A data-driven robotic Chinese calligraphy system using convolutional auto-encoder and differential evolution

The Chinese stroke evaluation and generation systems required in an autonomous calligraphy robot play a crucial role in producing high-quality writing results with good diversity. These systems often suffer from inefficiency and non-optima despite of intensive research effort investment by the robotic community. This paper proposes a new learning system to allow a robot to automatically learn to write Chinese calligraphy effectively. In the proposed system, the writing quality evaluation subsystem assesses written strokes using a convolutional auto-encoder network (CAE), which enables the generation of aesthetic strokes with various writing styles. The trained CAE network effectively excludes poorly written strokes through stroke reconstruction, but guarantees the inheritance of information from well-written ones. With the support of the evaluation subsystem, the writing trajectory model generation subsystem is realized by multivariate normal distributions optimized by differential evolution (DE), a type of heuristic optimization search algorithm. The proposed approach was validated and evaluated using a dataset of nine stroke categories; high-quality written strokes have been resulted with good diversity which shows the robustness and efficacy of the proposed approach and its potential in autonomous action-state space exploration for other real-world applications

Integration of an actor-critic model and generative adversarial networks for a Chinese calligraphy robot

As a combination of robotic motion planning and Chinese calligraphy culture, robotic calligraphy plays a significant role in the inheritance and education of Chinese calligraphy culture. Most existing calligraphy robots focus on enabling the robots to learn writing through human participation, such as human–robot interactions and manually designed evaluation functions. However, because of the subjectivity of art aesthetics, these existing methods require a large amount of implementation work from human engineers. In addition, the written results cannot be accurately evaluated. To overcome these limitations, in this paper, we propose a robotic calligraphy model that combines a generative adversarial network (GAN) and deep reinforcement learning to enable a calligraphy robot to learn to write Chinese character strokes directly from images captured from Chinese calligraphic textbooks. In our proposed model, to automatically establish an aesthetic evaluation system for Chinese calligraphy, a GAN is first trained to understand and reconstruct stroke images. Then, the discriminator network is independently extracted from the trained GAN and embedded into a variant of the reinforcement learning method, the “actor-critic model”, as a reward function. Thus, a calligraphy robot adopts the improved actor-critic model to learn to write multiple character strokes. The experimental results demonstrate that the proposed model successfully allows a calligraphy robot to write Chinese character strokes based on input stroke images. The performance of our model, compared with the state-of-the-art deep reinforcement learning method, shows the efficacy of the combination approach. In addition, the key technology in this work shows promise as a solution for robotic autonomous assembly

Space station needs, attributes, and architectural options study. Volume 1: Missions and requirements

Science and applications, NOAA environmental observation, commercial resource observations, commercial space processing, commercial communications, national security, technology development, and GEO servicing are addressed. Approach to time phasing of mission requirements, system sizing summary, time-phased user mission payload support, space station facility requirements, and integrated time-phased system requirements are also addressed

Dynamic Performance Management

This book explores how to design and implement planning & control (P&C) systems that can help organizations to manage their growth and restructuring processes in a sustainability perspective. The book is not designed to enable the reader to become an experienced system dynamics modeler; rather, it aims to develop the reader’s capabilities to design and implement performance management systems by using a system dynamics approach. More specifically, the book shows how to develop system dynamics models that can better support an understanding of: -What is organizational performance and how to frame and measure it; -How to identify and map the processes underlying performance; -How to design and implement a dynamic performance management system and link it to strategic planning; -How to tie strategic resource dynamics to processes and performance indicators; -How to link strategic resources, and performance indicators to responsibility and incentive systems. Using a dynamic performance management approach can improve an organization’s capability to understand and manage the forces driving performance over time, as well as set goals and objectives that may properly and selectively gauge results and match them to the key responsibility areas in the planning process. The dynamic performance management approaches covered in the book are beneficial to performance management analysts, enabling them to frame their professional field within the broader context of the system. The book also includes numerous case studies and dynamic performance management models for providing examples of how dynamic performance management works in practice. In addition, a literature review is included to provide a guideline for further improvements to those readers who wish to develop relevant, specific, and detailed system dynamics modeling skills and to establish the foundation for teaching system dynamics applied to performance management in organizational and inter-organizational contexts. This is particularly relevant for graduate students who have taken system dynamics courses and need to apply their own skills to business and public management

