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Page 673 - Shaping smarter and more sustainable cities - Striving for sustainable development goals

Users could realize the city integrated emergency decision by combining and optimizing these models on decision terminals. The framework of the model web technology used in smart sustainable cities is presented in Figure 9. Spatio-Temporal Models in Smart Sustainable Cities Trend prediction models Gas diffusion models Visibility analysis models Spatial statistical models Network analysis models Spatial analysis models Terrain analysis models Optimization models ... Model Representation in Smart Sustainable Cities Analysis of Description Model for Models Rapid Modeling for Models Model Registration & Discovery in Smart Sustainable Cities Model Representation Model Registration Model Query Model Inference Mapping from representation model to Automatic model match Discover new relations between models and registration model based on key words and apply them in new fields and registration other constraints Model Optimization and Combination Model Chain Generating Model Chain Optimization Abstract Instantiation Instantiated Instantiated Optimization Optimized Model Chain Model Model Model Model Execution Chain Chain Chain Chain Figure 9 – Framework of the model web technology used in smart sustainable cities The service web technology [b‐ESTO] is to overcome the obstacles in the workflow and sensor web technologies, and to achieve the goal of network intelligent service which is in accordance with specific sensor actions and interface specifications. The framework of the model web technology used in smart sustainable cities is demonstrated in Figure 10.

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Score: 1123691.4 - https://www.itu.int/en/publica.../files/basic-html/page673.html

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Page 307 - Shaping smarter and more sustainable cities - Striving for sustainable development goals

Web‐based Communication and Information System tools Information and knowledge management are increasingly recognized as important features in effective and efficient work in the water sector (Dondeynaz et al. 2009). (...) Figure 55 – The Brisbane City Web based Flood Map Source: http://www.sweetmaps.com/blog/wp‐content/uploads/2011/01/BCC_esri_flood_map.jpg Web‐based servers offer access to integrated information from heterogeneous data sources, as well as the innovative tools for analysis and assessment of a broad area such as climate change, water scarcity, human health, sanitation and urbanization necessary for proper urban water management. Integration of such web based communication tools using open communication standards allow a range of stakeholders to connect to the system, to use or add to its resources.

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Score: 1106422.1 - https://www.itu.int/en/publica.../files/basic-html/page307.html

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Page 531 - Shaping smarter and more sustainable cities - Striving for sustainable development goals

Web‐based communication and information system tools Information and knowledge management are increasingly recognized as an 32 important features for the effectiveness of the water sector . A key problem faced within the sector is the existence of a large body of complex, unstructured and fragmented data. Web‐based interfaces and online platforms provide a solution to enable the effective management, display, and retrieval of relevant information required by water managers/operators, urban planners, governments and the public. Figure 11 provides an example of the role played by web‐based technologies in city‐based flood maps.

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Score: 1089103.1 - https://www.itu.int/en/publica.../files/basic-html/page531.html

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Page 240 - 5G Basics - Core Network Aspects

Preconditions Three service function entities are deployed by the operator, which are uniform resource locator (URL) filter, protocol optimization and video optimization. Policies are defined such that the flow of a hypertext transport protocol (HTTP) video service should go to the URL filter and video optimization, and the flow of the web browsing service should go to the URL filter and protocol optimization. Operational flows Figure I.1 – Flexible traffic steering scenario 1) There are two groups of flows initiated by users: HTTP video; web browsing. 2) The flows of each group are identified and separated by mobile core network. 3) The HTTP video flow goes through URL filter and video optimization. The web browsing flow goes through URL filter and protocol optimization.

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Score: 1082781 - https://www.itu.int/en/publica.../files/basic-html/page240.html

