Matching Items (190)
Description
The Internet-of-Things (IoT) paradigm is reshaping the ways to interact with the physical space. Many emerging IoT applications need to acquire, process, gain insights from, and act upon the massive amount of data continuously produced by ubiquitous IoT sensors. It is nevertheless technically challenging and economically prohibitive for each IoT application to deploy and maintain a dedicated large-scale sensor network over distributed wide geographic areas. Built upon the Sensing-as-a-Service paradigm, cloud-sensing service providers are emerging to provide heterogeneous sensing data to various IoT applications with a shared sensing substrate. Cyber threats are among the biggest obstacles against the faster development of cloud-sensing services. This dissertation presents novel solutions to achieve trustworthy IoT sensing-as-a-service. Chapter 1 introduces the cloud-sensing system architecture and the outline of this dissertation. Chapter 2 presents MagAuth, a secure and usable two-factor authentication scheme that explores commercial off-the-shelf wrist wearables with magnetic strap bands to enhance the security and usability of password-based authentication for touchscreen IoT devices. Chapter 3 presents SmartMagnet, a novel scheme that combines smartphones and cheap magnets to achieve proximity-based access control for IoT devices. Chapter 4 proposes SpecKriging, a new spatial-interpolation technique based on graphic neural networks for secure cooperative spectrum sensing which is an important application of cloud-sensing systems. Chapter 5 proposes a trustworthy multi-transmitter localization scheme based on SpecKriging. Chapter 6 discusses the future work.
ContributorsZhang, Yan (Author) / Zhang, Yanchao YZ (Thesis advisor) / Fan, Deliang (Committee member) / Xue, Guoliang (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2022
Description
This dissertation investigates the problem of efficiently and effectively prioritizing a vulnerability risk in a computer networking system. Vulnerability prioritization is one of the most challenging issues in vulnerability management, which affects allocating preventive and defensive resources in a computer networking system. Due to the large number of identified vulnerabilities, it is very challenging to remediate them all in a timely fashion. Thus, an efficient and effective vulnerability prioritization framework is required. To deal with this challenge, this dissertation proposes a novel risk-based vulnerability prioritization framework that integrates the recent artificial intelligence techniques (i.e., neuro-symbolic computing and logic reasoning). The proposed work enhances the vulnerability management process by prioritizing vulnerabilities with high risk by refining the initial risk assessment with the network constraints. This dissertation is organized as follows. The first part of this dissertation presents the overview of the proposed risk-based vulnerability prioritization framework, which contains two stages. The second part of the dissertation investigates vulnerability risk features in a computer networking system. The third part proposes the first stage of this framework, a vulnerability risk assessment model. The proposed assessment model captures the pattern of vulnerability risk features to provide a more comprehensive risk assessment for a vulnerability. The fourth part proposes the second stage of this framework, a vulnerability prioritization reasoning engine. This reasoning engine derives network constraints from interactions between vulnerabilities and network environment elements based on network and system setups. This proposed framework assesses a vulnerability in a computer networking system based on its actual security impact by refining the initial risk assessment with the network constraints.
