Update papers.bib

Author: jackbergus

_bibliography/papers.bib

@@ -62,13 +62,26 @@ @article{logics2020004
 }
 
 
-@inproceedings{ideas2024a,
-	author = {Bergami, Giacomo and  Packer, Emma and  Scott, Kirsty and Del Din, Silvia},
-	title = {Predicting Dyskinetic Events through Verified Multivariate Time Series Classification},
-	year = {2025},
-	publisher = {Springer},
-	booktitle = {Database Engineered Applications},
-	series = {IDEAS '24 (in press)},
+
+@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",
 	dimensions = {true},
 	bibtex_show = {true},
 	selected={true},
@@ -77,13 +90,27 @@ @inproceedings{ideas2024a
 }
 
 
-@inproceedings{ideas2024b,
-	author = {Fox, Oliver Robert and Bergami, Giacomo and  Morgan, Graham},
-	title = { 	LaSSI: Logical, Structural, and Semantic text Interpretation},
-	year = {2025},
-	publisher = {Springer},
-	booktitle = {Database Engineered Applications},
-	series = {IDEAS '24 (in press)},
+
+
+
+@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",
 	dimensions = {true},
 	bibtex_show = {true},
 abbr={IDEAS},
@@ -92,6 +119,7 @@ @inproceedings{ideas2024b
 }
 
 
+
 @INPROCEEDINGS{simbridger2,
   author={Almutairi, Reham and Gillgallon, Rohin and Bergami, Giacomo and Morgan, Graham},
   booktitle={2024 20th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)},