6,426 research outputs found
์ ์ฌ ์๋ฒ ๋ฉ์ ํตํ ์๊ฐ์ ์คํ ๋ฆฌ๋ก๋ถํฐ์ ์์ฌ ํ ์คํธ ์์ฑ๊ธฐ ํ์ต
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ, 2019. 2. ์ฅ๋ณํ.The ability to understand the story is essential to make humans unique from other primates as well as animals. The capability of story understanding is crucial for AI agents to live with people in everyday life and understand their context. However, most research on story AI focuses on automated story generation based on closed
worlds designed manually, which are widely used for computation authoring. Machine learning techniques on story corpora face similar problems of natural language processing such as omitting details and commonsense knowledge. Since the remarkable success of deep learning on computer vision field, increasing our interest in research on bridging between vision and language, vision-grounded story data will potentially improve the performance of story understanding and narrative text generation.
Let us assume that AI agents lie in the environment in which the sensing information is input by the camera. Those agents observe the surroundings, translate them into the story in natural language, and predict the following event or multiple ones sequentially. This dissertation study on the related problems: learning stories or generating the narrative text from image streams or videos.
The first problem is to generate a narrative text from a sequence of ordered images. As a solution, we introduce a GLAC Net (Global-local Attention Cascading Network). It translates from image sequences to narrative paragraphs in text as a encoder-decoder framework with sequence-to-sequence setting. It has
convolutional neural networks for extracting information from images, and recurrent neural networks for text generation. We introduce visual cue encoders with stacked bidirectional LSTMs, and all of the outputs of each layer are aggregated as contextualized image vectors to extract visual clues. The coherency of the generated text is further improved by conveying (cascading) the information of the previous sentence to the next sentence serially in the decoders. We evaluate the performance of it on the Visual storytelling (VIST) dataset. It outperforms other state-of-the-art results and shows the best scores in total score and all of 6 aspects in the visual storytelling challenge with evaluation of human judges.
