Using visualization tools to understand concurrency

A visualization tool that provides an aggregate view of execution through a graph of events called the causality graph, which is suitable for systems with hundreds or thousands of processors, coarse-grained parallelism, and for a language that makes communication and synchronization explicit, is discussed. The methods for computing causality graphs and stepping through an execution with causality graphs are described. The properties of the abstraction algorithms and super nodes, the subgraphs in causality graphs, are also discussed     

Using recurrent neural networks for adaptive communication channelequalization

Nonlinear adaptive filters based on a variety of neural network models have been used successfully for system identification and noise-cancellation in a wide class of applications. An important problem in data communications is that of channel equalization, i.e., the removal of interferences introduced by linear or nonlinear message corrupting mechanisms, so that the originally transmitted symbols can be recovered correctly at the receiver. In this paper we introduce an adaptive recurrent neural network (RNN) based equalizer whose small size and high performance makes it suitable for high-speed channel equalization. We propose RNN based structures for both trained adaptation and blind equalization, and we evaluate their performance via extensive simulations for a variety of signal modulations and communication channel models. It is shown that the RNN equalizers have comparable performance with traditional linear filter based equalizers when the channel interferences are relatively mild, and that they outperform them by several orders of magnitude when either the channel's transfer function has spectral nulls or severe nonlinear distortion is present. In addition, the small-size RNN equalizers, being essentially generalized IIR filters, are shown to outperform multilayer perceptron equalizers of larger computational complexity in linear and nonlinear channel equalization cases.     

Using cognitive artifacts to understand distributed cognition

Studies of patient safety have identified gaps in current work including the need for research about communication and information sharing among healthcare providers. They have also encouraged the use of decision support tools to improve human performance. Distributed cognition is the shared awareness of goals, plans, and details that no single individual grasps. Cognitive artifacts are objects such as: schedules, display boards, lists, and worksheets that form part of a distributed cognition. Cognitive artifacts that are related to operating room (OR) scheduling include: the availabilities sheet, master schedule, OR graph, and OR board. All provide a "way in" to understand how teams in the acute care setting dynamically plan and manage the balance between demand for care and the resources available to provide it. This work has import for the way that information technology supports the organization, management, and use of healthcare resources. Better computer-supported cognitive artifacts will benefit patient safety by making teamwork processes, planning, communications, and resource management more resilient.     

Using function abstraction to understand program behavior

The authors explain how to understand programs by abstracting program function. This abstraction is made possible by the algebraic structure and mathematical properties of structured programs. They describe an abstraction algorithm that provides a basis for a tool for automatic abstraction of program functions. They also explore what the goals of a program-abstraction tool should be. A miniature Cobol program for a banking application is used as an example     

Using scenarios to understand the frontiers of IS

As we arrive at the millenium, the literature is filled with predictions and forecasts of the state of affairs in the 21st century. Most of these forecasts are single point prognoses. This paper uses scenario-building ideas to describe a richer set of possible states of Information Systems in the year 2010. The scenarios are integrative and consider a set of possible events and their impacts. Two major driving forces: (1) telecommunications development and (2) social acceptance of information systems (IS) are assumed to define the scenario space. Based on these driving forces, four scenarios are created: Utopian, Dystopian, Status Quo, and Technology. The Utopian (ubiquitous telecommunications, high social acceptance of IS) and Dystopian (limited gains in telecommunications, low social acceptance of IS) scenarios are described in detail. The scenarios provide a basis for assessing the frontiers of information systems.     

Using spreadsheet languages to understand sequence analysis algorithms

Spreadsheet languages have gained enormous popularity for simple numerical computations, but their power in non-numerical contexts has not been widely recognized. This paper presents spreadsheet implementations of several familiar sequence analysis tools. In many cases, spreadsheet notation clarifies the underlying algorithm, facilitating understanding of existing algorithms and promoting spontaneous experimentation. Spreadsheets also reveal, through their visible parallelism, opportunities for applying parallel processors to computationally challenging problems.     

