ppxscal/embedding_network huggingface dataset:
Interoperability for geospatial analysis: a semantics and ontology-based approach
Authors: Zarine Kemp, Lei Tan, Jacqueline Whalley
Venue: Proceedings of the eighteenth conference on Australasian database - Volume 63 (2007)

Information extraction and integration from heterogeneous, autonomous data resources are major requirements for many spatial applications. Geospatial analysis for scientific discovery involves identification of relevant information resources, extraction and fusion of requisite subsets of the information, application of spatial analytical techniques and visualization of the results in an appropriate form. The motivating application domain underlying this research is marine environmental management, although the principles discussed apply to a wide range of scientific disciplines. The research discussed in the paper focuses on integration of data sources, data exploration and interactive data analysis. A knowledge base is used to capture the semantics of the spatial, temporal and thematic dimensions at a domain level, and the context-aware framework exploited to meet the requirements of a varied and distributed user community with differing objectives.
On the submodularity of influence in social networks
Authors: Elchanan Mossel, Sebastien Roch
Venue: Proceedings of the thirty-ninth annual ACM symposium on Theory of computing (2007)

We prove and extend a conjecture of Kempe, Kleinberg, and Tardos (KKT) on the spread of influence in social networks. A social network can be represented by a directed graph where the nodes are individuals and the edges indicate a form of social relationship. A simple way to model the diffusion of ideas, innovative behavior, or "word-of-mouth" effects on such a graph is to consider an increasing process of "infected" (or active) nodes: each node becomes infected once an activation function of the set of its infected neighbors crosses a certain threshold value. Such a model was introduced by KKT in [7,8] where the authors also impose several natural assumptions: the threshold values are (uniformly) random to account for our lack of knowledge of the true values; and the activation functions are monotone and submodular, i.e. have "diminishing returns." The monotonicity condition indicates that a node is more likely to become active if more of its neighbors are active, while the submodularity condition, indicates that the marginal effect of each neighbor is decreasing when the set of active neighbors increases. For an initial set of active nodes s, let σ(S) denote the expected number of active nodes at termination. Here we prove a conjecture of KKT: we show that the function σ(S) is submodular under the assumptions above. We prove the same result for the expected value of any monotone, submodular function of the set of active nodes at termination. In other words, our results demonstrate that "local" submodularity is preserved "globally" under diffusion processes. This is of natural computational interest, as many optimization problems have good approximation algorithms for submodular functions. In particular, our results coupled with an argument in [7] imply that a greedy algorithm gives an (1-1/e-ε)-approximation algorithm for maximizing σ(S) among all sets s of a given size. This result has important practical implications for many social network analysis problems, notably viral marketing.
Time windows-based team performance measures: a framework to measure team performance in dynamic environments
Authors: Hari Thiruvengada, Ling Rothrock
Venue: Cognition, Technology and Work (2007)

In this paper, we present a framework for implementing Team Performance Measures based on a temporal accuracy measure called the relative accuracy index (RAI) to evaluate and compare team performance in a command-and-control human-in-the-loop simulation. The framework allows researchers to collect and analyze team outcomes in an unbiased objective manner based on a temporal performance measure known as a Time Window. Our framework provides experimenters and subject matter experts the necessary tools to evaluate performance in terms of task demands. We also provide a sample analysis of individual and team performance using the RAI.
Message confidentiality and authenticity in dynamic peer groups and sensor networks
Authors: Chinya V. Ravishankar
Venue:  (2006)

Two new classes of network applications have gained importance in the past decade: dynamic peer group-oriented applications and wireless sensor network applications. The Internet has enhanced the importance of group-oriented applications, such as pay-TV, distributed interactive games, teleconferencing and chat rooms. Wireless sensor networks are expected to be extremely useful for many environmental, civil, military and homeland security applications. A variety of security issues arise in the context of group-oriented applications and sensor networks. First, it is important to ensure in group-oriented applications that only current group members are allowed to participate in group communication. In sensor-network applications, we must ensure secure communication among nodes that include the base station, static sensors and mobile sinks, in the presence of node compromises. Achieving this goal is challenging since network scales can be large, system resources are highly limited, and the nodes are prone to compromise.In this thesis, we present efficient security mechanisms to address two important security problems: message confidentiality and authenticity. To support message confidentiality, we present an efficient, authenticated fault-tolerant protocol for dynamic peer groups, and a key establishment scheme for mobile sensor networks. To support message authenticity, we present two fault-localized schemes for false report filtering in sensor networks, and an efficient, fault-tolerant scheme for limiting and verifying privileges of mobile sinks.
Estimation of CAR processes observed in noise using Bayesian inference
Authors: P. Giannopoulos
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05 (2001)