The second is to predict the following events or narrative texts with the former parts of stories. It should be possible to predict at any step with an arbitrary length. We propose recurrent event retrieval models as a solution. They train a context accumulation function and two embedding functions, where make close the distance between the cumulative context at current time and the next probable events on a latent space. They update the cumulative context with a new event as a input using bilinear operations, and we can find the next event candidates with the updated cumulative context. We evaluate them for Story Cloze Test, they show competitive performance and the best in open-ended generation setting. Also, it demonstrates the working examples in an interactive setting.
The third deals with the study on composite representation learning for semantics and order for video stories. We embed each episode as a trajectory-like sequence of events on the latent space, and propose a ViStoryNet to regenerate video stories with them (tasks of story completion). We convert event sentences to thought vectors, and train functions to make successive event embed close each other to form episodes as trajectories. Bi-directional LSTMs are trained as sequence models, and decoders to generate event sentences with GRUs. We test them experimentally with PororoQA dataset, and observe that most of episodes show the form of trajectories. We use them to complete the blocked part of stories, and they show not perfect but overall similar result.
Those results above can be applied to AI agents in the living area sensing with their cameras, explain the situation as stories, infer some unobserved parts, and predict the future story.์คํ ๋ฆฌ๋ฅผ ์ดํดํ๋ ๋ฅ๋ ฅ์ ๋๋ฌผ๋ค ๋ฟ๋ง ์๋๋ผ ๋ค๋ฅธ ์ ์ธ์๊ณผ ์ธ๋ฅ๋ฅผ ๊ตฌ๋ณ์ง๋ ์ค์ํ ๋ฅ๋ ฅ์ด๋ค. ์ธ๊ณต์ง๋ฅ์ด ์ผ์์ํ ์์์ ์ฌ๋๋ค๊ณผ ํจ๊ป ์ง๋ด๋ฉด์ ๊ทธ๋ค์ ์ํ ์ ๋งฅ๋ฝ์ ์ดํดํ๊ธฐ ์ํด์๋ ์คํ ๋ฆฌ๋ฅผ ์ดํดํ๋ ๋ฅ๋ ฅ์ด ๋งค์ฐ ์ค์ํ๋ค. ํ์ง๋ง,
๊ธฐ์กด์ ์คํ ๋ฆฌ์ ๊ดํ ์ฐ๊ตฌ๋ ์ธ์ด์ฒ๋ฆฌ์ ์ด๋ ค์์ผ๋ก ์ธํด ์ฌ์ ์ ์ ์๋ ์ธ๊ณ ๋ชจ๋ธ ํ์์ ์ข์ ํ์ง์ ์ ์๋ฌผ์ ์์ฑํ๋ ค๋ ๊ธฐ์ ์ด ์ฃผ๋ก ์ฐ๊ตฌ๋์ด ์๋ค. ๊ธฐ๊ณํ์ต ๊ธฐ๋ฒ์ ํตํด ์คํ ๋ฆฌ๋ฅผ ๋ค๋ฃจ๋ ค๋ ์๋๋ค์ ๋์ฒด๋ก ์์ฐ์ด๋ก ํํ๋ ๋ฐ์ดํฐ์ ๊ธฐ๋ฐํ ์ ๋ฐ์ ์์ด ์์ฐ์ด ์ฒ๋ฆฌ์์ ๊ฒช๋ ๋ฌธ์ ๋ค์ ๋์ผํ๊ฒ ๊ฒช๋๋ค. ์ด๋ฅผ ๊ทน๋ณตํ๊ธฐ ์ํด์๋ ์๊ฐ์ ์ ๋ณด๊ฐ ํจ๊ป ์ฐ๋๋ ๋ฐ์ดํฐ๊ฐ ๋์์ด ๋ ์ ์๋ค. ์ต๊ทผ ๋ฅ๋ฌ๋์ ๋๋ถ์ ๋ฐ์ ์ ํ์
์ด ์๊ฐ๊ณผ ์ธ์ด ์ฌ์ด์ ๊ด๊ณ๋ฅผ ๋ค๋ฃจ๋ ์ฐ๊ตฌ๋ค์ด ๋์ด๋๊ณ
์๋ค. ์ฐ๊ตฌ์ ๋น์ ์ผ๋ก์, ์ธ๊ณต์ง๋ฅ ์์ด์ ํธ๊ฐ ์ฃผ๋ณ ์ ๋ณด๋ฅผ ์นด๋ฉ๋ผ๋ก ์
๋ ฅ๋ฐ๋ ํ๊ฒฝ ์์ ๋์ฌ์๋ ์ํฉ์ ์๊ฐํด ๋ณผ ์ ์๋ค. ์ด ์์์ ์ธ๊ณต์ง๋ฅ ์์ด์ ํธ๋ ์ฃผ๋ณ์ ๊ด์ฐฐํ๋ฉด์ ๊ทธ์ ๋ํ ์คํ ๋ฆฌ๋ฅผ ์์ฐ์ด ํํ๋ก ์์ฑํ๊ณ , ์์ฑ๋ ์คํ ๋ฆฌ๋ฅผ
๋ฐํ์ผ๋ก ๋ค์์ ์ผ์ด๋ ์คํ ๋ฆฌ๋ฅผ ํ ๋จ๊ณ์์ ์ฌ๋ฌ ๋จ๊ณ๊น์ง ์์ธกํ ์ ์๋ค. ๋ณธ ํ์ ๋
ผ๋ฌธ์์๋ ์ฌ์ง ๋ฐ ๋น๋์ค ์์ ๋ํ๋๋ ์คํ ๋ฆฌ(visual story)๋ฅผ ํ์ตํ๋ ๋ฐฉ๋ฒ, ๋ด๋ฌํฐ๋ธ ํ
์คํธ๋ก์ ๋ณํ, ๊ฐ๋ ค์ง ์ฌ๊ฑด ๋ฐ ๋ค์ ์ฌ๊ฑด์ ์ถ๋ก ํ๋ ์ฐ๊ตฌ๋ค์
๋ค๋ฃฌ๋ค.