Using targeted Bayesian network learning for suspect identification in communication networks

This paper proposes a machine learning application to identify mobile phone users suspected of involvement in criminal activities. The application characterizes the behavioral patterns of suspect users versus non-suspect users based on usage metadata such as call duration, call distribution, interaction time preferences and text-to-call ratios while avoiding any access to the content of calls or messages. The application is based on targeted Bayesian network learning method. It generates a graphical network that can be used by domain experts to gain intuitive insights about the key features that can help identify suspect users. The method enables experts to manage the trade-off between model complexity and accuracy using information theory metrics. Unlike other graphical Bayesian classifiers, the proposed application accomplishes the task required of a security company, namely an accurate suspect identification rate (recall) of at least 50% with no more than a 1% false identification rate. The targeted Bayesian network learning method is also used for additional tasks such as anomaly detection, distinction between “relevant” and “irrelevant” anomalies, and for associating anonymous telephone numbers with existing users by matching behavioral patterns.     

Using DEKAF to understand data modelling in the practitioner domain

Despite the ubiquitous use of entity-relationship modelling for more than twenty years, there is surprisingly little evidence of how effective data modelling is in the commercial domain, and this evidence suggests that modelling is problematic. This paper evaluates the literature on the effectiveness of data modelling in the practitioner domain, showing that implicit objectivist assumptions about narrative are questionable. A domain expert knowledge approach framework (DEKAF) is described in the context of overcoming problems of research generalisability. DEKAF provides both a useful way of understanding and thinking about the data modelling process and a way of making assumptions explicit in a particular practitioner domain. A summary of the findings of action research shows that DEKAF can be successfully used and can give insight into effective practitioner domain modelling.     

Using a multi-method approach to understand Agile software product lines

ContextSoftware product lines (SPLs) and Agile are approaches that share similar objectives. The main difference is the way in which these objectives are met. Typically evidence on what activities of Agile and SPL can be combined and how they can be integrated stems from different research methods performed separately. The generalizability of this evidence is low, as the research topic is still relatively new and previous studies have been conducted using only one research method.ObjectiveThis study aims to increase understanding of Agile SPL and improve the generalizability of the identified evidence through the use of a multi-method approach.MethodOur multi-method research combines three complementary methods (Mapping Study, Case Study and Expert Opinion) to consolidate the evidence.ResultsThis combination results in 23 findings that provide evidence on how Agile and SPL could be combined.ConclusionAlthough multi-method research is time consuming and requires a high degree of effort to plan, design, and perform, it helps to increase the understanding on Agile SPL and leads to more generalizable evidence. The findings confirm a synergy between Agile and SPL and serve to improve the body of evidence in Agile SPL. When researchers and practitioners develop new Agile SPL approaches, it will be important to consider these synergies.     

Using citing information to understand the logical structure of document images

The most noticeable characteristic of a construction tender document is that its hierarchical architecture is not obviously expressed but is implied in the citing information. Currently available methods cannot deal with such documents. In this paper, the intra-page and inter-page relationships are analyzed in detail. The creation of citing relationships is essential to extracting the logical structure of tender documents. The hierarchy of tender documents naturally leads to extracting and displaying the logical structure as tree structure. This method is successfully implemented in VHTender, and is the key to the efficiency and flexibility of the whole system. Received February 28, 2000 / Revised October 20, 2000     

Using multi-agent simulation to understand trading dynamics of a derivatives market

A fundamental question that arises in derivative pricing is why investors trade in a particular derivative at a “fair” price supplied by Arbitrage Pricing Theory (APT). APT establishes a price that is fair for a disinterested investor with a particular set of beliefs about market evolution and attributes trading to differences in those beliefs entertained by the opposite sides of the transaction.We present a model for an investor in a frictionless market that combines investors’ incentives in the form of pre-existing liability structures with derivatives pricing procedure tailored for a particular investor. This model enables us to show, through a series of experiments, that investors trade even when their belief structures are identical and accurate.More generally, our study suggests that multi-agent simulation of a financial market can provide a mechanism for conducting experiments that shed light on fundamental properties of the market. As all processes in financial markets (including decision making) become automated, it becomes crucial to have a mechanism by which we can observe the patterns that emerge from a variety of possible investor behaviors. Our simulator, designed as a dealer’s market, provides such a mechanism within a certain range of models.     

