MindSpaces:Art-driven Adaptive Outdoors and Indoors Design

MindSpaces provides solutions for creating functionally and emotionally appealing architectural designs in urban spaces. Social media services, physiological sensing devices and video cameras provide data from sensing environments. State-of-the-Art technology including VR, 3D design tools, emotion extraction, visual behaviour analysis, and textual analysis will be incorporated in MindSpaces platform for analysing data and adapting the design of spaces.</p

A case study on smart band

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2020. 8. 윤명환 .The aim of this study is to prove that the consumer review-based text mining methods proposed in the paper for cross-cultural design are effective. To prove it, we took Mi band 3 as a case study where we compared the cross-cultural differences in product preference of users from different cultural regions with this method. With the development of global market, more and more products and services are sold across the globe. Users from different cultures have different behaviors, cognitive styles, and value systems. Therefore, product should be designed to meet the needs and preferences of users from different cultural groups. In the field of cross-cultural design, existing studies are mainly focused on traditional usability and UX research methods. However, these methods expose some disadvantages when applied into cross-cultural design contexts. E-commerce websites provide a large volume of product reviews and it is easy to collect review data online. There is no need to employ foreign participants or make a survey onsite or remotely, which will save much more cost and time. There is a new trend that customer reviews are examined to know consumer opinions. Neverlessness, there are not many studies by analyzing online reviews in the field of cross-cultural design. Thus, my research proposed consumer review-based text mining methods for cross-cultural design, which consist of aspect-level opinion mining, sentiment analysis, and semantic network analysis. We collected review data from the following three websites: Naver of South Korea, Jingdong of China, and Amazon of the United States. Text mining methods including opinion mining, sentiment analysis, and semantic network analysis were performed. Firstly, product aspects were extracted from reviews according to word frequency. This indicates how much users are paying attention to different aspects of the product. Aspect-level sentiment analysis was conducted to find out customer satisfaction with different product aspects. Then, the words most associated with each product aspect were listed. Cluster analysis was conducted and the topic of each cluster was summarized. Data visualization of each dataset was done. Lastly, cross-cultural difference among three countries from the results was observed and discussed. Though there exist similar issues in product preferences of users from South Korea, China, and the United States, cross-cultural differences about Mi band 3 are shown in many product aspects. Korean tend to take Mi band as a fashionable, cool, yet not useful wearable device. They often buy it as a nice gift. They are interested in the appearance of the strap and often buy straps of different colors and materials. Korean do not enjoy outdoor activities as much as American. And the function of NFC is not prevalent in Korea. Thus, the smart band is not useful to Korean. These can explain why Korean do not care about quality of the smart band and do not want to buy Mi band at a high price. Korean think that the language of Korean on the display, application, and manual is the most important feature. The length of Korean texts is longer than Chinese to convey the same information. On the other hand, Korean prefer to check message notification on smart band rather than call notification. Therefore, Korean need a larger size for screen. Chinese are more concerned about different kinds of functions including fitness tracker (step counting, heart rate monitoring, and sleep monitoring), notification, and NFC. These different functions are all important and practical to Chinese. American enjoy outdoor activities and tend to use smart band mostly as activity tracker. They care more about activity tracker function including heart rate monitoring and step counting than Korean and Chinese. They have a higher requirement about the accuracy of measured data and have more negative reviews on activity tracker function than Korean and Chinese. Besides, they need the mode for swimming. Because American usually use the smart band for outdoor activities, they complain a lot that the screen is prone to scratches and is invisible under the outdoor sunlight. Also, they pay attention to the quality of screen and strap, expecting the material make the screen and strap durable. Besides, battery is the most significant aspect to American. They always try to test each function to find which function makes battery life short. The results of the case study prove that the consumer review-based text mining method proposed in the paper can generate cross-cultural difference in product preference effectively, which is helpful to cross-cultural design research. And this method is relatively easy and fast compared to other conventional methods.Chapter 1. Introduction 1 1.1 Background and Motivation 1 1.2 Research Objective 3 1.3 Organization of the Thesis 4 Chapter 2. Literature Review 5 2.1 Cross-Cultural Design 5 2.1.1 Definition 5 2.1.2 Necessity 6 2.1.3 Method 7 2.2 Opinion Mining and Sentiment Analysis 10 2.2.1 Aspect Level Opinion Mining 10 2.2.2 Cross-Lingual Opinion Mining 11 2.3 Semantic Network Analysis 13 Chapter 3. Methodology 15 3.1 Data Collection 15 3.2 Data Processing 16 3.2.1 Text Preprocessing 16 3.2.2 Opinion Mining and Sentiment Analysis 16 3.2.3 Semantic Network Analysis 17 3.2.4 Result Sample 18 Chapter 4. Result 20 4.1 Overview 20 4.2 Opinion Mining and Sentiment Analysis 21 4.2.1 Normalized Frequency 21 4.2.2 Sentiment Analysis 23 4.3 Semantic Network Analysis 26 4.3.1 Associated Words 26 4.3.1 Cluster Analysis 31 4.3.1 Data Visualization 34 4.4 Results based on Aspects 37 4.4.1 Battery 37 4.4.2 Price 39 4.4.3 Function 41 4.4.4 Step Counting 43 4.4.5 Korean 45 4.4.6 Heart Rate Monitoring 47 4.4.7 Sleep Monitoring 49 4.4.8 Quality 51 4.4.9 Notification 53 4.4.10 Screen 55 4.4.11 Exercise 57 4.4.12 App 59 4.4.13 Call 61 4.4.14 Connection 63 4.4.15 Waterproof 65 4.4.16 Display 67 4.4.17 Message 69 4.4.18 Alarm 71 4.4.19 Gift 73 4.4.20 Strap 75 Chapter 5. Conclusion 78 5.1 Summary of Findings 78 5.2 Future Research 80 Bibliography 82Maste

