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Xiong, Wenjie, Shan, Chun, Sun, Zhaoliang, Meng, Qinglei.  2018.  Real-time Processing and Storage of Multimedia Data with Content Delivery Network in Vehicle Monitoring System. 2018 6th International Conference on Wireless Networks and Mobile Communications (WINCOM). :1—4.

With the rapid development of the Internet of vehicles, there is a huge amount of multimedia data becoming a hidden trouble in the Internet of Things. Therefore, it is necessary to process and store them in real time as a way of big data curation. In this paper, a method of real-time processing and storage based on CDN in vehicle monitoring system is proposed. The MPEG-DASH standard is used to process the multimedia data by dividing them into MPD files and media segments. A real-time monitoring system of vehicle on the basis of the method introduced is designed and implemented.

Xiong, Wenjun, Carlsson, Per, Lagerström, Robert.  2019.  Re-Using Enterprise Architecture Repositories for Agile Threat Modeling. 2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop (EDOCW). :118–127.

Digitization has increased exposure and opened up for more cyber threats and attacks. To proactively handle this issue, enterprise modeling needs to include threat management during the design phase that considers antagonists, attack vectors, and damage domains. Agile methods are commonly adopted to efficiently develop and manage software and systems. This paper proposes to use an enterprise architecture repository to analyze not only shipped components but the overall architecture, to improve the traditional designs represented by legacy systems in the situated IT-landscape. It shows how the hidden structure method (with Design Structure Matrices) can be used to evaluate the enterprise architecture, and how it can contribute to agile development. Our case study uses an architectural descriptive language called ArchiMate for architecture modeling and shows how to predict the ripple effect in a damaging domain if an attacker's malicious components are operating within the network.

Xiong, X., Yang, L..  2017.  Multi End-Hopping Modeling and Optimization Using Cooperative Game. 2017 4th International Conference on Information Science and Control Engineering (ICISCE). :470–474.

End-hopping is an effective component of Moving Target Defense (MTD) by randomly hopping network configuration of host, which is a game changing technique against cyber-attack and can interrupt cyber kill chain in the early stage. In this paper, a novel end-hopping model, Multi End-hopping (MEH), is proposed to exploit the full potentials of MTD techniques by hosts cooperating with others to share possible configurable space (PCS). And an optimization method based on cooperative game is presented to make hosts form optimal alliances against reconnaissance, scanning and blind probing DoS attack. Those model and method confuse adversaries by establishing alliances of hosts to enlarge their PCS, which thwarts various malicious scanning and mitigates probing DoS attack intensity. Through simulations, we validate the correctness of MEH model and the effectiveness of optimization method. Experiment results show that the proposed model and method increase system stable operational probability while introduces a low overhead in optimization.

Xiong, Xinli, Zhao, Guangsheng, Wang, Xian.  2018.  A System Attack Surface Based MTD Effectiveness and Cost Quantification Framework. Proceedings of the 2Nd International Conference on Cryptography, Security and Privacy. :175-179.

Moving Target Defense (MTD) is a game-changing method to thwart adversaries and reverses the imbalance situation in network countermeasures. Introducing Attack Surface (AS) into MTD security assessment brings productive concepts to qualitative and quantitative analysis. The quantification of MTD effectiveness and cost (E&C) has been under researched, using simulation models and emulation testbeds, to give accurate and reliable results for MTD technologies. However, the lack of system-view evaluation impedes MTD to move toward large-scale applications. In this paper, a System Attack Surface Based Quantification Framework (SASQF) is proposed to establish a system-view based framework for further research in Attack Surface and MTD E&C quantification. And a simulated model based on SASQF is developed to provide illustrations and software simulation methods. A typical C/S scenario and Cyber Kill Chain (CKC) attacks are presented in case study and several simulated results are given. From the simulated results, IP mutation frequency is the key to increase consumptions of adversaries, while the IP mutation pool is not the principal factor to thwart adversaries in reconnaissance and delivery of CKC steps. For system user operational cost, IP mutation frequency influence legitimate connections in relative values under ideal link state without delay, packet lose and jitter. The simulated model based on SASQF also provides a basic method to find the optimal IP mutation frequency through simulations.