Earth Resources, A Continuing Bibliography with Indexes

This bibliography lists 460 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1984. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis

Consumer risk reflections on organic and local food in Seattle, with reference to Newcastle upon Tyne

Central questions of human geography can be explored in contemporary turns to organic and local foods (Goodman 2003, 2004; Murdoch & Міеle 2001). Why do people adapt differently to similar places, or vice-versa? Patterns are emerging in global trends of organic food consumption, such as the correlation of upper education and income levels with organic demand but these indicators do not explain everything, and too little is known on the micro-scale of everyday practices by different types of consumers in different countries (Raynolds 2004; IFOAM 2004). Buck, Getz & Guthman (1997) identified the Bay Area in northern California as one of the most significant centres of organic production and consumption in the us. My study focuses on Seattle and presents evidence that it is an organic growth pole in the same league as San Francisco, because so many Seattleites are concerned with food-related issues including animal welfare, environmental sustainability, social justice and nutrition. These ecotopic attitudes (Callenbach 1975) manifest themselves in behaviours linked to alternative food networks (AFNs), booming farmers' markets - and Puget Consumers Co-op, the largest in the US with 38,000 members and $93m sales which promotes organic and local foods, preserves farmland, and joined a boycott of organic-industrial milk brands because customers feared violations of USDA 'access to pasture' grazing rules in what I term the organic pasture wars (Pollan 2001; Cornucopia Institute August 10, 2006; USDA 2002; PCC 2006a&b; Scholten 2007e). Personal and family health is part of Seattle's turn to organics, but grassroots resistance to vertical integration in globalising food systems, evidenced by some Greens' vow to go beyond organic in USDA organic rules, may be termed altruistic, i.e. marked by care for others and the environment. Newcastle upon Tyne in the UK is, like Seattle, a former node for coal, steel and ships, but its champions such as Siemens have not been the economic drivers that Boeing and Microsoft have been on Puget Sound. Tyneside's consumption may have less to do with altruism than food scares such as anthropogenically-exacerbated mad cow disease (BSE/vCJD) which raised reflection among rich and poor, and induced vegetarianism in many young women (Whatmore 2002; Atkins & Bowler 2001). Foot and mouth disease, which spread from Newcastle in 2001, exacerbated doubts on food safety, and drove a turn to natural foods. Thus, while Newcastle is not claimed to be the equivalent of Seattle, both post-fordist cities host similar actors, often women, whose geographical imaginations transcend political economy (Marsden, Munton & Ward 1996). Ironically fieldwork was completed shortly before discovery of BSE near Seattle in 2003. The thesis brings risk theory into discussion of food. Its theoretical touchstone is the risk society thesis of Beck (1986) and Beck, Giddens & Lash (1994), attended by insights of Mary Douglas (1996) and Deborah Lupton (1999). Methodology includes interviews, focus groups and questionnaires from 404 UK/US respondents. Snowball sampling (Atkinson & Flint 2001) targeted groups in a range of stereotyped relationships to risks:• Academics: stereotypically risk-averse, undergraduates to professors, teachers & educators;• Firefighters: variably risk-embracing, or managing risk for career advancement' (Lupton, 1999: 156);• Motorcyclists: risk-embracing 'edgeworkers' justifying risk in work or hobbies (Lyng, 1990: 859);• Others: not fitting above groups, e.g. academic bikers, or motos with higher degrees if also teachers. Key claims are that Newcastle's organic use (three-times that found in Edinburgh a decade before) is on a continuum toward Seattle which has better prices and availability - evidence that the organic diet can be multi-ethnically democratic and not limited to elites (Tregear et al. 1997; Goodman 2004; Hartman 2004; Scholten 2006a & b). After a BSE scare, consumers often flirt with organics from afar before returning to conventional diets. But repeated scares may permanently dislodge the commodity fetish of industrial food, and as consumers' knowledge grows, more of them adopt food from trusted local farmers which better satisfies values such as health, local economic security, and ecological sustainability (Caplan 2000; Winter 2003). Seattle's political power as an organic pole is world class, but Newcastle also shows ethical strengths in AFNs and fair trade. In the new bio-fuel boom Seattle and Newcastle can learn from each other to resolve global issues such as food miles

