Interpretation of Natural-language Robot Instructions: Probabilistic Knowledge Representation, Learning, and Reasoning

A robot that can be simply told in natural language what to do -- this has been one of the ultimate long-standing goals in both Artificial Intelligence and Robotics research. In near-future applications, robotic assistants and companions will have to understand and perform commands such as set the table for dinner'', make pancakes for breakfast'', or cut the pizza into 8 pieces.'' Although such instructions are only vaguely formulated, complex sequences of sophisticated and accurate manipulation activities need to be carried out in order to accomplish the respective tasks. The acquisition of knowledge about how to perform these activities from huge collections of natural-language instructions from the Internet has garnered a lot of attention within the last decade. However, natural language is typically massively unspecific, incomplete, ambiguous and vague and thus requires powerful means for interpretation. This work presents PRAC -- Probabilistic Action Cores -- an interpreter for natural-language instructions which is able to resolve vagueness and ambiguity in natural language and infer missing information pieces that are required to render an instruction executable by a robot. To this end, PRAC formulates the problem of instruction interpretation as a reasoning problem in first-order probabilistic knowledge bases. In particular, the system uses Markov logic networks as a carrier formalism for encoding uncertain knowledge. A novel framework for reasoning about unmodeled symbolic concepts is introduced, which incorporates ontological knowledge from taxonomies and exploits semantically similar relational structures in a domain of discourse. The resulting reasoning framework thus enables more compact representations of knowledge and exhibits strong generalization performance when being learnt from very sparse data. Furthermore, a novel approach for completing directives is presented, which applies semantic analogical reasoning to transfer knowledge collected from thousands of natural-language instruction sheets to new situations. In addition, a cohesive processing pipeline is described that transforms vague and incomplete task formulations into sequences of formally specified robot plans. The system is connected to a plan executive that is able to execute the computed plans in a simulator. Experiments conducted in a publicly accessible, browser-based web interface showcase that PRAC is capable of closing the loop from natural-language instructions to their execution by a robot

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda

Perspectives on industrial clustering and the product, resource and knowledge based views of management

This project examines the theoretical basis for linking industrial clustering to the strategic management of firms. Specifically, a recently deployed theory building framework defined three perspectives on clustering, the competitiveness perspective, the externalities perspective and the territorial perspective, but stopped short of explaining when, where and to whom these perspectives are relevant. This thesis proposes that firms are the central recipient of cluster effects and that the product-based, resource-based and knowledge-based approaches to management provide the theoretical base from which the operational contexts of each cluster perspective can be defined. Three cluster-management relationships are modelled and beta-tested on a sample of cluster-based firms. The empirical analysis is designed to provide feedback to the theory building process and not to prove or disprove the theory itself. The analysis yielded little if any evidence that the proposed cluster-management relationships are present in the sample that was studied. This result was a surprise as the exuberance with which clusters and their benefits are often promoted suggests that in a cluster there should be a pronounced correlation between firm performance and cluster attributes. The statistical limitations of this analysis mean the results can not be inferred to the general population and that the theoretical propositions are not actually disproved. Nonetheless, the muted observations do cast attention on the need for better modelling and measurement instruments in the field of cluster research. In addition, this project initiates a deductive process by which subsequent research can focus on the causal pathways that comprise the phenomenon of industrial clustering; including the pathway that links clusters to firms and then to economic performance

Visual Analytics Methodologies on Causality Analysis

abstract: Causality analysis is the process of identifying cause-effect relationships among variables. This process is challenging because causal relationships cannot be tested solely based on statistical indicators as additional information is always needed to reduce the ambiguity caused by factors beyond those covered by the statistical test. Traditionally, controlled experiments are carried out to identify causal relationships, but recently there is a growing interest in causality analysis with observational data due to the increasing availability of data and tools. This type of analysis will often involve automatic algorithms that extract causal relations from large amounts of data and rely on expert judgment to scrutinize and verify the relations. Over-reliance on these automatic algorithms is dangerous because models trained on observational data are susceptible to bias that can be difficult to spot even with expert oversight. Visualization has proven to be effective at bridging the gap between human experts and statistical models by enabling an interactive exploration and manipulation of the data and models. This thesis develops a visual analytics framework to support the interaction between human experts and automatic models in causality analysis. Three case studies were conducted to demonstrate the application of the visual analytics framework in which feature engineering, insight generation, correlation analysis, and causality inspections were showcased.Dissertation/ThesisDoctoral Dissertation Computer Science 201

