95 research outputs found
How to Perform Reproducible Experiments in the ELLIOT Recommendation Framework: Data Processing, Model Selection, and Performance Evaluation
Recommender Systems have shown to be an efective way to alleviate the over-choice problem and provide
accurate and tailored recommendations. However, the impressive number of proposed recommendation
algorithms, splitting strategies, evaluation protocols, metrics, and tasks, has made rigorous experimental
evaluation particularly challenging. ELLIOT is a comprehensive recommendation framework that aims
to run and reproduce an entire experimental pipeline by processing a simple confguration fle. The
framework loads, flters, and splits the data considering a vast set of strategies. Then, it optimizes
hyperparameters for several recommendation algorithms, selects the best models, compares them with
the baselines, computes metrics spanning from accuracy to beyond-accuracy, bias, and fairness, and
conducts statistical analysis. The aim is to provide researchers a tool to ease all the experimental
evaluation phases (and make them reproducible), from data reading to results collection. ELLIOT is
freely available on GitHub at https://github.com/sisinflab/ellio
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
Air Force Institute of Technology Contributions to Air Force Research and Development, Calendar Year 1987
From the introduction:The primary mission of the Air Force Institute of Technology (AFIT) is education, but research and consulting are essential integral elements in the process. This report highlights AFIT\u27s contributions to Air Force research and development activities [in 1987]
Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks
[EN] In this paper we develop a framework for analysing the impact of Artificial Intelligence (AI) on
occupations. This framework maps 59 generic tasks from worker surveys and an occupational
database to 14 cognitive abilities (that we extract from the cognitive science literature) and these
to a comprehensive list of 328 AI benchmarks used to evaluate research intensity across a broad
range of different AI areas. The use of cognitive abilities as an intermediate layer, instead of
mapping work tasks to AI benchmarks directly, allows for an identification of potential AI exposure for tasks for which AI applications have not been explicitly created. An application of
our framework to occupational databases gives insights into the abilities through which AI is
most likely to affect jobs and allows for a ranking of occupations with respect to AI exposure.
Moreover, we show that some jobs that were not known to be affected by previous waves of automation may now be subject to higher AI exposure. Finally, we find that some of the abilities
where AI research is currently very intense are linked to tasks with comparatively limited labour
input in the labour markets of advanced economies (e.g., visual and auditory processing using
deep learning, and sensorimotor interaction through (deep) reinforcement learning).Tolan, S.; Pesole, A.; Martínez-Plumed, F.; Fernández-Macías, E.; Hernández-Orallo, J.; Gómez, E. (2021). Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks. Journal of Artificial Intelligence Research. 71:191-236. https://doi.org/10.1613/jair.1.12647S1912367
Privacy Intelligence: A Survey on Image Sharing on Online Social Networks
Image sharing on online social networks (OSNs) has become an indispensable
part of daily social activities, but it has also led to an increased risk of
privacy invasion. The recent image leaks from popular OSN services and the
abuse of personal photos using advanced algorithms (e.g. DeepFake) have
prompted the public to rethink individual privacy needs when sharing images on
OSNs. However, OSN image sharing itself is relatively complicated, and systems
currently in place to manage privacy in practice are labor-intensive yet fail
to provide personalized, accurate and flexible privacy protection. As a result,
an more intelligent environment for privacy-friendly OSN image sharing is in
demand. To fill the gap, we contribute a systematic survey of 'privacy
intelligence' solutions that target modern privacy issues related to OSN image
sharing. Specifically, we present a high-level analysis framework based on the
entire lifecycle of OSN image sharing to address the various privacy issues and
solutions facing this interdisciplinary field. The framework is divided into
three main stages: local management, online management and social experience.
At each stage, we identify typical sharing-related user behaviors, the privacy
issues generated by those behaviors, and review representative intelligent
solutions. The resulting analysis describes an intelligent privacy-enhancing
chain for closed-loop privacy management. We also discuss the challenges and
future directions existing at each stage, as well as in publicly available
datasets.Comment: 32 pages, 9 figures. Under revie
Air Force Institute of Technology Research Report 2017
This Research Report presents the FY18 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs)
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