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The underlying data source for web usage mining (WUM) is commonly thought to be server logs. However, access log files ensure quite limited data about the clients. Identifying sessions from this messy data takes a considerable effort, and operations performed for this purpose do not always yield excellent results. Also, this data cannot be used for web analytics efficiently. This study proposes an innovative method for user tracking, session management, and collecting web usage data. The method is mainly based on a new approach for using collected data for web analytics extraction as the data source in web usage mining. An application-based API has been developed with a different strategy from conventional client-side methods to obtain and process log data. The log data has been successfully gathered by integrating the technique into an enterprise web application. The results reveal that the homogeneous structured data collected and stored with this method is more convenient to browse, filter, and process than web server logs. This data stored on a relational database can be used effortlessly as a reliable data source for high-performance web usage mining activity, real-time web analytics, or a functional recommendation system., Comment: 15 pages, 8 figures

This paper reports an analysis of aspects of the project planning stage. The object of research is the decision-making processes that take place at this stage. This work considers the problem of building a hierarchy of tasks, their distribution among performers, taking into account restrictions on financial costs and duration of project implementation. Verbal and mathematical models of the task of constructing a hierarchy of tasks and other tasks that take place at the stage of project planning were constructed. Such indicators of the project implementation process efficiency were introduced as the time, cost, and cost-time efficiency. In order to be able to apply these criteria, the tasks of estimating the minimum value of the duration of the project and its minimum required cost were considered. Appropriate methods have been developed to solve them. The developed iterative method for assessing the minimum duration of project implementation is based on taking into account the possibility of simultaneous execution of various tasks. The method of estimating the minimum cost of the project is to build and solve the problem of Boolean programming. The values obtained as a result of solving these problems form an {\guillemotleft}ideal point{\guillemotright}, approaching which is enabled by the developed iterative method of constructing a hierarchy of tasks based on the method of sequential concessions. This method makes it possible to devise options for management decisions to obtain valid solutions to the problem. According to them, the decision maker can introduce a concession on the value of one or both components of the {\guillemotleft}ideal point{\guillemotright} or change the input data to the task. The models and methods built can be used when planning projects in education, science, production, etc., Comment: 7 pages, 1 figure

Automated, secure software development is an important task of digitalization, which is solved with the DevSecOps approach. An important part of the DevSecOps approach is continuous risk assessment, which is necessary to identify and evaluate risk factors. Combining the development cycle with continuous risk assessment creates synergies in software development and operation and minimizes vulnerabilities. The article presents the main methods of deploying web applications, ways to increase the level of information security at all stages of product development, compares different types of infrastructures and cloud computing providers, and analyzes modern tools used to automate processes. The cloud cluster was deployed using Terraform and the Jenkins pipeline, which is written in the Groovy programming language, which checks program code for vulnerabilities and allows you to fix violations at the earliest stages of developing secure web applications. The developed cluster implements the proposed algorithm for automated risk assessment based on the calculation (modeling) of threats and vulnerabilities of cloud infrastructure, which operates in real time, periodically collecting all information and adjusting the system in accordance with the risk and applied controls. The algorithm for calculating risk and losses is based on statistical data and the concept of the FAIR information risk assessment methodology. The risk value obtained using the proposed method is quantitative, which allows more efficient forecasting of information security costs in software development., Comment: 23 pages, 3 figures

Automating the theory-experiment cycle requires effective distributed workflows that utilize a computing continuum spanning lab instruments, edge sensors, computing resources at multiple facilities, data sets distributed across multiple information sources, and potentially cloud. Unfortunately, the obvious methods for constructing continuum platforms, orchestrating workflow tasks, and curating datasets over time fail to achieve scientific requirements for performance, energy, security, and reliability. Furthermore, achieving the best use of continuum resources depends upon the efficient composition and execution of workflow tasks, i.e., combinations of numerical solvers, data analytics, and machine learning. Pacific Northwest National Laboratory's LDRD "Cloud, High-Performance Computing (HPC), and Edge for Science and Security" (CHESS) has developed a set of interrelated capabilities for enabling distributed scientific workflows and curating datasets. This report describes the results and successes of CHESS from the perspective of open science.