SYNERGY OF BUILDING CYBERSECURITY SYSTEMS

The development of the modern world community is closely related to advances in computing resources and cyberspace. The formation and expansion of the range of services is based on the achievements of mankind in the field of high technologies. However, the rapid growth of computing resources, the emergence of a full-scale quantum computer tightens the requirements for security systems not only for information and communication systems, but also for cyber-physical systems and technologies. The methodological foundations of building security systems for critical infrastructure facilities based on modeling the processes of behavior of antagonistic agents in security systems are discussed in the first chapter. The concept of information security in social networks, based on mathematical models of data protection, taking into account the influence of specific parameters of the social network, the effects on the network are proposed in second chapter. The nonlinear relationships of the parameters of the defense system, attacks, social networks, as well as the influence of individual characteristics of users and the nature of the relationships between them, takes into account. In the third section, practical aspects of the methodology for constructing post-quantum algorithms for asymmetric McEliece and Niederreiter cryptosystems on algebraic codes (elliptic and modified elliptic codes), their mathematical models and practical algorithms are considered. Hybrid crypto-code constructions of McEliece and Niederreiter on defective codes are proposed. They can significantly reduce the energy costs for implementation, while ensuring the required level of cryptographic strength of the system as a whole. The concept of security of corporate information and educational systems based on the construction of an adaptive information security system is proposed. ISBN 978-617-7319-31-2 (on-line)ISBN 978-617-7319-32-9 (print) ------------------------------------------------------------------------------------------------------------------ How to Cite: Yevseiev, S., Ponomarenko, V., Laptiev, O., Milov, O., Korol, O., Milevskyi, S. et. al.; Yevseiev, S., Ponomarenko, V., Laptiev, O., Milov, O. (Eds.) (2021). Synergy of building cybersecurity systems. Kharkiv: РС ТЕСHNOLOGY СЕNTЕR, 188. doi: http://doi.org/10.15587/978-617-7319-31-2 ------------------------------------------------------------------------------------------------------------------ Indexing:                    Розвиток сучасної світової спільноти тісно пов’язаний з досягненнями в області обчислювальних ресурсів і кіберпростору. Формування та розширення асортименту послуг базується на досягненнях людства у галузі високих технологій. Однак стрімке зростання обчислювальних ресурсів, поява повномасштабного квантового комп’ютера посилює вимоги до систем безпеки не тільки інформаційно-комунікаційних, але і до кіберфізичних систем і технологій. У першому розділі обговорюються методологічні основи побудови систем безпеки для об'єктів критичної інфраструктури на основі моделювання процесів поведінки антагоністичних агентів у систем безпеки. У другому розділі пропонується концепція інформаційної безпеки в соціальних мережах, яка заснована на математичних моделях захисту даних, з урахуванням впливу конкретних параметрів соціальної мережі та наслідків для неї. Враховуються нелінійні взаємозв'язки параметрів системи захисту, атак, соціальних мереж, а також вплив індивідуальних характеристик користувачів і характеру взаємовідносин між ними. У третьому розділі розглядаються практичні аспекти методології побудови постквантових алгоритмів для асиметричних криптосистем Мак-Еліса та Нідеррейтера на алгебраїчних кодах (еліптичних та модифікованих еліптичних кодах), їх математичні моделі та практичні алгоритми. Запропоновано гібридні конструкції криптокоду Мак-Еліса та Нідеррейтера на дефектних кодах. Вони дозволяють істотно знизити енергетичні витрати на реалізацію, забезпечуючи при цьому необхідний рівень криптографічної стійкості системи в цілому. Запропоновано концепцію безпеки корпоративних інформаційних та освітніх систем, які засновані на побудові адаптивної системи захисту інформації. ISBN 978-617-7319-31-2 (on-line)ISBN 978-617-7319-32-9 (print) ------------------------------------------------------------------------------------------------------------------ Як цитувати: Yevseiev, S., Ponomarenko, V., Laptiev, O., Milov, O., Korol, O., Milevskyi, S. et. al.; Yevseiev, S., Ponomarenko, V., Laptiev, O., Milov, O. (Eds.) (2021). Synergy of building cybersecurity systems. Kharkiv: РС ТЕСHNOLOGY СЕNTЕR, 188. doi: http://doi.org/10.15587/978-617-7319-31-2 ------------------------------------------------------------------------------------------------------------------ Індексація:                 &nbsp