ContributorsZeng, Zhen (Author) / Xue, Guoliang (Thesis advisor) / Liu, Huan (Committee member) / Zhao, Ming (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2022
Description
High-temperature mechanical behaviors of metal alloys and underlying microstructural variations responsible for such behaviors are essential areas of interest for many industries, particularly for applications such as jet engines. Anisotropic grain structures, change of preferred grain orientation, and other transformations of grains occur both during metal powder bed fusion additive manufacturing processes, due to variation of thermal gradient and cooling rates, and afterward during different thermomechanical loads, which parts experience in their specific applications, could also impact its mechanical properties both at room and high temperatures. In this study, an in-depth analysis of how different microstructural features, such as crystallographic texture, grain size, grain boundary misorientation angles, and inherent defects, as byproducts of electron beam powder bed fusion (EB-PBF) AM process, impact its anisotropic mechanical behaviors and softening behaviors due to interacting mechanisms. Mechanical testing is conducted for EB-PBF Ti6Al4V parts made at different build orientations up to 600°C temperature. Microstructural analysis using electron backscattered diffraction (EBSD) is conducted on samples before and after mechanical testing to understand the interacting impact that temperature and mechanical load have on the activation of certain mechanisms. The vertical samples showed larger grain sizes, with an average of 6.6 µm, a lower average misorientation angle, and subsequently lower strength values than the other two horizontal samples. Among the three strong preferred grain orientations of the α phases, <1 1 2 ̅ 1> and <1 1 2 ̅ 0> were dominant in horizontally built samples, whereas the <0 0 0 1> was dominant in vertically built samples. Thus, strong microstructural variation, as observed among different EB-PBF Ti6Al4V samples, mainly resulted in anisotropic behaviors. Furthermore, alpha grain showed a significant increase in average grain size for all samples with the increasing test temperature, especially from 400°C to 600°C, indicating grain growth and coarsening as potential softening mechanisms along with temperature-induced possible dislocation motion. The severity of internal and external defects on fatigue strength has been evaluated non-destructively using quantitative methods, i.e., Murakami’s square root of area parameter model and Basquin’s model, and the external surface defects were rendered to be more critical as potential crack initiation sites.
ContributorsMian, Md Jamal (Author) / Ladani, Leila (Thesis advisor) / Razmi, Jafar (Committee member) / Shuaib, Abdelrahman (Committee member) / Mobasher, Barzin (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2022
Description
The emerging multimodal mobility as a service (MaaS) and connected and automated mobility (CAM) are expected to improve individual travel experience and entire transportation system performance in various aspects, such as convenience, safety, and reliability. There have been extensive efforts in the literature devoted to enhancing existing and developing new methodologies and tools to investigate the impacts and potentials of CAM systems. Due to the hierarchical nature of CAM systems and associated intrinsic correlated human factors and physical infrastructures from various resolutions, simply considering components across different levels into a single model may be practically infeasible and computationally prohibitive in operation and decision stages. One of the greatest challenges in existing studies is to construct a theoretically sound and computationally efficient architecture such that CAM system modeling can be performed in an inherently consistent cross-resolution manner. This research aims to contribute to the modeling of CAM systems on layered transportation networks, with a special focus on the following three aspects: (1) layered CAM system architecture with a tight network and modeling consistency, in which different levels of tasks can be efficiently performed at dedicated layers; (2) cross-resolution traffic state estimation in CAM systems using heterogeneous observations; and (3) integrated city logistics operation optimization in CAM for improving system performance.
ContributorsLu, Jiawei (Author) / Zhou, Xuesong (Thesis advisor) / Pendyala, Ram (Committee member) / Xue, Guoliang (Committee member) / Mittelmann, Hans (Committee member) / Arizona State University (Publisher)
Created2022
Description
Composite materials have gained interest in the aerospace, mechanical and civil engineering industries due to their desirable properties - high specific strength and modulus, and superior resistance to fatigue. Design engineers greatly benefit from a reliable predictive tool that can calculate the deformations, strains, and stresses of composites under uniaxial and multiaxial states of loading including damage and failure predictions. Obtaining this information from (laboratory) experimental testing is costly, time consuming, and sometimes, impractical. On the other hand, numerical modeling of composite materials provides a tool (virtual testing) that can be used as a supplemental and an alternate procedure to obtain data that either cannot be readily obtained via experiments or is not possible with the currently available experimental setup. In this study, a unidirectional composite (Toray T800-F3900) is modeled at the constituent level using repeated unit cells (RUC) so as to obtain homogenized response all the way from the unloaded state up until failure (defined as complete loss of load carrying capacity). The RUC-based model is first calibrated and validated against the principal material direction laboratory tests involving unidirectional loading states. Subsequently, the models are subjected to multi-directional states of loading to generate a point cloud failure data under in-plane and out-of-plane biaxial loading conditions. Failure surfaces thus generated are plotted and compared against analytical failure theories. Results indicate that the developed process and framework can be used to generate a reliable failure prediction procedure that can possibly be used for a variety of composite systems.
