Physical Sciences and Mathematics Commons^™

Contextual Path Retrieval: A Contextual Entity Relation Embedding-Based Approach, Pei-Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Contextual path retrieval (CPR) refers to the task of finding contextual path(s) between a pair of entities in a knowledge graph that explains the connection between them in a given context. For this novel retrieval task, we propose the Embedding-based Contextual Path Retrieval (ECPR) framework. ECPR is based on a three-component structure that includes a context encoder and path encoder that encode query context and path, respectively, and a path ranker that assigns a ranking score to each candidate path to determine the one that should be the contextual path. For context encoding, we propose two novel context encoding methods, …

Extracting Microservice Dependencies Using Log Analysis, Andres O. Rodriguez Ishida

Electronic Thesis and Dissertation Repository

Microservice architecture is an architectural style that supports the design and implementation of very scalable systems by distributing complex functionality to highly granular components. These highly granular components are referred to as microservices and can be dynamically deployed on Docker containers. These microservice architecture systems are very extensible since new microservices can be added or replaced as the system evolves. In such highly granular architectures, a major challenge that arises is how to quickly identify whether any changes in the system’s structure violate any policies or design constraints. Examples of policies and design constraints include whether a microservice can call …

Nonparametric Contextual Reasoning For Question Answering Over Large Knowledge Bases, Rajarshi Das

Doctoral Dissertations

Question answering (QA) over knowledge bases provides a user-friendly way of accessing the massive amount of information stored in them. We have experienced tremendous progress in the performance of QA systems, thanks to the recent advancements in representation learning by deep neural models. However, such deep models function as black boxes with an opaque reasoning process, are brittle, and offer very limited control (e.g. for debugging an erroneous model prediction). It is also unclear how to reliably add or update knowledge stored in their model parameters. This thesis proposes nonparametric models for question answering that disentangle logic from knowledge. For …

Assessing Evidence Relevance By Disallowing Assessment, John Licato, Michael Cooper

OSSA Conference Archive

Guidelines for assessing whether potential evidence is relevant to some argument tend to rely on criteria that are subject to well-known biasing effects. We describe a framework for argumentation that does not allow participants to directly decide whether evidence is potentially relevant to an argument---instead, evidence must prove its relevance through demonstration. This framework, called WG-A, is designed to translate into a dialogical game playable by minimally trained participants.

Transparency And Algorithmic Governance, Cary Coglianese, David Lehr

All Faculty Scholarship

Machine-learning algorithms are improving and automating important functions in medicine, transportation, and business. Government officials have also started to take notice of the accuracy and speed that such algorithms provide, increasingly relying on them to aid with consequential public-sector functions, including tax administration, regulatory oversight, and benefits administration. Despite machine-learning algorithms’ superior predictive power over conventional analytic tools, algorithmic forecasts are difficult to understand and explain. Machine learning’s “black-box” nature has thus raised concern: Can algorithmic governance be squared with legal principles of governmental transparency? We analyze this question and conclude that machine-learning algorithms’ relative inscrutability does not pose a …

Mixed Logical And Probabilistic Reasoning In The Game Of Clue, Todd W. Neller, Ziqian Luo

Computer Science Faculty Publications

Neller and Ziqian Luo ’18 presented a means of mixed logical and probabilistic reasoning with knowledge in the popular deductive mystery game Clue. Using at-least constraints, we more efficiently represented and reasoned about cardinality constraints on Clue card deal knowledge, and then employed a WalkSAT-based solution sampling algorithm with a tabu search metaheuristic in order to estimate the probabilities of unknown card places.

