This paper proposes RAMAN, a framework of approaches for multimodal anomaly detection that is robust to different anomaly types, input data, and domain constraints for the Mars Science Laboratory (MSL) power subsystem. Existing anomaly detection systems focus on a subset of anomalies and require a human expert for verification, making the process prone to bias and scaling issues. RAMAN combines the strengths of unsupervised feature extraction methods with the domain knowledge of Martian power systems. It uses thresholding methods based on autoencoder architectures and spline approximations to find anomalies in different data modalities. The system is evaluated on the power systems data of the Curiosity rover across multiple data types focusing on the equipment configuration, sensor data, and telemetry data received from the rover. Preliminary results for all these modalities show a high qualitative evaluation from field experts. The main strengths of RAMAN are its unit composition that helps detect anomalies in subsystems, easy extension to other Martian and non-Martian systems, and easy usability through interactive interfaces.

The results of many machine learning (ML) algorithms often yield very complicated black box models. While these models can have superb accuracy, their challenge lies in articulating the rationale behind specific outputs for given inputs, making verification and trust-building problematic in numerous applications or scenarios. In response to this challenge, ML researchers have started to devise methods that tackle this issue, creating a relatively heterogeneous set of tools which are often covered under the umbrella term of eXplainable AI, and abbreviated as XAI. Historically, system identification researchers have also faced similar problems with estimating meaningful parameters in order to obtain more interpretable models. Nevertheless, results from these fields currently seem to draw little from each other. This part of the tutorial, tailored as an introduction, aims at providing system identification researchers with a foundational overview of the motivations behind eXplainable AI (XAI) and the main connections with their field. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licneses/by-nc-nd/4.0/)

In combinatory logic it is known that the set of two combinators K and S are universal; in the sense that any other combinator can be expressed in terms of these two. K combinator can not be expressed only in terms of the S combinator. This will answer a question raised by Stephen Wolfram as ``Is the S combinator on its own computation universal?''

A successful policy negotiation scheme for Policy-Based Management (PBM) has been implemented. Policy negotiation is the process of determining the communication policy that all of the parties involved can agree on. Specifically, the problem is how to reconcile the various (and possibly conflicting) communication protocols used by different divisions. The solution must use protocols available to all parties involved, and should attempt to do so in the best way possible. Which protocols are commonly available, and what the definition of is will be dependent on the parties involved and their individual communications priorities.

Today's advanced network management systems can automate many aspects of the tactical networking operations within a military domain. However, automation of joint and coalition tactical networking across multiple domains remains challenging. Due to potentially conflicting goals and priorities, human agreement is often required before implementation into the network operations. This is further complicated by incompatible network management systems and security policies, rendering it difficult to implement automatic network management, thus requiring manual human intervention to the communication protocols used at various network routers and endpoints. This process of manual human intervention is tedious, error-prone, and slow. In order to facilitate a better solution, we are pursuing a technology which makes network management automated, reliable, and fast. Automating the negotiation of the common network communication parameters between different parties is the subject of this paper. We present the technology that enables inter-force dynamic communication resource negotiations to enable ad-hoc inter-operation in the field between force domains, without pre-planning. It also will enable a dynamic response to changing conditions within the area of operations. Our solution enables the rapid blending of intra-domain policies so that the forces involved are able to inter-operate effectively without overwhelming each other's networks with in-appropriate or unwarranted traffic. It will evaluate the policy rules and configuration data for each of the domains, then generate a compatible inter-domain policy and configuration that will update the gateway systems between the two domains.

In this paper we present a novel fast model-based diagnosis engine. Our novel engine is based on a two-step approach to diagnosis, i.e., off-line system analysis and on-line diagnosis. The efficiency of our novel method results from the fact that, by performing a detailed analysis of the target system, it drastically reduces the amount of computation needed for diagnosis. In particular, our new algorithm relies on the concept and use of minimal set of ARRs to achieve a much better efficiency in the diagnosis process. Our novel diagnosis engine is based on our two recent results. First, it uses our recently developed method for generation of the complete set of ARRs. Second, it uses the minimal set of ARRs; as we have recently shown that for any given number of faults, i.e., single, double, triple, etc., there is a corresponding minimal set of ARRs which is usually significantly smaller than the complete set of ARRs. We present and discuss the performance of our diagnosis engine by its application to several examples. We show that, even by using a non-exoneration assumption, we achieve a much better efficiency over the GDE as well as full ARR-based approaches for model-based diagnosis.

Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in situ platforms. One of NASA s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation can be considered to be part of the larger effort of improving reliability and safety. The standard methods for solving the sensor validation problem are based on probabilistic analysis of the system, from which the method based on Bayesian networks is most popular. Therefore, these methods can only predict the most probable faulty sensors, which are subject to the initial probabilities defined for the failures. The method developed in this work is based on a model-based approach and provides the faulty sensors (if any), which can be logically inferred from the model of the system and the sensor readings (observations). The method is also more suitable for the systems when it is hard, or even impossible, to find the probability functions of the system. The method starts by a new mathematical description of the problem and develops a very efficient and systematic algorithm for its solution. The method builds on the concepts of analytical redundant relations (ARRs).

This paper has been withdrawn by the author as one of the coauthors needs institutional permission.