ContributorsKatusele, Daniel Mutahwa (Author) / Rajan, Subramaniam (Thesis advisor) / Mobasher, Barzin (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2021
Description
Data mining, also known as big data analysis, has been identified as a critical and challenging process for a variety of applications in real-world problems. Numerous datasets are collected and generated every day to store the information. The rise in the number of data volumes and data modality has resulted in the increased demand for data mining methods and strategies of finding anomalies, patterns, and correlations within large data sets to predict outcomes. Effective machine learning methods are widely adapted to build the data mining pipeline for various purposes like business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The major challenges for effectively and efficiently mining big data include (1) data heterogeneity and (2) missing data. Heterogeneity is the natural characteristic of big data, as the data is typically collected from different sources with diverse formats. The missing value is the most common issue faced by the heterogeneous data analysis, which resulted from variety of factors including the data collecting processing, user initiatives, erroneous data entries, and so on. In response to these challenges, in this thesis, three main research directions with application scenarios have been investigated: (1) Mining and Formulating Heterogeneous Data, (2) missing value imputation strategy in various application scenarios in both offline and online manner, and (3) missing value imputation for multi-modality data. Multiple strategies with theoretical analysis are presented, and the evaluation of the effectiveness of the proposed algorithms compared with state-of-the-art methods is discussed.
Contributorsliu, Xu (Author) / He, Jingrui (Thesis advisor) / Xue, Guoliang (Thesis advisor) / Li, Baoxin (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2021
Description
The omnipresent data, growing number of network devices, and evolving attack techniques have been challenging organizations’ security defenses over the past decade. With humongous volumes of logs generated by those network devices, looking for patterns of malicious activities and identifying them in time is growing beyond the capabilities of their defense systems. Deep Learning, a subset of Machine Learning (ML) and Artificial Intelligence (AI), fills in this gapwith its ability to learn from huge amounts of data, and improve its performance as the data it learns from increases. In this dissertation, I bring forward security issues pertaining to two top threats that most organizations fear, Advanced Persistent Threat (APT), and Distributed Denial of Service (DDoS), along with deep learning models built towards addressing those
security issues. First, I present a deep learning model, APT Detection, capable of detecting anomalous activities in a system. Evaluation of this model demonstrates how it can contribute to early detection of an APT attack with an Area Under the Curve (AUC) of up to 91% on a Receiver Operating Characteristic (ROC) curve. Second, I present DAPT2020, a first of its kind dataset capturing an APT attack exploiting web and system vulnerabilities in an emulated organization’s production network. Evaluation of the dataset using well known machine learning models demonstrates the need for better deep learning models to detect APT attacks. I then present DAPT2021, a semi-synthetic dataset capturing an APT attackexploiting human vulnerabilities, alongside 2 less skilled attacks. By emulating the normal behavior of the employees in a set target organization, DAPT2021 has been created to enable researchers study the causations and correlations among the captured data, a much-needed information to detect an underlying threat early. Finally, I present a distributed defense
framework, SmartDefense, that can detect and mitigate over 90% of DDoS traffic at the source and over 97.5% of the remaining DDoS traffic at the Internet Service Provider’s (ISP’s) edge network. Evaluation of this work shows how by using attributes sent by customer edge network, SmartDefense can further help ISPs prevent up to 51.95% of the DDoS traffic
from going to the destination.