As vehicle systems become increasingly complex, with more features, services, sensors, actuators, and processing units, it is important to view vehicles not just as modes of transportation moving toward full autonomy, but also as adaptive systems that respond to the needs of their occupants. Vehicular services can be developed to support these adaptations. However, the increasing complexity of vehicular service development, even with current standardizations, best practices and guidelines, are insufficient to tackle the high complexity of development, with expectations of up to 1 (U.S.) billion lines of code for a fully (level 5) autonomous vehicle. Within this survey, the paradigm of Deterministic Software Defined Vehicles is explored, aiming to enhance the quality and ease of developing automotive services by focusing on service-oriented architectures, virtualization techniques, and the necessary deterministic intra- and inter-vehicular communications. Considering the main open challenges for such verticals, a vision architecture towards improved services development and orchestration is presented, focusing on: a) a deterministic network configurator; b) a data layer configurator; c) a hypervisor configurator; d) the vehicle abstraction layer; and e) a software orchestrator.

Community Question-Answering (CQA) forums have revolutionized how people seek information, especially those related to their healthcare needs, placing their trust in the collective wisdom of the public. However, there can be several answers in response to a single query, which makes it hard to grasp the key information related to the specific health concern. Typically, CQA forums feature a single top-voted answer as a representative summary for each query. However, a single answer overlooks the alternative solutions and other information frequently offered in other responses. Our research focuses on aspect-based summarization of health answers to address this limitation. Summarization of responses under different aspects such as suggestions, information, personal experiences, and questions can enhance the usability of the platforms. We formalize a multi-stage annotation guideline and contribute a unique dataset comprising aspect-based human-written health answer summaries. We build an automated multi-faceted answer summarization pipeline with this dataset based on task-specific fine-tuning of several state-of-the-art models. The pipeline leverages question similarity to retrieve relevant answer sentences, subsequently classifying them into the appropriate aspect type. Following this, we employ several recent abstractive summarization models to generate aspect-based summaries. Finally, we present a comprehensive human analysis and find that our summaries rank high in capturing relevant content and a wide range of solutions.

Auditing of AI systems is a promising way to understand and manage ethical problems and societal risks associated with contemporary AI systems, as well as some anticipated future risks. Efforts to develop standards for auditing Artificial Intelligence (AI) systems have therefore understandably gained momentum. However, we argue that creating auditing standards is not just insufficient, but actively harmful by proliferating unheeded and inconsistent standards, especially in light of the rapid evolution and ethical and safety challenges of AI. Instead, the paper proposes the establishment of an AI Audit Standards Board, responsible for developing and updating auditing methods and standards in line with the evolving nature of AI technologies. Such a body would ensure that auditing practices remain relevant, robust, and responsive to the rapid advancements in AI. The paper argues that such a governance structure would also be helpful for maintaining public trust in AI and for promoting a culture of safety and ethical responsibility within the AI industry. Throughout the paper, we draw parallels with other industries, including safety-critical industries like aviation and nuclear energy, as well as more prosaic ones such as financial accounting and pharmaceuticals. AI auditing should emulate those fields, and extend beyond technical assessments to include ethical considerations and stakeholder engagement, but we explain that this is not enough; emulating other fields' governance mechanisms for these processes, and for audit standards creation, is a necessity. We also emphasize the importance of auditing the entire development process of AI systems, not just the final products...

In the emerging space economy, autonomous robotic missions with specialized goals such as mapping and mining are gaining traction, with agencies and enterprises increasingly investing resources. Multirobot systems (MRS) research has provided many approaches to establish control and communication layers to facilitate collaboration from a technical perspective, such as granting more autonomy to heterogeneous robotic groups through auction-based interactions in mesh networks. However, stakeholders' competing economic interests often prevent them from cooperating within a proprietary ecosystem. Related work suggests that distributed ledger technology (DLT) might serve as a mechanism for enterprises to coordinate workflows and trade services to explore space resources through a transparent, reliable, non-proprietary digital platform. We challenge this perspective by pointing to the core technical weaknesses of blockchains, in particular, increased energy consumption, low throughput, and full transparency through redundancy. Our objective is to advance the discussion in a direction where the benefits of DLT from an economic perspective are weighted against the drawbacks from a technical perspective. We finally present a possible DLT-driven heterogeneous MRS for map exploration to study the opportunities for economic collaboration and competitiveness., Comment: 5 pages, 1 figure, 1 table, accepted for conference (ICSE24-NIER)

