Qualitative and quantitative temporal constraints and relationaldatabases: theory, architecture, and applications

Many different applications in different areas need to deal with both: databases, in order to take into account large amounts of structured data; and quantitative and qualitative temporal constraints about such data. We propose an approach that extends: temporal databases and artificial intelligence temporal reasoning techniques and integrate them in order to face such a need. Regarding temporal reasoning, we consider some results that we proved recently about efficient query answering in the Simple Temporal Problem framework and we extend them in order to deal with partitioned sets of constraints and to support relational database operations. Regarding databases, we extend the relational model in order to consider also qualitative and quantitative temporal constraints both in the data (data expressiveness) and in the queries (query expressiveness). We then propose a modular architecture integrating a relational database with a temporal reasoner. We also consider classes of applications that fit into our approach and consider patient management in a hospital as an example     

High-throughput, low-memory applications on the Pica architecture

This paper describes Pica, a fine-grain, message-passing architecture designed to efficiently support high-throughput, low-memory parallel applications, such as image processing, object recognition, and data compression. By specializing the processor and reducing local memory (4,096 36-bit words), multiple nodes can be implemented on a single chip. This allows high-performance systems for high-throughput applications to be realized at lower cost. The architecture minimizes overhead for basic parallel operations. An operand-addressed context cache and round-robin task manager support fast task swapping. Fixed-sized activation contexts simplify storage management. Word-tag synchronization bits provide low-cost synchronization. Several applications have been developed for this architecture, including thermal relaxation, matrix multiplication, JPEG image compression, and Positron Emission Tomography image reconstruction. These applications have been executed using an instrumented instruction-level simulator. The results of these experiments and an evaluation of Pica's architectural features are presented     

Phase synchronization: Theory and applications

The state-of-the-art of the phase synchronization theory was reviewed. Consideration was given to its applications to the synchronous and induction electrical motors, phase locked loops, and autosynchronization of the unbalanced rotors. The Yakubovich-Kalman frequency theorem was widely used to study the phase synchronization systems for global stability.     

Continuation methods: Theory and applications

This paper surveys in a tutorial fashion theoretical and applications aspects of the confirmation method for the solution of large scale system engineering problems. The continuation method is motivated and defined. The existence of the method is formulated in terms of degree theory a la Algebraic Topology. Examples are given throughout the paper and applications to engineering and economic problems are cited and described.     

Network-based distributed planning using coevolutionary agents: architecture and evaluation

A novel evolutionary planning framework (coevolutionary virtual design environment) particularly suited to distributed network-enabled design and manufacturing organizations is presented. The approach utilizes distributed evolutionary agents and mobile agents as principal object-oriented software entities that support a network-efficient evolutionary exploration of planning alternatives in which successive populations systematically select planning alternatives that reduce cost and increase throughput. This paper presents the architecture of the coevolutionary virtual design environment, and examines the network-based performance of the coevolutionary algorithms that execute in this environment. Simulation analysis examines the percentage convergence error and percentage computational advantage comparing the distributed network-based implementation to a centralized network-based implementation. The algorithms and architectures are evaluated in a realistic network setting and analyzed using models of network delays and processing times.     

Flame-MR: An event-driven architecture for MapReduce applications

Nowadays, many organizations analyze their data with the MapReduce paradigm, most of them using the popular Apache Hadoop framework. As the data size managed by MapReduce applications is steadily increasing, the need for improving the Hadoop performance also grows. Existing modifications of Hadoop (e.g., Mellanox Unstructured Data Accelerator) attempt to improve performance by changing some of its underlying subsystems. However, they are not always capable to cope with all its performance bottlenecks or they hinder its portability. Furthermore, new frameworks like Apache Spark or DataMPI can achieve good performance improvements, but they do not keep compatibility with existing MapReduce applications. This paper proposes Flame-MR, a new event-driven MapReduce architecture that increases Hadoop performance by avoiding memory copies and pipelining data movements, without modifying the source code of the applications. The performance evaluation on two representative systems (an HPC cluster and a public cloud platform) has shown experimental evidence of significant performance increases, reducing the execution time by up to 54% on the Amazon EC2 cloud.     

