scholarly journals Magnitude, Causes, and Solutions of the Performance Gap of Buildings: A Review

2019 ◽  
Vol 11 (3) ◽  
pp. 937 ◽  
Author(s):  
Xing Shi ◽  
Binghui Si ◽  
Jiangshan Zhao ◽  
Zhichao Tian ◽  
Chao Wang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Design Stage ◽  
Future Research ◽  
Research Subject ◽  
Performance Gap ◽  
Research Directions ◽  
Future Research Directions ◽  
The Difference ◽  
Review State ◽  
Reported Data

The performance gap of buildings is commonly defined as the difference between the performance value predicted in the design stage and that measured in the post-occupancy stage. Knowledge about the performance gap of buildings is valuable in many aspects and thus is a research subject drawing much attention. Important questions that should be asked include: (1) Definition: what is the performance gap of buildings? (2) Magnitude: how large is the performance gap of buildings? (3) Techniques: how to determine the performance gap of buildings? (4) Causes: what are the reasons leading to the performance gap of buildings? (5) Solutions: how to bridge the performance gap of buildings. By collecting and analyzing more than 20 published works with reported data on the performance gap of buildings and other research articles, these important questions are addressed. Through this review state-of-the-art knowledge regarding the performance gap of buildings is presented. Major conclusions are drawn and future research directions are pointed out.

scholarly journals Deep Learning for Video Captioning: A Review

2019 ◽  
Author(s):  
Shaoxiang Chen ◽  
Ting Yao ◽  
Yu-Gang Jiang
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Future Research ◽  
Research Directions ◽  
Video Captioning ◽  
Future Research Directions ◽  
Review State ◽  
Vision And Language

Deep learning has achieved great successes in solving specific artificial intelligence problems recently. Substantial progresses are made on Computer Vision (CV) and Natural Language Processing (NLP). As a connection between the two worlds of vision and language, video captioning is the task of producing a natural-language utterance (usually a sentence) that describes the visual content of a video. The task is naturally decomposed into two sub-tasks. One is to encode a video via a thorough understanding and learn visual representation. The other is caption generation, which decodes the learned representation into a sequential sentence, word by word. In this survey, we first formulate the problem of video captioning, then review state-of-the-art methods categorized by their emphasis on vision or language, and followed by a summary of standard datasets and representative approaches. Finally, we highlight the challenges which are not yet fully understood in this task and present future research directions.

scholarly journals Social commerce—state-of-the-art and future research directions

2016 ◽  
Vol 26 (3) ◽  
pp. 269-290 ◽  
Author(s):  
Catherine Baethge ◽  
Julia Klier ◽  
Mathias Klier
Keyword(s):  
State Of The Art ◽  
Future Research ◽  
Social Commerce ◽  
Research Directions ◽  
Future Research Directions

scholarly journals Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification

2020 ◽  
Author(s):  
Zheng Wang ◽  
Zhixiang Wang ◽  
Yinqiang Zheng ◽  
Yang Wu ◽  
Wenjun Zeng ◽  
...  
Keyword(s):  
Video Analysis ◽  
State Of The Art ◽  
Future Research ◽  
Low Resolution ◽  
Research Directions ◽  
Comprehensive Review ◽  
Practical Applications ◽  
Future Research Directions

An efficient and effective person re-identification (ReID) system relieves the users from painful and boring video watching and accelerates the process of video analysis. Recently, with the explosive demands of practical applications, a lot of research efforts have been dedicated to heterogeneous person re-identification (Hetero-ReID). In this paper, we provide a comprehensive review of state-of-the-art Hetero-ReID methods that address the challenge of inter-modality discrepancies. According to the application scenario, we classify the methods into four categories --- low-resolution, infrared, sketch, and text. We begin with an introduction of ReID, and make a comparison between Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and compare existing datasets for performing evaluations, and survey the models that have been widely employed in Hetero-ReID. We also summarize and compare the representative approaches from two perspectives, i.e., the application scenario and the learning pipeline. We conclude by a discussion of some future research directions. Follow-up updates are available at https://github.com/lightChaserX/Awesome-Hetero-reID

Energy-Storage Modeling: State-of-the-Art and Future Research Directions

2021 ◽  
pp. 1-1
Author(s):  
Ramteen Sioshansi ◽  
Paul Denholm ◽  
Juan Arteaga ◽  
Sarah Awara ◽  
Shubhrajit Bhattacharjee ◽  
...  
Keyword(s):  
Energy Storage ◽  
State Of The Art ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions

Retail Customer Engagement

2019 ◽  
pp. 1-26
Author(s):  
Andrea Moretta Tartaglione ◽  
Giuseppe Granata
Keyword(s):  
Literature Review ◽  
Bibliometric Analysis ◽  
Scientific Community ◽  
State Of The Art ◽  
Customer Engagement ◽  
Future Research ◽  
Research Directions ◽  
Study Results ◽  
Research Findings ◽  
Future Research Directions

Customer engagement is one of the most debated topics in marketing literature. The great interest of the scientific community resulted in a large amount of research on this topic making it difficult for scholars to understand how to really contribute to advance the research. Based on these considerations, this chapter aims to provide an overview of the research findings and trends of previous studies to guide the researcher to the most influential works, results, and issues that need more insights. In particular, this chapter offers a literature review on customer engagement and retail customer engagement using bibliometric analysis and scientific mapping study. Results show the most productive authors, most cited publications, most frequent words, and clusters of related words. The analysis provides a description of the state of the art of retail customer engagement and suggests future research directions.

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