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Xu, Z., Zhu, Q..  2018.  Cross-Layer Secure and Resilient Control of Delay-Sensitive Networked Robot Operating Systems. 2018 IEEE Conference on Control Technology and Applications (CCTA). :1712—1717.

A Robot Operating System (ROS) plays a significant role in organizing industrial robots for manufacturing. With an increasing number of the robots, the operators integrate a ROS with networked communication to share the data. This cyber-physical nature exposes the ROS to cyber attacks. To this end, this paper proposes a cross-layer approach to achieve secure and resilient control of a ROS. In the physical layer, due to the delay caused by the security mechanism, we design a time-delay controller for the ROS agent. In the cyber layer, we define cyber states and use Markov Decision Process to evaluate the tradeoffs between physical and security performance. Due to the uncertainty of the cyber state, we extend the MDP to a Partially Observed Markov Decision Process (POMDP). We propose a threshold solution based on our theoretical results. Finally, we present numerical examples to evaluate the performance of the secure and resilient mechanism.

Prakash, A., Walambe, R..  2018.  Military Surveillance Robot Implementation Using Robot Operating System. 2018 IEEE Punecon. :1—5.

Robots are becoming more and more prevalent in many real world scenarios. Housekeeping, medical aid, human assistance are a few common implementations of robots. Military and Security are also major areas where robotics is being researched and implemented. Robots with the purpose of surveillance in war zones and terrorist scenarios need specific functionalities to perform their tasks with precision and efficiency. In this paper, we present a model of Military Surveillance Robot developed using Robot Operating System. The map generation based on Kinect sensor is presented and some test case scenarios are discussed with results.

Correia, A., Fonseca, B., Paredes, H., Schneider, D., Jameel, S..  2019.  Development of a Crowd-Powered System Architecture for Knowledge Discovery in Scientific Domains. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). :1372—1377.
A substantial amount of work is often overlooked due to the exponential rate of growth in global scientific output across all disciplines. Current approaches for addressing this issue are usually limited in scope and often restrict the possibility of obtaining multidisciplinary views in practice. To tackle this problem, researchers can now leverage an ecosystem of citizens, volunteers and crowd workers to perform complex tasks that are either difficult for humans and machines to solve alone. Motivated by the idea that human crowds and computer algorithms have complementary strengths, we present an approach where the machine will learn from crowd behavior in an iterative way. This approach is embodied in the architecture of SciCrowd, a crowd-powered human-machine hybrid system designed to improve the analysis and processing of large amounts of publication records. To validate the proposal's feasibility, a prototype was developed and an initial evaluation was conducted to measure its robustness and reliability. We conclude this paper with a set of implications for design.
Wang, Q., Zhao, W., Yang, J., Wu, J., Hu, W., Xing, Q..  2019.  DeepTrust: A Deep User Model of Homophily Effect for Trust Prediction. 2019 IEEE International Conference on Data Mining (ICDM). :618—627.

Trust prediction in online social networks is crucial for information dissemination, product promotion, and decision making. Existing work on trust prediction mainly utilizes the network structure or the low-rank approximation of a trust network. These approaches can suffer from the problem of data sparsity and prediction accuracy. Inspired by the homophily theory, which shows a pervasive feature of social and economic networks that trust relations tend to be developed among similar people, we propose a novel deep user model for trust prediction based on user similarity measurement. It is a comprehensive data sparsity insensitive model that combines a user review behavior and the item characteristics that this user is interested in. With this user model, we firstly generate a user's latent features mined from user review behavior and the item properties that the user cares. Then we develop a pair-wise deep neural network to further learn and represent these user features. Finally, we measure the trust relations between a pair of people by calculating the user feature vector cosine similarity. Extensive experiments are conducted on two real-world datasets, which demonstrate the superior performance of the proposed approach over the representative baseline works.

Narang, S., Byali, M., Dayama, P., Pandit, V., Narahari, Y..  2019.  Design of Trusted B2B Market Platforms using Permissioned Blockchains and Game Theory. 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). :385—393.

