Methodologies for Assessment of Building's Energy Efficiency and Conservation: A Policy-Maker View

Recent global peer-review reports have concluded on importance of buildings in tacking the energy security and climate change challenges. To integrate the buildings energy efficiency into the policy agenda, significant research efforts have been recently done. More specifically, the public domain provides a bulk of literature on the application of buildings-related efficiency technologies and behavioural patterns, barriers to penetration of these practices, policies to overcome these barriers. From the policy-making perspective it is useful to understand how far our understanding of building energy efficiency goes and the approaches and methodologies are behind such assessment.Buildings, energy efficiency potential, greenhouse gas mitigation, policy assessment, energy policy impact evaluation, sectoral efficiency targets

The innovative capacity of a territory in behavioral assessments of its population

The paper provides a rationale for substantive and formalized definition of a territory’s capacity, outlines its innovative component in the unity of «subject/process/object-oriented» approach to its substantive content and performance assessment. It elaborates a system of mechanisms and institutions to build the innovative capacity of regions and territories, outlines the most effective areas of its use for spatial socioeconomic development. The paper also defines priority problems that require solutions and can ensure the increasing performance of a territory. These problems and variants of their solutions featured in the discussions held at the Gaidar International Economic Forum in Moscow (2015) and the 12th Krasnoyarsk Economic Forum, as demonstrated in this paper by the analysis of some presentations made at the forums. The paper shows the change in priorities of global innovative development in the second half of the 20th and early 21st century. It examines and provides the summary of research and practices in the area of using the innovative solutions for developing individual teams and territories, making a spatial arrangement of regions and the Russian Federation as a whole. The development of a territory and its capacity depends on many factors; however, elevating the role of knowledge, intellectual resources and involving the population in the governance process by developing and implementing various programs and projects play an increasing role in the current environment. The paper analyzes positive aspects of using the business projects as the primary mechanism for implementing the programs and plans involving the market institutions and public-private partnership (PPP). It assesses the role of teams and population in boosting the innovative activities and systemic development of territories.This paper was prepared with the funds of Subprogram No. 14 "Fundamental Problems of Regional Economy," the Project No. 15-14-7-13 "Scenario Approaches to the Implementation of the Ural Vector in the Reclamation and Development of the Russian Arctic Amid the Global Instability.

Impact of globalization processes on place branding

Процессы глобализации не просто оказывают существенное влияние на процесс создания и продвижения брендов территории, но требуют выработки новых технологий геобрендинга. Новые технологии должны позволять увязывать между собой интересы субъектов, действующих на территории, создавать бренд, легко читаемый представителями различных целевых аудиторий, а также корректировать созданный бренд с учетом специфики циркуляции информации в современном мире.The processes of globalization not just have a significant impact on the process of creating and promoting brand place, but they stimulate the development of new technologies of branding. New technology should connect the interests of audiences and create a brand relevant to the representatives of different target audiences

RankME: Reliable Human Ratings for Natural Language Generation

Human evaluation for natural language generation (NLG) often suffers from inconsistent user ratings. While previous research tends to attribute this problem to individual user preferences, we show that the quality of human judgements can also be improved by experimental design. We present a novel rank-based magnitude estimation method (RankME), which combines the use of continuous scales and relative assessments. We show that RankME significantly improves the reliability and consistency of human ratings compared to traditional evaluation methods. In addition, we show that it is possible to evaluate NLG systems according to multiple, distinct criteria, which is important for error analysis. Finally, we demonstrate that RankME, in combination with Bayesian estimation of system quality, is a cost-effective alternative for ranking multiple NLG systems.Comment: Accepted to NAACL 2018 (The 2018 Conference of the North American Chapter of the Association for Computational Linguistics

Findings of the E2E NLG Challenge

This paper summarises the experimental setup and results of the first shared task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue systems. Recent end-to-end generation systems are promising since they reduce the need for data annotation. However, they are currently limited to small, delexicalised datasets. The E2E NLG shared task aims to assess whether these novel approaches can generate better-quality output by learning from a dataset containing higher lexical richness, syntactic complexity and diverse discourse phenomena. We compare 62 systems submitted by 17 institutions, covering a wide range of approaches, including machine learning architectures -- with the majority implementing sequence-to-sequence models (seq2seq) -- as well as systems based on grammatical rules and templates.Comment: Accepted to INLG 201

Data-driven Natural Language Generation: Paving the Road to Success

We argue that there are currently two major bottlenecks to the commercial use of statistical machine learning approaches for natural language generation (NLG): (a) The lack of reliable automatic evaluation metrics for NLG, and (b) The scarcity of high quality in-domain corpora. We address the first problem by thoroughly analysing current evaluation metrics and motivating the need for a new, more reliable metric. The second problem is addressed by presenting a novel framework for developing and evaluating a high quality corpus for NLG training.Comment: WiNLP workshop at ACL 201

Crowd-sourcing NLG Data: Pictures Elicit Better Data

Recent advances in corpus-based Natural Language Generation (NLG) hold the promise of being easily portable across domains, but require costly training data, consisting of meaning representations (MRs) paired with Natural Language (NL) utterances. In this work, we propose a novel framework for crowdsourcing high quality NLG training data, using automatic quality control measures and evaluating different MRs with which to elicit data. We show that pictorial MRs result in better NL data being collected than logic-based MRs: utterances elicited by pictorial MRs are judged as significantly more natural, more informative, and better phrased, with a significant increase in average quality ratings (around 0.5 points on a 6-point scale), compared to using the logical MRs. As the MR becomes more complex, the benefits of pictorial stimuli increase. The collected data will be released as part of this submission.Comment: The 9th International Natural Language Generation conference INLG, 2016. 10 pages, 2 figures, 3 table

The E2E Dataset: New Challenges For End-to-End Generation

This paper describes the E2E data, a new dataset for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area. The E2E dataset poses new challenges: (1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena; (2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances. We also establish a baseline on this dataset, which illustrates some of the difficulties associated with this data.Comment: Accepted as a short paper for SIGDIAL 2017 (final submission including supplementary material