Xiong, Yi, Li, Zhongkui.  2020.  Privacy Preserving Average Consensus by Adding Edge-based Perturbation Signals. 2020 IEEE Conference on Control Technology and Applications (CCTA). :712—717.
In this paper, the privacy preserving average consensus problem of multi-agent systems with strongly connected and weight balanced graph is considered. In most existing consensus algorithms, the agents need to exchange their state information, which leads to the disclosure of their initial states. This might be undesirable because agents' initial states may contain some important and sensitive information. To solve the problem, we propose a novel distributed algorithm, which can guarantee average consensus and meanwhile preserve the agents' privacy. This algorithm assigns some additive perturbation signals on the communication edges and these perturbations signals will be added to original true states for information exchanging. This ensures that direct disclosure of initial states can be avoided. Then a rigid analysis of our algorithm's privacy preserving performance is provided. For any individual agent in the network, we present a necessary and sufficient condition under which its privacy is preserved. The effectiveness of our algorithm is demonstrated by a numerical simulation.
Xu, A., Dai, T., Chen, H., Ming, Z., Li, W..  2018.  Vulnerability Detection for Source Code Using Contextual LSTM. 2018 5th International Conference on Systems and Informatics (ICSAI). :1225–1230.

With the development of Internet technology, software vulnerabilities have become a major threat to current computer security. In this work, we propose the vulnerability detection for source code using Contextual LSTM. Compared with CNN and LSTM, we evaluated the CLSTM on 23185 programs, which are collected from SARD. We extracted the features through the program slicing. Based on the features, we used the natural language processing to analysis programs with source code. The experimental results demonstrate that CLSTM has the best performance for vulnerability detection, reaching the accuracy of 96.711% and the F1 score of 0.96984.

Xu, B., Lu, M., Zhang, D..  2017.  A Software Security Case Developing Method Based on Hierarchical Argument Strategy. 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :632–633.

Security cases-which document the rationale for believing that a system is adequately secure-have not been sufficiently used for a lack of practical construction method. This paper presents a hierarchical software security case development method to address this issue. We present a security concept relationship model first, then come up with a hierarchical asset-threat-control measure argument strategy, together with the consideration of an asset classification and threat classification for software security case. Lastly, we propose 11 software security case patterns and illustrate one of them.

Xu, Bin, Chang, Pamara, Welker, Christopher L., Bazarova, Natalya N., Cosley, Dan.  2016.  Automatic Archiving Versus Default Deletion: What Snapchat Tells Us About Ephemerality in Design. Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. :1662–1675.

Unlike most social media, where automatic archiving of data is the default, Snapchat defaults to ephemerality: deleting content shortly after it is viewed by a receiver. Interviews with 25 Snapchat users show that ephemerality plays a key role in shaping their practices. Along with friend-adding features that facilitate a network of mostly close relations, default deletion affords everyday, mundane talk and reduces self-consciousness while encouraging playful interaction. Further, although receivers can save content through screenshots, senders are notified; this selective saving with notification supports complex information norms that preserve the feel of ephemeral communication while supporting the capture of meaningful content. This dance of giving and taking, sharing and showing, and agency for both senders and receivers provides the basis for a rich design space of mechanisms, levels, and domains for ephemerality.

Xu, Bowen, Shirani, Amirreza, Lo, David, Alipour, Mohammad Amin.  2018.  Prediction of Relatedness in Stack Overflow: Deep Learning vs. SVM: A Reproducibility Study. Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. :21:1–21:10.
Background Xu et al. used a deep neural network (DNN) technique to classify the degree of relatedness between two knowledge units (question-answer threads) on Stack Overflow. More recently, extending Xu et al.'s work, Fu and Menzies proposed a simpler classification technique based on a fine-tuned support vector machine (SVM) that achieves similar performance but in a much shorter time. Thus, they suggested that researchers need to compare their sophisticated methods against simpler alternatives. Aim The aim of this work is to replicate the previous studies and further investigate the validity of Fu and Menzies' claim by evaluating the DNN- and SVM-based approaches on a larger dataset. We also compare the effectiveness of these two approaches against SimBow, a lightweight SVM-based method that was previously used for general community question-answering. Method We (1) collect a large dataset containing knowledge units from Stack Overflow, (2) show the value of the new dataset addressing shortcomings of the original one, (3) re-evaluate both the DNN-and SVM-based approaches on the new dataset, and (4) compare the performance of the two approaches against that of SimBow. Results We find that: (1) there are several limitations in the original dataset used in the previous studies, (2) effectiveness of both Xu et al.'s and Fu and Menzies' approaches (as measured using F1-score) drop sharply on the new dataset, (3) similar to the previous finding, performance of SVM-based approaches (Fu and Menzies' approach and SimBow) are slightly better than the DNN-based approach, (4) contrary to the previous findings, Fu and Menzies' approach runs much slower than DNN-based approach on the larger dataset - its runtime grows sharply with increase in dataset size, and (5) SimBow outperforms both Xu et al. and Fu and Menzies' approaches in terms of runtime. Conclusion We conclude that, for this task, simpler approaches based on SVM performs adequately well. We also illustrate the challenges brought by the increased size of the dataset and show the benefit of a lightweight SVM-based approach for this task.
Xu, Cheng, Xu, Jianliang, Hu, Haibo, Au, Man Ho.  2018.  When Query Authentication Meets Fine-Grained Access Control: A Zero-Knowledge Approach. Proceedings of the 2018 International Conference on Management of Data. :147-162.

