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Found 28 papers, 5 papers with code
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.
Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification without Prior Knowledge
Recent advances in Hierarchical Multi-label Classification (HMC), particularly neurosymbolic-based approaches, have demonstrated improved consistency and accuracy by enforcing constraints on a neural model during training.
Hierarchical Multi-label Classification
MUlTI-LABEL-ClASSIFICATION
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.
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.
Machine Learning Driven Biomarker Selection for Medical Diagnosis
Recent advances in experimental methods have enabled researchers to collect data on thousands of analytes simultaneously.
Scalable Semantic Non-Markovian Simulation Proxy for Reinforcement Learning
Recent advances in reinforcement learning (RL) have shown much promise across a variety of applications.
Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classification
Classification of movement trajectories has many applications in transportation and is a key component for large-scale movement trajectory generation and anomaly detection which has key safety applications in the aftermath of a disaster or other external shock.
Diversity Measures: Domain-Independent Proxies for Failure in Language Model Queries
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.
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.
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.
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.
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.
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.
Detecting Pathogenic Social Media Accounts without Content or Network Structure
The spread of harmful mis-information in social media is a pressing problem.
DARKMENTION: A Deployed System to Predict Enterprise-Targeted External Cyberattacks
In particular, we show that it outperforms baseline systems that attempt to generate warnings of cyber attacks related to two enterprises with an average increase in F1 score of about 45% and 57%.
Early Identification of Pathogenic Social Media Accounts
Pathogenic Social Media (PSM) accounts such as terrorist supporters exploit large communities of supporters for conducting attacks on social media.
Causal Inference for Early Detection of Pathogenic Social Media Accounts
Pathogenic social media accounts such as terrorist supporters exploit communities of supporters for conducting attacks on social media.
Strongly Hierarchical Factorization Machines and ANOVA Kernel Regression
High-order parametric models that include terms for feature interactions are applied to various data mining tasks, where ground truth depends on interactions of features.
Semi-Supervised Learning for Detecting Human Trafficking
Human trafficking is one of the most atrocious crimes and among the challenging problems facing law enforcement which demands attention of global magnitude.
A Non-Parametric Learning Approach to Identify Online Human Trafficking
Human trafficking is among the most challenging law enforcement problems which demands persistent fight against from all over the globe.
Darknet and Deepnet Mining for Proactive Cybersecurity Threat Intelligence
We have developed an operational system for obtaining information from these sites for the purposes of identifying emerging cyber threats.
MIST: Missing Person Intelligence Synthesis Toolkit
Each day, approximately 500 missing persons cases occur that go unsolved/unresolved in the United States.
Product Offerings in Malicious Hacker Markets
Marketplaces specializing in malicious hacking products - including malware and exploits - have recently become more prominent on the darkweb and deepweb.
Argumentation Models for Cyber Attribution
A major challenge in cyber-threat analysis is combining information from different sources to find the person or the group responsible for the cyber-attack.
Mining for Causal Relationships: A Data-Driven Study of the Islamic State
The Islamic State of Iraq and al-Sham (ISIS) is a dominant insurgent group operating in Iraq and Syria that rose to prominence when it took over Mosul in June, 2014.
An Argumentation-Based Framework to Address the Attribution Problem in Cyber-Warfare
Attributing a cyber-operation through the use of multiple pieces of technical evidence (i. e., malware reverse-engineering and source tracking) and conventional intelligence sources (i. e., human or signals intelligence) is a difficult problem not only due to the effort required to obtain evidence, but the ease with which an adversary can plant false evidence.
Belief Revision in Structured Probabilistic Argumentation
In real-world applications, knowledge bases consisting of all the information at hand for a specific domain, along with the current state of affairs, are bound to contain contradictory data coming from different sources, as well as data with varying degrees of uncertainty attached.
MANCaLog: A Logic for Multi-Attribute Network Cascades (Technical Report)
The modeling of cascade processes in multi-agent systems in the form of 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.
