ppxscal/embedding_network huggingface dataset:
Synthesis, Analysis, and Modeling of Large-Scale Mission-Critical Embedded Software Systems
Authors: Richard W. Selby
Venue: Proceedings of the International Conference on Software Process: Trustworthy Software Development Processes (2009)

Mission-critical embedded software performs the core processing logic for pervasive systems that affect people and enterprises everyday, ranging from aerospace systems to financial markets to automotive systems. In order to function properly, these embedded software systems rely on and are highly interdependent with other hardware and software systems. This research identifies design principles for large-scale mission-critical embedded software and investigates their application in development strategies, architectures, and techniques. We have examined actual embedded software systems from two different problem domains, advanced robotic spacecraft and financial market systems, and these analyses established the foundations for these design principles. Both system types embody solutions that respond to detailed specifications defined and modeled with heavy user involvement. Both system types possess mission-critical logic represented using state machines and other structured techniques. They both use a layered architecture approach with a foundation that provides infrastructure services, a layer with a simple set of foreground and background tasks, a layer with deterministic synchronous processing steps, and a layer with event-driven monitoring, commanding, and sequencing capabilities. The architectural approach supports a domain-specific command sequencing macro language that defines table-driven executable specifications and enables developers to work at higher abstraction levels throughout the lifecycle. The architectural approach also facilitates extensibility, reuse, and portability across multi-processor execution environments. The systems rely on extensive built-in self-tests, invariants, and redundant calculations that assess states and detect faults. From a development standpoint, both systems use risk-driven incremental lifecycles, system modeling, end-to-end prototyping, and statistical analysis of development processes. Based on insights gained from embedded software design principles and their application on these as well as other systems, improvement opportunities and research directions will be identified.
Distributed Barter--Based Directory Services
Authors: David Cabanillas, Steven Willmott, Ulises Cortés
Venue: Proceeding of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence (2008)

The allocation of resources amongst a set of autonomous entities with contradicting interests is a recurring and relevant theme in distributed domains. In this paper we present the design, implementation and evaluation of a distributed directory service based on a bartering mechanism in order to improve robustness in data retention. A directory service maps the names of network resources to their respective network addresses. A distributed directory service maintains this information not at a single node in a network, but across many nodes. The major challenge addressed in this paper is to build a workable system which not only responds to queries from users but A) ensures that directory items are never lost in the system and B) optimizes query response time with respect to different patterns of query arrival.
Ant colony optimization inspired resource discovery in P2P Grid systems
Authors: Yuhui Deng, Frank Wang, Adrian Ciura
Venue: The Journal of Supercomputing (2009)

It is a challenge for the traditional centralized or hierarchical Grid architecture to manage the large-scale and dynamic resources, while providing scalability. The Peer-to-Peer (P2P) model offers a prospect of dynamicity, scalability, and availability of a large pool of resources. By integrating the P2P philosophy and techniques into a Grid architecture, P2P Grid system is emerging as a promising platform for executing large-scale, resource intensive applications. There are two typical resource discovery approaches for a large-scale P2P system. The first one is an unstructured approach which propagates the query messages to all nodes to locate the required resources. The method does not scale well because each individual query generates a large amount of traffic and the network quickly becomes overwhelmed by the messages. The second one is a structured approach which places resources at specified locations to make subsequent queries easier to satisfy. However, the method does not support multi-attribute range queries and may not work well in the network which has an extremely transient population. This paper proposes and designs a large-scale P2P Grid system which employs an Ant Colony Optimization (ACO) algorithm to locate the required resources. The ACO method avoids a large-scale flat flooding and supports multi-attribute range query. Multiple ants can be employed to improve the parallelism of the method. A simulator is developed to evaluate the proposed resource discovery mechanism. Comprehensive simulation results validate the effectiveness of the proposed method compared with the traditional unstructured and structured approaches.
Comparative Study on Three Voidage Measurement Methods for Two-Phase Flow
Authors: Youmin Guo, Zhenrui Peng
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