We consider the problem of estimating continuous-time autoregressive (CAR) processes from discrete-time noisy observations. This can be done within a Bayesian framework using Markov chain Monte Carlo (MCMC) methods. Existing methods include the standard random walk Metropolis algorithm. On the other hand, least-squares (LS) algorithms exist where derivatives are approximated by differences and parameter estimation is done in a least-squares manner. In this paper, we incorporate the LS estimation into the MCMC framework to develop a new MCMC algorithm. This new algorithm is combined with the standard Metropolis algorithm and is found to improve performance compared to the standard MCMC algorithm. Simulation results are presented to support our findings.
Simple eight bit, emulated computers for illustrating computer architecture concepts and providing a starting point for student designs
Authors: Timothy D. Stanley, Thanh Quach Xuan, Leslie Fife, Don Colton
Venue: Proceedings of the ninth Australasian conference on Computing education - Volume 66 (2007)

Students learn better when they both hear and do. In computer architecture courses "doing" can be difficult in small schools without hardware labs hosted by computer engineering, electrical engineering, or similar departments. Software solutions exist. Our success with George Mills' Multimedia Logic (MML) is the focus of this paper. We have found that students learn and understand more, and experience less frustration, without the additional complexity of hardware details. MML provides a graphical computer architecture solution with convenient I/O support and the ability to build and emulate a variety of computer designs. It has proven highly motivational to upper-division computer science students designing and constructing emulated computers. Student projects resulted in excellent student understanding of the detailed inner workings of computers. Students also developed better teamwork skills and produced useful training aids for the lower-division computer organization class. Designs implemented include 8-bit and 16-bit, von Neumann and Harvard architectures, from single-cycle to twelve-cycle instructions. Issues resolved during the learning process include timing, initialization, instruction set architecture, I/O, and assembler design. We provide two demonstration computers used to illustrate to students a design approach and an expected outcome in their individual design activities. One example is an eight-bit Harvard architecture with eight instructions that execute in a single clock cycle. The second is an eight-bit von Neumann architecture that has four instructions and executes each instruction in three clock cycles. This paper describes these two example computers.
Developing accessible engineering courseware
Authors: J. C. Gillette, J. C. Huston, R. M. Johnson, C. Hiemcke
Venue: Proceedings of the Frontiers in Education Conference, on 1995. Proceedings., 1995 - Volume 02 (1995)

The Courseware Development Studio is part of the NSF Synthesis Coalition and has been authoring engineering courseware since 1990. One primary goal of the Coalition has been the creation of a nationally accessible database of academic courseware (NEEDS). A further mission of the Studio is to provide courseware authors with examples of assessable quality courseware. While the courseware is being developed, the type of delivery system that will be used to present the material in a classroom must be decided. Some of the Studio's experiences and difficulties using a dedicated multimedia classroom are detailed. This setup is compared with a portable display system that can be used in a conventional classroom. The Internet is becoming a valuable resource and an integral part of today's educational environment. Setup procedures that the studio had to follow to become connected to the World Wide Web are outlined. In addition, instructions on how to access the NEEDS database via the WWW are given. Suggestions on how to avoid problems between high-powered authoring tools and database accessibility by potential users are explored. In order to maintain a high degree of quality on the database some guidelines are outlined for evaluating courseware. Courseware evaluation is intended to guarantee high standards in learning materials, and to serve as a reference for authors during the development phase.
Computational approaches for microbial community data analysis
Authors: Fengzhu Sun
Venue:  (2006)

