Impact of remote sensing upon the planning, management, and development of water resources

Principal water resources users were surveyed to determine the impact of remote data streams on hydrologic computer models. Analysis of responses demonstrated that: most water resources effort suitable to remote sensing inputs is conducted through federal agencies or through federally stimulated research; and, most hydrologic models suitable to remote sensing data are federally developed. Computer usage by major water resources users was analyzed to determine the trends of usage and costs for the principal hydrologic users/models. The laws and empirical relationships governing the growth of the data processing loads were described and applied to project the future data loads. Data loads for ERTS CCT image processing were computed and projected through the 1985 era

The effect of information systems on middle management in the aerospace industry : the Westco case.

This is a Case Study involving a company in the aerospace industry. The case attempts to analyze the trends of decentralization in an organizational structure. Additionally, the analysis will probe the effects these trends are having on middle management positions. The time frame of this thesis includes economic recession and a significant strategy shift due to current world situations. Organizational strategy, culture, subcultures, mission priorities and education are just a few of the elements that will be reviewed as contributors to these issues.http://archive.org/details/effectofinformat00gaudCaptain, United States Marine CorpsApproved for public release; distribution is unlimited

CLiFF Notes: Research in the Language, Information and Computation Laboratory of the University of Pennsylvania

One concern of the Computer Graphics Research Lab is in simulating human task behavior and understanding why the visualization of the appearance, capabilities and performance of humans is so challenging. Our research has produced a system, called Jack, for the definition, manipulation, animation and human factors analysis of simulated human figures. Jack permits the envisionment of human motion by interactive specification and simultaneous execution of multiple constraints, and is sensitive to such issues as body shape and size, linkage, and plausible motions. Enhanced control is provided by natural behaviors such as looking, reaching, balancing, lifting, stepping, walking, grasping, and so on. Although intended for highly interactive applications, Jack is a foundation for other research. The very ubiquitousness of other people in our lives poses a tantalizing challenge to the computational modeler: people are at once the most common object around us, and yet the most structurally complex. Their everyday movements are amazingly fluid, yet demanding to reproduce, with actions driven not just mechanically by muscles and bones but also cognitively by beliefs and intentions. Our motor systems manage to learn how to make us move without leaving us the burden or pleasure of knowing how we did it. Likewise we learn how to describe the actions and behaviors of others without consciously struggling with the processes of perception, recognition, and language. Present technology lets us approach human appearance and motion through computer graphics modeling and three dimensional animation, but there is considerable distance to go before purely synthesized figures trick our senses. We seek to build computational models of human like figures which manifest animacy and convincing behavior. Towards this end, we: Create an interactive computer graphics human model; Endow it with reasonable biomechanical properties; Provide it with human like behaviors; Use this simulated figure as an agent to effect changes in its world; Describe and guide its tasks through natural language instructions. There are presently no perfect solutions to any of these problems; ultimately, however, we should be able to give our surrogate human directions that, in conjunction with suitable symbolic reasoning processes, make it appear to behave in a natural, appropriate, and intelligent fashion. Compromises will be essential, due to limits in computation, throughput of display hardware, and demands of real-time interaction, but our algorithms aim to balance the physical device constraints with carefully crafted models, general solutions, and thoughtful organization. The Jack software is built on Silicon Graphics Iris 4D workstations because those systems have 3-D graphics features that greatly aid the process of interacting with highly articulated figures such as the human body. Of course, graphics capabilities themselves do not make a usable system. Our research has therefore focused on software to make the manipulation of a simulated human figure easy for a rather specific user population: human factors design engineers or ergonomics analysts involved in visualizing and assessing human motor performance, fit, reach, view, and other physical tasks in a workplace environment. The software also happens to be quite usable by others, including graduate students and animators. The point, however, is that program design has tried to take into account a wide variety of physical problem oriented tasks, rather than just offer a computer graphics and animation tool for the already computer sophisticated or skilled animator. As an alternative to interactive specification, a simulation system allows a convenient temporal and spatial parallel programming language for behaviors. The Graphics Lab is working with the Natural Language Group to explore the possibility of using natural language instructions, such as those found in assembly or maintenance manuals, to drive the behavior of our animated human agents. (See the CLiFF note entry for the AnimNL group for details.) Even though Jack is under continual development, it has nonetheless already proved to be a substantial computational tool in analyzing human abilities in physical workplaces. It is being applied to actual problems involving space vehicle inhabitants, helicopter pilots, maintenance technicians, foot soldiers, and tractor drivers. This broad range of applications is precisely the target we intended to reach. The general capabilities embedded in Jack attempt to mirror certain aspects of human performance, rather than the specific requirements of the corresponding workplace. We view the Jack system as the basis of a virtual animated agent that can carry out tasks and instructions in a simulated 3D environment. While we have not yet fooled anyone into believing that the Jack figure is real , its behaviors are becoming more reasonable and its repertoire of actions more extensive. When interactive control becomes more labor intensive than natural language instructional control, we will have reached a significant milestone toward an intelligent agent