Trusted collaboration satisfying the requirements of (a) adequate transparency and (b) preservation of privacy of business sensitive information is a key factor to ensure the success and adoption of online business-to-business (B2B) collaboration platforms. Our work proposes novel ways of stringing together game theoretic modeling, blockchain technology, and cryptographic techniques to build such a platform for B2B collaboration involving enterprise buyers and sellers who may be strategic. The B2B platform builds upon three ideas. The first is to use a permissioned blockchain with smart contracts as the technical infrastructure for building the platform. Second, the above smart contracts implement deep business logic which is derived using a rigorous analysis of a repeated game model of the strategic interactions between buyers and sellers to devise strategies to induce honest behavior from buyers and sellers. Third, we present a formal framework that captures the essential requirements for secure and private B2B collaboration, and, in this direction, we develop cryptographic regulation protocols that, in conjunction with the blockchain, help implement such a framework. We believe our work is an important first step in the direction of building a platform that enables B2B collaboration among strategic and competitive agents while maximizing social welfare and addressing the privacy concerns of the agents.

Vaka, A., Manasa, G., Sameer, G., Das, B..  2019.  Generation And Analysis Of Trust Networks. 2019 1st International Conference on Advances in Information Technology (ICAIT). :443—448.

Trust is known to be a key component in human social relationships. It is trust that defines human behavior with others to a large extent. Generative models have been extensively used in social networks study to simulate different characteristics and phenomena in social graphs. In this work, an attempt is made to understand how trust in social graphs can be combined with generative modeling techniques to generate trust-based social graphs. These generated social graphs are then compared with the original social graphs to evaluate how trust helps in generative modeling. Two well-known social network data sets i.e. the soc-Bitcoin and the wiki administrator network data sets are used in this work. Social graphs are generated from these data sets and then compared with the original graphs along with other standard generative modeling techniques to see how trust is a good component in this. Other Generative modeling techniques have been available for a while but this investigation with the real social graph data sets validate that trust can be an important factor in generative modeling.

Wang, W., Xuan, S., Yang, W., Chen, Y..  2019.  User Credibility Assessment Based on Trust Propagation in Microblog. 2019 Computing, Communications and IoT Applications (ComComAp). :270—275.

Nowadays, Microblog has become an important online social networking platform, and a large number of users share information through Microblog. Many malicious users have released various false news driven by various interests, which seriously affects the availability of Microblog platform. Therefore, the evaluation of Microblog user credibility has become an important research issue. This paper proposes a microblog user credibility evaluation algorithm based on trust propagation. In view of the high consumption and low precision caused by malicious users' attacking algorithms and manual selection of seed sets by establishing false social relationships, this paper proposes two optimization strategies: pruning algorithm based on social activity and similarity and based on The seed node selection algorithm of clustering. The pruning algorithm can trim off the attack edges established by malicious users and normal users. The seed node selection algorithm can efficiently select the highly available seed node set, and finally use the user social relationship graph to perform the two-way propagation trust scoring, so that the low trusted user has a lower trusted score and thus identifies the malicious user. The related experiments verify the effectiveness of the trustworthiness-based user credibility evaluation algorithm in the evaluation of Microblog user credibility.

Abeysekara, P., Dong, H., Qin, A. K..  2019.  Machine Learning-Driven Trust Prediction for MEC-Based IoT Services. 2019 IEEE International Conference on Web Services (ICWS). :188—192.

We propose a distributed machine-learning architecture to predict trustworthiness of sensor services in Mobile Edge Computing (MEC) based Internet of Things (IoT) services, which aligns well with the goals of MEC and requirements of modern IoT systems. The proposed machine-learning architecture models training a distributed trust prediction model over a topology of MEC-environments as a Network Lasso problem, which allows simultaneous clustering and optimization on large-scale networked-graphs. We then attempt to solve it using Alternate Direction Method of Multipliers (ADMM) in a way that makes it suitable for MEC-based IoT systems. We present analytical and simulation results to show the validity and efficiency of the proposed solution.

Niz, D. de, Andersson, B., Klein, M., Lehoczky, J., Vasudevan, A., Kim, H., Moreno, G..  2019.  Mixed-Trust Computing for Real-Time Systems. 2019 IEEE 25th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA). :1—11.