Query authentication has been extensively studied to ensure the integrity of query results for outsourced databases, which are often not fully trusted. However, access control, another important security concern, is largely ignored by existing works. Notably, recent breakthroughs in cryptography have enabled fine-grained access control over outsourced data. In this paper, we take the first step toward studying the problem of authenticating relational queries with fine-grained access control. The key challenge is how to protect information confidentiality during query authentication, which is essential to many critical applications. To address this challenge, we propose a novel access-policy-preserving (APP) signature as the primitive authenticated data structure. A useful property of the APP signature is that it can be used to derive customized signatures for unauthorized users to prove the inaccessibility while achieving the zero-knowledge confidentiality. We also propose a grid-index-based tree structure that can aggregate APP signatures for efficient range and join query authentication. In addition to this, a number of optimization techniques are proposed to further improve the authentication performance. Security analysis and performance evaluation show that the proposed solutions and techniques are robust and efficient under various system settings.

Xu, D., Xiao, L., Mandayam, N. B., Poor, H. V..  2017.  Cumulative Prospect Theoretic Study of a Cloud Storage Defense Game against Advanced Persistent Threats. 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :541–546.

Cloud storage is vulnerable to advanced persistent threats (APTs), in which an attacker launches stealthy, continuous, well-funded and targeted attacks on storage devices. In this paper, cumulative prospect theory (CPT) is applied to study the interactions between a defender of cloud storage and an APT attacker when each of them makes subjective decisions to choose the scan interval and attack interval, respectively. Both the probability weighting effect and the framing effect are applied to model the deviation of subjective decisions of end-users from the objective decisions governed by expected utility theory, under uncertain attack durations. Cumulative decision weights are used to describe the probability weighting effect and the value distortion functions are used to represent the framing effect of subjective APT attackers and defenders in the CPT-based APT defense game, rather than discrete decision weights, as in earlier prospect theoretic study of APT defense. The Nash equilibria of the CPT-based APT defense game are derived, showing that a subjective attacker becomes risk-seeking if the frame of reference for evaluating the utility is large, and becomes risk-averse if the frame of reference for evaluating the utility is small.

Xu, D., Xiao, L., Sun, L., Lei, M..  2017.  Game theoretic study on blockchain based secure edge networks. 2017 IEEE/CIC International Conference on Communications in China (ICCC). :1–5.

Blockchain has been applied to study data privacy and network security recently. In this paper, we propose a punishment scheme based on the action record on the blockchain to suppress the attack motivation of the edge servers and the mobile devices in the edge network. The interactions between a mobile device and an edge server are formulated as a blockchain security game, in which the mobile device sends a request to the server to obtain real-time service or launches attacks against the server for illegal security gains, and the server chooses to perform the request from the device or attack it. The Nash equilibria (NEs) of the game are derived and the conditions that each NE exists are provided to disclose how the punishment scheme impacts the adversary behaviors of the mobile device and the edge server.

Xu, Dianxiang, Shrestha, Roshan, Shen, Ning.  2018.  Automated Coverage-Based Testing of XACML Policies. Proceedings of the 23Nd ACM on Symposium on Access Control Models and Technologies. :3–14.
While the standard language XACML is very expressive for specifying fine-grained access control policies, defects can get into XACML policies for various reasons, such as misunderstanding of access control requirements, omissions, and coding errors. These defects may result in unauthorized accesses, escalation of privileges, and denial of service. Therefore, quality assurance of XACML policies for real-world information systems has become an important issue. To address this issue, this paper presents a family of coverage criteria for XACML policies, such as rule coverage, rule pair coverage, decision coverage, and Modified Condition/Decision Coverage (MC/DC). To demonstrate the assurance levels of these coverage criteria, we have developed methods for automatically generating tests, i.e., access requests, to satisfy the coverage criteria using a constraint solver. We have evaluated these methods through mutation analysis of various policies with different levels of complexity. The experiment results have shown that the rule coverage is far from adequate for revealing the majority of defects in XACML policies, and that both MC/DC and decision coverage tests have outperformed the existing methods for testing XACML policies. In particular, MC/DC tests achieve a very high level of quality assurance of XACML policies.
Xu, Dongyan.  2014.  Virtualization and Security: Happily Ever After? Proceedings of the 4th ACM Conference on Data and Application Security and Privacy. :73–74.