A framework for an adaptable and personalised e-learning system based on free web resources

An adaptable and personalised E-learning system (APELS) architecture is developed to provide a framework for the development of comprehensive learning environments for learners who cannot follow a conventional programme of study. The system extracts information from freely available resources on the Web taking into consideration the learners' background and requirements to design modules and a planner system to organise the extracted learning material to facilitate the learning process. The process is supported by the development of an ontology to optimise and support the information extraction process. Additionally, natural language processing techniques are utilised to evaluate a topic's content against a set of learning outcomes as defined by standard curricula. An application in the computer science field is used to illustrate the working mechanisms of the proposed framework and its evaluation based on the ACM/IEEE Computing Curriculum.A variety of models are developed and techniques used to support the adaptability and personalisation features of APELS. First, a learner’s model was designed by incorporating students’ details, students’ requirements and the domain they wish to study into the system. In addition, learning style theories were adopted as a way of identifying and categorising the individuals, to improve their on-line learning experience and applying it to the learner’s model. Secondly, the knowledge extraction model is responsible for the extraction of the learning resources from the Web that would satisfy the learners’ needs and learning outcomes. To support this process, an ontology was developed to retrieve the relevant information as per users’ needs. In addition, it transforms HTML documents to XHTML to provide the information in an accessible format and easier for extraction and comparison purposes. Moreover, a matching process was implemented to compute the similarity measure between the ontology concepts that are used in the ACM/IEEE Computer Science Curriculum and those extracted from the websites. The website with the highest similarity score is selected as the best matching website that satisfies the learners’ request. A further step is required to evaluate whether the content extracted by the system is the appropriate learning material of the subject. For this purpose, the learning outcome validation process is added to ensure that the content of the selected websites will enable the appropriate learning based to the learning outcomes set by standard curricula. Finally, the information extracted by the system will be passed to a Planner model that will structure the content into lectures, tutorials and workshops based on some predefined learning constraints. The APELS system provides a novel addition to the field of adaptive E-learning systems by providing more personalized learning material to each user in a time-efficient way saving his/her time looking for the right course from the hugely available resources on the Web or going through the large number of websites and links returned by traditional search engines. The APELS system will adapt better to the learner’s style based on feedback and assessment once the learning process is initiated by the learner. The APELS system is expected to develop over time with more users