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Fast Infoset & Fast

X.891 | ISO/IEC 24824-1 ● Fast Web Services – ITU-T Rec. X.892 | ISO/IEC 24824-2 Paul.Sandoz@Sun.Com Sun Microsystems Standards for optimizing the use of XML and Web services ● Generic Applications of ASN.1 (new category) – Fast Infoset – Fast Web Services Paul.Sandoz@Sun.Com Sun Microsystems Standards for optimizing the use of XML and Web services ● Generic Applications of ASN.1 (new category) – Fast Infoset – Fast Web Services ● ASN.1 Encoding Rules – Mapping W3C XML Schema Definitions into ASN.1 ● ITU-T Rec. X.694 | ISO/IEC 8825-5 ● International standard Paul.Sandoz@Sun.Com Sun Microsystems Overview ● Optimization of XML documents ● XML Information Set and Fast Infoset ● XML Schema and X.694 ● Web services and Fast Web Services Paul.Sandoz@Sun.Com Sun Microsystems Optimization of XML documents: Why? (...) PER) Application Binding layer Encoding Application layer Paul.Sandoz@Sun.Com Sun Microsystems X.694 features ● Does not retain self-description or self- structuring property of XML – Creator and processor require same schema to interoperate ● 100% XML Schema support ● Compact documents that are fast to serialize and parse Paul.Sandoz@Sun.Com Sun Microsystems X.694 optimization ● Round trip encoding/decoding of PER encoded documents 4x to 10x faster than serializing/parsing XML documents ● PER encoded documents 80% smaller than XML documents ● For small XML documents equivalent PER documents can be smaller than GZIPed XML Paul.Sandoz@Sun.Com Sun Microsystems X.694 adoption ● Fundamental part of Fast Web Services ● OSS Nokalva has implementation – OSS XML Schema Tools Paul.Sandoz@Sun.Com Sun Microsystems Web services and Fast Web Services Paul.Sandoz@Sun.Com Sun Microsystems Stub/Tie Web services WSDL XML SOAP message Discover Compile Parse request Serialize Response Application Paul.Sandoz@Sun.Com Sun Microsystems Stub/Tie Fast Web Services WSDL XML SOAP message Discover Compile Application Fast infoset SOAP message ASN.1 SOAP message Paul.Sandoz@Sun.Com Sun Microsystems ASN.1 SOAP messages ● Binary representation of SOAP 1.2 message infoset ● Use X.694 and PER for the content of SOAP messages ● SOAP processing model extended to process ASN.1 SOAP messages Paul.Sandoz@Sun.Com Sun Microsystems Extended SOAP processing model Conceptual processing ASN.1 SOAP message (PER) SOAP message infoset ASN.1 SOAP message value Decode Generate Process SOAP message infoset ASN.1 SOAP message value ASN.1 SOAP message (PER) Produce Generate Encode Paul.Sandoz@Sun.Com Sun Microsystems Fast Web Services features ● Supports WS-I conforming WSDL with no modifications ● Annotations to WSDL – Declare Fast Web Services on WSDL port – Declare use of OIDs and ROIDs instead of URIs ● ASN.1 SOAP message content – X.694 encoded PER – Fast infoset documents Paul.Sandoz@Sun.Com Sun Microsystems Fast Web Services adoption ● OSS Nokalva will have product in FYQ1 ● Sun integrating Fast Web Services support for Fast Infoset into Java Web Services Developer Pack ● Web Service for Module database – Fast Web Services support for Fast Infoset Paul.Sandoz@Sun.Com Sun Microsystems Summary ● Fast Infoset – Self-describing or self-structured messages ● X.694 – Highly compact and efficient non-self-describing messages ● Fast Web Services – SOAP message infosets as fast infoset documents – SOAP message infosets as ASN.1 SOAP messages Paul.Sandoz@Sun.Com Sun Microsystems And in the future... ● Fast Infoset and with X.694?

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Score: 1081335.2 - https://www.itu.int/dms_pub/it.../15/02/T15020000120001PDFE.pdf

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Fast Infoset & Fast

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Score: 1081335.2 - https://www.itu.int/ITU-T/stud...tutorial_2005_03_30_sandoz.pdf

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Page 28 - ITU Journal Future and evolving technologies – Volume 2 (2021), Issue 2

Technique Web worker migration, machine • Solution is very limited. learning, Gradient algorithm, Data • Mainly suitable for web applications only. segmentation Mixed‑integer non linear programming, Gaussian ran‑ • Intelligence in fog computing is still domization, Subgradient algorithm, in its infancy, and the assumptions made etc. are not realistic yet. (...) Energy Consumption, • Latency in links is not the only major Improvement of parameter to be considered. handover QoS Description • A hybrid edge and central cloud computing architecture was proposed, including one macro cell with a Macro Base Station (MBS) and multiple small cells each with an SBS, [15, 40, 41, 42, 43, 44] and an iterative algorithm used to solve the combinatorial mixed‑integer and non‑convex optimization problems. • Web worker migration techniques and machine learning were proposed to detect user’s behaviors, ind optimal servers and make ef icient of loading decision. • Spatial and channel‑wise reduction units were applied to create a compressed represen‑ tation of the feature tensor which is transmitted to the cloud. • Energy consumed during the handover process can be reduced by computing the mini‑ mum distance between the UE and the handover BS. • Subgradient algorithm was applied to compute the minimum latency between links in an edge and perform resource allocation accordingly. Technique Finite Horizon Markov Decision • The solution only reduces the complexity Process, Ant Colony Optimization, of the problem. Divide‑and‑conquer based near optimal placement algorithms, etc. • Not appropriate for real time systems which Data Of loading & require real time performance guarantee.