Virtualization has been a major enabling technology for improving trustworthiness and tamper-resistance of computer security functions. In the past decade, we have witnessed the development of virtualization-based techniques for attack/malware monitoring, detection, prevention, and profiling. Virtual platforms have been widely adopted for system security experimentation and evaluation, because of their strong isolation, maneuverability, and scalability properties. Conversely, the demand from security research has led to significant advances in virtualization technology itself, for example, in the aspects of virtual machine introspection, check-pointing, and replay. In this talk, I will present an overview of research efforts (including our own) in virtualization-based security and security-driven virtualization. I will also discuss a number of challenges and opportunities in maintaining and elevating the synergies between virtualization and security.

Xu, F., Peng, R., Zheng, T., Xu, X..  2019.  Development and Validation of Numerical Magnetic Force and Torque Model for Magnetically Levitated Actuator. IEEE Transactions on Magnetics. 55:1–9.

To decouple the multi-axis motion in the 6 degrees of freedom magnetically levitated actuators (MLAs), this paper introduces a numerical method to model the force and torque distribution. Taking advantage of the Gaussian quadrature, the concept of coil node is developed to simplify the Lorentz integral into the summation of the interaction between each magnetic node in the remanence region and each coil node in the coil region. Utilizing the coordinate transformation in the numerical method, the computation burden is independent of the position and the rotation angle of the moving part. Finally, the experimental results prove that the force and torque predicted by the numerical model are rigidly consistent with the measurement, and the force and torque in all directions are decoupled properly based on the numerical solution. Compared with the harmonic model, the numerical wrench model is more suitable for the MLAs undertaking both the translational and rotational displacements.

Xu, Guangwu, Yan, Zheng.  2016.  A Survey on Trust Evaluation in Mobile Ad Hoc Networks. Proceedings of the 9th EAI International Conference on Mobile Multimedia Communications. :140–148.

Mobile Ad Hoc Network (MANET) is a multi-hop temporary and autonomic network comprised of a set of mobile nodes. MANETs have the features of non-center, dynamically changing topology, multi-hop routing, mobile nodes, limited resources and so on, which make it face more threats. Trust evaluation is used to support nodes to cooperate in a secure and trustworthy way through evaluating the trust of participating nodes in MANETs. However, many trust evaluation models proposed for MANETs still have many problems and shortcomings. In this paper, we review the existing researches, then analyze and compare the proposed trust evaluation models by presenting and applying uniform criteria in order to point out a number of open issues and challenges and suggest future research trends.

Xu, Guanshuo, Wu, Han-Zhou, Shi, Yun Q...  2016.  Ensemble of CNNs for Steganalysis: An Empirical Study. Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security. :103–107.

There has been growing interest in using convolutional neural networks (CNNs) in the fields of image forensics and steganalysis, and some promising results have been reported recently. These works mainly focus on the architectural design of CNNs, usually, a single CNN model is trained and then tested in experiments. It is known that, neural networks, including CNNs, are suitable to form ensembles. From this perspective, in this paper, we employ CNNs as base learners and test several different ensemble strategies. In our study, at first, a recently proposed CNN architecture is adopted to build a group of CNNs, each of them is trained on a random subsample of the training dataset. The output probabilities, or some intermediate feature representations, of each CNN, are then extracted from the original data and pooled together to form new features ready for the second level of classification. To make best use of the trained CNN models, we manage to partially recover the lost information due to spatial subsampling in the pooling layers when forming feature vectors. Performance of the ensemble methods are evaluated on BOSSbase by detecting S-UNIWARD at 0.4 bpp embedding rate. Results have indicated that both the recovery of the lost information, and learning from intermediate representation in CNNs instead of output probabilities, have led to performance improvement.