In the new space economy, space agencies, large enterprises, and start-ups aim to launch space multi-robot systems (MRS) for various in-situ resource utilization (ISRU) purposes, such as mapping, soil evaluation, and utility provisioning. However, these stakeholders' competing economic interests may hinder effective collaboration on a centralized digital platform. To address this issue, neutral and transparent infrastructures could facilitate coordination and value exchange among heterogeneous space MRS. While related work has expressed legitimate concerns about the technical challenges associated with blockchain use in space, we argue that weighing its potential economic benefits against its drawbacks is necessary. This paper presents a novel architectural framework and a comprehensive set of requirements for integrating blockchain technology in MRS, aiming to enhance coordination and data integrity in space exploration missions. We explored distributed ledger technology (DLT) to design a non-proprietary architecture for heterogeneous MRS and validated the prototype in a simulated lunar environment. The analyses of our implementation suggest global ISRU efficiency improvements for map exploration, compared to a corresponding group of individually acting robots, and that fostering a coopetitive environment may provide additional revenue opportunities for stakeholders., Comment: 10 pages, 4 figures, accepted for conference (SAC24)

As a broader set of applications from simulations to data analysis and machine learning require more parallel computational capability, the demand for interactive and urgent high performance computing (HPC) continues to increase. This paper overviews the progress made so far and elucidates the challenges and opportunities for greater integration of interactive and urgent HPC policies, techniques, and technologies into HPC ecosystems., Comment: 10 pages, submitted to IEEE CiSE journal

Serverless cloud is an innovative cloud service model that frees customers from most cloud management duties. It also offers the same advantages as other cloud models but at much lower costs. As a result, the serverless cloud has been increasingly employed in high-impact areas such as system security, banking, and health care. A big threat to the serverless cloud's performance is cold-start, which is when the time of provisioning the needed cloud resource to serve customers' requests incurs unacceptable costs to the service providers and/or the customers. This paper proposes a novel low-coupling, high-cohesion ensemble policy that addresses the cold-start problem at infrastructure- and function-levels of the serverless cloud stack, while the state of the art policies have a more narrowed focus. This ensemble policy anchors on the prediction of function instance arrivals, 10 to 15 minutes into the future. It is achievable by using the temporal convolutional network (TCN) deep-learning method. Bench-marking results on a real-world dataset from a large-scale serverless cloud provider show that TCN out-performs other popular machine learning algorithms for time series. Going beyond cold-start management, the proposed policy and publicly available codes can be adopted in solving other cloud problems such as optimizing the provisioning of virtual software-defined network assets., Comment: 8 pages, 7 figures, 3 tables

Making sure that users understand privacy policies that impact them is a key challenge for a real GDPR deployment. Research studies are mostly carried in English, but in Europe and elsewhere, users speak a language that is not English. Replicating studies in different languages requires the availability of comparable cross-language privacy policies corpora. This work provides a methodology for building comparable cross-language in a national language and a reference study language. We provide an application example of our methodology comparing English and Italian extending the corpus of one of the first studies about users understanding of technical terms in privacy policies. We also investigate other open issues that can make replication harder.

The Science Gateways Community Institute (SGCI) is an NSF Software Infrastructure for Sustained Innovation (S2I2) funded project that leads and supports the science gateway community. Major activities for SGCI include a) sustainability training, including the Focus Week week-long course designed to help science gateway operators develop sustainability plans, and the Jumpstart virtual short-course; b) usability and user experience consulting; c) a community catalog of science gateways and science gateway software; d) workforce development activities, including a coding institute for students, internship opportunities, and hackathons; e) an annual conference; and f) in-depth technical support for client gateway projects. The goals of SGCI's Embedded Technical Support component are to help the institute's clients to create new science gateways or to significantly enhance existing science gateways. Examples of the latter include helping to implement major new capabilities and to implement significant usability improvements suggested by SGCI's usability consultants. The Embedded Technical Support component was managed by Indiana University and involved research software engineers at San Diego Supercomputer Center, Texas Advanced Computing Center, Indiana University, and Purdue University (through 2019). Since 2016, the component has involved 20 research software engineers as consultants and has conducted 59 client consultations. This short paper provides a summary of lessons learned from the Embedded Technical Support program that may be useful for the research software engineering community., Comment: 3 pages, no figures

