| Nome |
# |
|
Discovering Generalized Association Rules from Twitter, file e384c42e-192d-d4b2-e053-9f05fe0a1d67
|
991
|
|
Data mining by means of generalized patterns, file e384c42e-1887-d4b2-e053-9f05fe0a1d67
|
833
|
|
Infrequent Weighted Itemset Mining Using Frequent Pattern Growth, file e384c42e-28d0-d4b2-e053-9f05fe0a1d67
|
788
|
|
Generalized association rule mining with constraints, file e384c42e-110c-d4b2-e053-9f05fe0a1d67
|
762
|
|
Multi-document summarization based on the Yago ontology, file e384c42e-2d97-d4b2-e053-9f05fe0a1d67
|
665
|
|
GraphSum: discovering correlations among multiple terms for graph-based summarization, file e384c42e-2e1b-d4b2-e053-9f05fe0a1d67
|
657
|
|
PaWI: Parallel Weighted Itemset Mining by means of MapReduce, file e384c42e-d2e4-d4b2-e053-9f05fe0a1d67
|
576
|
|
Expressive generalized itemsets, file e384c42e-3120-d4b2-e053-9f05fe0a1d67
|
546
|
|
Twitter data analysis by means of Strong Flipping Generalized Itemsets, file e384c42e-311f-d4b2-e053-9f05fe0a1d67
|
543
|
|
EnBay: A Novel Pattern-Based Bayesian Classifier, file e384c42e-2423-d4b2-e053-9f05fe0a1d67
|
499
|
|
Modeling correlations among air pollution-related data through generalized association rules, file e384c42f-1713-d4b2-e053-9f05fe0a1d67
|
490
|
|
CAS-MINE: Providing personalized services in context-aware applications by means of generalized rules., file e384c42e-10cf-d4b2-e053-9f05fe0a1d67
|
484
|
|
Misleading Generalized Itemset discovery, file e384c42e-2b2d-d4b2-e053-9f05fe0a1d67
|
471
|
|
Digging deep into weighted patient data through multiple-level patterns, file e384c42d-b1f8-d4b2-e053-9f05fe0a1d67
|
467
|
|
NEMICO: Mining network data through cloud-based data mining techniques, file e384c42e-38c8-d4b2-e053-9f05fe0a1d67
|
422
|
|
Combining news sentiment and technical analysis to predict stock trend reversal, file e384c431-44f3-d4b2-e053-9f05fe0a1d67
|
421
|
|
Personalized tag recommendation based on generalized rules, file e384c42e-1b4e-d4b2-e053-9f05fe0a1d67
|
409
|
|
Pattern Set Mining with Schema-based Constraint, file e384c42d-ac9e-d4b2-e053-9f05fe0a1d67
|
389
|
|
Discovering temporal change patterns in the presence of taxonomies, file e384c42e-1100-d4b2-e053-9f05fe0a1d67
|
384
|
|
Monitoring the citizens’ perception on urban security
in Smart City environments, file e384c42e-cf96-d4b2-e053-9f05fe0a1d67
|
373
|
|
MeTA: Characterization of medical treatments at different abstraction levels, file e384c42d-b258-d4b2-e053-9f05fe0a1d67
|
359
|
|
Discovering High-Utility Itemsets at Multiple Abstraction Levels, file e384c42f-a7e6-d4b2-e053-9f05fe0a1d67
|
344
|
|
Test-driven summarization: combining formative assessment with teaching document summarization, file e384c42f-a361-d4b2-e053-9f05fe0a1d67
|
331
|
|
Experimental validation of a massive educational service in a blended learning environment, file e384c42f-9f6a-d4b2-e053-9f05fe0a1d67
|
292
|
|
Itemset generalization with cardinality-based constraints, file e384c42e-28ce-d4b2-e053-9f05fe0a1d67
|
249
|
|
Educational video services in universities: a systematic effectiveness analysis, file e384c42f-dfd9-d4b2-e053-9f05fe0a1d67
|
247
|
|
