy of 0.26 m and 1.38 m, respectively.Comment: Accepted at 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC

    Crowdsourced 3D Mapping: A Combined Multi-View Geometry and Self-Supervised Learning Approach

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    The ability to efficiently utilize crowdsourced visual data carries immense potential for the domains of large scale dynamic mapping and autonomous driving. However, state-of-the-art methods for crowdsourced 3D mapping assume prior knowledge of camera intrinsics. In this work, we propose a framework that estimates the 3D positions of semantically meaningful landmarks such as traffic signs without assuming known camera intrinsics, using only monocular color camera and GPS. We utilize multi-view geometry as well as deep learning based self-calibration, depth, and ego-motion estimation for traffic sign positioning, and show that combining their strengths is important for increasing the map coverage. To facilitate research on this task, we construct and make available a KITTI based 3D traffic sign ground truth positioning dataset. Using our proposed framework, we achieve an average single-journey relative and absolute positioning accuracy of 39cm and 1.26m respectively, on this dataset.Comment: Accepted at 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
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