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Universal representation learning for multivariate time series using the instance-level and cluster-level supervised contrastive learning
The multivariate time series classification (MTSC) task aims to predict a class label for a given time series. Recently, modern deep learning-based...
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Efficient Top-k Frequent Itemset Mining on Massive Data
Top- k frequent itemset mining (top- k FIM) plays an important role in many practical applications. It reports the k itemsets with the highest...
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VEM\(^2\)L: an easy but effective framework for fusing text and structure knowledge on sparse knowledge graph completion
The task of Knowledge Graph Completion (KGC) is to infer missing links for Knowledge Graphs (KGs) by analyzing graph structures. However, with...
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Revealing the structural behaviour of Brunelleschi’s Dome with machine learning techniques
The Brunelleschi’s Dome is one of the most iconic symbols of the Renaissance and is among the largest masonry domes ever constructed. Since the late...
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MASS: distance profile of a query over a time series
Given a long time series, the distance profile of a query time series computes distances between the query and every possible subsequence of a long...
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Central node identification via weighted kernel density estimation
The detection of central nodes in a network is a fundamental task in network science and graph data analysis. During the past decades, numerous...
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Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms
Machine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate...
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Where To Go at the Next Timestamp
The next Point of Interest ( POI ) recommendation is the core technology of smart city. Current state-of-the-art models attempt to improve the accuracy...
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Construct and Query A Fine-Grained Geospatial Knowledge Graph
In this paper, we propose the fine-grained geospatial knowledge graph (FineGeoKG), which can capture the neighboring relations between geospatial...
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Fusing structural information with knowledge enhanced text representation for knowledge graph completion
Although knowledge graphs store a large number of facts in the form of triplets, they are still limited by incompleteness. Hence, Knowledge Graph...
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Explicit Behavior Interaction with Heterogeneous Graph for Multi-behavior Recommendation
Multi-behavior recommendation systems exploit multi-type user–item interactions (e.g., clicking, adding to cart and collecting) as auxiliary...
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DB-GPT: Large Language Model Meets Database
Large language models (LLMs) have shown superior performance in various areas. And LLMs have the potential to revolutionize data management by...
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Adaptive Bernstein change detector for high-dimensional data streams
Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and...
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When graph convolution meets double attention: online privacy disclosure detection with multi-label text classification
With the rise of Web 2.0 platforms such as online social media, people’s private information, such as their location, occupation and even family...
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CompTrails: comparing hypotheses across behavioral networks
The term Behavioral Networks describes networks that contain relational information on human behavior. This ranges from social networks that contain...
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Effective signal reconstruction from multiple ranked lists via convex optimization
The ranking of objects is widely used to rate their relative quality or relevance across multiple assessments. Beyond classical rank aggregation, it...
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Predicting consumer choice from raw eye-movement data using the RETINA deep learning architecture
We propose the use of a deep learning architecture, called RETINA, to predict multi-alternative, multi-attribute consumer choice from eye movement...
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Session-based recommendation by exploiting substitutable and complementary relationships from multi-behavior data
Session-based recommendation (SR) aims to dynamically recommend items to a user based on a sequence of the most recent user-item interactions. Most...