SPECSA: a scalable, policy-driven, extensible, and customizable security architecture for wireless enterprise applications

This paper presents SPECSA, a new, optimized, policy-driven security architecture for wireless enterprise applications. SPECSA is scalable, extensible, flexible, and customizable. It supports end-to-end client authentication, data integrity and confidentiality between wireless clients and enterprise servers. The security services provided by SPECSA are customized and controlled by an easily configurable security policy that specifies several security-related attributes, classifies network data based on sensitivity and content, and provides an abstraction for the communication and messaging between the client and the server. In addition, SPECSA provides a standard Application Programming Interface (API) that conceals to a great extent the complexity of security operations and programming from the application developer who may not be experienced with enterprise security programming. SPECSA was designed in a platform-neutral manner and can be implemented on a wide range of wireless clients ranging from low-end platforms such as the Java 2 Mobile Edition/Connected Limited Device Configuration (J2ME/CLDC) on limited-memory mobile devices to Personal Java and the Net Compact Framework on PDAs. On the server side, SPECSA can be implemented on any of the available enterprise server platforms. A sample implementation of SPECSA was developed for J2ME on the client-side and for Java 2 Enterprise Edition (J2EE) on the server-side.     

MARS: a programmable coordination architecture for mobile agents

Mobile agents offer much promise, but agent mobility and Internet openness make coordination more difficult. Mobile Agent Reactive Spaces (MARS), a Linda-like coordination architecture with programming features, can handle a heterogeneous network while still allowing simple and flexible application design     

Roboskeleton: An architecture for coordinating robot soccer agents

SkeletonAgent is an agent framework whose main feature is to integrate different artificial intelligent skills, like planning or learning, to obtain new behaviours in a multi-agent environment. This framework has been previously instantiated in a deliberative domain (electronic tourism), where planning was used to integrate Web information in a tourist plan. RoboSkeleton results from the instantiation of the same framework, SkeletonAgent, in a very different domain, the robot soccer. This paper shows how this architecture is used to obtain collaborative behaviours in a reactive domain. The paper describes how the different modules of the architecture for the robot soccer agents are designed, directly showing the flexibility of our framework.     

Dai-depur architecture: Distributed agents for real-time wwtp supervision and control

One of the key problems in real-time control Artificial Intelligent systems design is the development of an architecture able to manage efficiently the different elements of the process. In the management of Waste Water Treatment Plants (WWTP), with a great interrelation between the different units, there is a problem in order to identify and solve the different specific situations. A Supervisory System recognizes predefined problems and uses a determined strategy in order to keep the process controlled. The main goal of the paper is to present a knowledge-based distributed architecture for real time supervision and control of WWTP that overcomes some of these troubles. It is discussed the development of the application and the methodology employed in it. The prototype's architecture being developed DAI-DEPUR is detailed together with some obtained results.     

Distributed XML processing: Theory and applications

Basic message processing tasks, such as well-formedness checking and grammar validation, common in Web service messaging, can be off-loaded from the service providers’ own infrastructures. The traditional ways to alleviate the overhead caused by these tasks is to use firewalls and gateways. However, these single processing point solutions do not scale well. To enable effective off-loading of common processing tasks, we introduce the Prefix Automata SyStem — PASS, a middleware architecture which distributively processes XML payloads of web service SOAP messages during their routing towards Web servers. PASS is based on a network of automata, where PASS-nodes independently but cooperatively process parts of the SOAP message XML payload. PASS allows autonomous and pipelined in-network processing of XML documents, where parts of a large message payload are processed by various PASS-nodes in tandem or simultaneously. The non-blocking, non-wasteful, and autonomous operation of PASS middleware is achieved by relying on the prefix nature of basic XML processing tasks, such as well-formedness checking and DTD validation. These properties ensure minimal distributed processing management overhead. We present necessary and sufficient conditions for outsourcing XML document processing tasks to PASS, as well as provide guidelines for rendering suitable applications to be PASS processable. We demonstrate the advantages of migrating XML document processing, such as well-formedness checking, DTD parsing, and filtering to the network via event driven simulations.     

