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
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
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
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
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
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
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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