6,463 research outputs found
Fairness Testing: A Comprehensive Survey and Analysis of Trends
Unfair behaviors of Machine Learning (ML) software have garnered increasing
attention and concern among software engineers. To tackle this issue, extensive
research has been dedicated to conducting fairness testing of ML software, and
this paper offers a comprehensive survey of existing studies in this field. We
collect 100 papers and organize them based on the testing workflow (i.e., how
to test) and testing components (i.e., what to test). Furthermore, we analyze
the research focus, trends, and promising directions in the realm of fairness
testing. We also identify widely-adopted datasets and open-source tools for
fairness testing
Reshaping Higher Education for a Post-COVID-19 World: Lessons Learned and Moving Forward
No abstract available
Tradition and Innovation in Construction Project Management
This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
Analog Photonics Computing for Information Processing, Inference and Optimisation
This review presents an overview of the current state-of-the-art in photonics
computing, which leverages photons, photons coupled with matter, and
optics-related technologies for effective and efficient computational purposes.
It covers the history and development of photonics computing and modern
analogue computing platforms and architectures, focusing on optimization tasks
and neural network implementations. The authors examine special-purpose
optimizers, mathematical descriptions of photonics optimizers, and their
various interconnections. Disparate applications are discussed, including
direct encoding, logistics, finance, phase retrieval, machine learning, neural
networks, probabilistic graphical models, and image processing, among many
others. The main directions of technological advancement and associated
challenges in photonics computing are explored, along with an assessment of its
efficiency. Finally, the paper discusses prospects and the field of optical
quantum computing, providing insights into the potential applications of this
technology.Comment: Invited submission by Journal of Advanced Quantum Technologies;
accepted version 5/06/202
A Survey on Multimodal Large Language Models
Multimodal Large Language Model (MLLM) recently has been a new rising
research hotspot, which uses powerful Large Language Models (LLMs) as a brain
to perform multimodal tasks. The surprising emergent capabilities of MLLM, such
as writing stories based on images and OCR-free math reasoning, are rare in
traditional methods, suggesting a potential path to artificial general
intelligence. In this paper, we aim to trace and summarize the recent progress
of MLLM. First of all, we present the formulation of MLLM and delineate its
related concepts. Then, we discuss the key techniques and applications,
including Multimodal Instruction Tuning (M-IT), Multimodal In-Context Learning
(M-ICL), Multimodal Chain of Thought (M-CoT), and LLM-Aided Visual Reasoning
(LAVR). Finally, we discuss existing challenges and point out promising
research directions. In light of the fact that the era of MLLM has only just
begun, we will keep updating this survey and hope it can inspire more research.
An associated GitHub link collecting the latest papers is available at
https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models.Comment: Project
page:https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Model
Augmented Behavioral Annotation Tools, with Application to Multimodal Datasets and Models: A Systematic Review
Annotation tools are an essential component in the creation of datasets for machine learning purposes. Annotation tools have evolved greatly since the turn of the century, and now commonly include collaborative features to divide labor efficiently, as well as automation employed to amplify human efforts. Recent developments in machine learning models, such as Transformers, allow for training upon very large and sophisticated multimodal datasets and enable generalization across domains of knowledge. These models also herald an increasing emphasis on prompt engineering to provide qualitative fine-tuning upon the model itself, adding a novel emerging layer of direct machine learning annotation. These capabilities enable machine intelligence to recognize, predict, and emulate human behavior with much greater accuracy and nuance, a noted shortfall of which have contributed to algorithmic injustice in previous techniques. However, the scale and complexity of training data required for multimodal models presents engineering challenges. Best practices for conducting annotation for large multimodal models in the most safe and ethical, yet efficient, manner have not been established. This paper presents a systematic literature review of crowd and machine learning augmented behavioral annotation methods to distill practices that may have value in multimodal implementations, cross-correlated across disciplines. Research questions were defined to provide an overview of the evolution of augmented behavioral annotation tools in the past, in relation to the present state of the art. (Contains five figures and four tables)
Art and Creativity for HIV/AIDS Awareness, Prevention, and Empowerment of Young People in Uganda