Automated Ribosomal Intergenic Spacer Analysis (ARISA) is one of the community profiling techniques to study microbial community composition and its variation in environmental ecology. ARISA uses 16S-23S rRNA intergenic spacer length heterogeneity at different times and places. As large amount of community profiling data are generated by these approaches, effective analytical approaches for the profiles are essential to draw meaningful conclusions. This dissertation focuses on computational approaches for bacterial community profiling data analysis and it mainly contains two parts: Part I. Data preprocessing. Due to errors from various sources, the profiling data read directly from the fragment analyzer need to be properly binned before any down stream statistical analysis. We developed a dynamic programming algorithm (DPA) based binning approach for binning ARISA profiling data which minimizes the overall differences between replicates from the same sampling location and time. Clustering analysis of the ARISA from different times based on the dynamic programming algorithm binned data revealed important features of the biodiversity of the microbial communities. The robustness of DPA binning, i.e. how stable the bins are as more data come in, was also discussed. Part II. Interaction analysis. In marine ecosystems, interactions between species are complex and dynamic. These interactions are often missed by Pearson Correlation Coefficient (PCC) analysis because PCC is good at capturing the linear relationships between two species. We introduced two approaches: Local Similarity Analysis (LSA) and Liquid Association (LA), to identify more complex dependent associations among species as well as associations between species and environmental factors. The results, combined with results from PCC analysis were used to construct a theoretical ecological network which allows for easy visualization of the most significant associations. In summary, several computational approaches are introduced to analyze microbial community profiles from culture independent techniques for better characterization of the natural community composition and better understanding of the ecological dynamics in the microbial community.
An Analysis-by-Synthesis Approach to Tracking of Textiles
Authors: N. Hasler, B. Rosenhahn, M. Asbach, J.-R. Ohm, H.-P. Seidel
Venue: Proceedings of the IEEE Workshop on Motion and Video Computing (2007)

Despite strong interest in cloth simulation on the one hand and tracking of deformable objects on the other, little effort has been put into tracking cloth motion by modelling the fabric. Here, an analysis-by-synthesis approach to tracking textiles is proposed which, by fitting a simulated textile to a set of contours, is able to reconstruct the 3D-cloth configuration. Fitting is accomplished by optimising the parameters of the mass-spring model that is used to simulate the textile as well as the positions of a limited number of constrained points. To improve tracking accuracy and to overcome the inherently chaotic behaviour of the real fabric several techniques for tracking features on the cloth's surface and the best way for them to influence the simulation are evaluated.
Introducing Impact Analysis for Architectural Decisions
Authors: Matthias Riebisch, Sven Wohlfarth
Venue: Proceedings of the 14th Annual IEEE International Conference and Workshops on the Engineering of Computer-Based Systems (2007)

Architectural quality constitutes a critical factor for contemporary software systems, especially because of their size and the needs for frequent, quick changes. For success-critical business systems, architectural decisions are of high risk for the market share and even for the existence of enterprises. These decisions are important for design processes as well as for refactoring. Because of the complexity of the decisions, e.g., uncertain, contradicting goals, unknown effects and risky conditions, decision-making is a difficult and risky task. Risks can be minimized if the decisions are made systematically. In an earlier paper, we introduced methods of Decision Theory to perform such decisions in a rational way. This paper introduces a method for evaluating alternatives of architectural decisions, for both architectural design and refactoring. This method adopts elements of the scenariobased evaluation method ALMA [1]. A practical example illustrates the application of the improved decision process.
Compiler-directed design space exploration for pipelined field-programmable gate array applications
Authors: Mary W. Hall, Viktor K. Prasanna
Venue:  (2006)

Configurable computing devices, such as field programmable gate arrays (FPGA), provide the opportunity to tailor computing and storage resources to the specific needs of an application. FPGAs have parallel compute fabric, interconnect and memory subsystems allowing for concurrent execution that translates into improved program performance. Mapping a program onto an FPGA is a difficult problem. The designer must manage the data movement among program and system components, and partition system resources among program components to maximize performance. The current approach requires that a designer perform a time-consuming translation into a low-level, hardware-oriented specification, searching many alternative solutions by hand. The designer needs assistance from automated design tools to navigate the design space efficiently, a process we call design space exploration. This dissertation describes a system-level compiler that combines parallelizing compiler analyses with behavioral hardware synthesis estimates to automatically map five unannotated multiple loop nest sequential signal and image processing kernels onto an FPGA with multiple off-chip memories. We represent the program in a pipelined execution model; by detecting intermediate variables and assigning them to on-chip storage, our analysis reduces the number of off-chip memory accesses from 3 to 71 percent compared to the original code. We incorporate knowledge about the properties of our design space into our exploration algorithms and are able to prune 16 to 25 percent of the original size. We develop metrics, beyond the traditional area and performance measures, that are used to evaluate a design's performance against other designs. We select from a set of heuristics, based on a combination of compiler and synthesis knowledge, to guide our DSE. We find that a heuristic based on design quality identifies better candidates, in some cases 100 percent of the time, as opposed to heuristics based on area or performance alone. As configurable devices continue to grow in capacity, the number of design choices will increase tremendously. Without tools, programmers will be unable to tap into the potential of these devices. The techniques in this dissertation aid in achieving this goal.
Interactive presentation: A new asymmetric SRAM cell to reduce soft errors and leakage power in FPGA
Authors: Balkaran S. Gill, Chris Papachristou, Francis G. Wolff
Venue: Proceedings of the conference on Design, automation and test in Europe (2007)

