Knowledge and Information Systems

While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Merging distributed computing with ...

Human activity recognition (HAR) has received the significant attention in the field of security and surveillance due to its high potential for real-time monitoring, identifying the abnormal activities and situational awareness. HAR is able to ...

Every year the volume of information is growing at a high rate; therefore, more modern approaches are required to deal with such issues efficiently. Distributed systems, such as Apache Spark, offer such a modern approach, resulting in more and ...

Recommendation (recommender) systems (RS) have played a significant role in both research and industry in recent years. In the area of academia, there is a need to help researchers discover the most appropriate and relevant scientific information ...

We propose a model that forecasts market correlation structure from link- and node-based financial network features using machine learning. For such, market structure is modeled as a dynamic asset network by quantifying time-dependent co-movement ...$\mathrm{}$

Based on the complex network, the relationship in the real complex system can be modeled, and the bipartite network is a special complex network, which can describe the complex system containing two kinds of objects. Although existing bipartite ...

Attributed graph clustering or community detection which learns to cluster the nodes of a graph is a challenging task in graph analysis. Recently contrastive learning has shown significant results in various unsupervised graph learning tasks. In ...

In some actual decision-making problems, experts may be hesitant to judge the performances of alternatives, which leads to experts providing decision matrices with incomplete information. However, most existing estimation methods for incomplete ...

Semi-supervised learning is a promising approach to dealing with the problem of insufficient labeled data. Recent methods grouped into paradigms of consistency regularization and pseudo-labeling have outstanding performances on image data, but ...

This work presents an approach for distributed and contextualized reasoning in multi-agent systems, considering environments in which agents may have incomplete, uncertain and inconsistent knowledge. Knowledge is represented by defeasible logic ...

Different embeddings capture various linguistic aspects, such as syntactic, semantic, and contextual information. Taking into account the diverse linguistic facets, we propose a novel hybrid model. This model hinges on the amalgamation of multiple ...

As deep neural networks for visual recognition gain momentum, many studies have modified the loss function to improve the classification performance on long-tailed data. Typical and effective improvement strategies are to assign different weights ...${}_{}$${}_{}$

A large number of association rules often minimizes the reliability of data mining results; hence, a dimensionality reduction technique is crucial for data analysis. When analyzing massive datasets, existing models take more time to scan the ...

Alzheimer’s detection is a challenging task for physicians. There are subtle differences in the bio-marker characteristics of Alzheimers and mild cognitive impairment patients which is very difficult to detect by a physician. Machine learning ...

Many time series data mining algorithms work by reasoning about the relationships the conserved shapes of subsequences. To facilitate this, the Matrix Profile is a data structure that annotates a time series by recording each subsequence’s ...

We study the certification of stability properties, such as robustness and individual fairness, of the k-nearest neighbor algorithm (kNN). Our approach leverages abstract interpretation, a well-established program analysis technique that has been ...

In multi-label classification, data can have multiple labels simultaneously. Two approaches to this issue are either transforming the multi-label data or adapting single-label algorithms for multi-label data. Despite the problem transformation’s ...

Ontologies constitute the semantic model of Knowledge Graphs (KGs). This structural association indicates the potential existence of methodological analogies in the development of ontologies and KGs. The deployment of fully and well-defined ...

One of the challenges practitioners face when applying structure learning algorithms to their data involves determining a set of hyperparameters; otherwise, a set of hyperparameter defaults is assumed. The optimal hyperparameter configuration ...

Multi-view learning consistently outperforms traditional single-view learning by leveraging multiple perspectives of data. However, the effectiveness of multi-view learning heavily relies on how the data are partitioned into feature sets. In many ...

Lexical answer type prediction is integral to biomedical question–answering systems. LAT prediction aims to predict the expected answer’s semantic type of a factoid or list-type biomedical question. It also aids in the answer processing stage of a ...

This paper identifies trends in the application of big data in the transport sector and categorizes research work across scientific subfields. The systematic analysis considered literature published between 2012 and 2022. A total of 2671 studies ...