Breast cancer is one of the deadliest malignancies worldwide. In mammography, the most reliable screening tool for its diagnosis, expert radiologists review the mammograms to determine whether the patient has any signs of disease. Unfortunately, the evaluation of breast abnormalities is challenging, even for experienced radiologists. Computer-Aided Detection (CAD) systems can assist in the detection of breast cancer. In this work, an algorithm for the automatic detection and classification of masses, based on subtraction of sequential digital mammograms, image registration and machine learning, is presented. Previous studies assessed the use of sequential mammograms to perform temporal analysis by creating a new temporal feature vector. Temporal subtraction registers and subtracts the prior mammogram from the current one, prior to performing mass detection and classification. A new dataset, which includes sequential pairs from 40 patients (160 mammograms) with precisely annotated mass locations (benign and suspicious), was created to assess the performance of the algorithm. For the classification, various features were extracted and six classifiers were used in a leave-one-patient-out cross-validation. The accuracy of the classification of masses as benign or suspicious increased from 90.83% (with the previously described temporal analysis) to 96.51% (with temporal subtraction). The improvement was statistically significant with p <; 0.05. These results demonstrate the effectiveness of the proposed technique of temporal subtraction of mammograms for the detection of masses.

Large-scale systems comprising of multiple heterogeneous entities are directly influenced by the interactions of their participating entities.Such entities, both physical and virtual, attempt to satisfy their objectives by dynamically collaborating with each other, and thus forming collective adaptive systems.These systems are subject to the dynamicity of the entities' objectives, and to changes to the environment.In this work we focus on the latter, i.e. on providing the means for entities in such systems to model, monitor and evaluate their perceived utility by participating in the system.This allows for them to make informed decisions about their interactions with other entities in the system.For this purpose we propose a utility-based approach for decision making, as well as an architecture that allows for the support of this approach.

One of the main goals in service science is to find efficient ways to analyze and increment the value in a service network. The approach we propose in this paper is to increase the agility of the system in such a way that the network and the underlying business processes are able to spontaneously react to changes in the requirements or in the environment. This is done by making extensive use of knowledge transfer and intangible interactions among network participants. The final outcome is a transformation and analysis methodology that may be applied to a wide variety of service networks with the aim of finding possible reconfigurations for increasing customer satisfaction, reducing transaction risks, and therefore increasing the overall value of the network.

The paper presents a technique for performing dynamic goal-oriented buffer pool management for database management systems. To dynamically adjust the buffer pool sizes for the multiple buffer pools provided by database management systems is a complex constrained optimization problem. In the goal-oriented approach, the user specifies each buffer pool's random access response time goal and the total available number of buffers for all buffer pools. The problem is to dynamically expand or contract the buffer pool sizes based on the database workload to achieve these pre-defined response time goals for each buffer pool while maintaining the same total number of buffers in the database system. Our goal satisfaction algorithm monitors goal satisfaction of each buffer pool and periodically changes buffer pool sizes to improve goal satisfaction. The expansion and contraction process does not allocate new or free up existing virtual storage. We demonstrate that dynamic tuning can greatly improve buffer pool goal satisfaction through trace driven simulations.

A new approach to performing resource allocation in autonomous distributed computer systems is explored. As opposed to previous work which is based on interprocessor cooperation, the distributed system is modeled as a competitive society of microeconomic agents. The model is applied to the problem of managing distributed, replicated data objects. In this economy, jobs and transactions submitting read/write operations on data objects pay the processors to perform the operations. The processors use the revenue they earn to lease copies of data objects on which they can make a profit. The data objects themselves are active agents and set the prices for leasing copies. The economy is evaluated by a simulation study. The experiments show that the economy can substantially improve performance by varying the placement and number of copies of each data object.>

The authors propose and evaluate an efficient hierarchical clustering and allocation algorithm that drastically reduces the interprocess communications cost while observing lower and upper bounds of utilization for the individual processors. They compare the algorithm with branch-and-bound-type algorithms that can produce allocations with minimal communication cost, and show a very encouraging time complexity/suboptimality tradeoff in favor of the algorithm, at least for a class of process clusters and their random combinations which it is believed occur naturally in distributed applications. The heuristic allocation is well suited for a changing environment, where processors may fail or be added to the system and where the workload patterns may change unpredictably and/or periodically.>

A flow control mechanism based on an economic model of optimum decentralized decision making is presented. The virtual circuits (VCs) of the network are considered as economic agents competing for link capacity resources. Each VC is equipped with a preference relation defining its individual throughput-delay goals. The work makes three contributions to the study of flow control protocols. First, it presents fully decentralized flow control algorithms that allocate capacity to reflect both, varying priorities among VCs as well as their preferences and needs. Second, it establishes that the flow control algorithms result in Pareto-optimal allocation of capacities. This implies that the algorithms accomplish optimally fair allocation of resources given the set priorities and preferences. Third, experimental evidence is provided that the algorithms converge rapidly.>

A novel approach to allocating and sharing communication and computational resources in a distributed system is described. The approach, which is based on concepts drawn from microeconomics, uses algorithms that are competitive rather than cooperative. The effectiveness of these concepts is demonstrated by describing an economy that improves the performance of a distributed system by implementing load balancing. In this economy, competition sets prices for the resources in the system. Jobs complete for the resources by issuing bids, and the resource allocation decisions are made through auctions held by the processors. The benefits of the method include limited complexity and algorithms that are intrinsically decentralized and modular. Simulation studies show that these economies achieve substantial performance benefits.>

In this paper, we view computer networks as distributed systems that provide their users with a set of services, in a way which hides the distinction between those services which are local and those which are remote. We conceive of a given target network configuration as a network of communicating virtual machines and its behavior is modelled by a system of communicating sequential processes. Network protocols are described by a high level concurrent language (CSP) and a methodology is developed which permits the verification of partial and total correctness assertions about the system in a simple and natural way. Global invariants are used to establish invariant properties of the whole system and histories to record the sequence of communication exchanges between every matching pair of processes. Eventuality properties are expressed using linear temporal logic.

In this paper, we investigate strategies for dynamically reconfiguring shared memory multiprocessor systems that are subject to common memory faults and unpredictable processor deaths. These strategies aim at determining a communication page, i.e., a page of common memory that can be used by a group of processors for storing crucial common resources such as global locks for synchronization and global data structures for voting algorithms. To ensure system reliability, the reconfiguration strategies must be distributed so that each processor independently arrives at exactly the same choice. This type of reconfiguration strategy is currently used in the STAGE operating system on the PLURIBUS multiprocessor [5]. We analyze the weak points of the PLURIBUS algorithm and examine alternative strategies satisfying optimization criteria such as maximization of the number of processors and the number of common memory pages in the reconfigured system. We also present a general distributed algorithm which enables the processors in such a system to exchange the local information that is needed to reach a consensus on system reconfiguration.