Art, youth engagement and informality in the context of HIV prevention have been generally ignored by most researchers and stakeholders within the HIV programming and policy arenas, thus silencing the plight of urban youth infected with and affected by HIV/AIDS. In response, this thesis draws on the case of peri-urban settings of Kampala, Uganda to bring geographies of applied sculpture, HIV/AIDS prevention, and youth empowerment into dialogue, informed by the notions of art having the capacity to move beyond the spaces of galleries into an expanded field, and thus, beyond the visual and into the social spheres. In liaison with local NGOs (The Uganda AIDS Support Organisation - TASO, National Guidance and Empowerment Network for People Living with HIV/AIDS - NGEN+ and Lungujja Community based Health care Organisation – LUCOHECO, it adopts a mixed methodological approach, including applied art and participatory techniques - observation, video, storytelling, and interviews, to understand the lived experiences of young people (15-24 years) in marginalized spaces in Kampala. The thesis first examines the general context of using ethnography and applied social sculpture to explore every day experiences by facilitating the engagement of young people in open communication about the epidemic. This is intended to enable them to act in confronting stigma, taboos, and their precarious existence, while raising their awareness about HIV/AIDS. The thesis then explores the everyday precarious existence of young people in informal settings in Kampala. It proceeds to examine how workshops with these young people allowed collective engagement which, in turn, influenced the creation of artworks envisioned to act as communication tools for raising awareness of HIV/AIDS with the potential for livelihood benefits. Finally, the thesis examines young people’s active involvement in participatory workshops for HIV/AIDS prevention, providing ethnographic evidence regarding the artmaking process, the conversations that ensued as they worked, and the creation of applied objects/forms that enabled them to build their confidence to freely express about the precarities affecting their lives, countering taboos, and encouraging them to change their behaviours and practices while potentially acting as change agents in their own communities. It highlights the significance of stimulating open conversations about HIV/AIDS - as a starting point towards confronting stigma and other aspects of precarity, while advocating for the incorporation of the approach into practice by public health experts, policymakers, and development practitioners. The thesis shows the strengths of applied sculpture as an approach that has potential for making sense of ordinary everyday experiences, finding meaning and crafting clarity of young people’s lived experiences in the context of HIV/AIDS. It concludes that applied sculpture is potentially an important tool in tackling HIV/AIDS and its attendant problems by engendering and facilitating open conversations and social economic development through an engagement with the voices and agency of young people in Uganda and beyond
AntNetAlign: Ant colony optimization for network alignment
The code (and data) in this article has been certified as Reproducible by Code Ocean: (https://codeocean.com/). More information on the Reproducibility Badge Initiative is available at https://www.elsevier.com/physical-sciences-andengineering/computer-science/journalsNetwork Alignment (NA) is a hard optimization problem with important applications such as, for example, the identification of orthologous relationships between different proteins and of phylogenetic relationships between species. Given two (or more) networks, the goal is to find an alignment between them, that is, a mapping between their respective nodes such that the topological and functional structure is well preserved. Although the problem has received great interest in recent years, there is still a need to unify the different trends that have emerged from diverse research areas. In this paper, we introduce AntNetAlign, an Ant Colony Optimization (ACO) approach for solving the problem. The proposed approach makes use of similarity information extracted from the input networks to guide the construction process. Combined with an improvement measure that depends on the current construction state, it is able to optimize any of the three main topological quality measures. We provide an extensive experimental evaluation using real-world instances that range from Protein–Protein Interaction (PPI) networks to Social Networks. Results show that our method outperforms other state-of-the-art approaches in two out of three of the tested scores within a reasonable amount of time, specially in the important score. Moreover, it is able to obtain near-optimal results when aligning networks with themselves. Furthermore, in larger instances, our algorithm was still able to compete with the best performing method in this regard.Christian Blum and Guillem RodrÃguez Corominas, Spain were supported by grants PID2019-104156GB-I00 and TED2021-
129319B-I00 funded by MCIN/AEI/10.13039/501100011033. Maria J. Blesa acknowledges support from AEI, Spain under grant PID2020-112581GB-C21 (MOTION) and the Catalan Agency for Management of University and Research Grants (AGAUR), Spain under
grant 2017-SGR-786 (ALBCOM).Peer ReviewedPostprint (published version
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