의미론적 환경 이해 기반 인간 로봇 협업

학위논문(박사)--서울대학교 대학원 :공과대학 전기·정보공학부,2020. 2. 이범희.Human-robot cooperation is unavoidable in various applications ranging from manufacturing to field robotics owing to the advantages of adaptability and high flexibility. Especially, complex task planning in large, unconstructed, and uncertain environments can employ the complementary capabilities of human and diverse robots. For a team to be effectives, knowledge regarding team goals and current situation needs to be effectively shared as they affect decision making. In this respect, semantic scene understanding in natural language is one of the most fundamental components for information sharing between humans and heterogeneous robots, as robots can perceive the surrounding environment in a form that both humans and other robots can understand. Moreover, natural-language-based scene understanding can reduce network congestion and improve the reliability of acquired data. Especially, in field robotics, transmission of raw sensor data increases network bandwidth and decreases quality of service. We can resolve this problem by transmitting information in the form of natural language that has encoded semantic representations of environments. In this dissertation, I introduce a human and heterogeneous robot cooperation scheme based on semantic scene understanding. I generate sentences and scene graphs, which is a natural language grounded graph over the detected objects and their relationships, with the graph map generated using a robot mapping algorithm. Subsequently, a framework that can utilize the results for cooperative mission planning of humans and robots is proposed. Experiments were performed to verify the effectiveness of the proposed methods. This dissertation comprises two parts: graph-based scene understanding and scene understanding based on the cooperation between human and heterogeneous robots. For the former, I introduce a novel natural language processing method using a semantic graph map. Although semantic graph maps have been widely applied to study the perceptual aspects of the environment, such maps do not find extensive application in natural language processing tasks. Several studies have been conducted on the understanding of workspace images in the field of computer vision; in these studies, the sentences were automatically generated, and therefore, multiple scenes have not yet been utilized for sentence generation. A graph-based convolutional neural network, which comprises spectral graph convolution and graph coarsening, and a recurrent neural network are employed to generate sentences attention over graphs. The proposed method outperforms the conventional methods on a publicly available dataset for single scenes and can be utilized for sequential scenes. Recently, deep learning has demonstrated impressive developments in scene understanding using natural language. However, it has not been extensively applied to high-level processes such as causal reasoning, analogical reasoning, or planning. The symbolic approach that calculates the sequence of appropriate actions by combining the available skills of agents outperforms in reasoning and planning; however, it does not entirely consider semantic knowledge acquisition for human-robot information sharing. An architecture that combines deep learning techniques and symbolic planner for human and heterogeneous robots to achieve a shared goal based on semantic scene understanding is proposed for scene understanding based on human-robot cooperation. In this study, graph-based perception is used for scene understanding. A planning domain definition language (PDDL) planner and JENA-TDB are utilized for mission planning and data acquisition storage, respectively. The effectiveness of the proposed method is verified in two situations: a mission failure, in which the dynamic environment changes, and object detection in a large and unseen environment.인간과 이종 로봇 간의 협업은 높은 유연성과 적응력을 보일 수 있다는 점에서 제조업에서 필드 로보틱스까지 다양한 분야에서 필연적이다. 특히, 서로 다른 능력을 지닌 로봇들과 인간으로 구성된 하나의 팀은 넓고 정형화되지 않은 공간에서 서로의 능력을 보완하며 복잡한 임무 수행을 가능하게 한다는 점에서 큰 장점을 갖는다. 효율적인 한 팀이 되기 위해서는, 팀의 공통된 목표 및 각 팀원의 현재 상황에 관한 정보를 실시간으로 공유할 수 있어야 하며 함께 의사 결정을 할 수 있어야 한다. 이러한 관점에서, 자연어를 통한 의미론적 환경 이해는 인간과 서로 다른 로봇들이 모두 이해할 수 있는 형태로 환경을 인지한다는 점에서 가장 필수적인 요소이다. 또한, 우리는 자연어 기반 환경 이해를 통해 네트워크 혼잡을 피함으로써 획득한 정보의 신뢰성을 높일 수 있다. 특히, 대량의 센서 데이터 전송에 의해 네트워크 대역폭이 증가하고 통신 QoS (Quality of Service) 신뢰도가 감소하는 문제가 빈번히 발생하는 필드 로보틱스 영역에서는 의미론적 환경 정보인 자연어를 전송함으로써 통신 대역폭을 감소시키고 통신 QoS 신뢰도를 증가시킬 수 있다. 본 학위 논문에서는 환경의 의미론적 이해 기반 인간 로봇 협동 방법에 대해 소개한다. 먼저, 로봇의 지도 작성 알고리즘을 통해 획득한 그래프 지도를 이용하여 자연어 문장과 검출한 객체 및 각 객체 간의 관계를 자연어 단어로 표현하는 그래프를 생성한다. 그리고 자연어 처리 결과를 이용하여 인간과 다양한 로봇들이 함께 협업하여 임무를 수행할 수 있도록 하는 프레임워크를 제안한다. 본 학위 논문은 크게 그래프를 이용한 의미론적 환경 이해와 의미론적 환경 이해를 통한 인간과 이종 로봇 간의 협업 방법으로 구성된다. 먼저, 그래프를 이용한 의미론적 환경 이해 부분에서는 의미론적 그래프 지도를 이용한 새로운 자연어 처리 방법에 대해 소개한다. 의미론적 그래프 지도 작성 방법은 로봇의 환경 인지 측면에서 많이 연구되었지만 이를 이용한 자연어 처리 방법은 거의 연구되지 않았다. 반면 컴퓨터 비전 분야에서는 이미지를 이용한 환경 이해 연구가 많이 이루어졌지만, 연속적인 장면들은 다루는데는 한계점이 있다. 따라서 우리는 그래프 스펙트럼 이론에 기반한 그래프 컨볼루션과 그래프 축소 레이어로 구성된 그래프 컨볼루션 신경망 및 순환 신경망을 이용하여 그래프를 설명하는 문장을 생성한다. 제안한 방법은 기존의 방법들보다 한 장면에 대해 향상된 성능을 보였으며 연속된 장면들에 대해서도 성공적으로 자연어 문장을 생성한다. 최근 딥러닝은 자연어 기반 환경 인지에 있어 급속도로 큰 발전을 이루었다. 하지만 인과 추론, 유추적 추론, 임무 계획과 같은 높은 수준의 프로세스에는 적용이 힘들다. 반면 임무를 수행하는 데 있어 각 에이전트의 능력에 맞게 행위들의 순서를 계산해주는 상징적 접근법(symbolic approach)은 추론과 임무 계획에 있어 뛰어난 성능을 보이지만 인간과 로봇들 사이의 의미론적 정보 공유 방법에 대해서는 거의 다루지 않는다. 따라서, 인간과 이종 로봇 간의 협업 방법 부분에서는 딥러닝 기법들과 상징적 플래너(symbolic planner)를 연결하는 프레임워크를 제안하여 의미론적 이해를 통한 인간 및 이종 로봇 간의 협업을 가능하게 한다. 우리는 의미론적 주변 환경 이해를 위해 이전 부분에서 제안한 그래프 기반 자연어 문장 생성을 수행한다. PDDL 플래너와 JENA-TDB는 각각 임무 계획 및 정보 획득 저장소로 사용한다. 제안한 방법의 효용성은 시뮬레이션을 통해 두 가지 상황에 대해서 검증한다. 하나는 동적 환경에서 임무 실패 상황이며 다른 하나는 넓은 공간에서 객체를 찾는 상황이다.1 Introduction 1 1.1 Background and Motivation 1 1.2 Literature Review 5 1.2.1 Natural Language-Based Human-Robot Cooperation 5 1.2.2 Artificial Intelligence Planning 5 1.3 The Problem Statement 10 1.4 Contributions 11 1.5 Dissertation Outline 12 2 Natural Language-Based Scene Graph Generation 14 2.1 Introduction 14 2.2 Related Work 16 2.3 Scene Graph Generation 18 2.3.1 Graph Construction 19 2.3.2 Graph Inference 19 2.4 Experiments 22 2.5 Summary 25 3 Language Description with 3D Semantic Graph 26 3.1 Introduction 26 3.2 Related Work 26 3.3 Natural Language Description 29 3.3.1 Preprocess 29 3.3.2 Graph Feature Extraction 33 3.3.3 Natural Language Description with Graph Features 34 3.4 Experiments 35 3.5 Summary 42 4 Natural Question with Semantic Graph 43 4.1 Introduction 43 4.2 Related Work 45 4.3 Natural Question Generation 47 4.3.1 Preprocess 49 4.3.2 Graph Feature Extraction 50 4.3.3 Natural Question with Graph Features 51 4.4 Experiments 52 4.5 Summary 58 5 PDDL Planning with Natural Language 59 5.1 Introduction 59 5.2 Related Work 60 5.3 PDDL Planning with Incomplete World Knowledge 61 5.3.1 Natural Language Process for PDDL Planning 63 5.3.2 PDDL Planning System 64 5.4 Experiments 65 5.5 Summary 69 6 PDDL Planning with Natural Language-Based Scene Understanding 70 6.1 Introduction 70 6.2 Related Work 74 6.3 A Framework for Heterogeneous Multi-Agent Cooperation 77 6.3.1 Natural Language-Based Cognition 78 6.3.2 Knowledge Engine 80 6.3.3 PDDL Planning Agent 81 6.4 Experiments 82 6.4.1 Experiment Setting 82 6.4.2 Scenario 84 6.4.3 Results 87 6.5 Summary 91 7 Conclusion 92Docto