ContributorsMyneni, Sowmya (Author) / Xue, Guoliang (Thesis advisor) / Doupe, Adam (Committee member) / Li, Baoxin (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)
Created2022
Description城投债是地方政府投融资平台作为发行主体发行的债券，所融资金多被投入地方政府基础设施建设或者公益性项目，拥有地方政府信用的隐性担保。城投债在一定程度上缓解了地方政府在城市发展过程中资金的短缺问题，在我国城市化进程，促进当地经济发展，引导产业转型升级等方面做出了重大贡献。 随着城投债不断发展，代表城投债信用风险的主要考量点-城投债信用利差愈发备受关注。因为无论是城投债的承销机构，还是城投债的投资机构，包括涉及到城投债风险管控的政策制定部门，都会关注到城投债信用利差，那么影响城投债信用利差的影响因素有哪些呢，这些影响因素有哪些是对城投债信用利差有显著影响呢。 本文首先对城投债相关理论概念，包括政府投融资平台、城投债概念以及相关文献综述做了介绍；并指出了之前研究的一些不足之处等问题。同时对城投债的发展概况做了简要描述并进行了相关统计；其次针对影响城投债信用风险的相关因素进行了详细的分析，主要包括宏观经济因素分析、地方政府影响因素分析、发债主体影响因素分析和债项自身影响因素分析；通过分析每一种影响因素的具体情况，假设相关因素与信用利差的关系。然后再提取二手数据通过实证验证回归分析的方法分别验证假设是否成立，找出影响城投债信用风险的主要共同影响因素，同时得出影响最为强烈的几种因素。最后根据上述分析得出的相关结论， 提出防范与降低城投债信用风险的对策和建议。 该研究一方面引导市场正视城投债信用利差的各种因素，明确我们平时认为的影响因素和理论研究得出的影响因素是否一致；继而找到影响城投债信用利差的关键因素，供城 投债承销机构及投资机构做参考，同时提示城投债风险防范应重点关注的核心问题，为防范和降低城投债风险提供重要参考。
ContributorsLi, Juhui (Author) / Gu, Bin (Thesis advisor) / Liang, Bing (Thesis advisor) / Wang, Tan (Committee member) / Arizona State University (Publisher)
Created2019
Description疫情期间，人们与社区发生了高频链接。流调、核酸、求助，社区工作者无时不刻出现在人们的生活中。社区作为中国基层治理的基础单元，政府需要上而下到社区执行防控工作，百姓则自下而上需要社区提供服务和帮助、向上反映各类居民诉求。伴随着中国的城镇化进程，全中国有9万个城市社区、400万社区工作者，社区参与主体从居委会、物业到民非组织，不断地进化。在人们对未来生活需求不断提高的时侯，社区服务提供者协同社区各主体，实现数字社区可持续发展？本研究围绕“社区服务提供者如何在后疫情时代构建可持续的社区协同机制？”这一研究问题，首先，对数字社区可持续发展和数字社区协同机制相关研究进行梳理和回顾；第二，将以数字社区建设和协同治理为出发点，以七彩集团为研究样本，分析其实际运营的滨江缤纷未来社区和萧山瓜沥未来社区案例，归纳总结其协同治理的具体措施；第三，结合理论规范分析，提出“数字社区协同机制-协同绩效”的理论框架和作用边界；第四，运用问卷调查结合因子分析的方法，对这些具体措施能够实际提高企业绩效进行问卷发放和数据验证。
本研究得到以下三个主要结论：（1）数字社区中可持续性发展协同治理机制是社区运营主体利用数字化技术对社区内参与提供和使用服务的治理活动进行约束、激励、引导和管理的一系列制度安排，同时包括政策治理、社区文化和市场共建三种不同作用机制。（2）数字社区中，治理政策机制仅在社团主体中作用显著；社区文化除了在社区物业中作用有限，在其他所有主体中均发挥重要作用；市场共建则是在社团、物业和居民业主中发挥作用。（3）协同机制通过政策治理、社区文化、市场共建影响社区内不同主体的感知和行为，而数字技术作用一种新兴的支撑性技术能够对上述作用产生不同的增强作用，进而促进协同绩效提升。
