CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Geographic information extraction from texts

A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction

In Crowd Veritas: Leveraging Human Intelligence To Fight Misinformation

The spread of online misinformation has important effects on the stability of democracy. The sheer size of digital content on the web and social media and the ability to immediately access and share it has made it difficult to perform timely fact-checking at scale. Truthfulness judgments are usually made by experts, like journalists for political statements. A different approach can be relying on a (non-expert) crowd of human judges to perform fact-checking. This leads to the following research question: can such human judges detect and objectively categorize online (mis)information? Several extensive studies based on crowdsourcing are performed to answer. Thousands of truthfulness judgments over two datasets are collected by recruiting a crowd of workers from crowdsourcing platforms and the expert judgments are compared with the crowd ones. The results obtained allow for concluding that the workers are indeed able to do such. There is a limited understanding of factors that influence worker participation in longitudinal studies across different crowdsourcing marketplaces. A large-scale survey aimed at understanding how these studies are performed using crowdsourcing is run across multiple platforms. The answers collected are analyzed from both a quantitative and a qualitative point of view. A list of recommendations for task requesters to conduct these studies effectively is provided together with a list of best practices for crowdsourcing platforms. Truthfulness is a subtle matter: statements can be just biased, imprecise, wrong, etc. and a unidimensional truth scale cannot account for such differences. The crowd workers are asked to judge seven different dimensions of truthfulness selected based on existing literature. The newly collected crowdsourced judgments show that the workers are indeed reliable when compared to an expert-provided gold standard. Cognitive biases are human processes that often help minimize the cost of making mistakes but keep assessors away from an objective judgment of information. A review of the cognitive biases which might manifest during the fact-checking process is presented together with a list of countermeasures that can be adopted. An exploratory study on the previously collected data set is thus performed. The findings are used to formulate hypotheses concerning which individual characteristics of statements or judges and what cognitive biases may affect crowd workers' truthfulness judgments. The findings suggest that crowd workers' degree of belief in science has an impact, that they generally overestimate truthfulness, and that their judgments are indeed affected by various cognitive biases. Automated fact-checking systems to combat misinformation spreading exist, however, their complexity usually makes them opaque to the end user, making it difficult to foster trust in the system. The E-BART model is introduced with the hope of making progress on this front. E-BART can provide a truthfulness prediction for a statement, and jointly generate a human-readable explanation. An extensive human evaluation of the impact of explanations generated by the model is conducted, showing that the explanations increase the human ability to spot misinformation. The whole set of data collected and analyzed in this thesis is publicly released to the research community at: https://doi.org/10.17605/OSF.IO/JR6VC.The spread of online misinformation has important effects on the stability of democracy. The information that is consumed every day influences human decision-making processes. The sheer size of digital content on the web and social media and the ability to immediately access and share it has made it difficult to perform timely fact-checking at scale. Indeed, fact-checking is a complex process that involves several activities. A long-term goal can be building a so-called human-in-the-loop system to cope with (mis)information by measuring truthfulness in real-time (e.g., as they appear on some social media, news outlets, and so on) using a combination of crowd-powered data, human intelligence, and machine learning techniques. In recent years, crowdsourcing has become a popular method for collecting to collect reliable truthfulness judgments in order to scale up and help study the manual fact-checking effort. Initially, this thesis investigates whether human judges can detect and objectively categorize online (mis)information and which is the environment that allows obtaining the best results. Then, the impact of cognitive biases on human assessors while judging information truthfulness is addressed. A categorization of cognitive biases is proposed together with countermeasures to combat their effects and a bias-aware judgment pipeline for fact-checking. Lastly, an approach able to predict information truthfulness and, at the same time, generate a natural language explanation supporting the prediction itself is proposed. The machine-generated explanations are evaluated to understand whether they are useful for the human assessors to better judge the truthfulness of information items. A collaborative process between systems, crowd workers, and expert fact checkers would provide a scalable and decentralized hybrid mechanism to cope with the increasing volume of online misinformation

Volume 33, Number 09 (September 1915)

Modernism in Pianoforte Study (interview with Percy Grainger) Better Understanding of Crescendo Musical Obligation to the Child Right Studio Equipment Dramatic Scenes from the Operas Passing of Paul Wachs How to Introduce Scales to the Tiny Tot Music a Human Necessity in Modern Life Be the Architect of Your Own Technic Sensible Practice Hints Value of Scales Music from the Cradle to the Grave Piano Study as an Aid to Good Health Tonal Technical Practice Straightening Out the Strausses How to Start a Musical Kindergarten Mothers, Do You Need These Don’t\u27s? Deeds Not Words! When Is a Piano in Tune?https://digitalcommons.gardner-webb.edu/etude/1616/thumbnail.jp

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

Tools and Algorithms for the Construction and Analysis of Systems

This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems

Tools and Algorithms for the Construction and Analysis of Systems

This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems