Traditional Software Packages Research Articles

Symbolic equation solving via reinforcement learning

Machine-learning methods are rapidly being adopted in a wide variety of social, economic, and scientific contexts, yet they are notorious for struggling with exact mathematics. A typical example is computer algebra, which includes tasks like simplifying mathematical terms, calculating formal derivatives, or finding exact solutions of algebraic equations. Traditional software packages for these purposes are commonly based on a huge database of rules for how a specific operation (e.g., differentiation) transforms a certain term (e.g., sine function) into another one (e.g., cosine function). These rules have usually needed to be discovered and subsequently programmed by humans. Efforts to automate this process by machine-learning approaches are faced with challenges like the singular nature of solutions to mathematical problems, when approximations are unacceptable, as well as hallucination effects leading to flawed reasoning. We propose a novel deep-learning interface involving a reinforcement-learning agent that operates a symbolic stack calculator to explore mathematical relations. By construction, this system is capable of exact transformations and immune to hallucination. Using the paradigmatic example of solving linear equations in symbolic form, we demonstrate how our reinforcement-learning agent autonomously discovers elementary transformation rules and step-by-step solutions.

A novel choice model combining utility maximization and the disjunctive decision rules, application to two case studies

Most choice models, e.g. Multinomial Logit (MNL), rely on random utility theory, which assumes that a compensatory utility maximization decision rule explains an individual’s choice behaviour. Research has shown, however, that behaviour is sometimes better explained by non-compensatory decision rules. While some research has used Latent Class Choice Models (LCCMs) to account for multiple decision rules, many of them – such as the disjunctive rule – have yet to be explored. This paper formulates, estimates, and evaluates a LCCM that combines the MNL with a Generalised Random Disjunctive Model (GRDM), a new choice model we develop. Addressing deficiencies of existing disjunctive choice models, the GRDM allows for relative importance between attributes and is insensitive to irrelevant attributes. Unlike most non-compensatory models, it is tractable and incorporates random error terms for capturing unobserved heterogeneity across choice situations. The GRDM can be expressed as a Universal Logit (UL) model, which helps derive welfare metrics such as Marginal Rates of Substitution and elasticities and makes it possible to estimate the model with traditional software packages. The LCCM combining the GRDM and the MNL is estimated in two large-scale case studies: cyclists’ route choice and public transport route choice. Results are compared with other relevant LCCM specifications and the individual choice models, where it is found that the MNL + GRDM LCCM provides the best fit to the data. We also interpret the fitted parameters and calculate the Marginal Rates of Substitution, which align with behavioural expectations.

Reproducing FSL's fMRI data analysis via Nipype: Relevance, challenges, and solutions.

The "replication crisis" in neuroscientific research has led to calls for improving reproducibility. In traditional neuroscience analyses, irreproducibility may occur as a result of issues across various stages of the methodological process. For example, different operating systems, different software packages, and even different versions of the same package can lead to variable results. Nipype, an open-source Python project, integrates different neuroimaging software packages uniformly to improve the reproducibility of neuroimaging analyses. Nipype has the advantage over traditional software packages (e.g., FSL, ANFI, SPM, etc.) by (1) providing comprehensive software development frameworks and usage information, (2) improving computational efficiency, (3) facilitating reproducibility through sufficient details, and (4) easing the steep learning curve. Despite the rich tutorials it has provided, the Nipype community lacks a standard three-level GLM tutorial for FSL. Using the classical Flanker task dataset, we first precisely reproduce a three-level GLM analysis with FSL via Nipype. Next, we point out some undocumented discrepancies between Nipype and FSL functions that led to substantial differences in results. Finally, we provide revised Nipype code in re-executable notebooks that assure result invariability between FSL and Nipype. Our analyses, notebooks, and operating software specifications (e.g., docker build files) are available on the Open Science Framework platform.

Investigation of Thermal Behavior of Lithium-Ion Batteries under Different Loads

In this study, the thermal behavior and performance of pouch type Lithium-Ion Batteries (LIB) which are used in Hybrid Electric Vehicles (HEVs) and Electrical Vehicles (EVs) has been investigated at different discharge rates based on numerical simulations. Numerical simulation was performed through a traditional software package using the dual potential Multi-Scale Multi-Dimensional (MSMD) battery model to analyze the cell discharge behavior and investigate its thermal performance. When the battery load is increased, non-uniform thermal distribution and temperature rise has been observed. Non-uniform thermal distribution causes loss of capacity and performance in the battery. Therefore, an accurate and effective cooling system is required to eliminate non-uniform temperature distribution. This study is a preliminary preparation for cooling system design.

Balancing fluid and cemented routines in a digital workplace

Advanced workplace technologies are increasingly used alongside traditional enterprise software packages (such as enterprise resource planning) in the workplace. However, we have only limited understanding of how different kinds of technologies are used to dynamically shape work routines and fluidity in a digital workplace. We conducted an in-depth six-year study of the use of enterprise resource planning (ERP) system by a large information technology service provider company. The company used the system to manage its global staffing processes. We explored how the users of this system sought to achieve the fluidity needed to do their work. Our findings show that users balance two patterns of routine performance through different technologies with varying degrees of malleability: one to generate fluidity and another to generate stability. We call this process ‘generative balancing’. Our research contributes to the literature on workplace technologies and ERP use by providing insights into how the use of technologies with different degrees of malleability helps to craft digital workspaces and enables users to deal with tensions between accomplishing local-level performance and realizing corporate-level strategic intents.

Design and Implement a MapReduce Framework for Executing Standalone Software Packages in Hadoop-based Distributed Environments

The Hadoop MapReduce is the programming model of designing the auto scalable distributed computing applications. It provides developer an effective environment to attain automatic parallelization. However, most existing manufacturing systems are arduous and restrictive to migrate to MapReduce private cloud, due to the platform incompatible and tremendous complexity of system reconstruction. For increasing the efficiency of manufacturing systems with minimum modification of existing systems, we design a framework in this thesis, called MC-Framework: Multi-uses-based Cloudizing-Application Framework. It provides the simple interface to users for fairly executing requested tasks worked with traditional standalone software packages in MapReduce-based private cloud environments. Moreover, this thesis focuses on the multiuser workloads, but the default Hadoop scheduling scheme, i.e., FIFO, would increase delay under multiuser scenarios. Hence, we also propose a new scheduling mechanism, called Job-Sharing Scheduling, to explore and fairly share the jobs to machines in the MapReduce-based private cloud. Then, we prototype an experimental virtual-metrology module of a manufacturing system as a case study to verify and analysis the proposed MC-Framework. The results of our experiments indicate that our proposed framework enormously improved the time performance compared with the original package.

A Proposal to Manage Lessons Learned in Projects: Web 2.0 Technologies to Promote Innovation

The web 2.0 is transforming the project management in organizations by improving communication and collaboration. The new generation of web-based collaborative tools provides much better experience than the traditional software package allowing document sharing, integrated task tracking, enforcing team processes and agile planning. Despite of the indubitable benefits brought by web 2.0, the use of these technologies to promote knowledge management remains unexplored. For many project managers to obtain and integrate information from different tools of previous similar projects in global organizations remains a challenge. This theoretical paper presents a proposal that suggests an innovation in the knowledge management area applying web 2.0 technologies. The main goal is to provide an integrated vision of a set of technologies that could be used by organizations in order to promote better management of lessons learned. The proposal includes the lessons learned processes (e.g. capture, share and dissemination), the process-based (e.g. project review and after action review) and documentation-based (e.g. micro article and learning histories) methods. Results show how web 2.0 technologies can help project managers and team project to cope with the main lessons learned processes and methods to learn from experience. Moreover, recommendations are made for the effective use of web 2.0 components promoting innovation and supporting lessons learned management in projects.Keywords: Project management; Lessons learned processes; lessons learned methods; project learning; web 2.0 technologies; innovation.

Coordination mechanism of SaaS service supply chain: Based on compensation contracts

Purpose: The purpose of this paper is to build new contracts theories of SaaS service supply chain. Software as a Service (SaaS) has become a hot topic in this industry . Compared with traditional manufacturing supply chain and general service supply chain, the new IT service supply chain which based on SaaS has characteristics of both service and IT. And SaaS is completely different from traditional software package model. Therefore the classic contracts, which be widely used in traditional manufacturing supply chain, can’t be directly applied in SaaS service supply chain. The necessary way of IT services developing is to study the SaaS service supply chain combining with characteristics of SaaS. Therefore, It focuses on the coordination of SaaS service supply chain. Design/methodology/approach: It tries to answer the following question: how do the ISV motivate SaaS operators to improve the service level through effective contracts mechanism under conditions of asymmetric information. In order to answer these questions, this paper does some researches including: Under the conditions of information asymmetry, supposing the service level (is related to the degree of effort) of SaaS operator was private information, we construct model of compensation contract, i.e., to motivate SaaS operator to improve service level through transfer payments of compensation price. Findings and Originality/value: The study finds out that when ISV get to “positive feedback”, instead of the traditional market equilibrium, compensation contract (linear) can coordinate satisfactorily the SaaS service supply chain. In the point of “positive feedback”, the marginal revenue equals the marginal cost, but it is not the equilibrium of ISV’profit-maximization. Research limitations/implications: There are some limitations in this research. In the linear compensation contracts, the compensation price is fixed value. If in the contract, we can create a change value which is related to the sales volume of service, the coordination efficiency of compensation will be increased. And in the future, the nonlinear compensation contracts should be researched. Practical implications: In the practical implication, it will promote the development of study and practice of SaaS and IT service industry. And it is beneficial to ISV, platform operators and customers. Originality/value: From the theoretical perspective, this paper put forward some new contracts and motivating mechanism for effective coordination of SaaS service supply chain and the conclusion will enrich content of service supply chain theory system. In theory, it has more significance for further researching on SaaS and service supply chain.

Customer Relationships Management in Organizations

In recent years, the market for on-demand CRM applications suffered especially in the segment of small and medium companies because of the concerns about the cost and the complexity of on request implementations. CRM on demand is a good choice for companies that want to implement standard processes that can use data structures “out of box” with little or not at all internal IT support and does not require complex or real-time integration with back office systems.However, on-demand CRM software is not always as simple as vendors want us to believe. Customizing can be problematic and interface tools of CRM vendors can not provide desired levels of integration. Implementing a hosted CRM system should not take so much as traditional software packages does, but with very large and complex variants it may take even a year. In addition, some companies that hold sensitive data such as financial services could refuse to entrust control of such data to third parties for security reasons. For these reasons, AMR Research believes that by 2012, hosted CRM applications market will be only 12 percent of the total CRM market.In the last decade, CRM products were a market with strong growth, which has attracted a lot of companies, causing them to create their own CRM solutions. They created “flagship” solutions of small companies, specialized in such services or solutions belonging to specialized divisions of major solutions providers such as SAP, Oracle, Microsoft.Among these solutions we enumerate: ERP (Enterprise Resource Planning), Supply Chain Management – SCM, Microsoft Dynamics CRM, Data warehouse (DWH), etc.

Design and Implement a MapReduce Framework for Executing Standalone Software Packages in Hadoop-based Distributed Environments

The Hadoop MapReduce is the programming model of designing the auto scalable distributed computing applications. It provides developer an effective environment to attain automatic parallelization. However, most existing manufacturing systems are arduous and restrictive to migrate to MapReduce private cloud, due to the platform incompatible and tremendous complexity of system reconstruction. For increasing the efficiency of manufacturing systems with minimum modification of existing systems, we design a framework in this thesis, called MC-Framework: Multi-uses-based Cloudizing-Application Framework. It provides the simple interface to users for fairly executing requested tasks worked with traditional standalone software packages in MapReduce-based private cloud environments. Moreover, this thesis focuses on the multiuser workloads, but the default Hadoop scheduling scheme, i.e., FIFO, would increase delay under multiuser scenarios. Hence, we also propose a new scheduling mechanism, called Job-Sharing Scheduling, to explore and fairly share the jobs to machines in the MapReduce-based private cloud. Then, we prototype an experimental virtual-metrology module of a manufacturing system as a case study to verify and analysis the proposed MC-Framework. The results of our experiments indicate that our proposed framework enormously improved the time performance compared with the original package.