本研究通过聚焦于社区运营六方主体的角色分工、各自诉求，进一步讨论如何应用最新数字经济和技术来找到可持续发展的协同机制，为后疫情时代中国社区的良性发展找到解决方案。
ContributorsXu, Xiaowei (Author) / Shao, Benjamin (Thesis advisor) / Zhang, Anmin (Thesis advisor) / Gu, Bin (Committee member) / Hong, Yili (Committee member) / Arizona State University (Publisher)
Created2023
Description并购重组一直是提升企业经营效率、增强市场活力的重要力量。我国政府也一直积极鼓励企业间的并购重组以推动资源的优化配置，使得我国并购重组市场一直较为活跃，交易数量和规模屡创新高。然而交易双方的信息不对称可能导致并购方支付过高的并购溢价，损害并购方价值。针对这一问题，证监会于 2008 年开始实施《上市公司重大资产重组管理办法》，要求当标的资产估值是基于未来预期收益时，如果标的资产未来三年的实际盈利低于承诺利润，交易对方需以现金或股份形式对并购方进行补偿。因此，业绩承诺的政策初衷是防止被并购方操纵资产价格、保护并购方和外部投资者的利益。但近年来，部分公司与标的方合谋制定高业绩承诺以换取高估值溢价，继而引发财务造假、减持套现、利益输送、股价崩盘等一系列问题，比如曾被称为“游戏第一股”的掌趣科技从 2013 年开始疯狂并购多家公司并签订相应业绩承诺，公司股价随之飙升，华谊兄弟等大股东和管理层趁机大规模减持，累计套现近60亿，导致中小股东利益严重受损，资本市场对业绩承诺的质疑也日益增加。因此并购重组业绩承诺实现的影响因素及其作用机制成为近年来学术界的研究热点。上市公司进行并购交易时，通常会根据自身财务状况、股权结构、融资成本等因素设计并购支付方式。根据中国证监会 2014 年发布的 《关于修改＜上市公司收购管理办法＞的决定》 的规定，上市公司收购可采用现金、证券、现金与证券相结合等合法方式支付收购价款。收购方无论采取何种支付方式，都是期望通过优化配置资源、改善经营绩效和增加股东财富。自股权分置改革以来，我国的资本市场正不断完善，并购支付方式也逐渐走向多元化。但是由于信息不对称等问题的存在，业绩承诺协议中的支付方式选择和补偿方式选择也可能会滋生并购双方进行利益输送的温床。因此，在该背景下，研究支付方式对业绩承诺的影响机制具有重要的理论和现实意义。
本文以2014-2018年我国A股主板上市公司为研究样本，以“提出问题-理论分析-实证分析-研究结论”为基本思路，运用委托代理理论、信息不对称理论、信号传递理论以及控制权等理论，分析企业并购支付方式的影响因素和支付方式对业绩承诺影响的作用机制，并提出本文的研究假设，通过描述性统计分析、二元逻辑回归和多元线性回归分析等研究方法对研究假设进行实证检验，得出本文的研究结论。
本文主要的研究工作和内容如下：
根据本文的研究主题，梳理了并购、支付方式、业绩承诺等方面已有研究文献，指出已有研究文献的贡献和不足，进而提出本文的研究问题。
在理论分析和作用机理方面，本文运用代理理论、信息不对称理论、融资优序理论、控制权理论等针对支付方式的影响因素以及支付方式对业绩承诺影响的机理进行分析，据此提出本文的研究假设。
在实证研究方面，运用描述性统计分析、二元逻辑回归和多元线性回归分析等方法检验了股权集中度、现金持有量和市场估值对支付方式选择的影响，利用中介效应检验验证了资本结构、股权制衡以及税负协同在支付方式与业绩承诺之间的作用路径，得出本文的实证结论，最后采用更换实证模型方法和主要研究变量的方法进行了相应的稳健性检验。