Soft errors in semiconductor memories occur due to charged particle strikes at the cell nodes. In this paper, we present a new asymmetric memory cell to increase the soft error tolerance of SRAM. At the same time, this cell can be used at the reduced supply voltage to decrease the leakage power without significantly increasing the soft error rate of SRAM. A major use of this cell is in the configuration memory of FPGA. The cell is designed using a 70nm process technology and verified using Spice simulations. Soft error tolerance results are presented and compared with standard SRAM cell and an existing increased soft error tolerance cell. Simulation results show that our cell has lowest soft error rate at the various supply voltages.
Intruder Deduction for the Equational Theory of Exclusive-or with Commutative and Distributive Encryption
Authors: Pascal Lafourcade
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2007)

The first step in the verification of cryptographic protocols is to decide the intruder deduction problem, that is the vulnerability to a so-called passive attacker. We extend the Dolev-Yao model in order to model this problem in presence of the equational theory of a commutative encryption operator which distributes over the exclusive-or operator. The interaction between the commutative distributive law of the encryption and exclusive-or offers more possibilities to decrypt an encrypted message than in the non-commutative case, which imply a more careful analysis of the proof system. We prove decidability of the intruder deduction problem for a commutative encryption which distributes over exclusive-or with a DOUBLE-EXP-TIME procedure. And we obtain that this problem is EXPSPACE-hard in the binary case.
SHAD: A Human-Centered Security Architecture for the Plan B Operating System
Authors: Enrique Soriano, Francisco J. Ballesteros, Gorka Guardiola
Venue: Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications (2007)

This paper describes SHAD, a novel architecture for security in pervasive computing environments, and a prototype implementation. SHAD is a Peer-to-Peer and human-centered security architecture. It is based in a general purpose personal device that manages the user's security: the UbiTerm. There are several other systems that, at first sight, seem to provide Single Sign-On in ubiquitous environments. We argue that they fail to do so in practice, and that SHAD offers for the first time a real SSO that works well in ubiquitous environments that require using multiple machines and services simultaneously. SHAD permits users to share their resources in an easy, natural, and intuitive way, even while being disconnected from the rest of the world. The architecture we propose is able to exploit context information, when it is available. It does not require hard administration tasks, and permits users to manage their own resources. We have been using SHAD for one year. This paper describes our prototype implementation, the experience using it, and some measures that confirm that our approach is reasonable in practice.
Modeling Dynamic Substate Chains among Massive States for Prediction
Authors: Nguyen Viet Phuong, Takashi Washio
Venue: Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops (2006)

Along the development of ubiquitous sensing technologies, the opportunity to have transaction time series data is increasing. We propose a novel framework named HISC modeling to predict the dynamic system behaviors based on the transaction time series which contains explosive states due to the combinatorics of massive sensors and their output values with noise. Its significant performance has been confirmed through the comparisons with high-order Markov chain models and the application to practical data analysis.
Learning control knowledge for ai planning domains
Authors: Robert Givan, Alan Fern
Venue:  (2006)

We consider techniques for learning to plan in deterministic and stochastic Artificial Intelligence (AI) planning domains. Many AI planning domains have structure that can be exploited during planning. However, traditional automated techniques fail to capture and use such structure and as a result do not scale well as the size of the problems grows. Planning systems that exploit human-specified domain structure have shown impressive performance. In this thesis, we explore automated techniques that can identify domain-specific structure in AI planning domains. We apply machine-learning algorithms to solved problem instances to find control knowledge describing useful structure in the target planning domain. One key component in our approach is the use of concept-based knowledge representation for control knowledge. We develop learning algorithms suited for learning control knowledge. Our planning system using the learned control knowledge provides state-of-the-art performance in benchmark deterministic and stochastic planning domains. Finally, we eliminate the need for solved problem instances for learning by using a novel form of approximate policy iteration and a random-walk-based sampling algorithm. The result of this extension is a domain-independent system that automatically finds a domain-specific control knowledge by learning from the domain definition.