Xu, Guowen, Li, Hongwei, Lu, Rongxing.  2018.  Practical and Privacy-Aware Truth Discovery in Mobile Crowd Sensing Systems. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :2312–2314.

We design a Practical and Privacy-Aware Truth Discovery (PPATD) approach in mobile crowd sensing systems, which supports users to go offline at any time while still achieving practical efficiency under working process. More notably, our PPATD is the first solution under single server setting to resolve the problem that users must be online at all times during the truth discovery. Moreover, we design a double-masking with one-time pads protocol to further ensure the strong security of users' privacy even if there is a collusion between the cloud server and multiple users.

Xu, H., Hu, L., Liu, P., Xiao, Y., Wang, W., Dayal, J., Wang, Q., Tang, Y..  2018.  Oases: An Online Scalable Spam Detection System for Social Networks. 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). :98–105.
Web-based social networks enable new community-based opportunities for participants to engage, share their thoughts, and interact with each other. Theses related activities such as searching and advertising are threatened by spammers, content polluters, and malware disseminators. We propose a scalable spam detection system, termed Oases, for uncovering social spam in social networks using an online and scalable approach. The novelty of our design lies in two key components: (1) a decentralized DHT-based tree overlay deployment for harvesting and uncovering deceptive spam from social communities; and (2) a progressive aggregation tree for aggregating the properties of these spam posts for creating new spam classifiers to actively filter out new spam. We design and implement the prototype of Oases and discuss the design considerations of the proposed approach. Our large-scale experiments using real-world Twitter data demonstrate scalability, attractive load-balancing, and graceful efficiency in online spam detection for social networks.
Xu, H., Jiang, H..  2019.  An Image Encryption Schema Based on Hybrid Optimized Chaotic System. 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE). :784–788.

The purpose of this paper is to improve the safety of chaotic image encryption algorithm. Firstly, to achieve this goal, it put forward two improved chaotic system logistic and henon, which covered an promoted henon chaotic system with better probability density, and an 2-dimension logistic chaotic system with high Lyapunov exponents. Secondly, the chaotic key stream was generated by the new 2D logistic chaotic system and optimized henon mapping, which mixed in dynamic proportions. The conducted sequence has better randomness and higher safety for image cryptosystem. Thirdly, we proposed algorithm takes advantage of the compounded chaotic system Simulation experiment results and security analysis showed that the proposed scheme was more effective and secure. It can resist various typical attacks, has high security, satisfies the requirements of image encryption theoretical.

Xu, Haifeng.  2016.  The Mysteries of Security Games: Equilibrium Computation Becomes Combinatorial Algorithm Design. Proceedings of the 2016 ACM Conference on Economics and Computation. :497–514.

The security game is a basic model for resource allocation in adversarial environments. Here there are two players, a defender and an attacker. The defender wants to allocate her limited resources to defend critical targets and the attacker seeks his most favorable target to attack. In the past decade, there has been a surge of research interest in analyzing and solving security games that are motivated by applications from various domains. Remarkably, these models and their game-theoretic solutions have led to real-world deployments in use by major security agencies like the LAX airport, the US Coast Guard and Federal Air Marshal Service, as well as non-governmental organizations. Among all these research and applications, equilibrium computation serves as a foundation. This paper examines security games from a theoretical perspective and provides a unified view of various security game models. In particular, each security game can be characterized by a set system E which consists of the defender's pure strategies; The defender's best response problem can be viewed as a combinatorial optimization problem over E. Our framework captures most of the basic security game models in the literature, including all the deployed systems; The set system E arising from various domains encodes standard combinatorial problems like bipartite matching, maximum coverage, min-cost flow, packing problems, etc. Our main result shows that equilibrium computation in security games is essentially a combinatorial problem. In particular, we prove that, for any set system \$E\$, the following problems can be reduced to each other in polynomial time: (0) combinatorial optimization over E; (1) computing the minimax equilibrium for zero-sum security games over E; (2) computing the strong Stackelberg equilibrium for security games over E; (3) computing the best or worst (for the defender) Nash equilibrium for security games over E. Therefore, the hardness [polynomial solvability] of any of these problems implies the hardness [polynomial solvability] of all the others. Here, by "games over E" we mean the class of security games with arbitrary payoff structures, but a fixed set E of defender pure strategies. This shows that the complexity of a security game is essentially determined by the set system E. We view drawing these connections as an important conceptual contribution of this paper.