Computational Methods for Medical and Cyber Security

Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields

Density-based algorithms for active and anytime clustering

Data intensive applications like biology, medicine, and neuroscience require effective and efficient data mining technologies. Advanced data acquisition methods produce a constantly increasing volume and complexity. As a consequence, the need of new data mining technologies to deal with complex data has emerged during the last decades. In this thesis, we focus on the data mining task of clustering in which objects are separated in different groups (clusters) such that objects inside a cluster are more similar than objects in different clusters. Particularly, we consider density-based clustering algorithms and their applications in biomedicine. The core idea of the density-based clustering algorithm DBSCAN is that each object within a cluster must have a certain number of other objects inside its neighborhood. Compared with other clustering algorithms, DBSCAN has many attractive benefits, e.g., it can detect clusters with arbitrary shape and is robust to outliers, etc. Thus, DBSCAN has attracted a lot of research interest during the last decades with many extensions and applications. In the first part of this thesis, we aim at developing new algorithms based on the DBSCAN paradigm to deal with the new challenges of complex data, particularly expensive distance measures and incomplete availability of the distance matrix. Like many other clustering algorithms, DBSCAN suffers from poor performance when facing expensive distance measures for complex data. To tackle this problem, we propose a new algorithm based on the DBSCAN paradigm, called Anytime Density-based Clustering (A-DBSCAN), that works in an anytime scheme: in contrast to the original batch scheme of DBSCAN, the algorithm A-DBSCAN first produces a quick approximation of the clustering result and then continuously refines the result during the further run. Experts can interrupt the algorithm, examine the results, and choose between (1) stopping the algorithm at any time whenever they are satisfied with the result to save runtime and (2) continuing the algorithm to achieve better results. Such kind of anytime scheme has been proven in the literature as a very useful technique when dealing with time consuming problems. We also introduced an extended version of A-DBSCAN called A-DBSCAN-XS which is more efficient and effective than A-DBSCAN when dealing with expensive distance measures. Since DBSCAN relies on the cardinality of the neighborhood of objects, it requires the full distance matrix to perform. For complex data, these distances are usually expensive, time consuming or even impossible to acquire due to high cost, high time complexity, noisy and missing data, etc. Motivated by these potential difficulties of acquiring the distances among objects, we propose another approach for DBSCAN, called Active Density-based Clustering (Act-DBSCAN). Given a budget limitation B, Act-DBSCAN is only allowed to use up to B pairwise distances ideally to produce the same result as if it has the entire distance matrix at hand. The general idea of Act-DBSCAN is that it actively selects the most promising pairs of objects to calculate the distances between them and tries to approximate as much as possible the desired clustering result with each distance calculation. This scheme provides an efficient way to reduce the total cost needed to perform the clustering. Thus it limits the potential weakness of DBSCAN when dealing with the distance sparseness problem of complex data. As a fundamental data clustering algorithm, density-based clustering has many applications in diverse fields. In the second part of this thesis, we focus on an application of density-based clustering in neuroscience: the segmentation of the white matter fiber tracts in human brain acquired from Diffusion Tensor Imaging (DTI). We propose a model to evaluate the similarity between two fibers as a combination of structural similarity and connectivity-related similarity of fiber tracts. Various distance measure techniques from fields like time-sequence mining are adapted to calculate the structural similarity of fibers. Density-based clustering is used as the segmentation algorithm. We show how A-DBSCAN and A-DBSCAN-XS are used as novel solutions for the segmentation of massive fiber datasets and provide unique features to assist experts during the fiber segmentation process.Datenintensive Anwendungen wie Biologie, Medizin und Neurowissenschaften erfordern effektive und effiziente Data-Mining-Technologien. Erweiterte Methoden der Datenerfassung erzeugen stetig wachsende Datenmengen und