Reliable Inference from Unreliable Agents

Distributed inference using multiple sensors has been an active area of research since the emergence of wireless sensor networks (WSNs). Several researchers have addressed the design issues to ensure optimal inference performance in such networks. The central goal of this thesis is to analyze distributed inference systems with potentially unreliable components and design strategies to ensure reliable inference in such systems. The inference process can be that of detection or estimation or classification, and the components/agents in the system can be sensors and/or humans. The system components can be unreliable due to a variety of reasons: faulty sensors, security attacks causing sensors to send falsified information, or unskilled human workers sending imperfect information. This thesis first quantifies the effect of such unreliable agents on the inference performance of the network and then designs schemes that ensure a reliable overall inference. In the first part of this thesis, we study the case when only sensors are present in the system, referred to as sensor networks. For sensor networks, the presence of malicious sensors, referred to as Byzantines, are considered. Byzantines are sensors that inject false information into the system. In such systems, the effect of Byzantines on the overall inference performance is characterized in terms of the optimal attack strategies. Game-theoretic formulations are explored to analyze two-player interactions. Next, Byzantine mitigation schemes are designed that address the problem from the system\u27s perspective. These mitigation schemes are of two kinds: Byzantine identification schemes and Byzantine tolerant schemes. Using learning based techniques, Byzantine identification schemes are designed that learn the identity of Byzantines in the network and use this information to improve system performance. When such schemes are not possible, Byzantine tolerant schemes using error-correcting codes are developed that tolerate the effect of Byzantines and maintain good performance in the network. Error-correcting codes help in correcting the erroneous information from these Byzantines and thereby counter their attack. The second line of research in this thesis considers humans-only networks, referred to as human networks. A similar research strategy is adopted for human networks where, the effect of unskilled humans sharing beliefs with a central observer called \emph{CEO} is analyzed, and the loss in performance due to the presence of such unskilled humans is characterized. This problem falls under the family of problems in information theory literature referred to as the \emph{CEO Problem}, but for belief sharing. The asymptotic behavior of the minimum achievable mean squared error distortion at the CEO is studied in the limit when the number of agents $L$ and the sum rate $R$ tend to infinity. An intermediate regime of performance between the exponential behavior in discrete CEO problems and the $1/R$ behavior in Gaussian CEO problems is established. This result can be summarized as the fact that sharing beliefs (uniform) is fundamentally easier in terms of convergence rate than sharing measurements (Gaussian), but sharing decisions is even easier (discrete). Besides theoretical analysis, experimental results are reported for experiments designed in collaboration with cognitive psychologists to understand the behavior of humans in the network. The act of fusing decisions from multiple agents is observed for humans and the behavior is statistically modeled using hierarchical Bayesian models. The implications of such modeling on the design of large human-machine systems is discussed. Furthermore, an error-correcting codes based scheme is proposed to improve system performance in the presence of unreliable humans in the inference process. For a crowdsourcing system consisting of unskilled human workers providing unreliable responses, the scheme helps in designing easy-to-perform tasks and also mitigates the effect of erroneous data. The benefits of using the proposed approach in comparison to the majority voting based approach are highlighted using simulated and real datasets. In the final part of the thesis, a human-machine inference framework is developed where humans and machines interact to perform complex tasks in a faster and more efficient manner. A mathematical framework is built to understand the benefits of human-machine collaboration. Such a study is extremely important for current scenarios where humans and machines are constantly interacting with each other to perform even the simplest of tasks. While machines perform best in some tasks, humans still give better results in tasks such as identifying new patterns. By using humans and machines together, one can extract complete information about a phenomenon of interest. Such an architecture, referred to as Human-Machine Inference Networks (HuMaINs), provides promising results for the two cases of human-machine collaboration: \emph{machine as a coach} and \emph{machine as a colleague}. For simple systems, we demonstrate tangible performance gains by such a collaboration which provides design modules for larger, and more complex human-machine systems. However, the details of such larger systems needs to be further explored

Replica determinism and flexible scheduling in hard real-time dependable systems

Fault-tolerant real-time systems are typically based on active replication where replicated entities are required to deliver their outputs in an identical order within a given time interval. Distributed scheduling of replicated tasks, however, violates this requirement if on-line scheduling, preemptive scheduling, or scheduling of dissimilar replicated task sets is employed. This problem of inconsistent task outputs has been solved previously by coordinating the decisions of the local schedulers such that replicated tasks are executed in an identical order. Global coordination results either in an extremely high communication effort to agree on each schedule decision or in an overly restrictive execution model where on-line scheduling, arbitrary preemptions, and nonidentically replicated task sets are not allowed. To overcome these restrictions, a new method, called timed messages, is introduced. Timed messages guarantee deterministic operation by presenting consistent message versions to the replicated tasks. This approach is based on simulated common knowledge and a sparse time base. Timed messages are very effective since they neither require communication between the local scheduler nor do they restrict usage of on-line flexible scheduling, preemptions and nonidentically replicated task sets

Lux Occidentale: The Eastern Mission of the Pontifical Commission for Russia, Origins to 1933

Although it was first a sub-commission within the Congregation for the Eastern Churches (CEO), the Pontifical Commission for Russia (PCpR) emerged as an independent commission under the presidency of the noted Vatican Russian expert, Michel d’Herbigny, S.J. in 1925, and remained so until 1933 when it was re-integrated into CEO. The PCpR was given authority over the spiritual and material mission to Soviet Russia, including refugees who had fled the Bolshevik Revolution. While most studies concerning the Catholic Church and Russia are religious or political histories which focus, respectively, on martyrdom or the contest between the so-called free world and Communism, this dissertation is instead a social history which employs religious anthropological categories. The dissertation argues that soft-Orientalist dynamics were at play in the PCpR through the structures which it managed and engaged– especially the Russian Catholic Church of the Byzantine-Slavonic Rite, and through its mission of evangelization as it managed forms of worship, taught Catholic belief– especially as formulated by Vladimir Soloviev, and enforced codes of behavior– especially concerning clerical celibacy and marriage. The sense of the barbarity of the Bolshevism, which at one point was compared to Islam, justified for the members of the PCpR their sense of superiority over the Russian people

Selecting effective blockchain solutions

Distributed ledger technologies (DLT) are becoming increasingly popular and seen as a panacea for a wide range of applications. However, it is clear that many organisations, and even engineers, are selecting DLT solutions without fully understanding their power or limitations. Those that make the assessment that blockchain is the best solution are provided little guidance on the vast array of types of blockchain; whether permissioned, permissionless or federated; which consensus algorithm to use; and a range of other considerations. This paper aims to addresses this gap

THE SECRET OF VENETIAN SUCCESS: THE ROLE OF THE STATE IN FINANCIAL MARKETS

The commercial success of Venice hinged on her merchants¿ ability to do business with borrowed money. However, to raise other people¿s capital, merchants needed to commit not to embezzle the capital received. Despite this commitment problem, the evidence indicates an active financial market through which the Venetians, by and large, mobilized their savings to investments. What were the institutional foundations of this market? This paper claims that neither reputation-based institutions that did not rely on the state nor a coercive legal system provided such foundations. Instead, the state generated the rents and information required to induce merchants to refrain from acting opportunistically.Institutions for Contract Enforcement; State Formation; Financial Markets; Late Medieval Venice