Verifying complex Cyber-Physical Systems (CPS) is increasingly important given the push to deploy safety-critical autonomous features. Unfortunately, traditional verification methods do not scale to the complexity of these systems and do not provide systematic methods to protect verified properties when not all the components can be verified. To address these challenges, this paper proposes a real-time mixed-trust computing framework that combines verification and protection. The framework introduces a new task model, where an application task can have both an untrusted and a trusted part. The untrusted part allows complex computations supported by a full OS with a realtime scheduler running in a VM hosted by a trusted hypervisor. The trusted part is executed by another scheduler within the hypervisor and is thus protected from the untrusted part. If the untrusted part fails to finish by a specific time, the trusted part is activated to preserve safety (e.g., prevent a crash) including its timing guarantees. This framework is the first allowing the use of untrusted components for CPS critical functions while preserving logical and timing guarantees, even in the presence of malicious attackers. We present the framework design and implementation along with the schedulability analysis and the coordination protocol between the trusted and untrusted parts. We also present our Raspberry Pi 3 implementation along with experiments showing the behavior of the system under failures of untrusted components, and a drone application to demonstrate its practicality.

Malvankar, A., Payne, J., Budhraja, K. K., Kundu, A., Chari, S., Mohania, M..  2019.  Malware Containment in Cloud. 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). :221—227.

Malware is pervasive and poses serious threats to normal operation of business processes in cloud. Cloud computing environments typically have hundreds of hosts that are connected to each other, often with high risk trust assumptions and/or protection mechanisms that are not difficult to break. Malware often exploits such weaknesses, as its immediate goal is often to spread itself to as many hosts as possible. Detecting this propagation is often difficult to address because the malware may reside in multiple components across the software or hardware stack. In this scenario, it is usually best to contain the malware to the smallest possible number of hosts, and it's also critical for system administration to resolve the issue in a timely manner. Furthermore, resolution often requires that several participants across different organizational teams scramble together to address the intrusion. In this vision paper, we define this problem in detail. We then present our vision of decentralized malware containment and the challenges and issues associated with this vision. The approach of containment involves detection and response using graph analytics coupled with a blockchain framework. We propose the use of a dominance frontier for profile nodes which must be involved in the containment process. Smart contracts are used to obtain consensus amongst the involved parties. The paper presents a basic implementation of this proposal. We have further discussed some open problems related to our vision.

Kalyanaraman, A., Halappanavar, M..  2018.  Guest Editorial: Advances in Parallel Graph Processing: Algorithms, Architectures, and Application Frameworks. IEEE Transactions on Multi-Scale Computing Systems. 4:188—189.

The papers in this special section explore recent advancements in parallel graph processing. In the sphere of modern data science and data-driven applications, graph algorithms have achieved a pivotal place in advancing the state of scientific discovery and knowledge. Nearly three centuries of ideas have made graph theory and its applications a mature area in computational sciences. Yet, today we find ourselves at a crossroads between theory and application. Spurred by the digital revolution, data from a diverse range of high throughput channels and devices, from across internet-scale applications, are starting to mark a new era in data-driven computing and discovery. Building robust graph models and implementing scalable graph application frameworks in the context of this new era are proving to be significant challenges. Concomitant to the digital revolution, we have also experienced an explosion in computing architectures, with a broad range of multicores, manycores, heterogeneous platforms, and hardware accelerators (CPUs, GPUs) being actively developed and deployed within servers and multinode clusters. Recent advances have started to show that in more than one way, these two fields—graph theory and architectures–are capable of benefiting and in fact spurring new research directions in one another. This special section is aimed at introducing some of the new avenues of cutting-edge research happening at the intersection of graph algorithm design and their implementation on advanced parallel architectures.

Xu, W., Peng, Y..  2018.  SharaBLE: A Software Framework for Shared Usage of BLE Devices over the Internet. 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). :381—385.

With the development of Internet of Things, numerous IoT devices have been brought into our daily lives. Bluetooth Low Energy (BLE), due to the low energy consumption and generic service stack, has become one of the most popular wireless communication technologies for IoT. However, because of the short communication range and exclusive connection pattern, a BLE-equipped device can only be used by a single user near the device. To fully explore the benefits of BLE and make BLE-equipped devices truly accessible over the Internet as IoT devices, in this paper, we propose a cloud-based software framework that can enable multiple users to interact with various BLE IoT devices over the Internet. This framework includes an agent program, a suite of services hosting in cloud, and a set of RESTful APIs exposed to Internet users. Given the availability of this framework, the access to BLE devices can be extended from local to the Internet scale without any software or hardware changes to BLE devices, and more importantly, shared usage of remote BLE devices over the Internet is also made available.

