Update papers.bib

Author: jackbergus

_bibliography/papers.bib

@@ -1,6 +1,6 @@
 @incollection{chapter2025xai,
   author      = "Bergami, Giacomo and Fox, Oliver Robert, and Morgan, Graham",
-  title       = "Extracting Specifications Through Verified and Explainable AI: Interpretability, Interoperability, and Trade-Offs",
+  title       = "Extracting Specifications Through Verified and Explainable AI: Interpretability, Interoperability, and Trade-Offs (forthcoming)",
   booktitle   = "Explainable Artificial Intelligence for Trustworthy Decisions in Smart Applications",
   publisher   = "Springer",
   address     = "Switzerland",
@@ -72,24 +72,30 @@ @article{logics2020004
 
 
 @InProceedings{ideas2024a,
-author="Bergami, Giacomo
-and Packer, Emma
-and Scott, Kirsty
-and Del Din, Silvia",
-editor="Chbeir, Richard
-and Ilarri, Sergio
-and Manolopoulos, Yannis
-and Revesz, Peter Z.
-and Bernardino, Jorge
-and Leung, Carson K.",
-title="Predicting Dyskinetic Events Through Verified Multivariate Time Series Classification",
-booktitle="Database Engineered Applications",
-year="2025",
-publisher="Springer Nature Switzerland",
-address="Cham",
-pages="49--62",
-abstract="While monitoring Parkinsonian patients with wearable sensors and tracking their drug assumption patterns, we want to differentiate the behaviours distinguishing periods of relative well-being from dyskinetic events. This requires solving a novel problem, where an entire multivariate time series (MTS) has its class label varying in time, thus leading to a generalised formulation of multivariate time series classification (MTSC). To achieve explainability, we premier the composition of data trend (DT) analysis with DECLAREd, a log temporal declarative language, to derive human-readable correlations across different MTS dimensions' trends. This is mediated by a novel temporal data representation, polyadic logs, supporting both MTS raw data and concurrent activity-labelled durative activities (constituents) for representing event-based classes and concurrent DTs across MTS dimensions. Our validation over a real patient dataset shows that our MTCS algorithm, EMeriTAte, outperforms state-of-the-art MTSC for a novel patient classification task.",
-isbn="978-3-031-83472-1",
+  author       = {Giacomo Bergami and
+                  Emma Packer and
+                  Kirsty Scott and
+                  Silvia Del Din},
+  editor       = {Richard Chbeir and
+                  Sergio Ilarri and
+                  Yannis Manolopoulos and
+                  Peter Z. Revesz and
+                  Jorge Bernardino and
+                  Carson K. Leung},
+  title        = {Predicting Dyskinetic Events Through Verified Multivariate Time Series
+                  Classification},
+  booktitle    = {Database Engineered Applications - 28th International Symposium, {IDEAS}
+                  2024, Bayonne, France, August 26-29, 2024, Proceedings},
+  series       = {Lecture Notes in Computer Science},
+  volume       = {15511},
+  pages        = {49--62},
+  publisher    = {Springer},
+  year         = {2024},
+  url          = {https://doi.org/10.1007/978-3-031-83472-1\_4},
+  doi          = {10.1007/978-3-031-83472-1\_4},
+  timestamp    = {Wed, 02 Apr 2025 17:00:22 +0200},
+  biburl       = {https://dblp.org/rec/conf/ideas/BergamiPSD24.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org},
 	dimensions = {true},
 	bibtex_show = {true},
 	selected={true},
@@ -103,23 +109,28 @@ @InProceedings{ideas2024a
 
 
 @InProceedings{ideas2024b,
-author="Fox, Oliver Robert
-and Bergami, Giacomo
-and Morgan, Graham",
-editor="Chbeir, Richard
-and Ilarri, Sergio
-and Manolopoulos, Yannis
-and Revesz, Peter Z.
-and Bernardino, Jorge
-and Leung, Carson K.",
-title="LaSSI: Logical, Structural, and Semantic Text Interpretation",
-booktitle="Database Engineered Applications",
-year="2025",
-publisher="Springer Nature Switzerland",
-address="Cham",
-pages="106--121",
-abstract="This paper proposes LaSSI, a Natural Language Processing (NLP) pipeline contextualising verified Artificial Intelligence (AI) by transforming text via Montague Grammars (MGs). We are approaching from the point-of-view of graphs and logic, in which we achieve explainable sentence similarity in terms of Knowledge Base (KB) expansion and possible worlds semantics. Experiments in the present paper excel current state-of-the-art, Graph Retrieval-Augmented Generation (RAG)-based technologies, through a novel method surpassing vector-based and graph-based sentence similarity metrics.",
-isbn="978-3-031-83472-1",
+  author       = {Oliver Robert Fox and
+                  Giacomo Bergami and
+                  Graham Morgan},
+  editor       = {Richard Chbeir and
+                  Sergio Ilarri and
+                  Yannis Manolopoulos and
+                  Peter Z. Revesz and
+                  Jorge Bernardino and
+                  Carson K. Leung},
+  title        = {LaSSI: Logical, Structural, and Semantic Text Interpretation},
+  booktitle    = {Database Engineered Applications - 28th International Symposium, {IDEAS}
+                  2024, Bayonne, France, August 26-29, 2024, Proceedings},
+  series       = {Lecture Notes in Computer Science},
+  volume       = {15511},
+  pages        = {106--121},
+  publisher    = {Springer},
+  year         = {2024},
+  url          = {https://doi.org/10.1007/978-3-031-83472-1\_8},
+  doi          = {10.1007/978-3-031-83472-1\_8},
+  timestamp    = {Wed, 02 Apr 2025 17:00:22 +0200},
+  biburl       = {https://dblp.org/rec/conf/ideas/FoxBM24.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org},
 	dimensions = {true},
 	bibtex_show = {true},
 abbr={IDEAS},