1,720,977 research outputs found
SEUPD@CLEF: Team Axolotl on Rumor Verification using Evidence from Authorities
No full textNowadays, Search Engines (SEs) are technologies that are employed by the majority of people daily to satisfy information needs. Even though SEs and their underlying algorithms have been improved for several years, there are many challenges that are still to be solved. In this paper, we propose a possible approach to address Task 5 proposed in the CheckThat! Lab at CLEF 2024. The task involves the identification of relevant tweets from a set of authorities that can be used to verify a given rumor expressed in another tweet (i.e., to determine if the rumor can be trusted or not). It is also necessary to report whether the retrieved tweets support or oppose the considered rumor. We also show the results achieved by our system according to some of its possible configurations, analyzing the results and discussing which parameters impacted the performances the most, both in terms of efficiency and effectiveness. We observe that the usage of Large Language Models (LLMs) can boost effectiveness but results in a severe loss in terms of efficiency compared to less complex models. We finally show that our proposed system manages to achieve better results in terms of effectiveness compared to the ones achieved by the baseline provided by the Lab organizers on the English dataset available for this task
Applying the SCM to a specific intergroup relationship: Northern versus southern Italians
No full textIn the present study the SCM was applied to a specific and culturally salient intergroup relationship: Northern versus Southern Italians. We aimed to investigate whether the perceptions of structural attributes and their related stereotypic traits would remain the same when judgments were expressed by group members. Two student samples were recruited, one from Northern (N = 183), the other from Southern Italy (N = 182). Using questionnaires, the SCM main hypotheses were tested. Results are consistent with the model's predictions, and reflect the cultural stereotypes of the two groups. However, some interesting unexpected results were found and discussed
Overview of QuantumCLEF 2024: The Quantum Computing Challenge for Information Retrieval and Recommender Systems at CLEF
Full text linkQuantum Computing (QC) is an innovative research field that has gathered the interest of many researchers in the last few years. In fact, it is believed that QC could potentially revolutionize the way we solve very complex problems by dramatically decreasing the time required to solve them. Even though QC is still in its early stages of development, it is already possible to tackle some problems by means of quantum computers and to start catching a glimpse of its potential. Therefore, the aim of the QuantumCLEF lab is to raise awareness about QC and to develop and evaluate new QC algorithms to solve challenges that can be encountered when implementing Information Retrieval (IR) and Recommender Systems (RS) systems. Furthermore, this lab represents a good opportunity to engage with QC technologies which are typically not easily accessible. In this work, we present an overview of the first edition of QuantumCLEF, a lab that focuses on the application of Quantum Annealing (QA), a specific QC paradigm, to solve two tasks: Feature Selection for IR and RS systems, and Clustering for IR systems. There have been a total of 26 teams who registered for this lab and eventually 7 teams managed to successfully submit their runs following the lab guidelines. Due to the novelty of the topics, participants have been provided with many examples and comprehensive materials that allowed them to understand how QA works and how to program quantum annealers
QuantumCLEF 2024: Overview of the Quantum Computing Challenge for Information Retrieval and Recommender Systems at CLEF
Full text linkThe emerging field of Quantum Computing (QC) in computational science is attracting significant research interest due to its potential for groundbreaking applications. In fact, it is believed that QC could potentially revolutionize the way we solve very complex problems by significantly decreasing the time required to solve them. Even though QC is still in its early stages of development, it is already possible to tackle some problems using quantum computers and, thus, begin to see its potential. Therefore, the aim of the QuantumCLEF lab is to raise awareness about QC and to develop and evaluate new QC algorithms to solve challenges that are usually faced when implementing Information Retrieval (IR) and Recommender Systems (RS) systems. Furthermore, this lab represents a good opportunity to engage with QC technologies, which are typically not easily accessible due to their early development stage. In this work, we present an overview of the first edition of QuantumCLEF, a lab that focuses on the application of Quantum Annealing (QA), a specific QC paradigm, to solve two tasks: Feature Selection for IR and RS systems, and Clustering for IR systems. There were a total of 26 teams who registered for this lab, and eventually, 7 teams successfully submitted their runs following the lab guidelines. Due to the novelty of the topics, participants were provided with many examples and comprehensive materials to help them understand how QA works and how to program quantum annealers
A Quantum Annealing Instance Selection Approach for Efficient and Effective Transformer Fine-Tuning
Full text linkDeep Learning approaches have become pervasive in recent years due to their ability to solve complex tasks. However, these models need huge datasets for proper training and good generalization. This translates into high training and fine-tuning time, even several days for the most complex models and large datasets. In this work, we present a novel quantum Instance Selection (IS) approach that allows to significantly reduce the size of the training datasets (by up to 28%) while maintaining the model's effectiveness, thus promoting (training) speedups and scalability. Our solution is innovative in the sense that it exploits a different computing paradigm - Quantum Annealing (QA) - a specific Quantum Computing paradigm that can be used to tackle optimization problems. To the best of our knowledge, there have been no prior attempts to tackle the IS problem using QA. Furthermore, we propose a new Quadratic Unconstrained Binary Optimization formulation specific for the IS problem, which is a contribution in itself. Through an extensive set of experiments with several Text Classification benchmarks, we empirically demonstrate our quantum solution's feasibility and competitiveness with the current state-of-the-art IS solutions
Predictive validity of the host community acculturation scale: The effects of social dominance orientation and the belief in biological determinism
No full textIn this study, the predictive validity of the Italian version of the host community acculturation scale (HCAS; Barrette, Bourhis, Capozza, & Hichy, 2005) was tested using multiple regression. Participants (university students) completed the HCAS for three target groups (Immigrants, the Chinese, Albanians). Acculturation attitudes were measured in the domains of employment and cultural heritage. Social dominance orientation (SDO; Sidanius & Pratto, 1999), national and political identification were used as predictors for each acculturation orientation. In line with previous research, results showed that SDO was the main predictor of the acculturation orientations. Authors hypothesized that the effect of SDO was mediated by the belief in genetic determinism (BDG; Keller, 2005), namely, the belief that members of social categories share immutable characteristics, fixed in the genes. Results supported the hypothesis, but only in the culture domain and for the rejection orientations
QuantumCLEF 2024: Overview of the Quantum Computing Challenge for Information Retrieval and Recommender Systems at CLEF
No full textThe emerging field of Quantum Computing (QC) in computational science is attracting significant research interest due to its potential for groundbreaking applications. In fact, it is believed that QC could potentially revolutionize the way we solve very complex problems by significantly decreasing the time required to solve them. Even though QC is still in its early stages of development, it is already possible to tackle some problems using quantum computers and, thus, begin to see its potential. Therefore, the aim of the QuantumCLEF lab is to raise awareness about QC and to develop and evaluate new QC algorithms to solve challenges that are usually faced when implementing Information Retrieval (IR) and Recommender Systems (RS) systems. Furthermore, this lab represents a good opportunity to engage with QC technologies, which are typically not easily accessible due to their early development stage. In this work, we present an overview of the first edition of QuantumCLEF, a lab that focuses on the application of Quantum Annealing (QA), a specific QC paradigm, to solve two tasks: Feature Selection for IR and RS systems, and Clustering for IR systems. There were a total of 26 teams who registered for this lab, and eventually, 7 teams successfully submitted their runs following the lab guidelines. Due to the novelty of the topics, participants were provided with many examples and comprehensive materials to help them understand how QA works and how to program quantum annealers
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