์ฒซ ๋ฒ์งธ๋ก, ์ฌ๋ฌ ์ฅ์ ์ฌ์ง์ด ์ฃผ์ด์ก์ ๋ ์ด๋ฅผ ๋ฐํ์ผ๋ก ์คํ ๋ฆฌ ํ
์คํธ๋ฅผ ์์ฑํ๋ ๋ฌธ์ (๋น์ฃผ์ผ ์คํ ๋ฆฌํ
๋ง)๋ฅผ ๋ค๋ฃฌ๋ค. ์ด ๋ฌธ์ ํด๊ฒฐ์ ์ํด ๊ธ๋๋ท(GLAC Net)์ ์ ์ํ์๋ค. ๋จผ์ , ์ฌ์ง๋ค๋ก๋ถํฐ ์ ๋ณด๋ฅผ ์ถ์ถํ๊ธฐ ์ํ ์ปจ๋ณผ๋ฃจ์
์ ๊ฒฝ๋ง, ๋ฌธ์ฅ์
์์ฑํ๊ธฐ ์ํด ์ํ์ ๊ฒฝ๋ง์ ์ด์ฉํ๋ค. ์ํ์ค-์ํ์ค ๊ตฌ์กฐ์ ์ธ์ฝ๋๋ก์, ์ ์ฒด์ ์ธ ์ด์ผ๊ธฐ ๊ตฌ์กฐ์ ํํ์ ์ํด ๋ค๊ณ์ธต ์๋ฐฉํฅ ์ํ์ ๊ฒฝ๋ง์ ๋ฐฐ์นํ๋ ๊ฐ ์ฌ์ง ๋ณ ์ ๋ณด๋ฅผ ํจ๊ป ์ด์ฉํ๊ธฐ ์ํด ์ ์ญ์ -๊ตญ๋ถ์ ์ฃผ์์ง์ค ๋ชจ๋ธ์ ์ ์ํ์๋ค. ๋ํ,
์ฌ๋ฌ ๋ฌธ์ฅ์ ์์ฑํ๋ ๋์ ๋งฅ๋ฝ์ ๋ณด์ ๊ตญ๋ถ์ ๋ณด๋ฅผ ์์ง ์๊ฒ ํ๊ธฐ ์ํด ์์ ๋ฌธ์ฅ ์ ๋ณด๋ฅผ ์ ๋ฌํ๋ ๋ฉ์ปค๋์ฆ์ ์ ์ํ์๋ค. ์ ์ ์ ๋ฐฉ๋ฒ์ผ๋ก ๋น์คํธ(VIST) ๋ฐ์ดํฐ ์งํฉ์ ํ์ตํ์๊ณ , ์ 1 ํ ์๊ฐ์ ์คํ ๋ฆฌํ
๋ง ๋ํ(visual storytelling challenge)์์ ์ฌ๋ ํ๊ฐ๋ฅผ ๊ธฐ์ค์ผ๋ก ์ ์ฒด ์ ์ ๋ฐ 6 ํญ๋ชฉ ๋ณ๋ก ๋ชจ๋ ์ต๊ณ ์ ์ ๋ฐ์๋ค.
๋ ๋ฒ์งธ๋ก, ์คํ ๋ฆฌ์ ์ผ๋ถ๊ฐ ๋ฌธ์ฅ๋ค๋ก ์ฃผ์ด์ก์ ๋ ์ด๋ฅผ ๋ฐํ์ผ๋ก ๋ค์ ๋ฌธ์ฅ์ ์์ธกํ๋ ๋ฌธ์ ๋ฅผ ๋ค๋ฃฌ๋ค. ์์์ ๊ธธ์ด์ ์คํ ๋ฆฌ์ ๋ํด ์์์ ์์น์์ ์์ธก์ด ๊ฐ๋ฅํด์ผ ํ๊ณ , ์์ธกํ๋ ค๋ ๋จ๊ณ ์์ ๋ฌด๊ดํ๊ฒ ์๋ํด์ผ ํ๋ค. ์ด๋ฅผ ์ํ ๋ฐฉ๋ฒ์ผ๋ก
์ํ ์ฌ๊ฑด ์ธ์ถ ๋ชจ๋ธ(Recurrent Event Retrieval Models)์ ์ ์ํ์๋ค. ์ด ๋ฐฉ๋ฒ์ ์๋ ๊ณต๊ฐ ์์์ ํ์ฌ๊น์ง ๋์ ๋ ๋งฅ๋ฝ๊ณผ ๋ค์์ ๋ฐ์ํ ์ ๋ ฅ ์ฌ๊ฑด ์ฌ์ด์ ๊ฑฐ๋ฆฌ๋ฅผ ๊ฐ๊น๊ฒ ํ๋๋ก ๋งฅ๋ฝ๋์ ํจ์์ ๋ ๊ฐ์ ์๋ฒ ๋ฉ ํจ์๋ฅผ ํ์ตํ๋ค. ์ด๋ฅผ ํตํด ์ด๋ฏธ ์
๋ ฅ๋์ด ์๋ ์คํ ๋ฆฌ์ ์๋ก์ด ์ฌ๊ฑด์ด ์
๋ ฅ๋๋ฉด ์์ ํ์ ์ฐ์ฐ์ ํตํด ๊ธฐ์กด์ ๋งฅ๋ฝ์ ๊ฐ์ ํ์ฌ ๋ค์์ ๋ฐ์ํ ์ ๋ ฅํ ์ฌ๊ฑด๋ค์ ์ฐพ๋๋ค. ์ด ๋ฐฉ๋ฒ์ผ๋ก ๋ฝ์คํ ๋ฆฌ(ROCStories) ๋ฐ์ดํฐ์งํฉ์ ํ์ตํ์๊ณ , ์คํ ๋ฆฌ ํด๋ก์ฆ ํ
์คํธ(Story Cloze Test)๋ฅผ ํตํด ํ๊ฐํ ๊ฒฐ๊ณผ ๊ฒฝ์๋ ฅ ์๋ ์ฑ๋ฅ์ ๋ณด์์ผ๋ฉฐ, ํนํ ์์์ ๊ธธ์ด๋ก ์ถ๋ก ํ ์ ์๋ ๊ธฐ๋ฒ ์ค์ ์ต๊ณ ์ฑ๋ฅ์ ๋ณด์๋ค.