Li, W., Guo, D., Li, K., Qi, H., Zhang, J..  2018.  iDaaS: Inter-Datacenter Network as a Service. IEEE Transactions on Parallel and Distributed Systems. 29:1515—1529.

Increasing number of Internet-scale applications, such as video streaming, incur huge amount of wide area traffic. Such traffic over the unreliable Internet without bandwidth guarantee suffers unpredictable network performance. This result, however, is unappealing to the application providers. Fortunately, Internet giants like Google and Microsoft are increasingly deploying their private wide area networks (WANs) to connect their global datacenters. Such high-speed private WANs are reliable, and can provide predictable network performance. In this paper, we propose a new type of service-inter-datacenter network as a service (iDaaS), where traditional application providers can reserve bandwidth from those Internet giants to guarantee their wide area traffic. Specifically, we design a bandwidth trading market among multiple iDaaS providers and application providers, and concentrate on the essential bandwidth pricing problem. The involved challenging issue is that the bandwidth price of each iDaaS provider is not only influenced by other iDaaS providers, but also affected by the application providers. To address this issue, we characterize the interaction between iDaaS providers and application providers using a Stackelberg game model, and analyze the existence and uniqueness of the equilibrium. We further present an efficient bandwidth pricing algorithm by blending the advantage of a geometrical Nash bargaining solution and the demand segmentation method. For comparison, we present two bandwidth reservation algorithms, where each iDaaS provider's bandwidth is reserved in a weighted fair manner and a max-min fair manner, respectively. Finally, we conduct comprehensive trace-driven experiments. The evaluation results show that our proposed algorithms not only ensure the revenue of iDaaS providers, but also provide bandwidth guarantee for application providers with lower bandwidth price per unit.

Yang, R., Ouyang, X., Chen, Y., Townend, P., Xu, J..  2018.  Intelligent Resource Scheduling at Scale: A Machine Learning Perspective. 2018 IEEE Symposium on Service-Oriented System Engineering (SOSE). :132—141.

Resource scheduling in a computing system addresses the problem of packing tasks with multi-dimensional resource requirements and non-functional constraints. The exhibited heterogeneity of workload and server characteristics in Cloud-scale or Internet-scale systems is adding further complexity and new challenges to the problem. Compared with,,,, existing solutions based on ad-hoc heuristics, Machine Learning (ML) has the potential to improve further the efficiency of resource management in large-scale systems. In this paper we,,,, will describe and discuss how ML could be used to understand automatically both workloads and environments, and to help to cope with scheduling-related challenges such as consolidating co-located workloads, handling resource requests, guaranteeing application's QoSs, and mitigating tailed stragglers. We will introduce a generalized ML-based solution to large-scale resource scheduling and demonstrate its effectiveness through a case study that deals with performance-centric node classification and straggler mitigation. We believe that an MLbased method will help to achieve architectural optimization and efficiency improvement.

Garbo, A., Quer, S..  2018.  A Fast MPEG’s CDVS Implementation for GPU Featured in Mobile Devices. IEEE Access. 6:52027—52046.
The Moving Picture Experts Group's Compact Descriptors for Visual Search (MPEG's CDVS) intends to standardize technologies in order to enable an interoperable, efficient, and cross-platform solution for internet-scale visual search applications and services. Among the key technologies within CDVS, we recall the format of visual descriptors, the descriptor extraction process, and the algorithms for indexing and matching. Unfortunately, these steps require precision and computation accuracy. Moreover, they are very time-consuming, as they need running times in the order of seconds when implemented on the central processing unit (CPU) of modern mobile devices. In this paper, to reduce computation times and maintain precision and accuracy, we re-design, for many-cores embedded graphical processor units (GPUs), all main local descriptor extraction pipeline phases of the MPEG's CDVS standard. To reach this goal, we introduce new techniques to adapt the standard algorithm to parallel processing. Furthermore, to reduce memory accesses and efficiently distribute the kernel workload, we use new approaches to store and retrieve CDVS information on proper GPU data structures. We present a complete experimental analysis on a large and standard test set. Our experiments show that our GPU-based approach is remarkably faster than the CPU-based reference implementation of the standard, and it maintains a comparable precision in terms of true and false positive rates.
Zhang, Y., Deng, L., Chen, M., Wang, P..  2018.  Joint Bidding and Geographical Load Balancing for Datacenters: Is Uncertainty a Blessing or a Curse? IEEE/ACM Transactions on Networking. 26:1049—1062.