Insight Provenance For Spatiotemporal Visual Analytics: Theory, Review, And Guidelines, Andreas Hall, Paula Ahonen-Rainio, Kirsi Virrantaus

Journal of Spatial Information Science

Research on provenance, which focuses on different ways to describe and record the history of changes and advances made throughout an analysis process, is an integral part of visual analytics. This paper focuses on providing the provenance of insight and rationale through visualizations while emphasizing, first, that this entails a profound understanding of human cognition and reasoning and that, second, the special nature of spatiotemporal data needs to be acknowledged in this process. A recently proposed human reasoning framework for spatiotemporal analysis, and four guidelines for the creation of visualizations that provide the provenance of insight and rationale published in …

Mlcaf: Multi-Level Cross-Domain Semantic Context Fusioning For Behavior Identification, Muhammad Asif Razzaq, Claudia Villalonga, Sungyoung Lee, Usman Akhtar, Maqbool Ali, Eun Soo Kim, Asad Masood Khattak, Hyonwoo Seung, Taeho Hur, Jaehun Bang, Dohyeong Kim, Wajahat Ali Khan

All Works

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. The emerging research on automatic identification of user’s contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user’s contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level …

Intuition: Role, Biases, Cognitive Basis, And A Hypothetical Synergistic Explanation Of Intuitive Brain Operations, Jens G. Pohl

Collaborative Agent Design (CAD) Research Center

This paper explores the characteristics of the intuitive responses that are generated by our brain continuously in an automatic and effortless manner. However, while intuition is a very powerful mechanism, it is also subject to many biasing influences. The author discusses the role of intuition, examines representative examples of biasing influences, compares cognitive theories of intuition advanced by Simon (2002), Klein (2003 and 1999), and Kahneman (2011), and then advances a hypothetical explanation of the neurological operations underlying intuition based on Hebbian rules (Hebb 1949) of plasticity in combination with synergetic principles.

A Scalable Backward Chaining-Based Reasoner For A Semantic Web, Hui Shi, Kurt Maly, Steven Zeil

Computer Science Faculty Publications

In this paper we consider knowledge bases that organize information using ontologies. Specifically, we investigate reasoning over a semantic web where the underlying knowledge base covers linked data about science research that are being harvested from the Web and are supplemented and edited by community members. In the semantic web over which we want to reason, frequent changes occur in the underlying knowledge base, and less frequent changes occur in the underlying ontology or the rule set that governs the reasoning. Interposing a backward chaining reasoner between a knowledge base and a query manager yields an architecture that can support …

Curated Reasoning By Formal Modeling Of Provenance, Kevin B. Shaw

University of New Orleans Theses and Dissertations

The core problem addressed in this research is the current lack of an ability to repurpose and curate scientific data among interdisciplinary scientists within a research enterprise environment. Explosive growth in sensor technology as well as the cost of collecting ocean data and airborne measurements has allowed for exponential increases in scientific data collection as well as substantial enterprise resources required for data collection. There is currently no framework for efficiently curating this scientific data for repurposing or intergenerational use.

There are several reasons why this problem has eluded solution to date to include the competitive requirements for funding and …

Demonstration: Dynamic Sensor Registration And Semantic Processing For Ad-Hoc Mobile Environments (Semmob), Pramod Anantharam, Gary Alan Smith, Josh Pschorr, Krishnaprasad Thirunarayan, Amit P. Sheth

Kno.e.sis Publications

SemMOB enables dynamic registration of sensors via mobile devices, search, and near real-time inference over sensor observations in ad-hoc mobile environments (e.g., fire fighting). We demonstrate SemMOB in the context of an emergency response use case that requires automatic and dynamic registrations of sensor devices and annotation of sensor observations, decoding of latitude-longitude information in terms of human sensible names, fusion and abstraction of sensor values using background knowledge, and their visualization using mash-up.