์ธ ๋ฒ์งธ๋ก, ๋น๋์ค ์คํ ๋ฆฌ์์ ์ฌ๊ฑด ์ํ์ค ์ค ์ผ๋ถ๊ฐ ๊ฐ๋ ค์ก์ ๋ ์ด๋ฅผ ๋ณต๊ตฌํ๋ ๋ฌธ์ ๋ฅผ ๋ค๋ฃฌ๋ค. ํนํ, ๊ฐ ์ฌ๊ฑด์ ์๋ฏธ ์ ๋ณด์ ์์๋ฅผ ๋ชจ๋ธ์ ํํ ํ์ต์ ๋ฐ์ํ๊ณ ์ ํ์๋ค. ์ด๋ฅผ ์ํด ์๋ ๊ณต๊ฐ ์์ ๊ฐ ์ํผ์๋๋ค์ ๊ถค์ ํํ๋ก ์๋ฒ ๋ฉํ๊ณ ,
์ด๋ฅผ ๋ฐํ์ผ๋ก ์คํ ๋ฆฌ๋ฅผ ์ฌ์์ฑ์ ํ์ฌ ์คํ ๋ฆฌ ์์ฑ์ ํ ์ ์๋ ๋ชจ๋ธ์ธ ๋น์คํ ๋ฆฌ๋ท(ViStoryNet)์ ์ ์ํ์๋ค. ๊ฐ ์ํผ์๋๋ฅผ ๊ถค์ ํํ๋ฅผ ๊ฐ์ง๊ฒ ํ๊ธฐ ์ํด ์ฌ๊ฑด ๋ฌธ์ฅ์ ์ฌ๊ณ ๋ฒกํฐ(thought vector)๋ก ๋ณํํ๊ณ , ์ฐ์ ์ด๋ฒคํธ ์์ ์๋ฒ ๋ฉ์
ํตํด ์ ํ ์ฌ๊ฑด๋ค์ด ์๋ก ๊ฐ๊น๊ฒ ์๋ฒ ๋ฉ๋๋๋ก ํ์ฌ ํ๋์ ์ํผ์๋๊ฐ ๊ถค์ ์ ๋ชจ์์ ๊ฐ์ง๋๋ก ํ์ตํ์๋ค. ๋ฝ๋ก๋กQA ๋ฐ์ดํฐ์งํฉ์ ํตํด ์คํ์ ์ผ๋ก ๊ฒฐ๊ณผ๋ฅผ ํ์ธํ์๋ค. ์๋ฒ ๋ฉ ๋ ์ํผ์๋๋ค์ ๊ถค์ ํํ๋ก ์ ๋ํ๋ฌ์ผ๋ฉฐ, ์ํผ์๋๋ค์ ์ฌ์์ฑ ํด๋ณธ ๊ฒฐ๊ณผ ์ ์ฒด์ ์ธ ์ธก๋ฉด์์ ์ ์ฌํ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์๋ค.