We consider a network of smart sensors for an edge computing application that sample a time-varying signal and send updates to a base station for remote global monitoring. Sensors are equipped with sensing and compute, and can either send raw data or process them on-board before transmission. Limited hardware resources at the edge generate a fundamental latency-accuracy trade-off: raw measurements are inaccurate but timely, whereas accurate processed updates are available after processing delay. Hence, one needs to decide when sensors should transmit raw measurements or rely on local processing to maximize network monitoring performance. To tackle this sensing design problem, we model an estimation-theoretic optimization framework that embeds both computation and communication latency, and propose a Reinforcement Learning-based approach that dynamically allocates computational resources at each sensor. Effectiveness of our proposed approach is validated through numerical experiments motivated by smart sensing for the Internet of Drones and self-driving vehicles. In particular, we show that, under constrained computation at the base station, monitoring performance can be further improved by an online sensor selection., Comment: 16 pages, 17 figures; submitted to IEEE TNSE; final accepted version

The most pressing problems in science are neither empirical nor theoretical, but infrastructural. Scientific practice is defined by coproductive, mutually reinforcing infrastructural deficits and incentive systems that everywhere constrain and contort our art of curiosity in service of profit and prestige. Our infrastructural problems are not unique to science, but reflective of the broader logic of digital enclosure where platformatized control of information production and extraction fuels some of the largest corporations in the world. I have taken lessons learned from decades of intertwined digital cultures within and beyond academia like wikis, pirates, and librarians in order to draft a path towards more liberatory infrastructures for both science and society. Based on a system of peer-to-peer linked data, I sketch interoperable systems for shared data, tools, and knowledge that map onto three domains of platform capture: storage, computation and communication. The challenge of infrastructure is not solely technical, but also social and cultural, and so I attempt to ground a practical development blueprint in an ethics for organizing and maintaining it. I intend this draft as a rallying call for organization, to be revised with the input of collaborators and through the challenges posed by its implementation. I argue that a more liberatory future for science is neither utopian nor impractical -- the truly impractical choice is to continue to organize science as prestige fiefdoms resting on a pyramid scheme of underpaid labor, playing out the clock as every part of our work is swallowed whole by circling information conglomerates. It was arguably scientists looking for a better way to communicate that created something as radical as the internet in the first place, and I believe we can do it again., Comment: Original Web Document: https://jon-e.net/infrastructure

An assurance case is intended to provide justifiable confidence in the truth of its top claim, which typically concerns safety or security. A natural question is then "how much" confidence does the case provide? We argue that confidence cannot be reduced to a single attribute or measurement. Instead, we suggest it should be based on attributes that draw on three different perspectives: positive, negative, and residual doubts. Positive Perspectives consider the extent to which the evidence and overall argument of the case combine to make a positive statement justifying belief in its claims. We set a high bar for justification, requiring it to be indefeasible. The primary positive measure for this is soundness, which interprets the argument as a logical proof. Confidence in evidence can be expressed probabilistically and we use confirmation measures to ensure that the "weight" of evidence crosses some threshold. In addition, probabilities can be aggregated from evidence through the steps of the argument using probability logics to yield what we call probabilistic valuations for the claims. Negative Perspectives record doubts and challenges to the case, typically expressed as defeaters, and their exploration and resolution. Assurance developers must guard against confirmation bias and should vigorously explore potential defeaters as they develop the case, and should record them and their resolution to avoid rework and to aid reviewers. Residual Doubts: the world is uncertain so not all potential defeaters can be resolved. We explore risks and may deem them acceptable or unavoidable. It is crucial however that these judgments are conscious ones and that they are recorded in the assurance case. This report examines the perspectives in detail and indicates how Clarissa, our prototype toolset for Assurance 2.0, assists in their evaluation., Comment: Second Edition

The goal of this paper is to establish a general framework for dynamic behaviors of coupled fractional-order stochastic dynamic systems of particles by using star-coupled models. In particular, the general mechanics on the dynamic behaviors related to the stochastic resonance (SR) phenomenon of a star-coupled harmonic oscillator subject to multiplicative fluctuation and periodic force in viscous media are established by considering couplings, memory effects, the occurring of synchronization linked to the occurring of SR induced. The multiplicative noise is modeled as dichotomous noise and the memory of viscous media is characterized by fractional power kernel function. By using the Shapiro-Loginov formula and Laplace transform, the analytical expressions for the first moment of the steady state response, the stability and relationship between the system response and the system parameters in the long-time limit in terms of systems'asymptotic stability are also established. All theoretical analyses are supported by the corresponding numerical simulations, and thus it seems that the results established in this paper would provide a possible fundamental mathematical framework for the study of Schumpeter's theory on the economic development under the innovation and capital paradigm and related disciplines. In particular, the framework established by this paper allows us at the first time logically concluding that in general, the ratio for SMEs grow up successfully is less than one third, this is consistent with what the market has been observed commonly, and the similar conclusion available from the existing literature today., Comment: 29 pages, 20 figures

The use of artificial intelligence (AI) and AI methods in the workplace holds both great opportunities as well as risks to occupational safety and discrimination. In addition to legal regulation, technical standards will play a key role in mitigating such risk by defining technical requirements for development and testing of AI systems. This paper provides an overview and assessment of existing international, European and German standards as well as those currently under development. The paper is part of the research project "ExamAI - Testing and Auditing of AI systems" and focusses on the use of AI in an industrial production environment as well as in the realm of human resource management (HR).

In system operations it is commonly assumed that arbitrary changes to a system can be reversed or `rolled back', when errors of judgement and procedure occur. We point out that this view is flawed and provide an alternative approach to determining the outcome of changes. Convergent operators are fixed-point generators that stem from the basic properties of multiplication by zero. They are capable of yielding a repeated and predictable outcome even in an incompletely specified or `open' system. We formulate such `convergent operators' for configuration change in the language of groups and rings and show that, in this form, the problem of convergent reversibility becomes equivalent to the `division by zero' problem. Hence, we discuss how recent work by Bergstra and Tucker on zero-totalised fields helps to clear up long-standing confusion about the options for `rollback' in change management.

There are situations where data relevant to machine learning problems are distributed across multiple locations that cannot share the data due to regulatory, competitiveness, or privacy reasons. Machine learning approaches that require data to be copied to a single location are hampered by the challenges of data sharing. Federated Learning (FL) is a promising approach to learn a joint model over all the available data across silos. In many cases, the sites participating in a federation have different data distributions and computational capabilities. In these heterogeneous environments, existing approaches exhibit poor performance: synchronous FL protocols are communication efficient, but have slow learning convergence and high energy cost; conversely, asynchronous FL protocols have faster convergence with lower energy cost, but higher communication. In this work, we introduce a novel energy-efficient Semi-Synchronous Federated Learning protocol that mixes local models periodically with minimal idle time and fast convergence. We show through extensive experiments over established benchmark datasets in the computer-vision domain as well as in real-world biomedical settings that our approach significantly outperforms previous work in data and computationally heterogeneous environments., Comment: 30 pages, 12 figures

The central role of the certificate authority (CA) in traditional public key infrastructure (PKI) makes it fragile and prone to compromises and operational failures. Maintaining CAs and revocation lists is demanding especially in loosely-connected and large systems. Log-based PKIs have been proposed as a remedy but they do not solve the problem effectively. We provide a general model and a solution for decentralized and dynamic PKI based on a blockchain and web of trust model where the traditional CA and digital certificates are removed and instead, everything is registered on the blockchain. Registration, revocation, and update of public keys are based on a consensus mechanism between a certain number of entities that are already part of the system. Any node which is part of the system can be an auditor and initiate the revocation procedure once it finds out malicious activities. Revocation lists are no longer required as any node can efficiently verify the public keys through witnesses., Comment: 19 pages, 5 figures

There are phenomena that cannot be measured without subjective testing. However, subjective testing is a complex issue with many influencing factors. These interplay to yield either precise or incorrect results. Researchers require a tool to classify results of subjective experiment as either consistent or inconsistent. This is necessary in order to decide whether to treat the gathered scores as quality ground truth data. Knowing if subjective scores can be trusted is key to drawing valid conclusions and building functional tools based on those scores (e.g., algorithms assessing the perceived quality of multimedia materials). We provide a tool to classify subjective experiment (and all its results) as either consistent or inconsistent. Additionally, the tool identifies stimuli having irregular score distribution. The approach is based on treating subjective scores as a random variable coming from the discrete Generalized Score Distribution (GSD). The GSD, in combination with a bootstrapped G-test of goodness-of-fit, allows to construct $p$-value P-P plot that visualizes experiment's consistency. The tool safeguards researchers from using inconsistent subjective data. In this way, it makes sure that conclusions they draw and tools they build are more precise and trustworthy. The proposed approach works in line with expectations drawn solely on experiment design descriptions of 21 real-life multimedia quality subjective experiments., Comment: 11 pages, 3 figures. Accepted to 28th ACM International Conference on Multimedia (MM '20). For associated data sets, source codes and documentation, see https://github.com/Qub3k/subjective-exp-consistency-check

There are situations where data relevant to a machine learning problem are distributed among multiple locations that cannot share the data due to regulatory, competitiveness, or privacy reasons. For example, data present in users' cellphones, manufacturing data of companies in a given industrial sector, or medical records located at different hospitals. Moreover, participating sites often have different data distributions and computational capabilities. Federated Learning provides an approach to learn a joint model over all the available data in these environments. In this paper, we introduce a novel distributed validation weighting scheme (DVW), which evaluates the performance of a learner in the federation against a distributed validation set. Each learner reserves a small portion (e.g., 5%) of its local training examples as a validation dataset and allows other learners models to be evaluated against it. We empirically show that DVW results in better performance compared to established methods, such as FedAvg, both under synchronous and asynchronous communication protocols in data and computationally heterogeneous environments.

Software Repositories contain knowledge on how software engineering teams work, communicate, and collaborate. It can be used to develop a data-informed view of a team's development process, which in turn can be employed for process improvement initiatives. In modern, Agile development methods, process improvement takes place in Retrospective meetings, in which the last development iteration is discussed. However, previously proposed activities that take place in these meetings often do not rely on project data, instead depending solely on the perceptions of team members. We propose new Retrospective activities, based on mining the software repositories of individual teams, to complement existing approaches with more objective, data-informed process views., Comment: In IEEE/ACM 42nd International Conference on Software Engineering Workshops (ICSEW'20)

Reusable microservice artefacts are often deployed as black or grey boxes, with little concern for their properties and quality, beyond a syntactical interface description. This leads application developers to chaotic and opportunistic assumptions about how a composite application will behave in the real world. Systematically analyzing and tracking these publicly available artefacts will grant much needed predictability to microservice-based deployments. By establishing a distributed observatory and knowledge base, it is possible to track microservice repositories and analyze the artefacts reliably, and provide insights on their properties and quality to developers and researchers alike. This position paper argues for a federated research infrastructure with consensus voting among participants to establish and preserve ground truth about the insights.

Most aspects of our lives are governed by large, highly developed institutions that integrate several governance tasks under one authority structure. But theorists differ as to the mechanisms that drive the development of such concentrated governance systems from rudimentary beginnings. Is the emergence of integrated governance schemes a symptom of consolidation of authority by small status groups? Or does integration occur because a complex institution has more potential responses to a complex environment? Here we examine the emergence of complex governance regimes in 5,000 sovereign, resource-constrained, self-governing online communities, ranging in scale from one to thousands of users. Each community begins with no community members and no governance infrastructure. As communities grow, they are subject to selection pressures that keep better managed servers better populated. We identify predictors of community success and test the hypothesis that governance complexity can enhance community fitness. We find that what predicts success depends on size: changes in complexity predict increased success with larger population servers. Specifically, governance rules in a large successful community are more numerous and broader in scope. They also tend to rely more on rules that concentrate power in administrators, and on rules that manage bad behavior and limited server resources. Overall, this work is consistent with theories that formal integrated governance systems emerge to organize collective responses to interdependent resource management problems, especially as factors such as population size exacerbate those problems., Comment: contains supplement

Workload characterization is an integral part of performance analysis of high performance computing (HPC) systems. An understanding of workload properties sheds light on resource utilization and can be used to inform performance optimization both at the software and system configuration levels. It can provide information on how computational science usage modalities are changing that could potentially aid holistic capacity planning for the wider HPC ecosystem. Here, we report on the results of a detailed workload analysis of the portfolio of supercomputers comprising the NSF Innovative HPC program in order to characterize its past and current workload and look for trends to understand the nature of how the broad portfolio of computational science research is being supported and how it is changing over time. The workload analysis also sought to illustrate a wide variety of usage patterns and performance requirements for jobs running on these systems. File system performance, memory utilization and the types of parallelism employed by users (MPI, threads, etc) were also studied for all systems for which job level performance data was available., Comment: 93 pages, 82 figures, 19 tables

Blue Waters is a Petascale-level supercomputer whose mission is to enable the national scientific and research community to solve "grand challenge" problems that are orders of magnitude more complex than can be carried out on other high performance computing systems. Given the important and unique role that Blue Waters plays in the U.S. research portfolio, it is important to have a detailed understanding of its workload in order to guide performance optimization both at the software and system configuration level as well as inform architectural balance tradeoffs. Furthermore, understanding the computing requirements of the Blue Water's workload (memory access, IO, communication, etc.), which is comprised of some of the most computationally demanding scientific problems, will help drive changes in future computing architectures, especially at the leading edge. With this objective in mind, the project team carried out a detailed workload analysis of Blue Waters., Comment: 107 pages, >100 figures (figure sizes reduced to save space, contact authors for version with full resolution)

The previous generation of astronomical instruments tended to consist of single receivers in the focal point of one or more physical reflectors. Because of this, most astronomical data sets were small enough that the raw data could easily be downloaded and processed on a single machine. In the last decade, several large, complex Radio Astronomy instruments have been built and the SKA is currently being designed. Many of these instruments have been designed by international teams, and, in the case of LOFAR span an area larger than a single country. Such systems are ICT telescopes and consist mainly of complex software. This causes the main operational issues to be related to the ICT systems and not the telescope hardware. However, it is important that the operations of the ICT systems are coordinated with the traditional operational work. Managing the operations of such telescopes therefore requires an approach that significantly differs from classical telescope operations. The goal of this session is to bring together members of operational teams responsible for such large-scale ICT telescopes. This gathering will be used to exchange experiences and knowledge between those teams. Also, we consider such a meeting as very valuable input for future instrumentation, especially the SKA and its regional centres., Comment: 4 pages; to be published in ADASS XXVI (held October 16-20, 2016) proceedings. Recording can be found here

In the competitive environment, companies have given increasing importance to the IT sector and the resources it delivers as strategic. As a result, IT becomes a living being within the company. This sector is being subject to continuous changes in this scenario. These changes can occur within the own IT sector or whether IT to other sectors of the company. For both scenarios, it is important to have a good change control to avoid unnecessary trouble and expense. This paper aims to show through a case study, the benefits and results obtained with the implementation of a process of managing and controlling changes in the information technology environment of a large government company in Brazil., Comment: 11 Pages, 4 figures in International Journal of Advanced Information Technology (IJAIT) Vol. 6, No. 1, February 2016

Many software projects are no longer done in-house by a single organization. Instead, we are in a new age where software is developed by a networked community of individuals and organizations, which base their relations to each other on mutual interest. Paradoxically, recent research suggests that software development can actually be jointly-developed by rival firms. For instance, it is known that the mobile-device makers Apple and Samsung kept collaborating in open source projects while running expensive patent wars in the court. Taking a case study approach, we explore how rival firms collaborate in the open source arena by employing a multi-method approach that combines qualitative analysis of archival data (QA) with mining software repositories (MSR) and Social Network Analysis (SNA). While exploring collaborative processes within the OpenStack ecosystem, our research contributes to Software Engineering research by exploring the role of groups, sub-communities and business models within a high-networked open source ecosystem. Surprising results point out that competition for the same revenue model (i.e., operating conflicting business models) does not necessary affect collaboration within the ecosystem. Moreover, while detecting the different sub-communities of the OpenStack community, we found out that the expected social tendency of developers to work with developers from same firm (i.e., homophily) did not hold within the OpenStack ecosystem. Furthermore, while addressing a novel, complex and unexplored open source case, this research also contributes to the management literature in coopetition strategy and high-tech entrepreneurship with a rich description on how heterogeneous actors within a high-networked ecosystem (involving individuals, startups, established firms and public organizations) joint-develop a complex infrastructure for big-data in the open source arena., Comment: As accepted by the Journal of Internet Services and Applications (JISA)

Context: Competitiveness is the key to a sustainable development and it demands agility at the business and organizational levels, which in turn requires a flexible and customizable IT environment and effective and responsive governance in order to deliver value to the business. Objective: This paper describes the conceptual development of a theory for analyze and describe agile governance in order to increasing the success rate of their practice, achieving organizational performance and business competitiveness. Method: We adopt a multi-method research, framing the theory conceptual development using Dubin's method of theory building. Results: We have developed a conceptual framework of the theory encompassing its constructs, laws of interaction, boundaries and system states. Conclusion: This theory can provide a better understanding of the nature of agile governance, by mapping of its constructs, mediators, moderators and disturbing factors, in order to help organizations reach better results., Comment: Conference Proceedings of 12th International Conference on Management of Technology and Information Systems, 22 pages, 8 figures. In 12th International Conference on Management of Technology and Information Systems. Sao Paulo: FEA-USP