EUSN 2021 Book of Abstracts, Fifth European Conference on Social Networks

Book of abstract of the fifth European conference on Social Networks EUSN 202

Strengthening Small Business Clusters Serving Minority Communities

The existence of thriving and numerous small businesses is instrumental to generating wealth within a community. They provide a diverse employment base for local residents, leading to greater economic stability. Small businesses create opportunities for keeping dollars circulating within the community, multiplying the benefits of those dollars. Creating such internal-multipliers within the economy of a community is a strategy for strengthening the community as a whole. This benefit extends to communities of color, immigrant communities, ethnic minority communities, and sexual minority communities. Not all minority communities share in common a geographic location. They can be found residing in a relatively compact neighborhood just as they are often dispersed over a larger geographic area, such as a region. However a minority community is distributed, clusters of businesses serving a particular community provide value. In addition to generating wealth, business clusters facilitate the creation of place, for a community to meet. These places become important to the sense of identity and pride for the communities they serve. The goal of this project is to facilitate the strengthening of small businesses 3:nd business clusters that serve minority communities. Small businesses are defined as businesses with less than 50 employees. Small businesses serving minority communities include businesses that provide goods and services that mayor may not be unique to a minority community. These businesses may be owned by and employ members of a minority community. They may provide third places where people can gather and relax, or they may support community events. These small businesses may cluster to share a clientele base or provide goods and services to other businesses in the cluster. These relationships create inter-dependencies among the businesses in the cluster and the communities they serve. Understanding and attending to the inter-dependencies of small business clusters is one strategy for maintaining the viability of the businesses comprising the cluster. With greater understanding of the local dynamics of these clusters, planning and public investment decisions can be made to effectively strengthen small businesses and the minority communities they serve. In Portland, Oregon, the Interstate Corridor Urban Renewal Area (ORA) and the planning process in the West End offer opportunities for such business- and community-strengthening efforts. Our team performed case studies in the Interstate corridor URA and downtown\u27s West End to identify small businesses serving minority communities, the presence of business clusters and their dynamics. These specific areas of interest include N. Killingsworth Avenue, between Interstate-5 and Cleveland, in the Interstate Corridor URA and the Burnside Triangle in the West End. Within each case study area, we inventoried businesses using Regional Land Information System (RLIS) and City of Portland Bureau of Licenses data. We then conducted interviews with a sample of these businesses to gain a greater understanding of business characteristics as well as to identify business clusters and their mutually supporting dynamics. The interviews included questions to business owners about what would help strengthen their business as well as questions to customers about their patronage habits. We then synthesized findings from these interviews with those from a review of economic development literature to develop strategies and recommendations for strengthe:o.i.rrg small businesses and the communities they serve. We further intend this project to serve as a model for identifying small business clusters serving minority communities and determining how planning efforts can strengthen those businesses and the communities they serve. Our project will aid both our clients, including the economic development efforts of the Portland Development Commission (PDC) in the Interstate Corridor URA and City of Portland Commissioner Jim Francesconi\u27s interest in and advocacy for small and minority businesses