A Web-Integrated Environment For Component-Based Software Reasoning, Charles Cook

All Theses

This thesis presents the Web IDE, a web-integrated environment for component-based software reasoning. The Web IDE is specifically tailored to emphasize the relationships among various components in component-based software engineering (CBSE) and to facilitate reasoning. It allows students to use RESOLVE, a component-based, integrated specification and programming language, to build components and systems, providing real-time feedback that can be used to reason about the correctness of their component implementations. Real-time interaction and relationship focused component presentation reinforces CBSE and reasoning principles in a way not possible with traditional programming exercises and file management systems.
The Web IDE has gone through …

Modeling Geographic Awareness Of Road Networks For Consistency Verification, Ari Kassin

Open Access Theses & Dissertations

Problems related to transportation and inspection of valuable or sensitive assets such as commercial products and materials, cultural items and works of art, and hazardous materials share similarities and can be modeled by a core set of abstract entities including a payload, a vehicle, a driver, and an inspector. To make the load handling capabilities of security monitoring and inspection systems more scalable, security can be increased by reducing the variability of transportation routes to a finite set of authorized routes between trip origin and destination locations. Then trip anomalies, which are unexpected trip variations, can be used in inspection …

Enforcing Behavioral Constraints In Evolving Aspect-Oriented Programs, Raffi T. Khatchadourian, Johan Dovland, Neelam Soundarajan

Publications and Research

Reasoning, specification, and verification of Aspect-Oriented (AO) programs presents unique challenges especially as such programs evolve over time. Components, base-code and aspects alike, may be easily added, removed, interchanged, or presently unavailable at unpredictable frequencies. Consequently, modular reasoning of such programs is highly attractive as it enables tractable evolution, otherwise necessitating that the entire program be reexamined each time a component is changed. It is well known, however, that modular reasoning about AO programs is difficult. In this paper, we present our ongoing work in constructing a rely-guarantee style reasoning system for the Aspect-Oriented Programming (AOP) paradigm, adopting a trace-based …

Elements Of Human Decision-Making, Jens G. Pohl

Collaborative Agent Design (CAD) Research Center

The purpose of this paper is to present some understandings of the human problem-solving activity that we have gained in the Collaborative Agent Design Research Center (CADRC) over the past two decades. Since we feel strongly that the human decision-maker should be an integral component of any computer-based decision-support system, it follows that we would have endeavored to incorporate many of the elements that appear to be important to the user in the design of these systems. The complexity of the human cognitive system is evidenced by the large body of literature that describes problem-solving behavior and the relatively fewer …

Modeling Multiple Granularities Of Spatial Objects, Chitra Ramalingam

Electronic Theses and Dissertations

People conceptualize objects in an information space over different levels of details or granularities and shift among these granularities as necessary for the task at hand. Shifting among granularities is fundamental for understanding and reasoning about an information space. In general, shifting to a coarser granularity can improve one's understanding of a complex information space, whereas shifting to a more detailed granularity reveals information that is otherwise unknown. To arrive at a coarser granularity. objects must be generalized. There are multiple ways to perform generalization. Several generalization methods have been adopted from the abstraction processes that are intuitively carried out …

The Brain As A Symbol-Processing Machine., Armando F. Rocha

Armando F Rocha

The knowledge accumulated about the biochemistry of the synapsis in the last decades completely changes the notion of brain processing founded exclusively over an electrical mechanism, toward that supported by a complex chemical message exchange occurring both locally, at the synaptic site, as well as at other localities, depending on the solubility of the involved chemical substances in the extracellular compartment. These biochemical transactions support a rich symbolic processing of the information both encoded by the genes and provided by actual data collected from the surrounding environment, by means of either special molecular or cellular receptor systems. In this processing, …

An Integrated Framework For Learning And Reasoning, Christophe G. Giraud-Carrier, Tony R. Martinez

Faculty Publications

Learning and reasoning are both aspects of what is considered to be intelligence. Their studies within AI have been separated historically, learning being the topic of machine learning and neural networks, and reasoning falling under classical (or symbolic) AI. However, learning and reasoning are in many ways interdependent. This paper discusses the nature of some of these interdependencies and proposes a general framework called FLARE, that combines inductive learning using prior knowledge together with reasoning in a propositional setting. Several examples that test the framework are presented, including classical induction, many important reasoning protocols and two simple expert systems.