ID-Services: an RFID middleware architecture for mobile applications

The use of RFID middleware to support application development for and integration of RFID hardware into information systems has become quite common in RFID applications where reader devices remain stationary, which currently represents the largest part of all RFID applications in use. Another field for applying RFID technology which is offering a huge set of novel possibilities and applications are mobile applications, where readers are no longer fixed. In order to address the specific issues of mobile RFID-enabled applications and to support developers in rapid application development, we present ID-Services, an architecture for an RFID middleware that is designed to support mobile applications. The ID-Services approach has been used to implement MoVIS (Mobile Visitor Information System), a mobile application which allows museum visitors to request individually adapted multimedia information about exhibits in an intuitive way.     

DENet: a deep architecture for audio surveillance applications

Neural Computing and Applications - In the last years, a big interest of both the scientific community and the market has been devoted to the design of audio surveillance systems, able to analyse...     

A real-time architecture for time-aware agents

This paper describes the specification and implementation of a new three-layer time-aware agent architecture. This architecture is designed for applications and environments where societies of humans and agents play equally active roles, but interact and operate in completely different time frames. The architecture consists of three layers: the April real-time run-time (ART) layer, the time aware layer (TAL), and the application agents layer (AAL). The ART layer forms the underlying real-time agent platform. An original online, real-time, dynamic priority-based scheduling algorithm is described for scheduling the computation time of agent processes, and it is shown that the algorithm's O(n) complexity and scalable performance are sufficient for application in real-time domains. The TAL layer forms an abstraction layer through which human and agent interactions are temporally unified, that is, handled in a common way irrespective of their temporal representation and scale. A novel O(n2) interaction scheduling algorithm is described for predicting and guaranteeing interactions' initiation and completion times. The time-aware predicting component of a workflow management system is also presented as an instance of the AAL layer. The described time-aware architecture addresses two key challenges in enabling agents to be effectively configured and applied in environments where humans and agents play equally active roles. It provides flexibility and adaptability in its real-time mechanisms while placing them under direct agent control, and it temporally unifies human and agent interactions.     

Counterfactuals and causability in explainable artificial intelligence: Theory,algorithms, and applications

Deep learning models have achieved high performance across different domains, such as medical decision-making, autonomous vehicles, decision support systems, among many others. However, despite this success, the inner mechanisms of these models are opaque because their internal representations are too complex for a human to understand. This opacity makes it hard to understand the how or the why of the predictions of deep learning models.There has been a growing interest in model-agnostic methods that make deep learning models more transparent and explainable to humans. Some researchers recently argued that for a machine to achieve human-level explainability, this machine needs to provide human causally understandable explanations, also known as causability. A specific class of algorithms that have the potential to provide causability are counterfactuals.This paper presents an in-depth systematic review of the diverse existing literature on counterfactuals and causability for explainable artificial intelligence (AI). We performed a Latent Dirichlet topic modelling analysis (LDA) under a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to find the most relevant literature articles. This analysis yielded a novel taxonomy that considers the grounding theories of the surveyed algorithms, together with their underlying properties and applications to real-world data.Our research suggests that current model-agnostic counterfactual algorithms for explainable AI are not grounded on a causal theoretical formalism and, consequently, cannot promote causability to a human decision-maker. Furthermore, our findings suggest that the explanations derived from popular algorithms in the literature provide spurious correlations rather than cause/effects relationships, leading to sub-optimal, erroneous, or even biased explanations. Thus, this paper also advances the literature with new directions and challenges on promoting causability in model-agnostic approaches for explainable AI.     

A GPU Tile-Load-Map architecture for terrain rendering: theory and applications

This paper describes a robust, modular, complete GPU architecture—the Tile-Load-Map (TLM)—designed for the real-time visualization of wide textured terrains created with arbitrary meshes. It extends and completes our previous succinct paper Amara et al. (ISVC 2007, Part 1, Lecture Notes in Computer Science, vol. 4841, pp. 586–597, Springer, Berlin, 2007) by giving further technical and implementation details. It provides new solutions to problems that had been left unresolved, in the context of a joint use of OpenGL and CUDA, optimized on the G80 graphics chip. We explain the crucial components of the shaders, and emphasize the progress we have proposed, while resolving some difficulties. We show that this texturing architecture is well suited to current challenges, and takes into account most of the distinctive aspects of terrain rendering. Finally, we demonstrate how the design of the TLM facilitates the integration of geomatic input-data into procedural selection/rendering tasks on the GPU, and immediate applications to amplification. Electronic Supplementary Material  The online version of this article () contains supplementary material, which is available to authorized users.