Using textual demographics to understand computer use: 1950-1990

Between 1950 and 1990, a large number of publications appeared in the USA discussing the nature and role of computers. By looking at the topics of these articles, we can begin to understand what such systems were used for and why. The sampling of more than 1,600 articles and books surveyed in this paper is such a beginning     

Broadband system to increase bitrate in train communication networks

MVB (Multifunction Vehicle Bus), defined in IEC 61375, has been broadly adopted as the communication standard between embedded control systems on-board modern trains. In this work a new method to take advantage of the full bandwidth of the channel using an OFDM technique is described. With this new method it is possible to share the physical medium between standard MVB traffic and new OFDM traffic. A 90 Mbps theoretical bitrate can be achieved. The results of this work have been validated in a test bench including standard MVB nodes transmitting on a line similar to a real vehicle bus.     

Swarm optimisation algorithms applied to large balanced communication networks

In the last years, several combinatorial optimisation problems have arisen in the computer communications networking field. In many cases, for solving these problems it is necessary the use of meta-heuristics. An important problem in communication networks is the Terminal Assignment Problem (TAP). Our goal is to minimise the link cost of large balanced communication networks. TAP is a NP-Hard problem. The intractability of this problem is the motivation for the pursuits of Swarm Intelligence (SI) algorithms that produce approximate, rather than exact, solutions. This paper makes a comparison among the effectiveness of three SI algorithms: Ant Colony Optimisation, Discrete Particle Swarm Optimisation and Artificial Bee Colony. We also compare the SI algorithms with several algorithms from literature. Simulation results verify the effectiveness of the proposed algorithms. The results show that SI algorithms provide good solutions in a better running time.     

Using TPACK as a framework to understand teacher candidates' technology integration decisions

This research uses the technological pedagogical and content knowledge (TPACK) framework as a lens for understanding how teacher candidates make decisions about the use of information and communication technology in their teaching. Pre‐ and post‐treatment assessments required elementary teacher candidates at Brigham Young University to articulate how and why they would integrate technology in three content teaching design tasks. Researchers identified themes from student rationales that mapped to the TPACK constructs. Rationales simultaneously supported subcategories of knowledge that could be helpful to other researchers trying to understand and measure TPACK. The research showed significant student growth in the use of rationales grounded in content‐specific knowledge and general pedagogical knowledge, while rationales related to general technological knowledge remained constant.     

Optimal polling in communication networks

Polling is the process in which an issuing node of a communication network (polling station) broadcasts a query to every other node in the network and waits to receive a unique response from each of them. Polling can be thought of as a combination of broadcasting and gathering and finds wide applications in the control of distributed systems. In this paper, we consider the problem of polling in minimum time. We give a general lower bound on the minimum number of time units to accomplish polling in any network and we present optimal polling algorithms for several classes of graphs, including hypercubes and recursively decomposable Cayley graphs     

Real-time communication in multihop networks

Communication in real-time systems has to be predictable, because unpredictable delays in the delivery of messages can adversely affect the execution of tasks dependent on these messages. We develop a scheme for providing predictable interprocess communication in real-time systems with (partially connected) point-to-point interconnection networks, which provide guarantees on the maximum delivery time for messages. This scheme is based on the concept of a real-time channel, a unidirectional connection between source and destination. A real-time channel has parameters that describe the performance requirements of the source-destination communication, e.g., from a sensor station to a control site. Once such a channel is established, the communications subsystem guarantees that these performance requirements will be met. We concentrate on methods to compute guarantees for the delivery time of messages belonging to real-time channels. We also address problems associated with allocating buffers for these messages and develop a scheme that preserves delivery time guarantees     

Using the theory of the professions to understand the IS identity crisis

Academic information systems (IS) is struggling with an identity crisis that is grounded in limited consensus about the discipline's core concepts, its important research questions and its boundaries. The result is a pluralistic discipline that has triggered an ongoing debate about whether the ‘liquidity’ characteristic of IS is a natural consequence of rapidly changing technologies or if it is indicative of a chaotic state that is unsustainable. This paper looks at IS through a new and different lens by using the theory of the professions to gain insight into current and heretofore unidentified problems facing the discipline. Casting IS as a profession presents an integrated view of academic IS, IS practice and IS education grounded in a framework that explores connections among these three elements. The paper concludes with a discussion of the processes by which professions evolve and likely consequences for IS research, practice and education.