Voidage measurement of two-phase flow is of great importance to the industrial sector, in terms of safety, environmental protection and energy saving. Electrical capacitance tomography (ECT) is an effective technique for elucidating the distribution of dielectric materials inside closed pipes or vessels. Three currently proposed voidage measurement methods, which are based on Genetic Algorithm - Partial Least Square (GA-PLS), Ant System Algorithm (ASA), and Least Squares Support Vector Machine (LS-SVM) are reviewed respectively. All the data under three voidage measurement methods come from the ECT sensor. Then a unifying model is proposed to provide a universal voidage measurement framework. Finally, the experimental data are used to evaluate the three methods. The evaluation results are compared in terms of mean squared error, maximum absolute error, mean absolute error and measurement time. Future possible voidage measurement method based on ECT capacitance information is also discussed.
Study of Parallel Linear Solvers for Three-Dimensional Subsurface Flow Problems
Authors: Hung V. Nguyen, Jing-Ru C. Cheng, Robert S. Maier
Venue: Proceedings of the 9th International Conference on Computational Science: Part I (2009)

The performance of an iterative method for solving a system of linear equations depends on the structure of the system to be solved and on the choice of iterative solvers in combination with preconditioners. In this paper, the performance of a specified set of linear solvers and preconditioners provided by PETSc and Hypre is evaluated based on three data sets from subsurface finite element flow models. The results show that simple preconditioners are robust but do not enable convergence behavior that scales with problem size. They also show that it is important to choose an appropriate type of solver for different kind of simulations.
Greedy Routing with Anti-Void Traversal for Wireless Sensor Networks
Authors: Wen-Jiunn Liu, Kai-Ten Feng
Venue: IEEE Transactions on Mobile Computing (2009)

The unreachability problem (i.e., the so-called void problem) that exists in the greedy routing algorithms has been studied for the wireless sensor networks. Some of the current research work cannot fully resolve the void problem, while there exist other schemes that can guarantee the delivery of packets with the excessive consumption of control overheads. In this paper, a greedy anti-void routing (GAR) protocol is proposed to solve the void problem with increased routing efficiency by exploiting the boundary finding technique for the unit disk graph (UDG). The proposed rolling-ball UDG boundary traversal (RUT) is employed to completely guarantee the delivery of packets from the source to the destination node under the UDG network. The boundary map (BM) and the indirect map searching (IMS) scheme are proposed as efficient algorithms for the realization of the RUT technique. Moreover, the hop count reduction (HCR) scheme is utilized as a short-cutting technique to reduce the routing hops by listening to the neighbor's traffic, while the intersection navigation (IN) mechanism is proposed to obtain the best rolling direction for boundary traversal with the adoption of shortest path criterion. In order to maintain the network requirement of the proposed RUT scheme under the non-UDG networks, the partial UDG construction (PUC) mechanism is proposed to transform the non-UDG into UDG setting for a portion of nodes that facilitate boundary traversal. These three schemes are incorporated within the GAR protocol to further enhance the routing performance with reduced communication overhead. The proofs of correctness for the GAR scheme are also given in this paper. Comparing with the existing localized routing algorithms, the simulation results show that the proposed GAR-based protocols can provide better routing efficiency.
Diagnosing patients with a combination of principal component analysis and case based reasoning
Authors: Carles Pous, Dani Caballero, Beatriz Lopez
Venue: International Journal of Hybrid Intelligent Systems (2009)

This paper addresses the application of a principal component analysis (PCA) of categorical data prior to diagnosing a patients dataset using a case-based reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical dataset contains many categorical data and alternative methods such as RS-PCA are required. Thus, we propose to hybridize RS-PCA (regular simplex PCA) and a simple CBR system. Results show how the hybrid system, when diagnosing a medical dataset, produces results similar to the ones obtained when using the original attributes. These results are quite promising since they allow diagnosis with less computation effort and memory storage.
Monitoring uncertain data for sensor-based real-time systems
Authors: Al Mok
Venue:  (2008)

