arXiv subjects

Paulo Shakarian

Publications and source records attributed to Paulo Shakarian.

At least 37 records · Page 2Linked to original sources

arXiv2025

Geospatial Trajectory Generation via Efficient Abduction: Deployment for Independent Testing

The ability to generate artificial human movement patterns while meeting location and time constraints is an important problem in the security community, particularly as it enables the study of the analog problem of detecting such patterns while maintaining privacy. We frame this problem as an instance of abduction guided by a novel parsimony function represented as an aggregate truth value over an annotated logic program. This approach has the added benefit of affording explainability to an analyst user. By showing that any subset of such a program can provide a lower bound on this parsimony requirement, we are able to abduce movement trajectories efficiently through an informed (i.e., A*) search. We describe how our implementation was enhanced with the application of multiple techniques in order to be scaled and integrated with a cloud-based software stack that included bottom-up rule learning, geolocated knowledge graph retrieval/management, and interfaces with government systems for independently conducted government-run tests for which we provide results. We also report on our own experiments showing that we not only provide exact results but also scale to very large scenarios and provide realistic agent trajectories that can go undetected by machine learning anomaly detectors.

cs.LO↗

arXiv2025

Sea-cret Agents: Maritime Abduction for Region Generation to Expose Dark Vessel Trajectories

Bad actors in the maritime industry engage in illegal behaviors after disabling their vessel's automatic identification system (AIS) - which makes finding such vessels difficult for analysts. Machine learning approaches only succeed in identifying the locations of these ``dark vessels'' in the immediate future. This work leverages ideas from the literature on abductive inference applied to locating adversarial agents to solve the problem. Specifically, we combine concepts from abduction, logic programming, and rule learning to create an efficient method that approaches full recall of dark vessels while requiring less search area than machine learning methods. We provide a logic-based paradigm for reasoning about maritime vessels, an abductive inference query method, an automatically extracted rule-based behavior model methodology, and a thorough suite of experiments.

cs.AI↗

arXiv2024

Metal Price Spike Prediction via a Neurosymbolic Ensemble Approach

Predicting price spikes in critical metals such as Cobalt, Copper, Magnesium, and Nickel is crucial for mitigating economic risks associated with global trends like the energy transition and reshoring of manufacturing. While traditional models have focused on regression-based approaches, our work introduces a neurosymbolic ensemble framework that integrates multiple neural models with symbolic error detection and correction rules. This framework is designed to enhance predictive accuracy by correcting individual model errors and offering interpretability through rule-based explanations. We show that our method provides up to 6.42% improvement in precision, 29.41% increase in recall at 13.24% increase in F1 over the best performing neural models. Further, our method, as it is based on logical rules, has the benefit of affording an explanation as to which combination of neural models directly contribute to a given prediction.

cs.LG↗

arXiv2024

Metacognitive AI: Framework and the Case for a Neurosymbolic Approach

Metacognition is the concept of reasoning about an agent's own internal processes and was originally introduced in the field of developmental psychology. In this position paper, we examine the concept of applying metacognition to artificial intelligence. We introduce a framework for understanding metacognitive artificial intelligence (AI) that we call TRAP: transparency, reasoning, adaptation, and perception. We discuss each of these aspects in-turn and explore how neurosymbolic AI (NSAI) can be leveraged to address challenges of metacognition.

cs.AI↗

arXiv2024

Machine Learning Driven Biomarker Selection for Medical Diagnosis

Recent advances in experimental methods have enabled researchers to collect data on thousands of analytes simultaneously. This has led to correlational studies that associated molecular measurements with diseases such as Alzheimer's, Liver, and Gastric Cancer. However, the use of thousands of biomarkers selected from the analytes is not practical for real-world medical diagnosis and is likely undesirable due to potentially formed spurious correlations. In this study, we evaluate 4 different methods for biomarker selection and 4 different machine learning (ML) classifiers for identifying correlations, evaluating 16 approaches in all. We found that contemporary methods outperform previously reported logistic regression in cases where 3 and 10 biomarkers are permitted. When specificity is fixed at 0.9, ML approaches produced a sensitivity of 0.240 (3 biomarkers) and 0.520 (10 biomarkers), while standard logistic regression provided a sensitivity of 0.000 (3 biomarkers) and 0.040 (10 biomarkers). We also noted that causal-based methods for biomarker selection proved to be the most performant when fewer biomarkers were permitted, while univariate feature selection was the most performant when a greater number of biomarkers were permitted.