Komplexit\"at. In den letzten Jahrzehnten hat sich daher ein Bedarf an neuen Data-Mining-Technologien f\"ur komplexe Daten ergeben. In dieser Arbeit konzentrieren wir uns auf die Data-Mining-Aufgabe des Clusterings, in der Objekte in verschiedenen Gruppen (Cluster) getrennt werden, so dass Objekte in einem Cluster untereinander viel \"ahnlicher sind als Objekte in verschiedenen Clustern. Insbesondere betrachten wir dichtebasierte Clustering-Algorithmen und ihre Anwendungen in der Biomedizin. Der Kerngedanke des dichtebasierten Clustering-Algorithmus DBSCAN ist, dass jedes Objekt in einem Cluster eine bestimmte Anzahl von anderen Objekten in seiner Nachbarschaft haben muss. Im Vergleich mit anderen Clustering-Algorithmen hat DBSCAN viele attraktive Vorteile, zum Beispiel kann es Cluster mit beliebiger Form erkennen und ist robust gegen\"uber Ausrei{\ss}ern. So hat DBSCAN in den letzten Jahrzehnten gro{\ss}es Forschungsinteresse mit vielen Erweiterungen und Anwendungen auf sich gezogen. Im ersten Teil dieser Arbeit wollen wir auf die Entwicklung neuer Algorithmen eingehen, die auf dem DBSCAN Paradigma basieren, um mit den neuen Herausforderungen der komplexen Daten, insbesondere teurer Abstandsma{\ss}e und unvollst\"andiger Verf\"ugbarkeit der Distanzmatrix umzugehen. Wie viele andere Clustering-Algorithmen leidet DBSCAN an schlechter Per- formanz, wenn es teuren Abstandsma{\ss}en f\"ur komplexe Daten gegen\"uber steht. Um dieses Problem zu l\"osen, schlagen wir einen neuen Algorithmus vor, der auf dem DBSCAN Paradigma basiert, genannt Anytime Density-based Clustering (A-DBSCAN), der mit einem Anytime Schema funktioniert. Im Gegensatz zu dem urspr\"unglichen Schema DBSCAN, erzeugt der Algorithmus A-DBSCAN zuerst eine schnelle Ann\"aherung des Clusterings-Ergebnisses und verfeinert dann kontinuierlich das Ergebnis im weiteren Verlauf. Experten k\"onnen den Algorithmus unterbrechen, die Ergebnisse pr\"ufen und w\"ahlen zwischen (1) Anhalten des Algorithmus zu jeder Zeit, wann immer sie mit dem Ergebnis zufrieden sind, um Laufzeit sparen und (2) Fortsetzen des Algorithmus, um bessere Ergebnisse zu erzielen. Eine solche Art eines "Anytime Schemas" ist in der Literatur als eine sehr n\"utzliche Technik erprobt, wenn zeitaufwendige Problemen anfallen. Wir stellen auch eine erweiterte Version von A-DBSCAN als A-DBSCAN-XS vor, die effizienter und effektiver als A-DBSCAN beim Umgang mit teuren Abstandsma{\ss}en ist. Da DBSCAN auf der Kardinalit\"at der Nachbarschaftsobjekte beruht, ist es notwendig, die volle Distanzmatrix auszurechen. F\"ur komplexe Daten sind diese Distanzen in der Regel teuer, zeitaufwendig oder sogar unm\"oglich zu errechnen, aufgrund der hohen Kosten, einer hohen Zeitkomplexit\"at oder verrauschten und fehlende Daten. Motiviert durch diese m\"oglichen Schwierigkeiten der Berechnung von Entfernungen zwischen Objekten, schlagen wir einen anderen Ansatz f\"ur DBSCAN vor, namentlich Active Density-based Clustering (Act-DBSCAN). Bei einer Budgetbegrenzung B, darf Act-DBSCAN nur bis zu B ideale paarweise Distanzen verwenden, um das gleiche Ergebnis zu produzieren, wie wenn es die gesamte Distanzmatrix zur Hand h\"atte. Die allgemeine Idee von Act-DBSCAN ist, dass es aktiv die erfolgversprechendsten Paare von Objekten w\"ahlt, um die Abst\"ande zwischen ihnen zu berechnen, und versucht, sich so viel wie m\"oglich dem gew\"unschten Clustering mit jeder Abstandsberechnung zu n\"ahern. Dieses Schema bietet eine effiziente M\"oglichkeit, die Gesamtkosten der Durchf\"uhrung des Clusterings zu reduzieren. So schr\"ankt sie die potenzielle Schw\"ache des DBSCAN beim Umgang mit dem Distance Sparseness Problem von komplexen Daten ein. Als fundamentaler Clustering-Algorithmus, hat dichte-basiertes Clustering viele Anwendungen in den unterschiedlichen Bereichen. Im zweiten Teil dieser Arbeit konzentrieren wir uns auf eine Anwendung des dichte-basierten Clusterings in den Neurowissenschaften: Die Segmentierung der wei{\ss}en Substanz bei Faserbahnen im menschlichen Gehirn, die vom Diffusion Tensor Imaging (DTI) erfasst werden. Wir schlagen ein Modell vor, um die \"Ahnlichkeit zwischen zwei Fasern als einer Kombination von struktureller und konnektivit\"atsbezogener \"Ahnlichkeit von Faserbahnen zu beurteilen. Verschiedene Abstandsma{\ss}e aus Bereichen wie dem Time-Sequence Mining werden angepasst, um die strukturelle \"Ahnlichkeit von Fasern zu berechnen. Dichte-basiertes Clustering wird als Segmentierungsalgorithmus verwendet. Wir zeigen, wie A-DBSCAN und A-DBSCAN-XS als neuartige L\"osungen f\"ur die Segmentierung von sehr gro{\ss}en Faserdatens\"atzen verwendet werden, und bieten innovative Funktionen, um Experten w\"ahrend des Fasersegmentierungsprozesses zu unterst\"utzen

