Nowadays, 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.
Quantum 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 rep- resents 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.
The 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.
The field of Quantum Computing (QC) has gained significant popularity in recent years, due to its potential to provide benefits in terms of efficiency and effectiveness when employed to solve certain computationally intensive tasks. In both Information Retrieval (IR) and Recommender Systems (RS) we are required to build methods that apply complex processing on large and heterogeneous datasets, it is natural therefore to wonder whether QC could also be applied to boost their performance. The tutorial aims to provide first an introduction to QC for an audience that is not familiar with the technology, then to show how to apply the QC paradigm of Quantum Annealing (QA) to solve practical problems that are currently faced by IR and RS systems. During the tutorial, participants will be provided with the fundamentals required to understand QC and to apply it in practice by using a real D-Wave quantum annealer through APIs.
Deep Learning approaches have become pervasive in recent years. In fact, they allow for solving tasks that were thought to be too complex a few decades ago, sometimes with superhuman effectiveness. However, these models require huge datasets to be properly trained and to provide a 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 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 -- QA -- a specific Quantum Computing paradigm that can be used to tackle practical 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 QUBO formulation specific for the IS problem, which is a contribution in itself. Through an extensive set of experiments with several ATC benchmarks, we empirically demonstrate both the feasibility of our quantum solution and its competitiveness with the current state-of-the-art IS solutions.
The National Recovery and Resilience Plan (PNRR) allocates funds to universities for participating in an initiative for delivering orientation courses to students in the last three years of secondary education. The University of Padua is among the institutions joining this initiative. The main objective of these courses is to help students understand the significance of higher education and its value to society. These courses also provide students with an opportunity to explore different educational offerings. Additionally, students can gain practical experience in active and laboratory-based disciplinary teaching, consolidate their knowledge, and develop reflective and transversal skills. Finally, students can also get an overview of various employment sectors and potential job prospects. However, this initiative requires a big effort to plan all the lectures and manage the huge amount of students, institutes, courses, and professors involved. Therefore, in this work we present a Web application, called PNRRorienta, we have designed and developed to manage and simplify all the tasks related to this initiative. The University of Padua has started to use this application in September 2023 and, as of February 2024, it handles more than 70 different secondary education institutes in the Veneto Region, almost 200 courses offered to students, more than 1,300 lectures, more than 400 professors, and almost 10,000 students actively enrolled.
Quantum Computing (QC) is a research field that has been in the limelight in recent years. In fact, many researchers and practitioners believe that it can provide benefits in terms of efficiency and effectiveness when employed to solve certain computationally intensive tasks. In Information Retrieval (IR) and Recommender Systems (RS) we are required to process very large and heterogeneous datasets by means of complex operations, it is natural therefore to wonder whether QC could also be applied to boost their performance. The goal of this tutorial is to show how QC works to an audience that is not familiar with the technology, as well as how to apply the QC paradigm of Quantum Annealing (QA) to solve practical problems that are currently faced by IR and RS systems. During the tutorial, participants will be provided with the fundamentals required to understand QC and to apply it in practice by using a real D-Wave quantum annealer through APIs.
Over the last few years, Quantum Computing (QC) has captured the attention of numerous researchers pertaining to different fields since, due to technological advancements, QC resources have become more available and also applicable in solving practical problems. In the current landscape, Information Retrieval (IR) and Recommender Systems (RS) need to perform computationally intensive operations on massive and heterogeneous datasets. Therefore, it could be possible to use QC and especially Quantum Annealing (QA) technologies to boost systems' performance both in terms of efficiency and effectiveness. The objective of this work is to present the first edition of the QuantumCLEF lab, which is composed of two tasks that aim at:
Quantum Computing (QC) has been a focus of research for many researchers over the last few years. As a result of technological development, QC resources are also becoming available and usable to solve practical problems in the Information Retrieval (IR) and Recommender Systems (RS) fields. Nowadays IR and RS need to perform complex operations on very large datasets. In this scenario, it could be possible to increase the performance of these systems both in terms of efficiency and effectiveness by employing QC and, especially, Quantum Annealing (QA). The goal of this work is to design a Lab composed of different Shared Tasks that aims to:
Search Engines play important roles in helping users to rapidly retrieve relevant information. The technology underlying Search Engines has been improved in the last years, both in terms of hardware capabilities and in terms of software. However, they are still affected by many issues due to the continuously growing amount of data and the various forms in which it comes. In this paper we discuss our solution to the Information Retrieval problem proposed by the CLEF 2022 Touché Task 1. We first describe in general the considered problem and subsequently present our Information Retrieval System implemented through Apache Lucene illustrating the various phases and methods applied to fulfil the objectives of the task. Eventually, we provide the obtained experimental results and possible explanations for them. In particular, we investigate the reasons for which some methods performed worse than others and describe possible ways to improve the system in the future.