Xu, Hong-Li, JIANG, HongHua.  2019.  An Image Encryption Schema Based on Hybrid Optimized Chaotic System. 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE). :784–788.
The purpose of this paper is to improve the safety of chaotic image encryption algorithm. Firstly, to achieve this goal, it put forward two improved chaotic system logistic and henon, which covered an promoted henon chaotic system with better probability density, and an 2-dimension logistic chaotic system with high Lyapunov exponents. Secondly, the chaotic key stream was generated by the new 2D logistic chaotic system and optimized henon mapping, which mixed in dynamic proportions. The conducted sequence has better randomness and higher safety for image cryptosystem. Thirdly, we proposed algorithm takes advantage of the compounded chaotic system Simulation experiment results and security analysis showed that the proposed scheme was more effective and secure. It can resist various typical attacks, has high security, satisfies the requirements of image encryption theoretical.
Xu, Hui, Zhou, Yangfan, Lyu, Michael.  2016.  N-version Obfuscation. Proceedings of the 2Nd ACM International Workshop on Cyber-Physical System Security. :22–33.

Although existing for decades, software tampering attack is still a main threat to systems, such as Android, and cyber physical systems. Many approaches have been proposed to thwart specific procedures of tampering, e.g., obfuscation and self-checksumming. However, none of them can achieve theoretically tamper-proof without the protection of hardware circuit. Rather than proposing new tricks against tampering attacks, we focus on impeding the replication of software tampering via program diversification, and thus pose a scalability barrier against the attacks. Our idea, namely N-version obfuscation (NVO), is to automatically generate and deliver same featured, but functionally nonequivalent software copies to different machines or users. In this paper, we investigate such an idea on Android platform. We carefully design a candidate NVO solution for networked apps, which leverages a Message Authentication Code (MAC) mechanism to generate the functionally nonequivalent diversities. Our evaluation result shows that the time required for breaking such a software system increases linearly with respect to the number of software versions. In this way, attackers would suffer great scalability issues, considering that an app can have millions of users. With minimal NVO costs, effective tamper-resistant security can therefore be established.

Xu, J., Howard, A..  2020.  How much do you Trust your Self-Driving Car? Exploring Human-Robot Trust in High-Risk Scenarios 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). :4273—4280.

Trust is an important characteristic of successful interactions between humans and agents in many scenarios. Self-driving scenarios are of particular relevance when discussing the issue of trust due to the high-risk nature of erroneous decisions being made. The present study aims to investigate decision-making and aspects of trust in a realistic driving scenario in which an autonomous agent provides guidance to humans. To this end, a simulated driving environment based on a college campus was developed and presented. An online and an in-person experiment were conducted to examine the impacts of mistakes made by the self-driving AI agent on participants’ decisions and trust. During the experiments, participants were asked to complete a series of driving tasks and make a sequence of decisions in a time-limited situation. Behavior analysis indicated a similar relative trend in the decisions across these two experiments. Survey results revealed that a mistake made by the self-driving AI agent at the beginning had a significant impact on participants’ trust. In addition, similar overall experience and feelings across the two experimental conditions were reported. The findings in this study add to our understanding of trust in human-robot interaction scenarios and provide valuable insights for future research work in the field of human-robot trust.

Xu, J., Howard, A..  2018.  The Impact of First Impressions on Human- Robot Trust During Problem-Solving Scenarios. 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). :435—441.

With recent advances in robotics, it is expected that robots will become increasingly common in human environments, such as in the home and workplaces. Robots will assist and collaborate with humans on a variety of tasks. During these collaborations, it is inevitable that disagreements in decisions would occur between humans and robots. Among factors that lead to which decision a human should ultimately follow, theirs or the robot, trust is a critical factor to consider. This study aims to investigate individuals' behaviors and aspects of trust in a problem-solving situation in which a decision must be made in a bounded amount of time. A between-subject experiment was conducted with 100 participants. With the assistance of a humanoid robot, participants were requested to tackle a cognitive-based task within a given time frame. Each participant was randomly assigned to one of the following initial conditions: 1) a working robot in which the robot provided a correct answer or 2) a faulty robot in which the robot provided an incorrect answer. Impacts of the faulty robot behavior on participant's decision to follow the robot's suggested answer were analyzed. Survey responses about trust were collected after interacting with the robot. Results indicated that the first impression has a significant impact on participant's behavior of trusting a robot's advice during a disagreement. In addition, this study discovered evidence supporting that individuals still have trust in a malfunctioning robot even after they have observed a robot's faulty behavior.