11,624 research outputs found
Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-based Distributed Deep Learning
One decade ago, Bitcoin was introduced, becoming the first cryptocurrency and
establishing the concept of "blockchain" as a distributed ledger. As of today,
there are many different implementations of cryptocurrencies working over a
blockchain, with different approaches and philosophies. However, many of them
share one common feature: they require proof-of-work to support the generation
of blocks (mining) and, eventually, the generation of money. This proof-of-work
scheme often consists in the resolution of a cryptography problem, most
commonly breaking a hash value, which can only be achieved through brute-force.
The main drawback of proof-of-work is that it requires ridiculously large
amounts of energy which do not have any useful outcome beyond supporting the
currency. In this paper, we present a theoretical proposal that introduces a
proof-of-useful-work scheme to support a cryptocurrency running over a
blockchain, which we named Coin.AI. In this system, the mining scheme requires
training deep learning models, and a block is only mined when the performance
of such model exceeds a threshold. The distributed system allows for nodes to
verify the models delivered by miners in an easy way (certainly much more
efficiently than the mining process itself), determining when a block is to be
generated. Additionally, this paper presents a proof-of-storage scheme for
rewarding users that provide storage for the deep learning models, as well as a
theoretical dissertation on how the mechanics of the system could be
articulated with the ultimate goal of democratizing access to artificial
intelligence.Comment: 17 pages, 5 figure
CItyMaker:
Due to its complexity, the evolution of cities is something that is difficult to predict and planning new developments for cities is therefore a difficult task. This complexity can be identified on two levels: on a micro level, it emerges from the multiple relations between the many components and actors in cities, whereas on a macro level it stems from the geographical, social and economic relations between cities. However, many of these relations can be measured.
The design of plans for cities can only be improved if designers are able to address measurements of some of the relationships between the components of cities during the design process. These measurements are called urban indicators. By calculating such measurements, designers can grasp the meaning of the changes being proposed, not just as simple alternative layouts, but also in terms of the changes in indicators adding a qualitative perception.
This thesis presents a method and a set of tools to generate alternative solutions for an urban context. The method proposes the use of a combined set of design patterns encoding typical design moves used by urban designers. The combination of patterns generates different layouts which can be adjusted by manipulating several parameters in relation to updated urban indicators. The patterns were developed from observation of typical urban design procedures, first encoded as discursive grammars and later translated into parametric design patterns. The CItyMaker method and tools allows the designer to compose a design solution from a set of programmatic premises and fine-tune it by pulling parameters whilst checking the changes in urban indicators. These tools improve the designer’s awareness of the consequences of their design moves
CItyMaker
Due to its complexity, the evolution of cities is something that is difficult to predict and planning new developments for cities is therefore a difficult task. This complexity can be identified on two levels: on a micro level, it emerges from the multiple relations between the many components and actors in cities, whereas on a macro level it stems from the geographical, social and economic relations between cities. However, many of these relations can be measured.
The design of plans for cities can only be improved if designers are able to address measurements of some of the relationships between the components of cities during the design process. These measurements are called urban indicators. By calculating such measurements, designers can grasp the meaning of the changes being proposed, not just as simple alternative layouts, but also in terms of the changes in indicators adding a qualitative perception.
This thesis presents a method and a set of tools to generate alternative solutions for an urban context. The method proposes the use of a combined set of design patterns encoding typical design moves used by urban designers. The combination of patterns generates different layouts which can be adjusted by manipulating several parameters in relation to updated urban indicators. The patterns were developed from observation of typical urban design procedures, first encoded as discursive grammars and later translated into parametric design patterns. The CItyMaker method and tools allows the designer to compose a design solution from a set of programmatic premises and fine-tune it by pulling parameters whilst checking the changes in urban indicators. These tools improve the designer’s awareness of the consequences of their design moves
Reinforcement Learning Based Design Methodology for Building Performance: A Case of Building Facades with Kinetic Elements
With the increasing complexity of design problems in building performance, traditional design methods are difficult to meet the growing demand of designers. For example, in building facades with kinetic elements, traditional design methods are facing many constraints due to the complex design variables and requirements. This study applied reinforcement learning(RL) to building performance optimization and proposed a novel design methodology. This design methodology consists of two parts: (1) a reinforcement learning-based design system; (2) an improved design process based on reinforcement learning.
The construction of the design methodology started from the analysis of design algorithms. Next, the design system was built based on Python and Grasshopper. Then, this research proposed a design process that incorporates the reinforcement learning algorithm. Finally, a full factorial experiment was conducted to verify the generalization and effectiveness of this design method in different scenarios. Results of the experiment showed that kinetic facades generated by the novel design method perform better than facades generated by traditional design methods in terms of blocking radiation heat and glare.
The application of reinforcement learning in architecture is still in the exploratory stage and has many unexplored research directions. By proposing a feasible and efficient reinforcement learning-based design methodology, this study will improve the performance of buildings and provide references for applying reinforcement learning in design
Knowledge-based control of an adaptive interface
The analysis, development strategy, and preliminary design for an intelligent, adaptive interface is reported. The design philosophy couples knowledge-based system technology with standard human factors approaches to interface development for computer workstations. An expert system has been designed to drive the interface for application software. The intelligent interface will be linked to application packages, one at a time, that are planned for multiple-application workstations aboard Space Station Freedom. Current requirements call for most Space Station activities to be conducted at the workstation consoles. One set of activities will consist of standard data management services (DMS). DMS software includes text processing, spreadsheets, data base management, etc. Text processing was selected for the first intelligent interface prototype because text-processing software can be developed initially as fully functional but limited with a small set of commands. The program's complexity then can be increased incrementally. The intelligent interface includes the operator's behavior and three types of instructions to the underlying application software are included in the rule base. A conventional expert-system inference engine searches the data base for antecedents to rules and sends the consequents of fired rules as commands to the underlying software. Plans for putting the expert system on top of a second application, a database management system, will be carried out following behavioral research on the first application. The intelligent interface design is suitable for use with ground-based workstations now common in government, industrial, and educational organizations
Syntactic Topic Models
The syntactic topic model (STM) is a Bayesian nonparametric model of language
that discovers latent distributions of words (topics) that are both
semantically and syntactically coherent. The STM models dependency parsed
corpora where sentences are grouped into documents. It assumes that each word
is drawn from a latent topic chosen by combining document-level features and
the local syntactic context. Each document has a distribution over latent
topics, as in topic models, which provides the semantic consistency. Each
element in the dependency parse tree also has a distribution over the topics of
its children, as in latent-state syntax models, which provides the syntactic
consistency. These distributions are convolved so that the topic of each word
is likely under both its document and syntactic context. We derive a fast
posterior inference algorithm based on variational methods. We report
qualitative and quantitative studies on both synthetic data and hand-parsed
documents. We show that the STM is a more predictive model of language than
current models based only on syntax or only on topics
Emerg. house 4all: a sensitive approach informed by new digital tools
The paper presents the progress of a multidisciplinary team who is collectively working
on an integrated design tool to generate humanized urban and self-built tailored incremental
housing. The ongoing project called “Emerg.cities4all” goal is to develop a system that could
use the revealed cultural, social and spatial dynamics involved in the genesis of informal
settlements (favelas, musseques and caniços) to autonomously generate adequate housing
designs while simultaneously generating all the information needed to direct fabrication. This
research explores grammar-based design and digital fabrication systems to provide modular,
adaptable and affordable housing. Emerg.cities4all research project suggests that computing
and fabrication algorithms are paving the way to achieve new design paradigms for low-income
populations
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