We consider the scenario where a cloud service provider (CSP) operates multiple geo-distributed datacenters to provide Internet-scale service. Our objective is to minimize the total electricity and bandwidth cost by jointly optimizing electricity procurement from wholesale markets and geographical load balancing (GLB), i.e., dynamically routing workloads to locations with cheaper electricity. Under the ideal setting where exact values of market prices and workloads are given, this problem reduces to a simple linear programming and is easy to solve. However, under the realistic setting where only distributions of these variables are available, the problem unfolds into a non-convex infinite-dimensional one and is challenging to solve. One of our main contributions is to develop an algorithm that is proven to solve the challenging problem optimally, by exploring the full design space of strategic bidding. Trace-driven evaluations corroborate our theoretical results, demonstrate fast convergence of our algorithm, and show that it can reduce the cost for the CSP by up to 20% as compared with baseline alternatives. This paper highlights the intriguing role of uncertainty in workloads and market prices, measured by their variances. While uncertainty in workloads deteriorates the cost-saving performance of joint electricity procurement and GLB, counter-intuitively, uncertainty in market prices can be exploited to achieve a cost reduction even larger than the setting without price uncertainty.

Kathiravelu, P., Chiesa, M., Marcos, P., Canini, M., Veiga, L..  2018.  Moving Bits with a Fleet of Shared Virtual Routers. 2018 IFIP Networking Conference (IFIP Networking) and Workshops. :1—9.

The steady decline of IP transit prices in the past two decades has helped fuel the growth of traffic demands in the Internet ecosystem. Despite the declining unit pricing, bandwidth costs remain significant due to ever-increasing scale and reach of the Internet, combined with the price disparity between the Internet's core hubs versus remote regions. In the meantime, cloud providers have been auctioning underutilized computing resources in their marketplace as spot instances for a much lower price, compared to their on-demand instances. This state of affairs has led the networking community to devote extensive efforts to cloud-assisted networks - the idea of offloading network functionality to cloud platforms, ultimately leading to more flexible and highly composable network service chains.We initiate a critical discussion on the economic and technological aspects of leveraging cloud-assisted networks for Internet-scale interconnections and data transfers. Namely, we investigate the prospect of constructing a large-scale virtualized network provider that does not own any fixed or dedicated resources and runs atop several spot instances. We construct a cloud-assisted overlay as a virtual network provider, by leveraging third-party cloud spot instances. We identify three use case scenarios where such approach will not only be economically and technologically viable but also provide performance benefits compared to current commercial offerings of connectivity and transit providers.

Sunny, S. M. N. A., Liu, X., Shahriar, M. R..  2018.  Remote Monitoring and Online Testing of Machine Tools for Fault Diagnosis and Maintenance Using MTComm in a Cyber-Physical Manufacturing Cloud. 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). :532—539.

Existing systems allow manufacturers to acquire factory floor data and perform analysis with cloud applications for machine health monitoring, product quality prediction, fault diagnosis and prognosis etc. However, they do not provide capabilities to perform testing of machine tools and associated components remotely, which is often crucial to identify causes of failure. This paper presents a fault diagnosis system in a cyber-physical manufacturing cloud (CPMC) that allows manufacturers to perform diagnosis and maintenance of manufacturing machine tools through remote monitoring and online testing using Machine Tool Communication (MTComm). MTComm is an Internet scale communication method that enables both monitoring and operation of heterogeneous machine tools through RESTful web services over the Internet. It allows manufacturers to perform testing operations from cloud applications at both machine and component level for regular maintenance and fault diagnosis. This paper describes different components of the system and their functionalities in CPMC and techniques used for anomaly detection and remote online testing using MTComm. It also presents the development of a prototype of the proposed system in a CPMC testbed. Experiments were conducted to evaluate its performance to diagnose faults and test machine tools remotely during various manufacturing scenarios. The results demonstrated excellent feasibility to detect anomaly during manufacturing operations and perform testing operations remotely from cloud applications using MTComm.