Monitoring of user-defined constraints on time-varying data is a fundamental functionality in various sensor-based real-time applications such as environmental monitoring, process control, location-based surveillance, etc. In general, these applications track real-world objects and constantly evaluate the constraints over the object trace to take a timely reaction upon their violation or satisfaction. While it is ideal that all the constraints are evaluated accurately in real-time, data streams often contain incomplete and delayed information, rendering the evaluation results of the constraints uncertain to some degree. In this dissertation, we provide a comprehensive approach to the problem of monitoring constraint-based queries over data streams for which the data or timestamp values are inherently uncertain.First, we propose a generic framework, namely PTMON, for monitoring timing constraints and detecting their violation early, based on the notion of probabilistic violation time. In doing so, we provide a systemic approach for deriving a set of necessary timing constraints at compilation time. Our work is innovative in that the framework is formulated to be modular with respect to the probability distributions on timestamp values. We demonstrate the applicability of the framework for different timestamp models.Second, we present a probabilistic timing join operator, namely P TJOIN, as an extended functionality of PTMON, which performs stream join operations based on temporal proximity as well as temporal uncertainty. To efficiently check the PTJOIN condition upon event arrivals, we introduce the stream-partitioning technique that delimits the probing range tightly.Third, we address the problem of monitoring value-based constraints that are in the form of range predicates on uncertain data values with confidence thresholds. A new monitoring scheme SPMON that can reduce the amount of data transmission and thus expedite the processing of uncertain data streams is introduced. The similarity concept that was originally intended for real-time databases is extended for our probabilistic data stream model where each data value is given by a probability distribution. In particular, for uniform and gaussian distributions, we show how we derive a set of constraints on distribution parameters as a metric of similarity distances, exploiting the semantics of probabilistic queries being monitored. The derived constraints enable us to formulate the probabilistic similarity region that suppresses unnecessary data transmission in a monitoring system.
Emerging marketsHow ICT advances might help developing nations
Authors: Mark Cleverley
Venue: Communications of the ACM (2009)

Some predictions for technology developments, deployments, and the associated societal implications.
Plenary lecture 3: statistical global metabolic control analysis
Authors: Zelimir Kurtanjek
Venue: Proceedings of the 10th WSEAS international conference on Mathematics and computers in biology and chemistry (2009)

Metabolic Control Analysis (MCA) is a mathematical theory stemming from elec-trical engineering network analysis applied to biological systems. Availability of annotated genome, metabolomics and proteomics of numerous industrial important microorganism leads fundamental research of industrial microbiology "in silico". From engineering point of view, open are possibilities for computer design of synthetic genome for development of new tech-nologies, most importantly for bioenergetics based on synthetic microorganism with integrated photosynthesis and fermentation metabolisms. The main obstacles toward this far reaching goal are not in chemical synthesis of genome, but rather in biological and computer analysis of intricate metabolism control on a molecular level. At present, most of MCA analy-sis is based on steady state (homeostatic constraint) analysis and study of "one factor at a time" infinitesimal effects of perturbations of each individual enzyme and metabolite concen-tration on metabolic fluxes and individual reaction rates. This work introduces the concept of global sensitivity based on simultaneous variation of a complete set of enzymes and metabo-lite concentrations (elasticities) in finite ranges of activities (concentrations). Perturbations are defined by corresponding finite ranges of concentrations and corresponding general probabil-ity density distributions. The flux sensitivities are determined as first and second order rela-tive multidimensional variances. The first order effects are reflection of variation of each individual enzyme. Importantly, the second order effects imply synergic effects of the whole ensemble of enzymes which completely escapes in the traditional MCA analysis. The disper-sions of fluxes due to each enzyme are evaluated through computer simulation and Fourier Amplitude Sensitivity Test algorithm. The implications of the proposed theory are demon-strated by computer simulation of theoretical metabolism pathways and experimental analysis of E. coli central metabolism unsteady perturbation by glucose impulse.
Decision making in large-scale domains: a case study
Authors: Mikhail Soutchanski, Huy Pham, John Mylopoulos
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

Planning under uncertainty attracted significant attention in AI and in other fields. To overcome computational problems associated with Markov Decision Processes (MDPs) in large scale domains researchers often take advantage of structural properties and look for approximately optimal solutions. DTGolog, a decision-theoretic agent programming language based on the situation calculus, was proposed to ease some of the computational difficulties by using natural ordering constraints on execution of actions. Using DTGolog, domain specific constraints on the set of available policies can be expressed in a high-level program and this program helps to reduce significantly computation required to find a policy optimal in this set. Our paper explores whether the DTGolog framework can be used to evaluate different designs of a decision making agent in a large real-world domain. Each design is understood as combination of a template (expressed as a Golog program) for available policies and a reward function. To evaluate and compare alternative designs we estimate the probability of goal satisfaction for each design. As a domain, we choose the London Ambulance Service (LAS) case study that is well known in software engineering, but remains unknown in AI. In our paper we demonstrate that DTGolog can be applied successfully to quantitative evaluation of alternative designs in terms of their ability to satisfy a system goal with a high probability. We provide a detailed axiomatization of the domain in the temporal situation calculus with stochastic actions. The main advantage of this representation is that neither actions, not states require explicit enumeration. We do an experimental analysis using an on-line implementation of DTGolog coupled with a simulator that models real time actions of many external agents.
A Hierarchical Model for the Recognition of Deformable Objects
Authors: Martin Stommel, Klaus-Dieter Kuhnert
Venue: Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers (2008)