q-bio.QM↗

arXiv2023

Scalable Semantic Non-Markovian Simulation Proxy for Reinforcement Learning

Recent advances in reinforcement learning (RL) have shown much promise across a variety of applications. However, issues such as scalability, explainability, and Markovian assumptions limit its applicability in certain domains. We observe that many of these shortcomings emanate from the simulator as opposed to the RL training algorithms themselves. As such, we propose a semantic proxy for simulation based on a temporal extension to annotated logic. In comparison with two high-fidelity simulators, we show up to three orders of magnitude speed-up while preserving the quality of policy learned. In addition, we show the ability to model and leverage non-Markovian dynamics and instantaneous actions while providing an explainable trace describing the outcomes of the agent actions.

cs.LG↗

arXiv2023

Diversity Measures: Domain-Independent Proxies for Failure in Language Model Queries

Error prediction in large language models often relies on domain-specific information. In this paper, we present measures for quantification of error in the response of a large language model based on the diversity of responses to a given prompt - hence independent of the underlying application. We describe how three such measures - based on entropy, Gini impurity, and centroid distance - can be employed. We perform a suite of experiments on multiple datasets and temperature settings to demonstrate that these measures strongly correlate with the probability of failure. Additionally, we present empirical results demonstrating how these measures can be applied to few-shot prompting, chain-of-thought reasoning, and error detection.

cs.CL↗

arXiv2023

PyReason: Software for Open World Temporal Logic

The growing popularity of neuro symbolic reasoning has led to the adoption of various forms of differentiable (i.e., fuzzy) first order logic. We introduce PyReason, a software framework based on generalized annotated logic that both captures the current cohort of differentiable logics and temporal extensions to support inference over finite periods of time with capabilities for open world reasoning. Further, PyReason is implemented to directly support reasoning over graphical structures (e.g., knowledge graphs, social networks, biological networks, etc.), produces fully explainable traces of inference, and includes various practical features such as type checking and a memory-efficient implementation. This paper reviews various extensions of generalized annotated logic integrated into our implementation, our modern, efficient Python-based implementation that conducts exact yet scalable deductive inference, and a suite of experiments. PyReason is available at: github.com/lab-v2/pyreason.

cs.LO↗

arXiv2023

An Independent Evaluation of ChatGPT on Mathematical Word Problems (MWP)

We study the performance of a commercially available large language model (LLM) known as ChatGPT on math word problems (MWPs) from the dataset DRAW-1K. To our knowledge, this is the first independent evaluation of ChatGPT. We found that ChatGPT's performance changes dramatically based on the requirement to show its work, failing 20% of the time when it provides work compared with 84% when it does not. Further several factors about MWPs relating to the number of unknowns and number of operations that lead to a higher probability of failure when compared with the prior, specifically noting (across all experiments) that the probability of failure increases linearly with the number of addition and subtraction operations. We also have released the dataset of ChatGPT's responses to the MWPs to support further work on the characterization of LLM performance and present baseline machine learning models to predict if ChatGPT can correctly answer an MWP. We have released a dataset comprised of ChatGPT's responses to support further research in this area.

cs.CL↗

arXiv2023

Extensions to Generalized Annotated Logic and an Equivalent Neural Architecture

While deep neural networks have led to major advances in image recognition, language translation, data mining, and game playing, there are well-known limits to the paradigm such as lack of explainability, difficulty of incorporating prior knowledge, and modularity. Neuro symbolic hybrid systems have recently emerged as a straightforward way to extend deep neural networks by incorporating ideas from symbolic reasoning such as computational logic. In this paper, we propose a list desirable criteria for neuro symbolic systems and examine how some of the existing approaches address these criteria. We then propose an extension to generalized annotated logic that allows for the creation of an equivalent neural architecture comprising an alternate neuro symbolic hybrid. However, unlike previous approaches that rely on continuous optimization for the training process, our framework is designed as a binarized neural network that uses discrete optimization. We provide proofs of correctness and discuss several of the challenges that must be overcome to realize this framework in an implemented system.