CLiFF Notes: Research in the Language Information and Computation Laboratory of The University of Pennsylvania

This report takes its name from the Computational Linguistics Feedback Forum (CLIFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science, Psychology, and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. With 48 individual contributors and six projects represented, this is the largest LINC Lab collection to date, and the most diverse

META-NET Strategic Research Agenda for Multilingual Europe 2020

In everyday communication, Europe’s citizens, business partners and politicians are inevitably confronted with language barriers. Language technology has the potential to overcome these barriers and to provide innovative interfaces to technologies and knowledge. This document presents a Strategic Research Agenda for Multilingual Europe 2020. The agenda was prepared by META-NET, a European Network of Excellence. META-NET consists of 60 research centres in 34 countries, who cooperate with stakeholders from economy, government agencies, research organisations, non-governmental organisations, language communities and European universities. META-NET’s vision is high-quality language technology for all European languages. “The research carried out in the area of language technology is of utmost importance for the consolidation of Portuguese as a language of global communication in the information society.” — Dr. Pedro Passos Coelho (Prime-Minister of Portugal) “It is imperative that language technologies for Slovene are developed systematically if we want Slovene to flourish also in the future digital world.” — Dr. Danilo Türk (President of the Republic of Slovenia) “For such small languages like Latvian keeping up with the ever increasing pace of time and technological development is crucial. The only way to ensure future existence of our language is to provide its users with equal opportunities as the users of larger languages enjoy. Therefore being on the forefront of modern technologies is our opportunity.” — Valdis Dombrovskis (Prime Minister of Latvia) “Europe’s inherent multilingualism and our scientific expertise are the perfect prerequisites for significantly advancing the challenge that language technology poses. META-NET opens up new opportunities for the development of ubiquitous multilingual technologies.” — Prof. Dr. Annette Schavan (German Minister of Education and Research

Developing and demonstrating an institutional mechanism for transferring remote sensing technology to 14 western states using northern California as the test site

There are no author-identified significant results in this report