In ensemble (or bulk) quantum computation, measurements of qubits in an individual computer cannot be performed. Instead, only expectation values can be measured. As a result of this limitation on the model of computation, various important algorithms cannot be processed directly on such computers, and must be modified. We provide modifications of various existing protocols, including algorithms for universal fault--tolerant computation, Shor's factorization algorithm (which can be extended to any algorithm computing an NP function), and some search algorithms to enable processing them on ensemble quantum computers.

Large-scale Web security systems usually involve cooperation between domains with non-identical policies. The network management and Web communication software used by the different organizations presents a stumbling block. Many of the tools used by the various divisions do not have the ability to communicate network management data with each other. At best, this means that manual human intervention into the communication protocols used at various network routers and endpoints is required. Developing practical, sound, and automated ways to compose policies to bridge these differences is a long-standing problem. One of the key subtleties is the need to deal with inconsistencies and defaults where one organization proposes a rule on a particular feature, and another has a different rule or expresses no rule. A general approach is to assign priorities to rules and observe the rules with the highest priorities when there are conflicts. The present methods have inherent inefficiency, which heavily restrict their practical applications. A new, efficient algorithm combines policies utilized for Web services. The method is based on an algorithm that allows an automatic and scalable composition of security policies between multiple organizations. It is based on defeasible policy composition, a promising approach for finding conflicts and resolving priorities between rules. In the general case, policy negotiation is an intractable problem. A promising method, suggested in the literature, is when policies are represented in defeasible logic, and composition is based on rules for non-monotonic inference. In this system, policy writers construct metapolicies describing both the policy that they wish to enforce and annotations describing their composition preferences. These annotations can indicate whether certain policy assertions are required by the policy writer or, if not, under what circumstances the policy writer is willing to compromise and allow other assertions to take precedence. Meta-policies are specified in defeasible logic, a computationally efficient non-monotonic logic developed to model human reasoning. One drawback of this method is that at one point the algorithm starts an exhaustive search of all subsets of the set of conclusions of a defeasible theory. Although the propositional defeasible logic has linear complexity, the set of conclusions here may be large, especially in real-life practical cases. This phenomenon leads to an inefficient exponential explosion of complexity. The current process of getting a Web security policy from combination of two meta-policies consists of two steps. The first is generating a new meta-policy that is a composition of the input meta-policies, and the second is mapping the meta-policy onto a security policy. The new algorithm avoids the exhaustive search in the current algorithm, and provides a security policy that matches all requirements of the involved metapolicies.

We present a novel concept of Minimal Set of Analytical Redundancy Relation (ARRs) and an efficient method for its calculation for application to system diagnosis. ARRs are one of the crucial tools for model-based diagnosis as well as for optimizing, analyzing, and validating the system of sensors. However, despite the importance of the ARRs for system diagnosis, it seems that less attention has been paid to their efficient application. In this paper, we first discuss the complexity of model-based diagnosis by using ARRs. We then present the concept of Minimal Set of ARRs which enables a faster system diagnosis by significantly reducing the number of ARRs to be evaluated for diagnosis purpose. We then show that the derivation of minimal set of ARRs can be mapped as a 0-1 Integer Programming problem and present an efficient branch-and-bound algorithm for this derivation. We also present the results of application of our method for generating the minimal set of ARRs, to both synthetic and industrial examples, to show the significant reduction in the computational cost that can be achieved for system diagnosis.

We will describe a dynamic federated autonomic networking system1 for the testing of netcentric systems across organizations. Using a suite of policy-based management software tools, our system can provide netcentric missions with self-configuring, self-debugging, self-healing, and self-protecting capabilities across end-to-end coalition networks. The novelty of our system originates from: (1) A multi-party dynamic policy negotiation algorithm and protocol for autonomous cross-organization negotiations on network resources; (2) A RDF/XML based language to enable automated understanding of network and application configurations across organizations; (3) A teachable, natural language policy capture system; (4) An advanced user interface tool for capturing and representing domain expert knowledge; and (5) A reasoning system for distributing and applying domain knowledge. This system has been developed for large-scale netcentric systems testing applications where the rapid configuration and management of dynamically changing netcentric test exercises is critical. Using a scenario from a real test exercise involving test ranges around the country, we have demonstrated in our testbed that we could reduce the debugging time for a network configuration problem from 6 hours, involving dozens of operators, to less than 2 seconds with minimal human involvement.

In this paper we present a new efficient algorithmic method for generating the conflicts set for model-based diagnosis. Our new method combines the strength of the two different approaches proposed in the literature, that is, the Fault Detection and Isolation (FDI), which is based on automatic control theory and statistical decision theory, and the other one, known as DX, which is based on artificial intelligence techniques. The first building block in our method is a new efficient algorithm for generation of the complete set of Analytical redundancy Relations (ARRs) for the system in an implicit form. For the diagnosis, our method first performs (similar to DX approaches) a system simulation to calculate the expected values of the measurements. Any discrepancy, i.e., the difference between expected and actual value of measurement, would trigger our diagnosis process. To this end, only those ARRs which involve the measurement with discrepancy are checked for consistency which lead a to a significant reduction in the number of consistency checks usually performed by DX approaches. We demonstrate the efficiency of our new method by its application to several synthetic systems and compare it with that of GDE.