cs.AI↗

arXiv2022

Reasoning about Complex Networks: A Logic Programming Approach

Reasoning about complex networks has in recent years become an important topic of study due to its many applications: the adoption of commercial products, spread of disease, the diffusion of an idea, etc. In this paper, we present the MANCaLog language, a formalism based on logic programming that satisfies a set of desiderata proposed in previous work as recommendations for the development of approaches to reasoning in complex networks. To the best of our knowledge, this is the first formalism that satisfies all such criteria. We first focus on algorithms for finding minimal models (on which multi-attribute analysis can be done), and then on how this formalism can be applied in certain real world scenarios. Towards this end, we study the problem of deciding group membership in social networks: given a social network and a set of groups where group membership of only some of the individuals in the network is known, we wish to determine a degree of membership for the remaining group-individual pairs. We develop a prototype implementation that we use to obtain experimental results on two real world datasets, including a current social network of criminal gangs in a major U.S.\ city. We then show how the assignment of degree of membership to nodes in this case allows for a better understanding of the criminal gang problem when combined with other social network mining techniques -- including detection of sub-groups and identification of core group members -- which would not be possible without further identification of additional group members.

cs.AI↗

arXiv2020

Mining user interaction patterns in the darkweb to predict enterprise cyber incidents

With rise in security breaches over the past few years, there has been an increasing need to mine insights from social media platforms to raise alerts of possible attacks in an attempt to defend conflict during competition. In this study, we attempt to build a framework that utilizes unconventional signals from the darkweb forums by leveraging the reply network structure of user interactions with the goal of predicting enterprise related external cyber attacks. We use both unsupervised and supervised learning models that address the challenges that come with the lack of enterprise attack metadata for ground truth validation as well as insufficient data for training the models. We validate our models on a binary classification problem that attempts to predict cyber attacks on a daily basis for an organization. Using several controlled studies on features leveraging the network structure, we measure the extent to which the indicators from the darkweb forums can be successfully used to predict attacks. We use information from 53 forums in the darkweb over a span of 17 months for the task. Our framework to predict real world organization cyber attacks of 3 different security events, suggest that focusing on the reply path structure between groups of users based on random walk transitions and community structures has an advantage in terms of better performance solely relying on forum or user posting statistics prior to attacks.

cs.SI↗

arXiv2020

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the pattern by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game makes a decision among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information.

cs.SI↗

arXiv2020

Leveraging Motifs to Model the Temporal Dynamics of Diffusion Networks

Information diffusion mechanisms based on social influence models are mainly studied using likelihood of adoption when active neighbors expose a user to a message. The problem arises primarily from the fact that for the most part, this explicit information of who-exposed-whom among a group of active neighbors in a social network, before a susceptible node is infected is not available. In this paper, we attempt to understand the diffusion process through information cascades by studying the temporal network structure of the cascades. In doing so, we accommodate the effect of exposures from active neighbors of a node through a network pruning technique that leverages network motifs to identify potential infectors responsible for exposures from among those active neighbors. We attempt to evaluate the effectiveness of the components used in modeling cascade dynamics and especially whether the additional effect of the exposure information is useful. Following this model, we develop an inference algorithm namely InferCut, that uses parameters learned from the model and the exposure information to predict the actual parent node of each potentially susceptible user in a given cascade. Empirical evaluation on a real world dataset from Weibo social network demonstrate the significance of incorporating exposure information in recovering the exact parents of the exposed users at the early stages of the diffusion process.

