On machine learning and knowledge organisation in Multimedia Information Retrieval

Recent technological developments have increased the use of machine learning to solve many problems, including many in information retrieval (IR). Deployment of machine-learning techniques is widespread in text search, notability web search engines (Dai et al., 2011). Multimedia information retrieval as a problem however still represents a significant challenge to machine learning as a technological solution, but some problems in IR can still be addressed by using appropriate AI techniques. In this paper we review the technological developments, and provide a perspective on the use of machine-learning techniques in conjunction with knowledge organisation techniques to address multimedia IR needs. We take the perspective from the MacFarlane (2016) position paper, that there are some problems in multimedia IR that AI and machine learning cannot currently solve. The semantic gap in multimedia IR (Enser, 2008) remains a significant problem in the field, and solutions to them are many years off. However, there are occasions where the new technological developments allow the use of knowledge organisation and machine learning in multimedia search systems and services. Specifically we argue that the improvement of detection of some classes of low level features in images (Karpathy and Li, 2015), music (Byrd and Crawford, 2002) and video (Hu et al., 2011) can be used in conjunction with knowledge organisation to tag or label multimedia content for better retrieval performance. We advocate the use of supervised learning techniques. We provide an overview of the use of knowledge organisation schemes in machine learning, and make recommendations to information professionals on the use of this technology with knowledge organisation techniques to solve multimedia IR problems

GGE Biplot Analysis of Forage Yield Performance and Stability Assessment of Tall Fescue Experimental Populations Selected Under Grazing Pressure in a Stress Environment

Integrating the yield and stability of genotypes selected under grazing pressure is an important objective in breeding forage crops. Genotype × environment (G x E) interaction is a major source of inconsistency in crop performance across locations. As a result, a genotype is considered stable if it has a low contribution to the G x E interaction. This study explores the effects of G x E interaction on yield and stability of 10 tall fescue experimental populations selected for persistence under grazing pressure outside the area of adaptation of the species (stress environment). Six standard checks were included. The populations were tested in a randomized complete block design with 5 replications in 9 environments. The pooled analysis of variance (ANOVA) revealed highly significant (p \u3c 0.01) variations between populations, locations, years, and G × E interaction. The first two principal components generated by the GGE biplot accounted for 46.78% and 28.45% variation in GGE for yield. The locations (Athens and Blairsville) were found to be the most significant causes of yield variation. The GGE biplot revealed three winning populations GALA1301 (ga1), GALA1302 (ga2), and GALA1306 (ga6) in terms of yield across environments. These populations performed better than all the checks. GALA1502T (g2t) was the most stable and GALA1502A(g2a), GALA1301(ga1), and GALA1303(ga3) are both comparatively stable and high yield performers. Comparison of the two populations g2t and g2a that were selected from the same base population but in different environments (g2t selected for persistence at Tifton under grazing pressure and g2a selected for yield without grazing in Athens) showed that g2t was the most stable across environments but lower in yield than g2a. Our results suggest that selection under grazing pressure in stress environments could result in improved stability across environments while yield performance will still depend on the genetic background of the germplasm

How to Blend Journalistic Expertise with Artificial Intelligence for Research and Verifying News Stories

The use of AI technology can help to automate news verification workflows, while significantly innovating journalism practices. However, most existing systems are designed in isolation without interactive collaboration with journalists. ‘DMINR’ project aims to bring humans-at-the-center of AI loop for developing a powerful tool that is sympathetic to the way journalists work. In this paper, we attempt to understand how AI can shape journalists’ practices and, crucially, be shaped by them; we aim to design human-centred AI tool that works in synergy with journalists’ practices and strike a useful balance between human and machine intelligence. In this paper, we conducted a Co-design workshop to inform the design of the ‘DMINR’ system. Based on the findings, we outline the main challenges for designing AI systems in the context of journalism, that can serve as a resource for Human-AI interaction design