This paper proposes a hierarchical model for the recognition of deformable objects. Object categories are modelled by multiple views, views in turn consist of several parts, and parts consist of several features. The main advantage of the proposed model is that its nodes can be tuned with regard to the spatial selectivity. Every node in a category, views or part can thus take on the shape of a simple bag of features or a geometrically selective constellation model including all forms in between. Together with the explicit modelling of multiple views this allows for the modelling of categories with high intra-class variance. Experimental results show a high precision for the recognition of a character from a cartoon data base.
A Survey on One-Hop Clustering Algorithms in Mobile Ad Hoc Networks
Authors: Suchismita Chinara, Santanu Kumar Rath
Venue: Journal of Network and Systems Management (2009)

Clustering in mobile ad hoc network (MANET) play a vital role in improving its basic network performance parameters like routing delay, bandwidth consumption and throughput. One-hop clustering scheme adopts the simple mechanism to make the logical partition of the dynamic network where the network topology changes constantly resulting an unstable clustering. This paper makes a comprehensive survey of some bench-mark one-hop clustering algorithms to understand the research trends in this area. The literature provides the logic of cluster formation for different algorithms in achieving a linked cluster architecture and an intensive simulation survey of their performance on the cluster maintenance aspects such as cluster density, frequency of cluster reelection, frequency of cluster changes by the nodes and the granularity of cluster heads. This paper should facilitate the researchers as well as practitioners in choosing a suitable clustering algorithm on the basis of their formation and maintenance overhead, before any routing scheme is adopted in the mobile ad hoc network.
Global Stability of Neural Networks with Delays and Impulses
Authors: Fengjian Yang, Chaolong Zhang, Dongqing Wu, Jianfu Yang, Yanshan Zeng, Lishi Liang, Qun Hong
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

This paper is concerned with a class of neural networks with delays and impulses. Some sufficient conditions are obtained for the existence and globally exponential stability of a unique equilibrium solution without assuming the activation function to be bounded, monotonic or differentiable. An example is given to demonstrate the effectiveness of the obtained results.
BlackBerry Curve 8900 Made Simple: For the Curve 8900, 8910, 8920, 8930, and all 89xx Series BlackBerry Smartphones.
Authors: Martin Trautschold
Venue:  (2009)

This guide book is unique in that it helps both Windows and Apple Mac users with all aspects of the BlackBerry Curve 89xx (8900, 8910, 8920, all 89xx models). Some of the highlights include: Dozens of hidden or little-known time-saving tips and tricks. How to configure and use Wi-Fi and GPS Synchronizing your BlackBerry with your Windows or Mac computer. How to squeeze every ounce of power from the Address Book, Calendar, Tasks and MemoPad. How to become Master of Your BlackBerry Email How to use Google Sync for true Wireless Sync without an Enterprise Server Support for both BlackBerry Internet Service and Corporate BlackBerry Enterprise Server users. Tips and tricks to unlock the rich Media Features of the BlackBerry Curve. How turn your Curve into something that could replace a video and audio iPod How to use your Curve for Mapping (including Google Maps.) Learn how to go Online and download Facebook and Myspace pages for your BlackBerry
On the Broad Implications of Reinforcement Learning based AGI
Authors: Scott Livingston, Jamie Garvey, Itamar Elhanany
Venue: Proceeding of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference (2008)

Reinforcement learning (RL) is an attractive machine learning discipline in the context of Artificial General Intelligence (AGI). This paper focuses on the intersection between RL and AGI by first speculating on what are the missing components that would facilitate the realization of RL-based AGI. Based on this paradigm, we touch on several of the key moral and practical issues that will inevitably arise.
Algorithm for Multi-sensor Asynchronous Track-to-Track Fusion
Authors: Cheng Cheng, Jinfeng Wang
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

This paper derives an algorithm for multi-sensor asynchronous track-to-track fusion that combines tracks provided by different sensors, which have each communication delay. In this algorithm, an adaptive approach for fusion in a multi-sensors environment is used. The measurements of two sensors tracking the same target are processed by linear Kalman Filters, and the outputs of the local trackers are sent to the central node. In this node, a decision logic, which is based on the comparison between distance metrics and thresholds, selects the method to obtain the global estimate. The simulation result illustrates that this algorithms approaches the Weighted Covariance Fusion (WCF) algorithm in the fusion precision, and the computational burden reduces one about the half.