Supporting stock trading in multiple foreign markets: a multilingual news summarization approach, file e384c42f-3fc3-d4b2-e053-9f05fe0a1d67
|
246
|
|
Discovering air quality patterns in urban environments, file e384c42f-13e2-d4b2-e053-9f05fe0a1d67
|
191
|
|
Identifying collaborations among researchers: a pattern-based approach, file e384c42f-96cd-d4b2-e053-9f05fe0a1d67
|
161
|
|
DSLE: A Smart Platform for Designing Data Science Competitions, file e384c432-5eb3-d4b2-e053-9f05fe0a1d67
|
160
|
|
Highlighter: automatic highlighting of electronic learning documents, file e384c42f-abcb-d4b2-e053-9f05fe0a1d67
|
140
|
|
Learning from summaries: supporting e-learning activities by means of document summarization, file e384c42f-32e7-d4b2-e053-9f05fe0a1d67
|
118
|
|
E-MIMIC: Empowering Multilingual Inclusive Communication, file e384c434-3cdb-d4b2-e053-9f05fe0a1d67
|
110
|
|
Heterogeneous industrial vehicle usage predictions: A real case, file e384c430-d5f7-d4b2-e053-9f05fe0a1d67
|
98
|
|
Quantitative cryptocurrency trading: exploring the use of machine learning techniques, file e384c433-d6b2-d4b2-e053-9f05fe0a1d67
|
83
|
|
Cross-Lingual Propagation of Sentiment Information Based on Bilingual Vector Space Alignment, file e384c432-2e88-d4b2-e053-9f05fe0a1d67
|
78
|
|
Predicting Car Availability in Free Floating Car Sharing Systems: Leveraging Machine Learning in Challenging Contexts, file e384c432-811f-d4b2-e053-9f05fe0a1d67
|
73
|
|
Summarize Dates First: A Paradigm Shift in Timeline Summarization, file e384c433-c88e-d4b2-e053-9f05fe0a1d67
|
71
|
|
Predicting student academic performance by means of associative classification, file e384c433-3ab8-d4b2-e053-9f05fe0a1d67
|
68
|
|
From teaching books to educational videos and vice versa: a cross-media content retrieval experience, file e384c434-0249-d4b2-e053-9f05fe0a1d67
|
67
|
|
An explainable data-driven approach to web directory taxonomy mapping, file e384c432-e2d5-d4b2-e053-9f05fe0a1d67
|
59
|
|
Training ensembles of faceted classification models for quantitative stock trading, file e384c433-a7c9-d4b2-e053-9f05fe0a1d67
|
59
|
|
From Hotel Reviews to City Similarities: A Unified Latent-Space Model, file e384c431-4486-d4b2-e053-9f05fe0a1d67
|
58
|
|
Leveraging the explainability of associative classifiers to support quantitative stock trading, file e384c432-51be-d4b2-e053-9f05fe0a1d67
|
54
|
|
Improving the effectiveness of SQL learning practice: a data-driven approach, file e384c430-2704-d4b2-e053-9f05fe0a1d67
|
53
|
|
UNIFORM: Automatic Alignment of Open Learning Datasets, file e384c432-8952-d4b2-e053-9f05fe0a1d67
|
53
|
|
Automatic slides generation in the absence of training data, file e384c433-f4e5-d4b2-e053-9f05fe0a1d67
|
50
|
|
End-to-end Training For Financial Report Summarization, file e384c432-a627-d4b2-e053-9f05fe0a1d67
|
46
|
|
REDTag: A Predictive Maintenance Framework for Parcel Delivery Services, file e384c431-b91a-d4b2-e053-9f05fe0a1d67
|
45
|
|
Recommending Personalized Summaries of Teaching Materials, file e384c431-72b4-d4b2-e053-9f05fe0a1d67
|
44
|
|
Preventive maintenance for heterogeneous industrial vehicles with incomplete usage data, file e384c433-9067-d4b2-e053-9f05fe0a1d67
|
44
|
|