The Case for Degree Completion: African American Transfer Students at a Traditionally White Institution

The purpose of this dissertation was to identify and understand the factors that contribute to the degree completion of African American transfer students at a traditionally White institution. Through qualitative methods and a case study design, the current study provides an examination of the educational journey of thirteen, African American recent college graduates. Using semi-structured individual interviews, data from the participants were collected, transcribed, and analyzed drawing from several major theoretical perspectives on college student persistence. Variables examined included interactions with faculty and with peers, racial experiences on campus, and support services offered to transfer students by the institution. Findings indicated that African American transfer students identified strong support networks, confidence in their ability to learn, intrinsic motivation and having clear educational goals as factors which contributed to their degree completion at a traditionally White institution. Implications for campus policies and practices, as well as recommendations for future research are presented

Innovative communication, effective coordination and knowledge management in UK local authority planning departments

This thesis sets out to examine the scope for integrated knowledge based planning systems. Five planning departments in the South East Midlands of the UK have been investigated through environmental appraisal, conceptual modelling and empirical evidence gathering. The results of analyses suggest a number of configurations, which could provide reformation instruments in the context of technological innovation, social coordination and knowledge management for sustainable development. This research study provided the insights and learning into how to successfully develop and implement an integrated knowledge based planning system. The primary aspiration of this research was to develop a robust pragmatic framework to support an efficient and effective delivery of the planning system in the UK local government towards sustainable development. A mixed research methodology was employed for the research fieldwork. Firstly, an extensive review of literature took place to summarise and synthesise the arguments of the key research propositions contributing to the development of an integrated knowledge based planning system. Secondly, exploratory fieldwork took place as an appropriate methodology in this study, applying the semi-structured interview and questionnaire techniques to gather data from senior level planning officials who were directly involved in the planning system transformation. This study was initiated by examining the previous planning environment in the UK local government and its transformation from its conventional state to a contemporary emergent state. The fieldwork was carried out to identify the key supportive and preventive knowledge factors for both explicit and tacit knowledge domains. As a result, the nature of successful technology based initiatives was determined and solutions to the possible emerging challenges were appraised