Spatial Effects of NAO on Temperature and Precipitation Anomalies in Italy

The NAO teleconnective pattern has a great influence on the European climate; however, the exact quantification of NAO pattern in the different areas is sometimes lacking, and at other times, highlights even large differences between the various studies. This motivation led to the identification of the aim of this research in the study of the relationship between the NAO index and temperature and precipitation anomalies over the period 1991-2020, through the analysis of 87 rain gauges and 86 thermometric stations distributed as homogeneously as possible over the Italian territory. The results were sometimes at odds with the scientific literature on the subject, as significance was also found outside the winter season, e.g., in the spring for temperatures and in the autumn for precipitation, and in some cases, correlations were found, especially in August, even in southern Italy, which is usually considered a poorly correlated area. In addition, the linear relationship between the NAO index and temperature and precipitation anomalies was verified, with many weather stations obtaining significant coefficients of determinations as high as 0.5-0.6 in December, with 29 degrees of freedom, and a p-value set at 95%. Finally, for both climatic parameters, the presence of clusters and outliers at seasonal and monthly levels was assessed, obtaining a spatial distribution using the local Moran index, and summarising them in maps. This analysis highlighted important clusters in Northern and Central Italy, while clusters in the summer months occur in the South. These results provide information that may further elucidate local atmospheric dynamics in relation to NAO phases, as well as encourage future studies that may link other teleconnective indices aimed at better explaining the variance of climate parameters

AI should embody our values: Investigating journalistic values to inform AI technology design

In the current climate of shrinking newsrooms and revenues, journalists face increasing pressures exerted by the industry’s for-profit focus and the expectation of intensified output. While AI-enabled journalism has great potential to help alleviate journalists’ pressures, it might also disrupt journalistic norms and, at worst, interfere with their duty to inform the public. For AI systems to be as useful as possible, designers should understand journalists’ professional values and incorporate them into their designs. We report findings from interviews with journalists to understand their perceptions of how professional values that are important to them (such as truth, impartiality and originality) might be supported and/or undermined by AI technologies. Based on these findings, we provide design insight and guidelines for incorporating values into the design of AI systems. We argue HCI design can achieve the strongest possible value alignment by moving beyond merely supporting important values, to truly embodying them

Journalists as Design Partners for AI

We report on a project exploring the development of an AI-enabled system for researching and verifying news articles. In particular, we underscore the value of journalists in the role of designers in a wider multi-disciplinary team including AI experts and interaction designers. We unpack our learnings by presenting three sensitizing concepts for Human-Centred AI technologies in the context of journalism. We contribute these concepts to provoke discussion and inspiration for design work

Analysis of Snow Cover in the Sibillini Mountains in Central Italy

Research on solid precipitation and snow cover, especially in mountainous areas, suffers from problems related to the lack of on-site observations and the low reliability of measurements, which is often due to instruments that are not suitable for the environmental conditions. In this context, the study area is the Monti Sibillini National Park, and it is no exception, as it is a mountainous area located in central Italy, where the measurements are scarce and fragmented. The purpose of this research is to provide a characterization of the snow cover with regard to maximum annual snow depth, average snow depth during the snowy period, and days with snow cover on the ground in the Monti Sibillini National Park area, by means of ground weather stations, and also analyzing any trends over the last 30 years. For this research, in order to obtain reliable snow cover data, only data from weather stations equipped with a sonar system and manual weather stations, where the surveyor goes to the site each morning and checks the thickness of the snowpack and records, it were collected. The data were collected from 1 November to 30 April each year for 30 years, from 1991 to 2020; six weather stations were taken into account, while four more were added as of 1 January 2010. The longer period was used to assess possible ongoing trends, which proved to be very heterogeneous in the results, predominantly negative in the case of days with snow cover on the ground, while trends were predominantly positive for maximum annual snow depth and distributed between positive and negative for the average annual snow depth. The shorter period, 2010–2022, on the other hand, ensured the presence of a larger number of weather stations and was used to assess the correlation and presence of clusters between the various weather stations and, consequently, in the study area. Furthermore, in this way, an up-to-date nivometric classification of the study area was obtained (in terms of days with snow on the ground, maximum height of snowpack, and average height of snowpack), filling a gap where there had been no nivometric study in the aforementioned area. The interpolations were processed using geostatistical techniques such as co-kriging with altitude as an independent variable, allowing fairly precise spatialization, analyzing the results of cross-validation. This analysis could be a useful tool for hydrological modeling of the area, as well as having a clear use related to tourism and vegetation, which is extremely influenced by the nivometric variables in its phenology. In addition, this analysis could also be considered a starting point for the calibration of more recent satellite products dedicated to snow cover detection, in order to further improve the compiled climate characterizatio