Characterizing situations of dock overload in bicycle sharing stations, file e384c430-bc23-d4b2-e053-9f05fe0a1d67
|
43
|
|
Discovering profitable stocks for intraday trading, file e384c433-18e7-d4b2-e053-9f05fe0a1d67
|
42
|
|
Profiling industrial vehicle duties using CAN bus signal segmentation and clustering, file e384c434-155e-d4b2-e053-9f05fe0a1d67
|
37
|
|
Machine learning supported next-maintenance prediction for industrial vehicles, file e384c432-790c-d4b2-e053-9f05fe0a1d67
|
35
|
|
L’analyse du discours et l’intelligence artificielle pour réaliser une écriture inclusive : le projet EMIMIC, file e384c434-ea63-d4b2-e053-9f05fe0a1d67
|
26
|
|
Exploiting pivot words to classify and summarize discourse facets of scientific papers, file e384c432-78ea-d4b2-e053-9f05fe0a1d67
|
22
|
|
Summarization of emergency news articles driven by relevance feedback, file e384c430-292f-d4b2-e053-9f05fe0a1d67
|
21
|
|
Leveraging full-text article exploration for citation analysis, file e384c433-c8fa-d4b2-e053-9f05fe0a1d67
|
21
|
|
Leveraging multimodal content for podcast summarization, file e384c434-7c07-d4b2-e053-9f05fe0a1d67
|
17
|
|
Cross-lingual timeline summarization, file e384c434-28a3-d4b2-e053-9f05fe0a1d67
|
15
|
|
Exploring Subgroup Performance In End-to-End Speech Models, file 58230c2a-c650-4083-a8d1-c92320fd43cb
|
12
|
|
Discovering cross-topic collaborations among researchers by exploiting weighted association rules, file e384c432-f85b-d4b2-e053-9f05fe0a1d67
|
11
|
|
Characterizing unpredictable patterns in Wireless Sensor Network data, file e384c431-80bb-d4b2-e053-9f05fe0a1d67
|
10
|
|
How Much Attention Should we Pay to Mosquitoes?, file a927875e-3d1e-4c89-8639-f0999c498a11
|
9
|
|
Inferring multilingual domain-specific word embeddings from large document corpora, file e384c434-3f01-d4b2-e053-9f05fe0a1d67
|
9
|
|
Transformer-based Non-Verbal Emotion Recognition: Exploring Model Portability across Speakers’ Genders, file 18f9639e-c5cf-4416-b3b3-5c638dadfc9d
|
8
|
|
Density-based Clustering by Means of Bridge Point Identification, file 19aead30-63dc-48fb-8857-a7a2ccc6053c
|
8
|
|
Summarising Multilingual Documents: The Unexpressed Potential of Deep Natural Language Processing, file 271e982e-a19d-480f-8a1c-609f38db121a
|
8
|
|
ViPER: Video-based Perceiver for Emotion Recognition, file 260d50ef-b7a8-4d19-95ab-e0b323f4fbd1
|
7
|
|
DSLE: A Smart Platform for Designing Data Science Competitions, file e384c432-3460-d4b2-e053-9f05fe0a1d67
|
7
|
|
Extracting highlights of scientific articles: A supervised summarization approach, file e384c432-847b-d4b2-e053-9f05fe0a1d67
|
7
|
|
Early portfolio pruning: a scalable approach to hybrid portfolio selection, file e9f5806f-ecc0-462d-b1b9-76db6042e7e7
|
7
|
|
DQNC2S: DQN-based Cross-stream Crisis event Summarizer, file 2313aaed-830b-48d1-b7f1-3707ff45a8b8
|
6
|
|
BART-IT: An Efficient Sequence-to-Sequence Model for Italian Text Summarization, file 94d08d47-a16c-4fab-ac23-7a95a55f8f74
|
6
|
|
CarPredictor: forecasting the number of free floating car sharing vehicles within restricted urban areas, file e384c430-f5e0-d4b2-e053-9f05fe0a1d67
|
6
|
|
Discovering profitable stocks for intraday trading, file e384c431-bd3a-d4b2-e053-9f05fe0a1d67
