2,485 research outputs found
Zero-Shot Retrieval with Search Agents and Hybrid Environments
Learning to search is the task of building artificial agents that learn to
autonomously use a search box to find information. So far, it has been shown
that current language models can learn symbolic query reformulation policies,
in combination with traditional term-based retrieval, but fall short of
outperforming neural retrievers. We extend the previous learning to search
setup to a hybrid environment, which accepts discrete query refinement
operations, after a first-pass retrieval step performed by a dual encoder.
Experiments on the BEIR task show that search agents, trained via behavioral
cloning, outperform the underlying search system based on a combined dual
encoder retriever and cross encoder reranker. Furthermore, we find that simple
heuristic Hybrid Retrieval Environments (HRE) can improve baseline performance
by several nDCG points. The search agent based on HRE (HARE) produces
state-of-the-art performance on both zero-shot and in-domain evaluations. We
carry out an extensive qualitative analysis to shed light on the agents
policies
Mining Helpdesk Databases For Professional Development Topic Discovery
This single-site, instrumental case study created and tested a methodological road map by which academic institutions can use text data mining techniques to derive technology skillset weaknesses and professional development topics from the site’s technical support helpdesk database. The methods employed were described in detail and applied to the helpdesk database of an independent, co-educational boarding high school in the northeastern United States. Standard text data mining procedures, including the formation of a wordlist (frequently occurring terms), and the creation and application of clustering (automated data grouping) and classification (automated data labeling) models generated meaningful and revealing themes from the helpdesk database. The results of the text mining procedures were bolstered and analyzed using human interpretation and spreadsheet-based summaries. Major findings included the discovery of four prominent technologies that warranted professional development at the site and a universally-applicable approach to undertaking successful helpdesk data mining endeavors. The case study’s conclusions included a call to action for researchers to leverage the methodology at other locations. Future data mining studies may yield practical and applicable knowledge at research sites. Shared methods, approaches, and findings from such studies will advance the field of helpdesk data mining used to glean professional development topics for the very people who have submitted technological support requests to helpdesk providers
Using Technology Enabled Qualitative Research to Develop Products for the Social Good, An Overview
This paper discusses the potential benefits of the convergence of three recent trends for the design of socially beneficial products and services: the increasing application of qualitative research techniques in a wide range of disciplines, the rapid mainstreaming of social media and mobile technologies, and the emergence of software as a service. Presented is a scenario facilitating the complex data collection, analysis, storage, and reporting required for the qualitative research recommended for the task of designing relevant solutions to address needs of the underserved. A pilot study is used as a basis for describing the infrastructure and services required to realize this scenario. Implications for innovation of enhanced forms of qualitative research are presented
An integrated ranking algorithm for efficient information computing in social networks
Social networks have ensured the expanding disproportion between the face of
WWW stored traditionally in search engine repositories and the actual ever
changing face of Web. Exponential growth of web users and the ease with which
they can upload contents on web highlights the need of content controls on
material published on the web. As definition of search is changing,
socially-enhanced interactive search methodologies are the need of the hour.
Ranking is pivotal for efficient web search as the search performance mainly
depends upon the ranking results. In this paper new integrated ranking model
based on fused rank of web object based on popularity factor earned over only
valid interlinks from multiple social forums is proposed. This model identifies
relationships between web objects in separate social networks based on the
object inheritance graph. Experimental study indicates the effectiveness of
proposed Fusion based ranking algorithm in terms of better search results.Comment: 14 pages, International Journal on Web Service Computing (IJWSC),
Vol.3, No.1, March 201
Application-aware optimization of Artificial Intelligence for deployment on resource constrained devices
Artificial intelligence (AI) is changing people's everyday life. AI techniques such as Deep Neural Networks (DNN) rely on heavy computational models, which are in principle designed to be executed on powerful HW platforms, such as desktop or server environments. However, the increasing need to apply such solutions in people's everyday life has encouraged the research for methods to allow their deployment on embedded, portable and stand-alone devices, such as mobile phones, which exhibit relatively low memory and computational resources. Such methods targets both the development of lightweight AI algorithms and their acceleration through dedicated HW.
This thesis focuses on the development of lightweight AI solutions, with attention to deep neural networks, to facilitate their deployment on resource constrained devices. Focusing on the computer vision field, we show how putting together the self learning ability of deep neural networks with application-specific knowledge, in the form of feature engineering, it is possible to dramatically reduce the total memory and computational burden, thus allowing the deployment on edge devices. The proposed approach aims to be complementary to already existing application-independent network compression solutions. In this work three main DNN optimization goals have been considered: increasing speed and accuracy, allowing training at the edge, and allowing execution on a microcontroller. For each of these we deployed the resulting algorithm to the target embedded device and measured its performance
Exploring 3D Chemical Plant Using VRML
The research project focused on how virtual reality could create an immersive
environment and improve in designing a chemical plant. The main problem is the
difficulties in designing chemical plant since 2D plant layout cannot provide the
real walking-through. The aim of this project is to develop and design 3D
Chemical Plant which allows users to explore the virtual plant environment
freely. The objectives of this project are to design and develop 3D Chemical
Plant in the virtual environment; to enable user to walkthrough the chemical
plant; and at the same time evaluate the effectiveness of the implementation of
3D Chemical Plant. In completion the project, the framework used is based on
the waterfall modeling theory. This study also examines the structure and
existing use of VRML (International standard for 3D modelling on the internet)
in constmction and architectural practice as a means of investigating its role and
potential for extensible construction information visualization in chemical plant.
The phases involved in the framework used for project development is the
initiation phase, design specification, project development, integration and
testing and lastly project implementation. Developments tools have been used in
the project are VRML and 3D Max 6. As a result from the evaluation conducted,
the mean of 3.5 from level of satisfaction ranking shows that mostly the
evaluators are satisfied with the project and feel that the realism of 3D chemical
plant and suitability of color and textures will improve the designing of chemical
plant in virtual environment. As conclusion, the research project show that
VR!VE are very useful and give a good impact for the chemical Engineer in
designing a chemical plant
SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds
Text-to-image diffusion models can create stunning images from natural
language descriptions that rival the work of professional artists and
photographers. However, these models are large, with complex network
architectures and tens of denoising iterations, making them computationally
expensive and slow to run. As a result, high-end GPUs and cloud-based inference
are required to run diffusion models at scale. This is costly and has privacy
implications, especially when user data is sent to a third party. To overcome
these challenges, we present a generic approach that, for the first time,
unlocks running text-to-image diffusion models on mobile devices in less than
seconds. We achieve so by introducing efficient network architecture and
improving step distillation. Specifically, we propose an efficient UNet by
identifying the redundancy of the original model and reducing the computation
of the image decoder via data distillation. Further, we enhance the step
distillation by exploring training strategies and introducing regularization
from classifier-free guidance. Our extensive experiments on MS-COCO show that
our model with denoising steps achieves better FID and CLIP scores than
Stable Diffusion v with steps. Our work democratizes content creation
by bringing powerful text-to-image diffusion models to the hands of users.Comment: Our project webpage: https://snap-research.github.io/SnapFusion
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