Open Source Alternatives for Business Intelligence: Critical Success Factors for Adoption

The purpose of this research is to identify critical factors that affect the adoption of Open Source Business Intelligence (OPBI) tools and to compare the differences between OPBI and Proprietary Business Intelligence (PBI) tools. Based on the Technology Acceptance Model, an organizational adoption model was designed to analyze four cases of organizations that have adopted Business Intelligence (BI) tools. The cases were documented using a tested protocol and a set of interviews. The analysis of the cases shows that organizations with fewer resources and simpler IT selection processes tend to adopt OPBI. The most cited reason for using OPBI software is cost savings. The results also reveal that for most users OPBI does not require sophisticated BI specialists and offers as many useful features as PBI tools. These findings are important to BI vendors, users, developers, and organizations interested in adopting BI technologies

Multi-level Fusion Network for 3D Object Detection from Camera and LiDAR Data

In 3D Object Detection (3DOD), the fusion of point cloud data and image data is of vital importance, because this can maximize the use of the high resolution information of RGB image and the rich 3D information of point cloud data. This thesis proposes a two-stage 3D object detection system, which takes input from the camera and LiDAR data, and outputs the localization and category of the 3D bounding box. The system uses a novel feature extractor to learn the full-resolution scale features while keeping the computation speed coupled with a multimodal fusion Region Proposal Network (RPN) architecture. The second stage detection network regresses the offsets between the 3D proposals generated by the RPN and the ground truth boxes using a 6-dimension encoding technique. Experiments conducted on the Kitti dataset showed the performance boost of the proposed algorithm over the state-of-the-arts on the 3D Object Detection tasks.Master of Science in EngineeringElectrical Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/166310/1/Zixuan Zhao Final Thesis.pdfDescription of Zixuan Zhao Final Thesis.pdf : Thesi

Corporate governance, board-shareholder relationship and firm performance: Evidence from financial institutions in China

The extant literature exhibits extensive but inconclusive empirical results related to the effectiveness of corporate governance and the influence of board-shareholder relationship on firm-level performance, partially owing to the neglect of industry differences and firm size effect. To offer incremental insight, this paper filled the gap by forming a fixed effect model, with a concentrated data set of 107 publicly listed A-share financial institutions in China for 10 years from 2009 to 2018, to further examine in detail whether ownership concentration, shareholder affiliation and board independence would affect the performance of firms in various sizes. This study finds that, ownership concentration and shareholder affiliation exert a negative impact on performance, which bears out the Stewardship Theory that board is trustworthy and should be given more autonomy. However, the influence of shareholder affiliation on firm performance becomes surprisingly positive as firm size goes up, which suggests that closely-connected large shareholders are more effective in regulating board behaviors in large corporations. Besides, this study also finds that board independence can stimulate firm-level performance and development, which is generally agreed by the agency theorists. Apart from providing additional evidence to examine governance theories, this paper provides practical suggestions that decision makers of Chinese financial institutions should make efforts in choosing a proper managerial structure concerning the firm size, and policymakers should deepen the financial reform regarding ownership structure and board features to facilitate the development of finance industry in China

Differentiable Frank-Wolfe Optimization Layer

Differentiable optimization has received a significant amount of attention due to its foundational role in the domain of machine learning based on neural networks. The existing methods leverages the optimality conditions and implicit function theorem to obtain the Jacobian matrix of the output, which increases the computational cost and limits the application of differentiable optimization. In addition, some non-differentiable constraints lead to more challenges when using prior differentiable optimization layers. This paper proposes a differentiable layer, named Differentiable Frank-Wolfe Layer (DFWLayer), by rolling out the Frank-Wolfe method, a well-known optimization algorithm which can solve constrained optimization problems without projections and Hessian matrix computations, thus leading to a efficient way of dealing with large-scale problems. Theoretically, we establish a bound on the suboptimality gap of the DFWLayer in the context of l1-norm constraints. Experimental assessments demonstrate that the DFWLayer not only attains competitive accuracy in solutions and gradients but also consistently adheres to constraints. Moreover, it surpasses the baselines in both forward and backward computational speeds

Trajectory Servoing: Image-Based Trajectory Tracking Using SLAM

This paper describes an image based visual servoing (IBVS) system for a nonholonomic robot to achieve good trajectory following without real-time robot pose information and without a known visual map of the environment. We call it trajectory servoing. The critical component is a feature-based, indirect SLAM method to provide a pool of available features with estimated depth, so that they may be propagated forward in time to generate image feature trajectories for visual servoing. Short and long distance experiments show the benefits of trajectory servoing for navigating unknown areas without absolute positioning. Trajectory servoing is shown to be more accurate than pose-based feedback when both rely on the same underlying SLAM system

The convergence investigation of meshless finite block method and finite element method

The finite element method is one of the most widely used numerical method in engineering analysis, however, the bad convergence and the complexity of meshing reduce the reliability of the simulated results. Therefore, in this work, a meshless finite block method was applied on heat transfer analysis and elastic deformation analysis. It combines the ideas of finite element and boundary element. A better convergence of meshless finite block method than finite element method was proved

Distributed Deep Learning Optimization of Heat Equation Inverse Problem Solvers

The inversion problem of partial differential equation plays a crucial role in cyber-physical systems applications. This paper presents a novel deep learning optimization approach to constructing a solver of heat equation inversion. To improve the computational efficiency in large-scale industrial applications, data and model parallelisms are incorporated on a platform of multiple GPUs. The advanced Ring-AllReduce architecture is harnessed to achieve an acceleration ratio of 3.46. Then a new multi-GPUs distributed optimization method GradReduce is proposed based on Ring-AllReduce architecture. This method optimizes the original data communication mechanism based on mechanical time and frequency by introducing the gradient transmission scheme solved by linear programming. The experimental results show that the proposed method can achieve an acceleration ratio of 3.84 on a heterogeneous system platform with two CPUs and four GPUs

SDT: A Low-cost and Topology-reconfigurable Testbed for Network Research

Network experiments are essential to network-related scientific research (e.g., congestion control, QoS, network topology design, and traffic engineering). However, (re)configuring various topologies on a real testbed is expensive, time-consuming, and error-prone. In this paper, we propose \emph{Software Defined Topology Testbed (SDT)}, a method for constructing a user-defined network topology using a few commodity switches. SDT is low-cost, deployment-friendly, and reconfigurable, which can run multiple sets of experiments under different topologies by simply using different topology configuration files at the controller we designed. We implement a prototype of SDT and conduct numerous experiments. Evaluations show that SDT only introduces at most 2\% extra overhead than full testbeds on multi-hop latency and is far more efficient than software simulators (reducing the evaluation time by up to 2899x). SDT is more cost-effective and scalable than existing Topology Projection (TP) solutions. Further experiments show that SDT can support various network research experiments at a low cost on topics including but not limited to topology design, congestion control, and traffic engineering.Comment: This paper will be published in IEEE CLUSTER 2023. Preview version onl