Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Scaling Machine Learning Systems using Domain Adaptation

Machine-learned components, particularly those trained using deep learning methods, are becoming integral parts of modern intelligent systems, with applications including computer vision, speech processing, natural language processing and human activity recognition. As these machine learning (ML) systems scale to real-world settings, they will encounter scenarios where the distribution of the data in the real-world (i.e., the target domain) is different from the data on which they were trained (i.e., the source domain). This phenomenon, known as domain shift, can significantly degrade the performance of ML systems in new deployment scenarios. In this thesis, we study the impact of domain shift caused by variations in system hardware, software and user preferences on the performance of ML systems. After quantifying the performance degradation of ML models in target domains due to the various types of domain shift, we propose unsupervised domain adaptation (uDA) algorithms that leverage unlabeled data collected in the target domain to improve the performance of the ML model. At its core, this thesis argues for the need to develop uDA solutions while adhering to practical scenarios in which ML systems will scale. More specifically, we consider four scenarios: (i) opaque ML systems, wherein parameters of the source prediction model are not made accessible in the target domain, (ii) transparent ML systems, wherein source model parameters are accessible and can be modified in the target domain, (iii) ML systems where source and target domains do not have identical label spaces, and (iv) distributed ML systems, wherein the source and target domains are geographically distributed, their datasets are private and cannot be exchanged using adaptation. We study the unique challenges and constraints of each scenario and propose novel uDA algorithms that outperform state-of-the-art baselines

Applications of electroencephalography in construction

A wearable electroencephalogram (EEG) is considered a means for investigating psychophysiological conditions of individuals in the workplace in order to ameliorate occupational health and safety. Following other sectors, construction scholars have adopted this technology over the past decade to strengthen evidence-based practices to improve the wellbeing of workers. This study presents the state-of-the-art hardware, algorithms, and applications of EEG as a platform that assists in dealing with the risk-prone and complex nature of construction tasks. After summarizing the background of EEG and its research paradigms in different sectors, a comprehensive review of EEG-enabled construction research is provided. First, through a macro-scale review aided by bibliometric analysis a big picture of the research streams is plotted. Second, a micro-scale review is conducted to spot the gaps in the literature. The identified gaps are used to classify the future research directions into theoretical, application, and methodological developments

Essays on Predictive Analytics in E-Commerce

Die Motivation für diese Dissertation ist dualer Natur: Einerseits ist die Dissertation methodologisch orientiert und entwickelt neue statistische Ansätze und Algorithmen für maschinelles Lernen. Gleichzeitig ist sie praktisch orientiert und fokussiert sich auf den konkreten Anwendungsfall von Produktretouren im Onlinehandel. Die “data explosion”, veursacht durch die Tatsache, dass die Kosten für das Speichern und Prozessieren großer Datenmengen signifikant gesunken sind (Bhimani and Willcocks, 2014), und die neuen Technologien, die daraus resultieren, stellen die größte Diskontinuität für die betriebliche Praxis und betriebswirtschaftliche Forschung seit Entwicklung des Internets dar (Agarwal and Dhar, 2014). Insbesondere die Business Intelligence (BI) wurde als wichtiges Forschungsthema für Praktiker und Akademiker im Bereich der Wirtschaftsinformatik (WI) identifiziert (Chen et al., 2012). Maschinelles Lernen wurde erfolgreich auf eine Reihe von BI-Problemen angewandt, wie zum Beispiel Absatzprognose (Choi et al., 2014; Sun et al., 2008), Prognose von Windstromerzeugung (Wan et al., 2014), Prognose des Krankheitsverlaufs von Patienten eines Krankenhauses (Liu et al., 2015), Identifikation von Betrug Abbasi et al., 2012) oder Recommender-Systeme (Sahoo et al., 2012). Allerdings gibt es nur wenig Forschung, die sich mit Fragestellungen um maschinelles Lernen mit spezifischen Bezug zu BI befasst: Obwohl existierende Algorithmen teilweise modifiziert werden, um sie auf ein bestimmtes Problem anzupassen (Abbasi et al., 2010; Sahoo et al., 2012), beschränkt sich die WI-Forschung im Allgemeinen darauf, existierende Algorithmen, die für andere Fragestellungen als BI entwickelt wurden, auf BI-Fragestellungen anzuwenden (Abbasi et al., 2010; Sahoo et al., 2012). Das erste wichtige Ziel dieser Dissertation besteht darin, einen Beitrag dazu zu leisten, diese Lücke zu schließen. Diese Dissertation fokussiert sich auf das wichtige BI-Problem von Produktretouren im Onlinehandel für eine Illustration und praktische Anwendung der vorgeschlagenen Konzepte. Viele Onlinehändler sind nicht profitabel (Rigby, 2014) und Produktretouren sind eine wichtige Ursache für dieses Problem (Grewal et al., 2004). Neben Kostenaspekten sind Produktretouren aus ökologischer Sicht problematisch. In der Logistikforschung ist es weitestgehend Konsens, dass die “letzte Meile” der Zulieferkette, nämlich dann wenn das Produkt an die Haustür des Kunden geliefert wird, am CO2-intensivsten ist (Browne et al., 2008; Halldórsson et al., 2010; Song et al., 2009). Werden Produkte retourniert, wird dieser energieintensive Schritt wiederholt, wodurch sich die Nachhaltigkeit und Umweltfreundlichkeit des Geschäftsmodells von Onlinehändlern relativ zum klassischen Vertrieb reduziert. Allerdings können Onlinehändler Produktretouren nicht einfach verbieten, da sie einen wichtigen Teil ihres Geschäftsmodells darstellen: So hat die Möglichkeit, Produkte zu retournieren positive Auswirkungen auf Kundenzufriedenheit (Cassill, 1998), Kaufverhalten (Wood, 2001), künftiges Kaufverhalten (Petersen and Kumar, 2009) und emotianale Reaktionen der Kunden (Suwelack et al., 2011). Ein vielversprechender Ansatz besteht darin, sich auf impulsives und kompulsives (LaRose, 2001) sowie betrügerisches Kaufverhalten zu fokussieren (Speights and Hilinski, 2005; Wachter et al., 2012). In gegenwärtigen akademschen Literatur zu dem Thema gibt es keine solchen Strategien. Die meisten Strategien unterscheiden nicht zwischen gewollten und ungewollten Retouren (Walsh et al., 2014). Das zweite Ziel dieser Dissertation besteht daher darin, die Basis für eine Strategie von Prognose und Intervention zu entwickeln, mit welcher Konsumverhalten mit hoher Retourenwahrscheinlichkeit im Vorfeld erkannt und rechtzeitig interveniert werden kann. In dieser Dissertation werden mehrere Prognosemodelle entwickelt, auf Basis welcher demonstriert wird, dass die Strategie, unter der Annahme moderat effektiver Interventionsstrategien, erhebliche Kosteneinsparungen mit sich bringt