Shahriar, M. R., Sunny, S. M. N. A., Liu, X., Leu, M. C., Hu, L., Nguyen, N..  2018.  MTComm Based Virtualization and Integration of Physical Machine Operations with Digital-Twins in Cyber-Physical Manufacturing Cloud. 2018 5th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2018 4th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). :46—51.

Digital-Twins simulate physical world objects by creating 'as-is' virtual images in a cyberspace. In order to create a well synchronized digital-twin simulator in manufacturing, information and activities of a physical machine need to be virtualized. Many existing digital-twins stream read-only data of machine sensors and do not incorporate operations of manufacturing machines through Internet. In this paper, a new method of virtualization is proposed to integrate machining data and operations into the digital-twins using Internet scale machine tool communication method. A fully functional digital-twin is implemented in CPMC testbed using MTComm and several manufacturing application scenarios are developed to evaluate the proposed method and system. Performance analysis shows that it is capable of providing data-driven visual monitoring of a manufacturing process and performing manufacturing operations through digital twins over the Internet. Results of the experiments also shows that the MTComm based digital twins have an excellent efficiency.

Shaikh, F., Bou-Harb, E., Neshenko, N., Wright, A. P., Ghani, N..  2018.  Internet of Malicious Things: Correlating Active and Passive Measurements for Inferring and Characterizing Internet-Scale Unsolicited IoT Devices. IEEE Communications Magazine. 56:170—177.

Advancements in computing, communication, and sensing technologies are making it possible to embed, control, and gather vital information from tiny devices that are being deployed and utilized in practically every aspect of our modernized society. From smart home appliances to municipal water and electric industrial facilities to our everyday work environments, the next Internet frontier, dubbed IoT, is promising to revolutionize our lives and tackle some of our nations' most pressing challenges. While the seamless interconnection of IoT devices with the physical realm is envisioned to bring a plethora of critical improvements in many aspects and diverse domains, it will undoubtedly pave the way for attackers that will target and exploit such devices, threatening the integrity of their data and the reliability of critical infrastructure. Further, such compromised devices will undeniably be leveraged as the next generation of botnets, given their increased processing capabilities and abundant bandwidth. While several demonstrations exist in the literature describing the exploitation procedures of a number of IoT devices, the up-to-date inference, characterization, and analysis of unsolicited IoT devices that are currently deployed "in the wild" is still in its infancy. In this article, we address this imperative task by leveraging active and passive measurements to report on unsolicited Internet-scale IoT devices. This work describes a first step toward exploring the utilization of passive measurements in combination with the results of active measurements to shed light on the Internet-scale insecurities of the IoT paradigm. By correlating results of Internet-wide scanning with Internet background radiation traffic, we disclose close to 14,000 compromised IoT devices in diverse sectors, including critical infrastructure and smart home appliances. To this end, we also analyze their generated traffic to create effective mitigation signatures that could be deployed in local IoT realms. To support largescale empirical data analytics in the context of IoT, we make available the inferred and extracted IoT malicious raw data through an authenticated front-end service. The outcomes of this work confirm the existence of such compromised devices on an Internet scale, while the generated inferences and insights are postulated to be employed for inferring other similarly compromised IoT devices, in addition to contributing to IoT cyber security situational awareness.

Zhang, H., Liu, H., Deng, L., Wang, P., Rong, X., Li, Y., Li, B., Wang, H..  2018.  Leader Recognition and Tracking for Quadruped Robots. 2018 IEEE International Conference on Information and Automation (ICIA). :1438—1443.

To meet the high requirement of human-machine interaction, quadruped robots with human recognition and tracking capability are studied in this paper. We first introduce a marker recognition system which uses multi-thread laser scanner and retro-reflective markers to distinguish the robot's leader and other objects. When the robot follows leader autonomously, the variant A* algorithm which having obstacle grids extended virtually (EA*) is used to plan the path. But if robots need to track and follow the leader's path as closely as possible, it will trust that the path which leader have traveled is safe enough and uses the incremental form of EA* algorithm (IEA*) to reproduce the trajectory. The simulation and experiment results illustrate the feasibility and effectiveness of the proposed algorithms.