Making newsworthy news: The integral role of creativity and verification in the Human Information Behaviour that drives news story creation

Creativity and verification are intrinsic to high quality journalism, but their role is often poorly visible in news story creation. Journalists face relentless commercial pressures that threaten to compromise story quality, in a digital era where their ethical obligation not to mislead the public has never been more important. It is therefore crucial to investigate how journalists can be supported to produce stories that are original, impactful, and factually accurate, under tight deadlines. We present findings from 14 semi-structured interviews, where we asked journalists to discuss the creation of a recent news story to understand the process and associated Human Information Behaviour (HIB). Six overarching behaviours were identified: discovering, collecting, organising, interrogating, contextualising, and publishing. Creativity and verification were embedded throughout news story creation and integral to journalists’ HIB, highlighting their ubiquity. They often manifested at a micro level; in small-scale but vital activities that drove and facilitated story creation. Their ubiquitous role highlights the importance of creativity and verification support being woven into functionality that facilitates information acquisition and use in digital information tools for journalists

A Question of Design: Strategies for Embedding AI-Driven Tools into Journalistic Work Routines

With the promise of AI, the use of emerging technologies in journalism has gained momentum. However, the question of how such technologies can be interwoven with newsroom practices, values, routines, and socio-cultural experiences is often neglected. This article investigates the ways in which AI-driven tools are permeating newswork and design strategies for blending technological capabilities with editorial requirements. We followed a multi-method approach to investigate the deployment of AI in news production at two London newsrooms: (1) a design ethnography at the BBC with journalists and technologists, and (2) interviews with journalists at The Times. Our findings show that while journalists are generally open to try AI-driven technologies that benefit their work, technologists struggle to integrate them into journalistic workflows. The consensus was that human judgement is required to make complex decisions in journalism and that journalistic values should be prioritised in AI tool design. We claim that AI tools need to fit with professional practices and values in journalism in order to be fully accepted as an editorial tool. Embedding new technologies into journalistic workflows requires therefore a close collaboration between journalists and technologists, and a sociotechnical design that blends in work routines and values

I'm the same, I'm the same, I'm trying to change: Investigating the role of human information behavior in view change

Information is powerful; it can influence peoples' views and, in turn, their behavior. Much recent research and discussion on the role information plays in view change has focused on filter bubbles, echo chambers and misinformation and how they might influence what people think and how they act. However, no prior work has focused specifically on understanding the human information behavior (HIB) that drives and facilitates view change. We report findings from interviews with 18 people who recently changed views on issues they considered important. We found a tight symbiotic relationship between HIB and view change; passive information encountering sparked change, often spurring follow‐up active seeking and verification which progressed the change to a “point of no return,” supported making the change and reinforced the decision to change. When shared, information that contributed to the change sometimes sparked changes in others (as did expressing or debating the change), serving as an information encounter that perpetuated a cycle of HIB and view change. This understanding of the integral role of HIB in view change can inform policy and systems design to promote view change autonomy and a broader research agenda of understanding HIB to support democratic principles and values