cs.SI↗

arXiv2020

Understanding and forecasting lifecycle events in information cascades

Most social network sites allow users to reshare a piece of information posted by a user. As time progresses, the cascade of reshares grows, eventually saturating after a certain time period. While previous studies have focused heavily on one aspect of the cascade phenomenon, specifically predicting when the cascade would go viral, in this paper, we take a more holistic approach by analyzing the occurrence of two events within the cascade lifecycle - the period of maximum growth in terms of surge in reshares and the period where the cascade starts declining in adoption. We address the challenges in identifying these periods and then proceed to make a comparative analysis of these periods from the perspective of network topology. We study the effect of several node-centric structural measures on the reshare responses using Granger causality which helps us quantify the significance of the network measures and understand the extent to which the network topology impacts the growth dynamics. This evaluation is performed on a dataset of 7407 cascades extracted from the Weibo social network. Using our causality framework, we found that an entropy measure based on nodal degree causally affects the occurrence of these events in 93.95% of cascades. Surprisingly, this outperformed clustering coefficient and PageRank which we hypothesized would be more indicative of the growth dynamics based on earlier studies. We also extend the Granger-causality Vector Autoregression (VAR) model to forecast the times at which the events occur in the cascade lifecycle.

cs.SI↗

arXiv2020

A Feature-Driven Approach for Identifying Pathogenic Social Media Accounts

Over the past few years, we have observed different media outlets' attempts to shift public opinion by framing information to support a narrative that facilitate their goals. Malicious users referred to as "pathogenic social media" (PSM) accounts are more likely to amplify this phenomena by spreading misinformation to viral proportions. Understanding the spread of misinformation from account-level perspective is thus a pressing problem. In this work, we aim to present a feature-driven approach to detect PSM accounts in social media. Inspired by the literature, we set out to assess PSMs from three broad perspectives: (1) user-related information (e.g., user activity, profile characteristics), (2) source-related information (i.e., information linked via URLs shared by users) and (3) content-related information (e.g., tweets characteristics). For the user-related information, we investigate malicious signals using causality analysis (i.e., if user is frequently a cause of viral cascades) and profile characteristics (e.g., number of followers, etc.). For the source-related information, we explore various malicious properties linked to URLs (e.g., URL address, content of the associated website, etc.). Finally, for the content-related information, we examine attributes (e.g., number of hashtags, suspicious hashtags, etc.) from tweets posted by users. Experiments on real-world Twitter data from different countries demonstrate the effectiveness of the proposed approach in identifying PSM users.

cs.SI↗

arXiv2019

Can social influence be exploited to compromise security: An online experimental evaluation

Social media has enabled users and organizations to obtain information about technology usage like software usage and even security feature usage. However, on the dark side it has also allowed an adversary to potentially exploit the users in a manner to either obtain information from them or influence them towards decisions that might have malicious settings or intents. While there have been substantial efforts into understanding how social influence affects one's likelihood to adopt a security technology, especially its correlation with the number of friends adopting the same technology, in this study we investigate whether peer influence can dictate what users decide over and above their own knowledge. To this end, we manipulate social signal exposure in an online controlled experiment with human participants to investigate whether social influence can be harnessed in a negative way to steer users towards harmful security choices. We analyze this through a controlled game where each participant selects one option when presented with six security technologies with differing utilities, with one choice having the most utility. Over multiple rounds of the game, we observe that social influence as a tool can be quite powerful in manipulating a user's decision towards adoption of security technologies that are less efficient. However, what stands out more in the process is that the manner in which a user receives social signals from its peers decides the extent to which social influence can be successful in changing a user's behavior.

cs.SI↗

arXiv2019

An End-to-End Framework to Identify Pathogenic Social Media Accounts on Twitter

Pathogenic Social Media (PSM) accounts such as terrorist supporter accounts and fake news writers have the capability of spreading disinformation to viral proportions. Early detection of PSM accounts is crucial as they are likely to be key users to make malicious information "viral". In this paper, we adopt the causal inference framework along with graph-based metrics in order to distinguish PSMs from normal users within a short time of their activities. We propose both supervised and semi-supervised approaches without taking the network information and content into account. Results on a real-world dataset from Twitter accentuates the advantage of our proposed frameworks. We show our approach achieves 0.28 improvement in F1 score over existing approaches with the precision of 0.90 and F1 score of 0.63.

cs.SI↗