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Text Classification

There is an abundance of text data in this world but most of it is raw. We need to extract information from this data to make use of it. One way to extract this information from raw text is to apply informative labels drawn from a pre-defined fixed set i.e. Text Classification. In this thesis, we focus on the general problem of text classification, and work towards solving challenges associated to binary/multi-class/multi-label classification. More specifically, we deal with the problem of (i) Zero-shot labels during testing; (ii) Active learning for text screening; (iii) Multi-label classification under low supervision; (iv) Structured label space; (v) Classifying pairs of words in raw text i.e. Relation Extraction. For (i), we use a zero-shot classification model that utilizes independently learned semantic embeddings. Regarding (ii), we propose a novel active learning algorithm that reduces problem of bias in naive active learning algorithms. For (iii), we propose neural candidate-selector architecture that starts from a set of high-recall candidate labels to obtain high-precision predictions. In the case of (iv), we proposed an attention based neural tree decoder that recursively decodes an abstract into the ontology tree. For (v), we propose using second-order relations that are derived by explicitly connecting pairs of words via context token(s) for improved relation extraction. We use a wide variety of both traditional and deep machine learning tools. More specifically, we used traditional machine learning models like multi-valued linear regression and logistic regression for (i, ii), deep convolutional neural networks for (iii), recurrent neural networks for (iv) and transformer networks for (v)

Leveraging human-computer interaction and crowdsourcing for scholarly knowledge graph creation