最后给出本文的主要研究结论，指出本文的研究局限和未来研究方向。
本文的主要研究结论如下： （1）股票支付方式更有利于实现业绩承诺。通过多元统计回归分析和中介效应检验，以及现金支付和股权支付下的业绩承诺兑现进行均值差异检验，均发现两种不同支付方式下的业绩承诺兑现效果是显著存在差异的。（2）在理论上解释了支付方式影响业绩承诺的机理。运用信息不对称下的信号理论、资本结构理论、公司控制权以及协同理论,阐述了支付方式影响业绩承诺的机理,业绩承诺的兑现是支付方式、资本结构、公司控制权结构等多种因素综合作用的结果。选择不同的支付方式来源于企业不同的融资方式，差异化的融资方式就会导致企业在并购完成后形成不同的资本结构和股权结构，从而给企业带来财务协同和管理协同效应，同时，由于我国税收制度的改革，对不同的支付方式均能够影响并购参与方的税收变化，能够产生税收协同效应，从而有利于实现企业的业绩承诺。
（3）在并购支付方式影响因素的研究中，运用多元回归统计回归的方法分析方法验证了现金持有量、股权集中度和市场估值是影响企业并购支付方式的重要因素。其中，股权集中度与支付方式的回归系数为负数，说明股权集中度越高，企业越倾向于选择现金支付；现金持有量与股份支付之间存在负相关的关系，即公司现金持有水平越高，企业越偏好选择现金支付；市场估值与股份支付的回归系数为正，说明企业并购支付方式具有择时效应，当上市公司股价较高时，上市公司会利用股票溢价来减少实际支付的金额。
（4）在支付方式对业绩承诺的兑现的研究中，运用逻辑回归和中介效应检验的方法，证实了支付方式能够对业绩承诺直接产生影响外，还发现资本结构、股权制衡和税负在其中发挥了中介的作用，其中相比现金支付，股权支付会引起股权结构和债务结构的变化，通过风险共担以及股权结构的优化，在一定程度上能够一直股权制衡带来的寻租成本，从而有利于业绩承诺的兑现。股权支付能够获得递延交纳资本利得税、应计折日和资产增加等税收协同，也助力了业绩承诺的实现。与此同时，发现并购企业自身规模、被并购企业的规模、并购的类型以及是否是关联并购也起到了一定的影响，这为企业如何实现业绩承诺提供了参考。
本文的创新之处体现在：
（1）研究视角的创新。以往关于并购支付方式的研究主要关注支付方式的选择对并购绩效的影响，鲜少考虑业绩承诺这一前提下二者之间的关系。本文综合考量当并购双方签订业绩承诺的情况下，企业的并购目标不同于简单追求控制权的转移，增加了对并购长期战略目标的关注，分析支付方式对业绩承诺的影响及其作用机制。本文基于业绩承诺视角研究支付方式对并购绩效的影响，进一步扩展了并购领域的研究视角。
（2）研究方法的创新。利用中介效应检验模型验证支付方式对业绩承诺的影响。支付方式作为并购战略的重要组成部分，对于能否顺利实施并购战略具有重要的意义，以往的研究大多选择结构方程进行因素检验，本文采用中介效应的三步法进行验证，发现支付方式既有直接效应，同时还验证了资本结构、股权制衡和税收协同的部分中介作用，本研究有助于丰富和完善支付方式对业绩承诺影响的研究内容。
（3）研究方向和内容的创新。本文采用本文改变现有文献通过设置虚拟变量的方式将股份支付和现金支付截然分开，把并购案例中股份支付对价与并购支付总对价之间的比率作为并购支付变量，并购支付变量设计成连续变量；同时，本文以并购完成后样本公司的资本结构、股权制衡和税负变化衡量并购带来的协同效应为中介变量，详细分析支付方式对业绩承诺影响的作用机制，完善并丰富了并购领域的研究。
ContributorsPan, Jie (Author) / Pei, Ker-Wei (Thesis advisor) / Chen, Xin (Thesis advisor) / Jiang, Zhan (Committee member) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2023