์ ๊ฒฐ๊ณผ๋ฌผ๋ค์ ์นด๋ฉ๋ผ๋ก ์
๋ ฅ๋๋ ์ฃผ๋ณ ์ ๋ณด๋ฅผ ๋ฐํ์ผ๋ก ์คํ ๋ฆฌ๋ฅผ ์ดํดํ๊ณ ์ผ๋ถ ๊ด์ธก๋์ง ์์ ๋ถ๋ถ์ ์ถ๋ก ํ๋ฉฐ, ํฅํ ์คํ ๋ฆฌ๋ฅผ ์์ธกํ๋ ๋ฐฉ๋ฒ๋ค์ ๋์๋๋ค.Abstract i
Chapter 1 Introduction 1
1.1 Story of Everyday lives in Videos and Story Understanding . . . 1
1.2 Problems to be addressed . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Approach and Contribution . . . . . . . . . . . . . . . . . . . . . 6
1.4 Organization of Dissertation . . . . . . . . . . . . . . . . . . . . . 9
Chapter 2 Background and Related Work 10
2.1 Why We Study Stories . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 Latent Embedding . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3 Order Embedding and Ordinal Embedding . . . . . . . . . . . . 14
2.4 Comparison to Story Understanding . . . . . . . . . . . . . . . . 15
2.5 Story Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.5.1 Abstract Event Representations . . . . . . . . . . . . . . . 17
2.5.2 Seq-to-seq Attentional Models . . . . . . . . . . . . . . . . 18
2.5.3 Story Generation from Images . . . . . . . . . . . . . . . 19
Chapter 3 Visual Storytelling via Global-local Attention Cascading
Networks 21
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 Evaluation for Visual Storytelling . . . . . . . . . . . . . . . . . . 26
3.3 Global-local Attention Cascading Networks (GLAC Net) . . . . . 27
3.3.1 Encoder: Contextualized Image Vector Extractor . . . . . 28
3.3.2 Decoder: Story Generator with Attention and Cascading
Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.4.1 VIST Dataset . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.4.2 Experiment Settings . . . . . . . . . . . . . . . . . . . . . 33
3.4.3 Network Training Details . . . . . . . . . . . . . . . . . . 36
3.4.4 Qualitative Analysis . . . . . . . . . . . . . . . . . . . . . 38
3.4.5 Quantitative Analysis . . . . . . . . . . . . . . . . . . . . 38
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Chapter 4 Common Space Learning on Cumulative Contexts
and the Next Events: Recurrent Event Retrieval
Models 44
4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.2 Problems of Context Accumulation . . . . . . . . . . . . . . . . . 45
4.3 Recurrent Event Retrieval Models for Next Event Prediction . . 46
4.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.4.2 Story Cloze Test . . . . . . . . . . . . . . . . . . . . . . . 52
4.4.3 Open-ended Story Generation . . . . . . . . . . . . . . . . 53
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Chapter 5 ViStoryNet: Order Embedding of Successive Events
and the Networks for Story Regeneration 58
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.2 Order Embedding with Triple Learning . . . . . . . . . . . . . . 60
5.2.1 Embedding Ordered Objects in Sequences . . . . . . . . . 62
5.3 Problems and Contextual Events . . . . . . . . . . . . . . . . . . 62
5.3.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . 62
5.3.2 Contextual Event Vectors from Kids Videos . . . . . . . . 64
5.4 Architectures for the Story Regeneration Task . . . . . . . . . . . 67
5.4.1 Two Sentence Generators as Decoders . . . . . . . . . . . 68
5.4.2 Successive Event Order Embedding (SEOE) . . . . . . . . 68
5.4.3 Sequence Models of the Event Space . . . . . . . . . . . . 72
5.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.5.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . 73
5.5.2 Quantitative Analysis . . . . . . . . . . . . . . . . . . . . 73
5.5.3 Qualitative Analysis . . . . . . . . . . . . . . . . . . . . . 74
5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Chapter 6 Concluding Remarks 80
6.1 Summary of Methods and Contributions . . . . . . . . . . . . . . 80
6.2 Limitation and Outlook . . . . . . . . . . . . . . . . . . . . . . . 81
6.3 Suggestions for Future Research . . . . . . . . . . . . . . . . . . . 81
์ด๋ก 101Docto
Flux Analysis in Process Models via Causality
We present an approach for flux analysis in process algebra models of
biological systems. We perceive flux as the flow of resources in stochastic
simulations. We resort to an established correspondence between event
structures, a broadly recognised model of concurrency, and state transitions of
process models, seen as Petri nets. We show that we can this way extract the
causal resource dependencies in simulations between individual state
transitions as partial orders of events. We propose transformations on the
partial orders that provide means for further analysis, and introduce a
software tool, which implements these ideas. By means of an example of a
published model of the Rho GTP-binding proteins, we argue that this approach
can provide the substitute for flux analysis techniques on ordinary
differential equation models within the stochastic setting of process algebras
Authoring virtual crowds: a survey