Practical Applications of Machine Learning to Underground Rock Engineering

Rock mechanics engineers have increasing access to large quantities of data from underground excavations as sensor technologies are developed, data storage becomes cheaper, and computational speed and power improve. Machine learning has emerged as a viable approach to process data for engineering decision making. This research investigates practical applications of machine learning algorithms (MLAs) to underground rock engineering problems using real datasets from a variety of rock mass deformation contexts. It was found that preserving the format of the original input data as much as possible reduces the introduction of bias during digitalization and results in more interpretable MLAs. A Convolutional Neural Network (CNN) is developed using a dataset from Cigar Lake Mine, Saskatchewan, Canada, to predict the tunnel liner yield class. Several hyperparameters are optimized: the amount of training data, the convolution filter size, and the error weighting scheme. Two CNN architectures are proposed to characterize the rock mass deformation: (i) a Global Balanced model that has a prediction accuracy >65% for all yield classes, and (ii) a Targeted Class 2/3 model that emphasizes the worst case yield and has a recall of >99% for Class 2. The interpretability of the CNN is investigated through three Input Variable Selection (IVS) methods. The three methods are Channel Activation Strength, Input Omission, and Partial Correlation. The latter two are novel methods proposed for CNNs using a spatial and temporal geomechanical dataset. Collectively, the IVS analyses indicate that all the available digitized inputs are needed to produce good CNN performances. A Long-Short Term Memory (LSTM) network is developed using a dataset for Garson Mine, near Sudbury, Ontario, Canada, to predict the stress state in a FLAC3D model. This is a novel method proposed to semi-automate calibration of finite-difference models of high-stress environments. A workflow for optimizing the hyperparameters of the LSTM network is proposed. The performance of the LSTM network predicting the three principal stresses is improved as compared to predicting the six-component stress tensor, with corrected Akaike Information Criterion (AICc) values of -59.62 and -45.50, respectively. General recommendations are made with respect to machine learning algorithm development for practical rock engineering problems, in terms of how to format and pre-process inputs, select architectures, tune hyperparameters, and determine engineering verification metrics. Recommendations are made to demonstrate how algorithms can be rendered interpretable with the application of tools that already exist in the field of machine learning

Skills and politics. General and specific

Skills and skill formation have become central topics in contemporary political economy. This essay traces a key concept in the current debate - the distinction between general and specific skills - back to its diverse origins in American postwar labor economics, comparative industrial relations, and human capital theory. To show how the distinction has evolved over time and between disciplines, it is related to other dual classifications of work skills, like high versus low, broad versus narrow, theoretical versus experiential, professional versus occupational, explicit versus tacit, extrafunctional versus functional, and certifiable versus noncertifiable. The aim is to reconstruct how notions of skill generality and skill specificity came to be used as the foundation of an economistic-functionalist 'production regime,' 'varieties of capitalism,' or 'asset' theory of welfare state development, and generally of politics under capitalism. -- Berufliche Qualifikationen und berufliche Bildung sind ein zentrales Thema gegenwärtiger politisch-ökonomischer Forschung. Der Aufsatz untersucht einen Schlüsselbegriff der Diskussion - die Unterscheidung zwischen allgemeinen und spezialisierten Fähigkeiten - mit Hinblick auf seine diversen Ursprünge in der amerikanischen Arbeitsökonomie der Nachkriegsjahre, der vergleichenden Forschung über industrielle Arbeitsbeziehungen und der Humankapitaltheorie. Um zu zeigen, wie die Begriffsbildung sich mit der Zeit und zwischen den verschiedenen Disziplinen entwickelt hat, wird sie mit anderen dualen Klassifikationen von beruflichen Fertigkeiten - hoch und niedrig, breit und eng, theoretisch und erfahrungsbasiert, explizit und implizit, extrafunktional und funktional, zertifizierbar und nicht zertifizierbar - in Beziehung gesetzt. Ziel ist herauszuarbeiten, wie die Unterscheidung zwischen allgemeinen und speziellen Qualifikationen zur Grundlage diverser ökonomistisch-funktionalistischer Theorien der wohlfahrtsstaatlichen Entwicklung und allgemein der Politik im Kapitalismus werden konnte.