|
6
|
|
Mining SpatioTemporally Invariant Patterns, file f98120d0-81cd-4a3f-bc09-4f0809f55b6d
|
6
|
|
Leveraging the momentum effect in machine learning-based cryptocurrency trading, file e008c2ff-f768-4dcf-a80c-1b52d39851eb
|
5
|
|
Infrequent Weighted Itemset Mining Using Frequent Pattern Growth, file e384c42e-a226-d4b2-e053-9f05fe0a1d67
|
5
|
|
ELSA: A multilingual document summarization algorithm based on frequent itemsets and latent semantic analysis, file e384c430-cd71-d4b2-e053-9f05fe0a1d67
|
5
|
|
Learning Confidence Intervals for Feature Importance: A Fast Shapley-based Approach, file 0510efed-a40b-4b62-bbac-40267e91ab52
|
4
|
|
Machine learning supported next-maintenance prediction for industrial vehicles, file e384c432-5c6c-d4b2-e053-9f05fe0a1d67
|
4
|
|
A multi-faceted characterization of free-floating car sharing service usage, file e384c433-1097-d4b2-e053-9f05fe0a1d67
|
4
|
|
From teaching books to educational videos and vice versa: a cross-media content retrieval experience, file e384c434-4b27-d4b2-e053-9f05fe0a1d67
|
4
|
|
On the use of Pretrained Language Models for Legal Italian Document Classification, file 5576bdd8-cc9b-404c-88a4-8a221b84721d
|
3
|
|
Leveraging Explainable AI to Support Cryptocurrency Investors, file 5f6a029e-ec56-41cc-8ab0-e97bfbb86607
|
3
|
|
Enhancing BERT-Based Visual Question Answering through Keyword-Driven Sentence Selection, file 98e3c00b-4026-4c02-b210-47f001784acd
|
3
|
|
Extractive Conversation Summarization Driven by Textual Entailment Prediction, file 99ee8d72-f383-4662-9c3e-ba965375241a
|
3
|
|
ITALIC: An Italian Intent Classification Dataset, file bfc1e161-a9ee-4eea-8cd9-a48139b20518
|
3
|
|
Discovering cross-topic collaborations among researchers by exploiting weighted association rules, file e384c430-3126-d4b2-e053-9f05fe0a1d67
|
3
|
|
Combining Machine Learning and Natural Language Processing for Language-Specific, Multi-Lingual, and Cross-Lingual Text Summarization: A Wide-Ranging Overview, file e384c431-767d-d4b2-e053-9f05fe0a1d67
|
3
|
|
Highlighter: automatic highlighting of electronic learning documents, file e384c431-7aaa-d4b2-e053-9f05fe0a1d67
|
3
|
|
An explainable data-driven approach to web directory taxonomy mapping, file e384c432-6855-d4b2-e053-9f05fe0a1d67
|
3
|
|
Leveraging the explainability of associative classifiers to support quantitative stock trading, file e384c432-6e4b-d4b2-e053-9f05fe0a1d67
|
3
|
|
Leveraging summarization techniques in educational technology systems, file 04d30fd6-f86d-4684-afa3-16e778a78a14
|
2
|
|
Transformer-based Non-Verbal Emotion Recognition: Exploring Model Portability across Speakers’ Genders, file 0aba1025-9088-4f91-bcc9-0936bf267b39
|
2
|
|
Detecting industrial vehicles’ duty levels using contrastive learning, file 0cd90813-b072-4035-bb7d-bdc64ef0442a
|
2
|
|
PoliTo at SemEval-2023 Task 1: CLIP-based Visual-Word Sense Disambiguation Based on Back-Translation, file 0e4d71b9-263f-46ba-b4f9-3adfd4249e12
|
2
|
|
Automatic Inference of Taxonomy Relationships Among Legal Documents, file 11dda044-37b1-482a-b934-72540130e4a4
|
2
|
| Totale |
15.704 |
Archivio Istituzionale della Ricerca