The number of scholarly publications continues to grow each year, as well as the number of journals and active researchers. Therefore, methods and tools to organize scholarly knowledge are becoming increasingly important. Without such tools, it becomes increasingly difficult to conduct research in an efficient and effective manner. One of the fundamental issues scholarly communication is facing relates to the format in which the knowledge is shared. Scholarly communication relies primarily on narrative document-based formats that are specifically designed for human consumption. Machines cannot easily access and interpret such knowledge, leaving machines unable to provide powerful tools to organize scholarly knowledge effectively. In this thesis, we propose to leverage knowledge graphs to represent, curate, and use scholarly knowledge. The systematic knowledge representation leads to machine-actionable knowledge, which enables machines to process scholarly knowledge with minimal human intervention. To generate and curate the knowledge graph, we propose a machine learning assisted crowdsourcing approach, in particular Natural Language Processing (NLP). Currently, NLP techniques are not able to satisfactorily extract high-quality scholarly knowledge in an autonomous manner. With our proposed approach, we intertwine human and machine intelligence, thus exploiting the strengths of both approaches. First, we discuss structured scholarly knowledge, where we present the Open Research Knowledge Graph (ORKG). Specifically, we focus on the design and development of the ORKG user interface (i.e., the frontend). One of the key challenges is to provide an interface that is powerful enough to create rich knowledge descriptions yet intuitive enough for researchers without a technical background to create such descriptions. The ORKG serves as the technical foundation for the rest of the work. Second, we focus on comparable scholarly knowledge, where we introduce the concept of ORKG comparisons. ORKG comparisons provide machine-actionable overviews of related literature in a tabular form. Also, we present a methodology to leverage existing literature reviews to populate ORKG comparisons via a human-in-the-loop approach. Additionally, we show how ORKG comparisons can be used to form ORKG SmartReviews. The SmartReviews provide dynamic literature reviews in the form of living documents. They are an attempt address the main weaknesses of the current literature review practice and outline how the future of review publishing can look like. Third, we focus designing suitable tasks to generate scholarly knowledge in a crowdsourced setting. We present an intelligent user interface that enables researchers to annotate key sentences in scholarly publications with a set of discourse classes. During this process, researchers are assisted by suggestions coming from NLP tools. In addition, we present an approach to validate NLP-generated statements using microtasks in a crowdsourced setting. With this approach, we lower the barrier to entering data in the ORKG and transform content consumers into content creators. With the work presented, we strive to transform scholarly communication to improve machine-actionability of scholarly knowledge. The approaches and tools are deployed in a production environment. As a result, the majority of the presented approaches and tools are currently in active use by various research communities and already have an impact on scholarly communication.Die Zahl der wissenschaftlichen Veröffentlichungen nimmt jedes Jahr weiter zu, ebenso wie die Zahl der Zeitschriften und der aktiven Forscher. Daher werden Methoden und Werkzeuge zur Organisation von wissenschaftlichem Wissen immer wichtiger. Ohne solche Werkzeuge wird es immer schwieriger, Forschung effizient und effektiv zu betreiben. Eines der grundlegenden Probleme, mit denen die wissenschaftliche Kommunikation konfrontiert ist, betrifft das Format, in dem das Wissen publiziert wird. Die wissenschaftliche Kommunikation beruht in erster Linie auf narrativen, dokumentenbasierten Formaten, die speziell für Experten konzipiert sind. Maschinen können auf dieses Wissen nicht ohne weiteres zugreifen und es interpretieren, so dass Maschinen nicht in der Lage sind, leistungsfähige Werkzeuge zur effektiven Organisation von wissenschaftlichem Wissen bereitzustellen. In dieser Arbeit schlagen wir vor, Wissensgraphen zu nutzen, um wissenschaftliches Wissen darzustellen, zu kuratieren und zu nutzen. Die systematische Wissensrepräsentation führt zu maschinenverarbeitbarem Wissen. Dieses ermöglicht es Maschinen wissenschaftliches Wissen mit minimalem menschlichen Eingriff zu verarbeiten. Um den Wissensgraphen zu generieren und zu kuratieren, schlagen wir einen Crowdsourcing-Ansatz vor, der durch maschinelles Lernen unterstützt wird, insbesondere durch natürliche Sprachverarbeitung (NLP). Derzeit sind NLP-Techniken nicht in der Lage, qualitativ hochwertiges wissenschaftliches Wissen auf autonome Weise zu extrahieren. Mit unserem vorgeschlagenen Ansatz verknüpfen wir menschliche und maschinelle Intelligenz und nutzen so die Stärken beider Ansätze. Zunächst erörtern wir strukturiertes wissenschaftliches Wissen, wobei wir den Open Research Knowledge Graph (ORKG) vorstellen.Insbesondere konzentrieren wir uns auf das Design und die Entwicklung der ORKG-Benutzeroberfläche (das Frontend). Eine der größten Herausforderungen besteht darin, eine Schnittstelle bereitzustellen, die leistungsfähig genug ist, um umfangreiche Wissensbeschreibungen zu erstellen und gleichzeitig intuitiv genug ist für Forscher ohne technischen Hintergrund, um solche Beschreibungen zu erstellen. Der ORKG dient als technische Grundlage für die Arbeit. Zweitens konzentrieren wir uns auf vergleichbares wissenschaftliches Wissen, wofür wir das Konzept der ORKG-Vergleiche einführen. ORKG-Vergleiche bieten maschinell verwertbare Übersichten über verwandtes wissenschaftliches Wissen in tabellarischer Form. Außerdem stellen wir eine Methode vor, mit der vorhandene Literaturübersichten genutzt werden können, um ORKG-Vergleiche mit Hilfe eines Human-in-the-Loop-Ansatzes zu erstellen. Darüber hinaus zeigen wir, wie ORKG-Vergleiche verwendet werden können, um ORKG SmartReviews zu erstellen. Die SmartReviews bieten dynamische Literaturübersichten in Form von lebenden Dokumenten. Sie stellen einen Versuch dar, die Hauptschwächen der gegenwärtigen Praxis des Literaturreviews zu beheben und zu skizzieren, wie die Zukunft der Veröffentlichung von Reviews aussehen kann. Drittens konzentrieren wir uns auf die Gestaltung geeigneter Aufgaben zur Generierung von wissenschaftlichem Wissen in einer Crowdsourced-Umgebung. Wir stellen eine intelligente Benutzeroberfläche vor, die es Forschern ermöglicht, Schlüsselsätze in wissenschaftlichen Publikationen mittles Diskursklassen zu annotieren. In diesem Prozess werden Forschende mit Vorschlägen von NLP-Tools unterstützt. Darüber hinaus stellen wir einen Ansatz zur Validierung von NLP-generierten Aussagen mit Hilfe von Mikroaufgaben in einer Crowdsourced-Umgebung vor. Mit diesem Ansatz senken wir die Hürde für die Eingabe von Daten in den ORKG und setzen Inhaltskonsumenten als Inhaltsersteller ein. Mit der Arbeit streben wir eine Transformation der wissenschaftlichen Kommunikation an, um die maschinelle Verwertbarkeit von wissenschaftlichem Wissen zu verbessern. Die Ansätze und Werkzeuge werden in einer Produktionsumgebung eingesetzt. Daher werden die meisten der vorgestellten Ansätze und Werkzeuge derzeit von verschiedenen Forschungsgemeinschaften aktiv genutzt und haben bereits einen Einfluss auf die wissenschaftliche Kommunikation.EC/ERC/819536/E

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N