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Score: 1053856.25 - https://www.itu.int/en/publica...2/files/basic-html/page28.html

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Call for Experts to

ARCHITECTURE AND DEVOPS Areas of expertise: • Expertise in design and implementation of machine learning based systems and workflows in web portal • Expertise in design and implementation of APIs Expertise in data engineering and processing model input/output data • Expertise in cloud and containerized computing for portal and automated assessment • Expertise with storage and compute requirements for ensembling and assessing models • Expertise in AI/ML modeling and data standards (input and output) MVP responsibilities: • Build API and design specifications, including format of data, where query will be hosted to support API query, and ongoing API maintenance • Build framework for operationalizing ensemble model in web portal • Determine requirements for storage and compute needed for ensembling and assessing models for future use cases • Determine, implement, and govern storage for input, output, and model • Create policies on end user access, configuration, and versioning • Provide API calls and requirements for end user interface http://www.itu.int/go/ai-data-commons https://www.itu.int/en/ITU-T/extcoop/ai-data-commons/Documents/MVP.pdf https://www.research.net/r/PRWG1MVP0122 mailto:pr@ai-commons.org 2. MACHINE LEARNING MODELERS Areas of expertise: • Expertise in data science, statistics, machine learning • Expertise in ensemble models • Background in optimization, operations research, Bayesian inference, or modeling prescriptors • Required fluency in python programming language with background with tensorflow, pytorch, and similar packages • Expertise in machine learning model validation and assessment • Experience in implementation of machine learning models in API-based workflows • Experience in data engineering and processing model input/output data MVP responsibilities: • Create ensemble model based on a set of identified models, expanding as use case is refined • Develop, test, optimize module to assess submitted models by making calls to models hosted by 3rd parties or submitters • Develop, test, optimize code to compare submitted models and produce set of performance metrics • Develop, test, optimize code to conduct ensembling of submitted models • Develop, test, optimize code to operationalize ensemble model in UX (Predictors: take context + actions as input, predict outcomes; Prescriptors: take context as input, prescribe actions optimizing desired balance of outcomes) 3. UX DEVELOPER Areas of expertise: • Expertise in design and implementation of web portal for machine learning model assessment and validation MVP responsibilities: • Responsible for design, implementation, and maintenance of web portal for assessing submitted models, comparing, reviewing KPIs 4.

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Score: 1052797.7 - https://www.itu.int/en/ITU-T/e...Documents/MVP-experts_call.pdf

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Tutorial on Fast Web Services

Tutorial on Fast Web Services Tutorial on Fast Web Services This document provides tutorial material on Fast Web Services (it is equivalent to Annex C of X.892 | ISO/IEC 24824-2). Some of the advantages of using Fast Web Services are described. The differences between the conceptual and optimized processing of SOAP messages are highlighted, followed by an example. (...) This provides the main advantage (fast computer processing and low message bandwidth) of Fast Web Services, but a number of further optimizations of XML SOAP are discussed below.

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Score: 1037860.7 - https://www.itu.int/en/ITU-T/asn1/Documents/tutorial-fws.pdf

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[COM13-D743] Virata LTD (Q7/13): CT supports optimized performance to networked web

[COM13-D743] Virata LTD (Q7/13): CT supports optimized performance to networked web English Français Copia Imprimible Oficina del Secretario General Radio- comunicaciones (ITU-R) Normalización (ITU-T) Desarrollo (ITU-D) TELECOM Exposiciones y Foros Miembros Pagina Principal : Publicaciones [COM13-D743] Virata LTD (Q7/13): CT supports optimized performance to networked web Disponibilidad : TIES Users Telecargar Document Formato Talla Colocado TIESUsers English Word for Windows 7.0 32.0 kb feb 05 1999 Comienzo de la página - Comentarios - Confidencial - Copyright © ITU 2001 Reservados todos los derechos Contacto public : webmaster@itu.int Puesto al dia el 2007-03-30

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Score: 1022304.7 - https://www.itu.int/itudoc/itu...13/dcontr/dc-feb99/743-es.html

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