Sun, P., Yin, S., Man, W., Tao, T..  2018.  Research of Personalized Recommendation Algorithm Based on Trust and User's Interest. 2018 International Conference on Robots Intelligent System (ICRIS). :153—156.

Most traditional recommendation algorithms only consider the binary relationship between users and projects, these can basically be converted into score prediction problems. But most of these algorithms ignore the users's interests, potential work factors or the other social factors of the recommending products. In this paper, based on the existing trustworthyness model and similarity measure, we puts forward the concept of trust similarity and design a joint interest-content recommendation framework to suggest users which videos to watch in the online video site. In this framework, we first analyze the user's viewing history records, tags and establish the user's interest characteristic vector. Then, based on the updated vector, users should be clustered by sparse subspace clust algorithm, which can improve the efficiency of the algorithm. We certainly improve the calculation of similarity to help users find better neighbors. Finally we conduct experiments using real traces from Tencent Weibo and Youku to verify our method and evaluate its performance. The results demonstrate the effectiveness of our approach and show that our approach can substantially improve the recommendation accuracy.

Attia, M., Hossny, M., Nahavandi, S., Dalvand, M., Asadi, H..  2018.  Towards Trusted Autonomous Surgical Robots. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). :4083—4088.

Throughout the last few decades, a breakthrough took place in the field of autonomous robotics. They have been introduced to perform dangerous, dirty, difficult, and dull tasks, to serve the community. They have been also used to address health-care related tasks, such as enhancing the surgical skills of the surgeons and enabling surgeries in remote areas. This may help to perform operations in remote areas efficiently and in timely manner, with or without human intervention. One of the main advantages is that robots are not affected with human-related problems such as: fatigue or momentary lapses of attention. Thus, they can perform repeated and tedious operations. In this paper, we propose a framework to establish trust in autonomous medical robots based on mutual understanding and transparency in decision making.

Weigelin, B. C., Mathiesen, M., Nielsen, C., Fischer, K., Nielsen, J..  2018.  Trust in Medical Human-Robot Interactions based on Kinesthetic guidance. 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). :901—908.

In medical human-robot interactions, trust plays an important role since for patients there may be more at stake than during other kinds of encounters with robots. In the current study, we address issues of trust in the interaction with a prototype of a therapeutic robot, the Universal RoboTrainer, in which the therapist records patient-specific tasks for the patient by means of kinesthetic guidance of the patients arm, which is connected to the robot. We carried out a user study with twelve pairs of participants who collaborate on recording a training program on the robot. We examine a) the degree with which participants identify the situation as uncomfortable or distressing, b) participants' own strategies to mitigate that stress, c) the degree to which the robot is held responsible for the problems occurring and the amount of agency ascribed to it, and d) when usability issues arise, what effect these have on participants' trust. We find signs of distress mostly in contexts with usability issues, as well as many verbal and kinesthetic mitigation strategies intuitively employed by the participants. Recommendations for robots to increase users' trust in kinesthetic interactions include the timely production of verbal cues that continuously confirm that everything is alright as well as increased contingency in the presentation of strategies for recovering from usability issues arising.

Nam, C., Li, H., Li, S., Lewis, M., Sycara, K..  2018.  Trust of Humans in Supervisory Control of Swarm Robots with Varied Levels of Autonomy. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). :825—830.

In this paper, we study trust-related human factors in supervisory control of swarm robots with varied levels of autonomy (LOA) in a target foraging task. We compare three LOAs: manual, mixed-initiative (MI), and fully autonomous LOA. In the manual LOA, the human operator chooses headings for a flocking swarm, issuing new headings as needed. In the fully autonomous LOA, the swarm is redirected automatically by changing headings using a search algorithm. In the mixed-initiative LOA, if performance declines, control is switched from human to swarm or swarm to human. The result of this work extends the current knowledge on human factors in swarm supervisory control. Specifically, the finding that the relationship between trust and performance improved for passively monitoring operators (i.e., improved situation awareness in higher LOAs) is particularly novel in its contradiction of earlier work. We also discover that operators switch the degree of autonomy when their trust in the swarm system is low. Last, our analysis shows that operator's preference for a lower LOA is confirmed for a new domain of swarm control.