227 research outputs found
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State-of-the-art on research and applications of machine learning in the building life cycle
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science
Semantic Routed Network for Distributed Search Engines
Searching for textual information has become an important activity on the web. To satisfy the
rising demand and user expectations, search systems should be fast, scalable and deliver relevant
results. To decide which objects should be retrieved, search systems should compare holistic
meanings of queries and text document objects, as perceived by humans. Existing techniques do
not enable correct comparison of composite holistic meanings like: "evidences on role of DR2
gene in development of diabetes in Caucasian population", which is composed of multiple
elementary meanings: "evidence", "DR2 gene", etc. Thus these techniques can not discern objects
that have a common set of keywords but convey different meanings. Hence we need new methods
to compare composite meanings for superior search quality.
In distributed search engines, for scalability, speed and efficiency, index entries should be
systematically distributed across multiple index-server nodes based on the meaning of the objects.
Furthermore, queries should be selectively sent to those index nodes which have relevant entries.
This requires an overlay Semantic Routed Network which will route messages, based on meaning.
This network will consist of fast response networking appliances called semantic routers. These
appliances need to: (a) carry out sophisticated meaning comparison computations at high speed; and (b) have the right kind of behavior to automatically organize an optimal index system. This
dissertation presents the following artifacts that enable the above requirements:
(1) An algebraic theory, a design of a data structure and related techniques to efficiently
compare composite meanings.
(2) Algorithms and accelerator architectures for high speed meaning comparisons inside
semantic routers and index-server nodes.
(3) An overlay network to deliver search queries to the index nodes based on meanings.
(4) Algorithms to construct a self-organizing, distributed meaning based index system.
The proposed techniques can compare composite meanings ~105 times faster than an equivalent
software code and existing hardware designs. Whereas, the proposed index organization approach
can lead to 33% savings in number of servers and power consumption in a model search engine
having 700,000 servers. Therefore, using all these techniques, it is possible to design a Semantic
Routed Network which has a potential to improve search results and response time, while saving
resources
Open Information Extraction: A Review of Baseline Techniques, Approaches, and Applications
With the abundant amount of available online and offline text data, there
arises a crucial need to extract the relation between phrases and summarize the
main content of each document in a few words. For this purpose, there have been
many studies recently in Open Information Extraction (OIE). OIE improves upon
relation extraction techniques by analyzing relations across different domains
and avoids requiring hand-labeling pre-specified relations in sentences. This
paper surveys recent approaches of OIE and its applications on Knowledge Graph
(KG), text summarization, and Question Answering (QA). Moreover, the paper
describes OIE basis methods in relation extraction. It briefly discusses the
main approaches and the pros and cons of each method. Finally, it gives an
overview about challenges, open issues, and future work opportunities for OIE,
relation extraction, and OIE applications.Comment: 15 pages, 9 figure
“So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy
Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT's capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT's use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts
Automatic characterization and generation of music loops and instrument samples for electronic music production
Repurposing audio material to create new music - also known as sampling - was a foundation of electronic music and is a fundamental component of this practice. Currently, large-scale databases of audio offer vast collections of audio material for users to work with. The navigation on these databases is heavily focused on hierarchical tree directories. Consequently, sound retrieval is tiresome and often identified as an undesired interruption in the creative process.
We address two fundamental methods for navigating sounds: characterization and generation. Characterizing loops and one-shots in terms of instruments or instrumentation allows for organizing unstructured collections and a faster retrieval for music-making. The generation of loops and one-shot sounds enables the creation of new sounds not present in an audio collection through interpolation or modification of the existing material. To achieve this, we employ deep-learning-based data-driven methodologies for classification and generation.Repurposing audio material to create new music - also known as sampling - was a foundation of electronic music and is a fundamental component of this practice. Currently, large-scale databases of audio offer vast collections of audio material for users to work with. The navigation on these databases is heavily focused on hierarchical tree directories. Consequently, sound retrieval is tiresome and often identified as an undesired interruption in the creative process.
We address two fundamental methods for navigating sounds: characterization and generation. Characterizing loops and one-shots in terms of instruments or instrumentation allows for organizing unstructured collections and a faster retrieval for music-making. The generation of loops and one-shot sounds enables the creation of new sounds not present in an audio collection through interpolation or modification of the existing material. To achieve this, we employ deep-learning-based data-driven methodologies for classification and generation
A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity
Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed.
A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition.
First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development.
An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists
Soccer on Social Media
In the era of digitalization, social media has become an integral part of our
lives, serving as a significant hub for individuals and businesses to share
information, communicate, and engage. This is also the case for professional
sports, where leagues, clubs and players are using social media to reach out to
their fans. In this respect, a huge amount of time is spent curating multimedia
content for various social media platforms and their target users. With the
emergence of Artificial Intelligence (AI), AI-based tools for automating
content generation and enhancing user experiences on social media have become
widely popular. However, to effectively utilize such tools, it is imperative to
comprehend the demographics and preferences of users on different platforms,
understand how content providers post information in these channels, and how
different types of multimedia are consumed by audiences. This report presents
an analysis of social media platforms, in terms of demographics, supported
multimedia modalities, and distinct features and specifications for different
modalities, followed by a comparative case study of select European soccer
leagues and teams, in terms of their social media practices. Through this
analysis, we demonstrate that social media, while being very important for and
widely used by supporters from all ages, also requires a fine-tuned effort on
the part of soccer professionals, in order to elevate fan experiences and
foster engagement
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