Towards robust real-world historical handwriting recognition

In this thesis, we make a bridge from the past to the future by using artificial-intelligence methods for text recognition in a historical Dutch collection of the Natuurkundige Commissie that explored Indonesia (1820-1850). In spite of the successes of systems like 'ChatGPT', reading historical handwriting is still quite challenging for AI. Whereas GPT-like methods work on digital texts, historical manuscripts are only available as an extremely diverse collections of (pixel) images. Despite the great results, current DL methods are very data greedy, time consuming, heavily dependent on the human expert from the humanities for labeling and require machine-learning experts for designing the models. Ideally, the use of deep learning methods should require minimal human effort, have an algorithm observe the evolution of the training process, and avoid inefficient use of the already sparse amount of labeled data. We present several approaches towards dealing with these problems, aiming to improve the robustness of current methods and to improve the autonomy in training. We applied our novel word and line text recognition approaches on nine data sets differing in time period, language, and difficulty: three locally collected historical Latin-based data sets from Naturalis, Leiden; four public Latin-based benchmark data sets for comparability with other approaches; and two Arabic data sets. Using ensemble voting of just five neural networks, a level of accuracy was achieved which required hundreds of neural networks in earlier studies. Moreover, we increased the speed of evaluation of each training epoch without the need of labeled data

Towards the Understanding of Private Content – Content-based Privacy Assessment and Protection in Social Networks

In the wake of the Facebook data breach scandal, users begin to realize how vulnerable their per-sonal data is and how blindly they trust the online social networks (OSNs) by giving them an inordinate amount of private data that touch on unlimited areas of their lives. In particular, stud-ies show that users sometimes reveal too much information or unintentionally release regretful messages, especially when they are careless, emotional, or unaware of privacy risks. Additionally, friends on social media platforms are also found to be adversarial and may leak one’s private in-formation. Threats from within users’ friend networks – insider threats by human or bots – may be more concerning because they are much less likely to be mitigated through existing solutions, e.g., the use of privacy settings. Therefore, we argue that the key component of privacy protection in social networks is protecting sensitive/private content, i.e. privacy as having the ability to control dissemination of information. A mechanism to automatically identify potentially sensitive/private posts and alert users before they are posted is urgently needed. In this dissertation, we propose a context-aware, text-based quantitative model for private in-formation assessment, namely PrivScore, which is expected to serve as the foundation of a privacy leakage alerting mechanism. We first explicitly research and study topics that might contain private content. Based on this knowledge, we solicit diverse opinions on the sensitiveness of private infor-mation from crowdsourcing workers, and examine the responses to discover a perceptual model behind the consensuses and disagreements. We then develop a computational scheme using deep neural networks to compute a context-free PrivScore (i.e., the “consensus” privacy score among average users). Finally, we integrate tweet histories, topic preferences and social contexts to gener-ate a personalized context-aware PrivScore. This privacy scoring mechanism could be employed to identify potentially-private messages and alert users to think again before posting them to OSNs. It could also benefit non-human users such as social media chatbots

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community