Recent advancements in crowd simulation unravel a wide range of functionalities for virtual agents, delivering highly-realistic,natural virtual crowds. Such systems are of particular importance to a variety of applications in fields such as: entertainment(e.g., movies, computer games); architectural and urban planning; and simulations for sports and training. However, providingtheir capabilities to untrained users necessitates the development of authoring frameworks. Authoring virtual crowds is acomplex and multi-level task, varying from assuming control and assisting users to realise their creative intents, to deliveringintuitive and easy to use interfaces, facilitating such control. In this paper, we present a categorisation of the authorable crowdsimulation components, ranging from high-level behaviours and path-planning to local movements, as well as animation andvisualisation. We provide a review of the most relevant methods in each area, emphasising the amount and nature of influencethat the users have over the final result. Moreover, we discuss the currently available authoring tools (e.g., graphical userinterfaces, drag-and-drop), identifying the trends of early and recent work. Finally, we suggest promising directions for futureresearch that mainly stem from the rise of learning-based methods, and the need for a unified authoring framework.This work has received funding from the European Unionโs Horizon 2020 research and innovation programme under the Marie Skลodowska Curie grant agreement No 860768 (CLIPE project). This project has received funding from the European Unionโs Horizon 2020 Research and Innovation Programme under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital PolicyPeer ReviewedPostprint (author's final draft
Configuring urban carbon governance: insights from Sydney, Australia
In the political geography of responses to climate change, and the governance of carbon more specifically, the urban has emerged as a strategic site. Although it is recognized that urban carbon governance occurs through diverse programs and projectsโinvolving multiple actors and working through multiple sites, mechanisms, objects, and subjectsโsurprisingly little attention has been paid to the actual processes through which these diverse elements are drawn together and held together in the exercise of governing. These processesโtermed configurationโremain underspecified. This article explores urban carbon governance interventions as relational configurations, excavating how their diverse elementsโhuman, institutional, representational, and materialโare assembled, drawn into relation, and held together in the exercise of governing. Through an analysis of two contrasting case studies of urban carbon governance interventions in Sydney, Australia, we draw out common processes of configuring and specific sets of devices and techniques that gather, align, and maintain the relations between actors and elements that constitute intervention projects. We conclude by reflecting on the implications of conceiving of governing projects as relational configurations for how we understand the nature and practice of urban carbon governance, especially by revealing the diverse modes of power at work within processes of configuring
Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective
Data-driven decision making is becoming an integral part of manufacturing
companies. Data is collected and commonly used to improve efficiency and
produce high quality items for the customers. IoT-based and other forms of
object tracking are an emerging tool for collecting movement data of
objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over
space and time. Movement data can provide valuable insights like process
bottlenecks, resource utilization, effective working time etc. that can be used
for decision making and improving efficiency.
Turning movement data into valuable information for industrial management and
decision making requires analysis methods. We refer to this process as movement
analytics. The purpose of this document is to review the current state of work
for movement analytics both in manufacturing and more broadly.
We survey relevant work from both a theoretical perspective and an
application perspective. From the theoretical perspective, we put an emphasis
on useful methods from two research areas: machine learning, and logic-based
knowledge representation. We also review their combinations in view of movement
analytics, and we discuss promising areas for future development and
application. Furthermore, we touch on constraint optimization.
From an application perspective, we review applications of these methods to
movement analytics in a general sense and across various industries. We also
describe currently available commercial off-the-shelf products for tracking in
manufacturing, and we overview main concepts of digital twins and their
applications
The Political Economy of Renewable Energy Investment in Ghana
The high level of fossil fuel consumption globally is wreaking havoc on the global climate through the emissions of greenhouse gases. Against this backdrop, there have been calls from national and international stakeholders for a transition towards renewable energy (RE). However, the investment and adoption of renewable energy technologies especially, in developing countries have been woefully inadequate. Even though various policy and legislative instruments in support of RE development abound in Ghana, the contribution of RE to the energy generation mix is notably insignificant, due to constraints that limit high investment. Using the Political Economy Analysis (PEA) approach, this article examines the deficiencies in these policy strategies, and unravels the complexity as well as the alignments of interests of stakeholders
regarding policies that could provide a more favourable investment in renewables in Ghana. The article recommends that Ghanaโs leaders champion those policies with the highest support across all stakeholders
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