Generalized Probabilistic Reasoning And Empirical Studies On Computational Efficiency And Scalability, Eric P. Baenen

Theses and Dissertations

Expert Systems are tools that can be very useful for diagnostic purposes, however current methods of storing and reasoning with knowledge have significant limitations. One set of limitations involves how to store and manipulate uncertain knowledge: much of the knowledge we are dealing with has some degree of uncertainty. These limitations include lack of complete information, not being able to model cyclic information and limitations on the size and complexity of the problems to be solved. If expert systems are ever going to be able to tackle significant real world problems then these deficiencies must be corrected. This paper describes …

Parallel Algorithm Fundamentals And Analysis, Bruce M. Mcmillin, Hanan Lutfiyya, Grace Tsai, Jun-Lin Liu

Computer Science Technical Reports

This session explores, through the use of formal methods, the “intuition” used in creating a parallel algorithm design and realizing this design on distributed memory hardware. The algorithm class NC and the LSTM machine are used to show why some algorithms realize their promise of speedup better than others and the algorithm class NP is used to show why other algorithms will never be good for parallelization. Performance and correctness through cooperative axiomatic reasoning and temporal reasoning provide an additional basis for understanding parallel algorithm design and specification. Finally, the realities of algorithm design are presented through partitioning and mapping …

Parallel Algorithm Fundamentals And Analysis, Bruce M. Mcmillin, Hanan Lutfiyya, Grace Tsai, Jun-Lin Liu

Computer Science Technical Reports

This session explores, through the use of formal methods, the “intuition” used in creating a parallel algorithm design and realizing this design on distributed memory hardware. The algorithm class NG and the LSTM machine are used to show why some algorithms realize their promise of speedup better than others and the algorithm class NP is used to show why other algorithms will never be good for parallelization. The realities of algorithm design are presented through partitioning and mapping issues and models. Finally, correctness through cooperative axiomatic reasoning provides an additional basis for understanding parallel algorithm design and specification and is …

Binary Resolution In Surface Reasoning, William C. Purdy

Electrical Engineering and Computer Science - Technical Reports

Intuition suggests the hypothesis that everyday human reasoning is conducted in the written or spoken natural language, rather than in some disparate representation into which the surface language is translated. An examination of human reasoning reveals patterns of inference that parallel binary resolution. But any standard implementation of resolution requires Skolemization. Skolemization would seem an unlikely component of human reasoning. This appears to contradict the hypothesis that human reasoning takes place at the surface. To reconcile these observations, this paper develops a new rule of inference, which operates on surface expressions directly. This rule is shown to produce results which …

Surface Reasoning, William C. Purdy

Electrical Engineering and Computer Science - Technical Reports

Surface reasoning is defined to be deduction conducted in the surface language in terms of certain primitive logical relations. The surface language is a spoken or written natural language (in this paper, English), in contrast to a "base language" or “deep structure" sometimes hypothesized to explain natural language phenomena. The primitive logical relations are inclusion, exclusion and overlap between classes of entities. A calculus for surface reasoning is presented. Then a model for reasoning in this calculus is developed. The model is similar to but more general than syllogistic. In this model, reasoning is represented as construction of fragments (subposets) …

Taxonomic Reasoning And Lexical Semantics, William C. Purdy

Electrical Engineering and Computer Science - Technical Reports

Taxonomic reasoning is used in many applications, including many-sorted logic, knowledge bases, document retrieval, and natural language processing. These various applications have been dealt with independently. Because they have so much in common, a general approach to taxonomic reasoning would seem to be justified. This paper presents a theory of lexical semantics as an example of such a general approach. The theory defines a representation and an algebra for that representation. The operations of the algebra are inherently parallel, making them well matched to the capabilities of modern computer systems.