Temperature Effect on Uncooled Semiconductor Laser Diode to the Network Performance System

Problem statement: The characteristics of a laser diode are highly de pendent on the temperature of the laser chip. Thus, the effect of temperature on the network performance of uncooled semiconductor laser diode are studied by simulating its equivalent electrical circuit, developed from the rate equations that governing optical component s directly into an electrical simulation framework. Approach: The simulations are carried out using circuit anal ysis program named OptiSPICE and OptiSystem. In this simulation study, the analysis is done based on four parameters in order to determine the quality of received signal which are; bit error rate, eye height, maximum Q factor and jitter. Results: As the temperature variance is fed to the laser di ode through an external thermal noise, the result will shows that the ambient tempe rature of laser diode is varied with time. In this case, the performance of laser diode will decreases as th e operating temperature increases or, conversely th e performance increases as the operating temperature decreases. The circuit design must be simulated using a SPICE engine that incorporates both electri cal and optical models, which is OptiSPICE software. It cannot be simulated using traditional software packages because they do not provide sufficient coupling between electrical and optical devices. Conclusion: We successfully analyze the effect of the temperature on laser source to the pe rformance of optical network system.

The Effect of Temperature on the Performance of Uncooled Semiconductor Laser Diode in Optical Network

Problem statement: The characteristics of a laser diode are highly dependent on the temperature of the laser chip. Thus, the effect of temperature on the network performance of uncooled semiconductor laser diode are studied by simulating its equivalent electrical circuit, developed from the rate equations that governing optical components directly into an electrical simulation framework. Approach: The simulations are carried out using circuit analysis program named OptiSPICE and Opti System. On this simulation study, the analysis is done based on three parameters in order to determine the quality of received signal which are; center wavelength, bandwidth and output power. Results: As the temperature variance is fed to the laser diode through an external thermal noise, the result will shows that the ambient temperature of laser diode is varied with time. In this case, the performance of laser diode will decreases as the operating temperature increases or, conversely the performance increases as the operating temperature decreases. Conclusion: The circuit design must be simulated using a SPICE engine that incorporates both electrical and optical models, which is Opti SPICE software. It cannot be simulated using traditional software packages because they do not provide sufficient coupling between electrical and optical devices.

Web-Based Applications quality factors: A survey and a proposed conceptual model

Web-Based Applications (WBA) are fast becoming more widespread, larger, more interactive, and more essential to the international use of computers. The most successful WBA companies are beginning to realize that key critical factors of success or failure of any WBA must be highly dependable on delivering on a high quality web site. To attain the desired quality of WBA, it is necessary to suggest a model that organizes and enables the identification of WBA quality perspectives. This paper addresses WBA quality model and categorizes its quality factors. The software is an essential part of any WBA. ISO9126 standard for software engineering product quality states that the main purpose of software quality evaluation is to provide quantitative reference for software products evaluation that is reliable, understandable, and acceptable. The main weakness point here is the lack of a formal specification of key factors for WBA quality. Traditional quality models are not adequate for WBA because they do not address all problems associated with the new features of WBA. Therefore, ISO9126 and different quality models of software were investigated and partially used as an initial step to identify a conceptual quality model for WBA. WBA have common characteristics with traditional software packages, and other distinct characteristics that are particular to WBA. In this paper a proposed conceptual quality model to organize WBA quality factors in terms of its sub factors was identified. In addition, the proposed conceptual quality model effectively reflects the main views of WBA based on the opinion of highly skilled professionals (visitor, owner, end user). The main goal of this paper is identifying, categorizing, and modeling WBA quality factors.

