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Antonio Anastasio Bruto da Costa

Publications and source records attributed to Antonio Anastasio Bruto da Costa.

6 recordsLinked to original sources

Explainable Rule Mining of IPv6 Extension-Header Presence Patterns from Paired-Vantage Captures

IPv6 extension headers (EHs), such as fragmentation, segment routing, and in-situ telemetry, are operationally important yetwidely dropped in transit, and characterising their behaviour from packet captures is a recurring measurement problem. We ask whetheran explainable miner can recover human-readable rules of EH behaviour, and we contribute two reusable tools: a negative-control protocol that diagnoses whether a mined "temporal" network rule reflects genuine cross-packet dynamics or mere within-packetco-occurrence, and a sender-conditioned, per-family EH-retention measurement. Applying an interpretable temporal-logic rule miner to the JAMES paired-vantage dataset, we recover a portable Fragment-EH rule that the protocol reveals to be a within-packet,near-definitional co-occurrence rather than a temporal pattern, so the temporal-logic machinery does no work for this dominant rule;the retention measurement independently recovers the expected within-window ordering of EH observability. Our main result istherefore an honest, controlled negative finding, corroborated by executed decision-tree and large-language-model baselines: on theevaluated JAMES traces network-temporal structure does not carry the dominant Fragment-EH signal, and we supply the controls thatestablish when it would, validated on a synthetic positive control containing a genuine cross-packet dependency.

cs.CR↗

Explaining Outcomes of Multi-Party Dialogues using Causal Learning

Multi-party dialogues are common in enterprise social media on technical as well as non-technical topics. The outcome of a conversation may be positive or negative. It is important to analyze why a dialogue ends with a particular sentiment from the point of view of conflict analysis as well as future collaboration design. We propose an explainable time series mining algorithm for such analysis. A dialogue is represented as an attributed time series of occurrences of keywords, EMPATH categories, and inferred sentiments at various points in its progress. A special decision tree, with decision metrics that take into account temporal relationships between dialogue events, is used for predicting the cause of the outcome sentiment. Interpretable rules mined from the classifier are used to explain the prediction. Experimental results are presented for the enterprise social media posts in a large company.

cs.AI↗

Learning Temporal Causal Sequence Relationships from Real-Time Time-Series

We aim to mine temporal causal sequences that explain observed events (consequents) in time-series traces. Causal explanations of key events in a time-series has applications in design debugging, anomaly detection, planning, root-cause analysis and many more. We make use of decision trees and interval arithmetic to mine sequences that explain defining events in the time-series. We propose modified decision tree construction metrics to handle the non-determinism introduced by the temporal dimension. The mined sequences are expressed in a readable temporal logic language that is easy to interpret. The application of the proposed methodology is illustrated through various examples.

cs.LG↗

Quantitative Corner Case Feature Analysis of Hybrid Automata with ForFET$^{SMT}$

The analysis and verification of hybrid automata (HA) models against rich formal properties can be a challenging task. Existing methods and tools can mainly reason whether a given property is satisfied or violated. However, such qualitative answers might not provide sufficient information about the model behaviors. This paper presents the ForFET$^{SMT}$ tool which can be used to reason quantitatively about such properties. It employs feature automata and can evaluate quantitative property corners of HA. ForFET$^{SMT}$ uses two third-party formal verification tools as its backbone: the SpaceEx reachability tool and the SMT solver dReach/dReal. Herein, we describe the design and implementation of ForFET$^{SMT}$ and present its functionalities and modules. To improve the usability of the tool for non-expert users, we also provide a list of quantitative property templates.

cs.FL↗

Recurrence in Dense-time AMS Assertions

The notion of recurrence over continuous or dense time, as required for expressing Analog and Mixed-Signal (AMS) behaviours, is fundamentally different from what is offered by the recurrence operators of SystemVerilog Assertions (SVA). This article introduces the formal semantics of recurrence over dense time and provides a methodology for the runtime verification of such properties using interval arithmetic. Our property language extends SVA with dense real-time intervals and predicates containing real-valued signals. We provide a tool kit which interfaces with off-the-shelf EDA tools through standard VPI.

cs.FL↗

Formal Feature Interpretation of Hybrid Systems

In current practice a formal analysis of hybrid system models is assertion-based. The work presented here is based on features that look beyond functional correctness toward a quantitative evaluation of behavioral attributes. A feature defines a real-valued evaluation function over a specific set of traces. This paper describes an improved method for the interpretation of features over hybrid automata models. It further demonstrates how satisfiability modulo theory solvers can be used for extracting behavioral traces corresponding to corner cases of a feature. Results are demonstrated on examples from the control and circuit domains.

cs.LO↗