Google Earth and Google Fusion Tables in support of time-critical collaboration: Mapping the deepwater horizon oil spill with the AVIRIS airborne spectrometer

Web interfaces have made remote sensing image resources more accessible and interactive. However, many web-based and Digital Earth opportunities for remote sensing have not yet been fully explored and could greatly facilitate scientific collaboration. In many cases, these resources can augment traditional proprietary software packages, which can have limited flexibility, spatiotemporal controls, and data synthesis abilities. In this paper, we discuss how web services and Google Earth were used for time-critical geovisualizations of the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Deepwater Horizon oil spill imaging campaign. In particular, we describe how (1) AVIRIS Google Earth products were used to visualize the spatial and temporal characteristics of the campaign’s image acquisitions, critically needed for flight planning, (2) the Google Fusion Table cloud-based service was applied to create a highly-interactive image archive and mapping display, and (3) the Google Fusion Table API was utilized to create a flexible PHP-based interface for metadata creation and as the basis for an interactive data catalog. Although there are other possible software and programming approaches to these activities, we highlight freely-accessible and flexible solutions and bring attention to the newly introduced Google Fusion Tables as a collaborative scientific platform.

JColorGrid: software for the visualization of biological measurements

BackgroundTwo-dimensional data colourings are an effective medium by which to represent three-dimensional data in two dimensions. Such "color-grid" representations have found increasing use in the biological sciences (e.g. microarray 'heat maps' and bioactivity data) as they are particularly suited to complex data sets and offer an alternative to the graphical representations included in traditional statistical software packages. The effectiveness of color-grids lies in their graphical design, which introduces a standard for customizable data representation. Currently, software applications capable of generating limited color-grid representations can be found only in advanced statistical packages or custom programs (e.g. micro-array analysis tools), often associated with steep learning curves and requiring expert knowledge.ResultsHere we describe JColorGrid, a Java library and platform independent application that renders color-grid graphics from data. The software can be used as a Java library, as a command-line application, and as a color-grid parameter interface and graphical viewer application. Data, titles, and data labels are input as tab-delimited text files or Microsoft Excel spreadsheets and the color-grid settings are specified through the graphical interface or a text configuration file. JColorGrid allows both user graphical data exploration as well as a means of automatically rendering color-grids from data as part of research pipelines.ConclusionThe program has been tested on Windows, Mac, and Linux operating systems, and the binary executables and source files are available for download at .

E-forecasting: challenges and opportunities

The major advantage of electronic markets over nonelectronic traditional market places is that trading partners use more sophisticated methods, rules and systems of information exchange. The internet has inevitably boosted this advantage over the last decade, making electronic markets the dominant 'playing field' for various industrial and business activities. Forecasting is one of them; it is important to firms because it can help ensure that resources are used effectively. In the market there are a number of off-the-self system products that provide forecasts. The new trend of moving traditional software packages to web services has pushed forecasting to a new dimension – e-forecasting, as called by the authors. In this paper we explore the challenges and opportunities arising from e-forecasting. Two very fertile e-areas for possible application are discussed in detail: e-government and forecasting community portals.

The use of Cox regression and genetic algorithm (CoRGA) for identifying risk factors for mortality in older people

The increase in the proportion and number of older people in developed countries has resulted in research investigating risk factors for adverse health outcomes, including mortality. However, research has been limited by the range of risk factors included in regression models. This is partly because traditional statistical methods and software packages allow a restricted number of variables and combinations of variables. This article describes ongoing research to overcome these limitations through the CoRGA program, which combines Cox regression with a genetic algorithm for the variable selection process. CoRGA was used to try and identify the best combination of risk factors for 4-year all-cause mortality. The combination of 10 risk factors identified by CoRGA included both known and new risk factors for mortality in older people. Further research is seeking to develop the program further and to identify further risk factors for all-cause mortality in older people.

A first approach to e‐forecasting: a survey of forecasting Web services

A great challenge for today’s companies is not only how to adapt to the changing business environment but also how to gain a competitive advantage from the way in which they choose to do so. As a basis for achieving such advantages, companies have started to seek to improve the performance of various operations. Forecasting is one of them; it is important to firms because it can help ensure that effective use of resources is made. In the market there are a number of off‐the‐shelf system products, which provide forecasts. The new trend, of moving traditional software packages to Web services, has pushed forecasting to a new dimension, named by the authors as “e‐forecasting”. In this paper, a first approach to e‐forecasting is made by throwing light on several aspects and a survey is presented which aims at identifying existing Web forecasting services.

Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach

In recent years, many of the developments in logistics are connected to the need of information of efficient supply chain flow. An important issue in the logistics system is to find the network strategy that can give the least cost of the physical distribution flow. In this paper, we consider the logistic chain network problem formulated by 0–1 mixed integer linear programming model. The design tasks of this problem involve the choice of the facilities (plants and distribution centers) to be opened and the distribution network design to satisfy the demand with minimum cost. As the solution method, we propose the spanning tree-based genetic algorithm by using Prüfer number representation. We design the feasibility criteria and develop the repairing procedure for the infeasible Prüfer number, so that it can work for relatively large size problems. The efficacy and the efficiency of this method are demonstrated by comparing its numerical experiment results with those of traditional matrix-based genetic algorithm and professional software package LINDO.

Analyzing complex survey data

Increasingly, social work researchers are performing secondary analyses using data from large-scale surveys such as the National Health Interview Survey (NHIS) (National Center for Health Statistics, 1997), the National Education Longitudinal Study of 1988 (NELS:88), and the Longitudinal Study of Aging (LSOA). Generally, these surveys use complex probability sampling procedures such as stratification, multiple stages of selection, and oversampling to obtain a representative sample of the target population (Cochran, 1977; La Vange, Stearns, Lafata, Koch, & Shah, 1996). Because of these sampling procedures, analyzing these data with traditional statistical software, such as SPSS or SAS, which use ordinary and generalized least squares estimation techniques, results in the underestimation of the standard error (Silbersiepe & Hardy, 1997), inappropriate confidence intervals, and misleading tests of significance (Carlson, Johnson, & Cohen, 1993). Also, these statistical software packages assume a simple random sampling (Silbersiepe & Hardy) and do not control for features of the sampling design that affect observed outcomes (La Vange et al.). Given these aspects of traditional statistical software packages, they are considered inappropriate for analyzing complex survey data (Brogan, Daniels, Rolka, Marsteller, & Chattopadhay, 1998; La Vange et al.). Analyzing complex survey data requires specialized software. SUDAAN (Shah, Barnwell, & Bider, 1997) and PC Carp (Fuller, Kennedy, Schnell, Sullivan, & Park, 1986) are the most widely used packages (La Vange et al., 1996) Other software used are WesVarPC (Brick, Broene, James, & Severynse, 1996) and Stata (StataCorp, 1996). Many social work researchers, however, are not using these specialized programs to analyze complex survey data. One reason may be that social work researchers may be unaware of the need to use such programs. This article uses data from the 1994 AIDS Knowledge and Attitudes Supplement to the National Health Interview Survey (NHIS) to illustrate that biased point estimates, inappropriate standard errors, and misleading tests of significance can result from using traditional software packages for complex survey analysis. We also illustrate how results are affected by assuming a simple random sample (with and without the option of weighting), by using the sampling weights without controlling for complex survey design effect, by and controlling for complex survey design effects and using national estimate weights. METHODOLOGICAL ISSUES When analyzing data from complex surveys, researchers need to account for the sampling design use the sampling weights and the appropriate statistical software. Accounting for the Sampling Design Because the complex probability procedures used in large-scale surveys to obtain a representative sample of the target population do not result in a simple random sample, the consequences of these procedures on statistical inferences must be taken into account (Johnson, & Elliott, 1998). To control for design effects, researchers need to alter the variance estimates by considering issues such as stratification, clustering, other proponents of nonindependence of observations, and oversampling or undersampling of subgroups of the population. The simple definition of a design effect is the ratio of the estimated population variance taking the complex design features into account over the estimated population variance based on the same sample and assuming a simple random sample. A design effect of one indicates that the variance estimate is the same regardless of the controls for the complex survey design. When the design effect is greater than one, the standard error (SE) calculated controlling for the complex survey design is larger than the SE calculated assuming a simple random sample. Therefore, the confidence interval around the point estimate is larger. …