The analysis of the theoretical material revealed the lack of consensus on defini-tion of the tax stimulation of innovation-active business entities within the re-gional taxation. The definition tax stimulation of innovation-active business en-tities is specified., Comment: innovation; business entity; taxation; tax rate; return on assets

This paper presents the basis of the Agile Governance in Information and Communication Technology (ICT), which is based on Agile Software Engineering Methodologies principles and values. Its development was done through a systematic review process, supported by Bibliometrics and Scientometrics methods and techniques, where the Critical Success Factors (CSF) of ICT Governance projects and the principles of the Agile Manifesto were analyzed. Next, through an inductive approach, focused on the convergence between the concepts involved, it was analyzed how agile principles could help to minimize the gap between ICT and business. Evidences of their occurrence were taken through a Conceptual Survey Research. As a result, the foundations and concepts of Agile Governance in ICT were defined and, finally, the development of a reference model was proposed as a future work., Comment: Journal of Information Systems and Technology Management (JISTEM), Vol 7, No 2, August 2010

Context: Agility at the business level requires Information Technology (IT) environment flexible and customizable, as well as effective and responsive governance in order to deliver value faster, better, and cheaper to the business. Objective: To understand better this context, our paper seeks to investigate how the domain of agile governance has evolved, as well as to derive implications for research and practice. Method: We conducted a systematic review about the state of art of the agile governance up to and including 2013. Our search strategy identified 1992 studies in 10 databases, of which 167 had the potential to answer our research questions. Results: We organized the studies into four major groups: software engineering, enterprise, manufacturing and multidisciplinary; classifying them into 16 emerging categories. As a result, the review provides a convergent definition for agile governance, six meta- principles, and a map of findings organized by topic and classified by relevance and convergence. Conclusion: The found evidence lead us to believe that agile governance is a relatively new, wide and multidisciplinary area focused on organizational performance and competitiveness that needs to be more intensively studied. Finally, we made improvements and additions to the methodological approach for systematic reviews and qualitative studies., Comment: International Journal of Computer Science & Information Technology (IJCSIT) Vol 6, No 5, October 2014 20 pages, 8 Figures, 7 Tables

Historical (Stressed-) Value-at-Risk ((S)VAR), and Expected Shortfall (ES), are widely used risk measures in regulatory capital and Initial Margin, i.e. funding, computations. However, whilst the definitions of VAR and ES are unambiguous, they depend on input distributions that are data-cleaning- and Data-Model-dependent. We quantify the scale of these effects from USD CDS (2004--2014), and from USD interest rates (1989--2014, single-curve setup before 2004, multi-curve setup after 2004), and make two standardisation proposals: for data; and for Data-Models. VAR and ES are required for lifetime portfolio calculations, i.e. collateral calls, which cover a wide range of market states. Hence we need standard, i.e. clean, complete, and common (i.e. identical for all banks), market data also covering this wide range of market states. This data is historically incomplete and not clean hence data standardization is required. Stressed VAR and ES require moving market movements during a past (usually not recent) window to current, and future, market states. All choices (e.g. absolute difference, relative, relative scaled by some function of market states) implicitly define a Data Model for transformation of extreme market moves (recall that 99th percentiles are typical, and the behaviour of the rest is irrelevant). Hence we propose standard Data Models. These are necessary because different banks have different stress windows. Where there is no data, or a requirement for simplicity, we propose standard lookup tables (one per window, etc.). Without this standardization of data and Data Models we demonstrate that VAR and ES are complex derivatives of subjective choices., Comment: 22 pages, 5 figures, 1 table

In an era of software crisis, the move of firms towards distributed software development teams is being challenged by emerging collaboration issues. On this matter, the open-source phenomenon may shed some light, as successful cases on distributed collaboration in the open-source community have been recurrently reported. In this paper, we explore the collaboration networks in the WebKit open-source project, by mining WebKit's source-code version-control-system data with Social Network Analysis (SNA). Our approach allows us to observe how key events in the mobile-device industry have affected the WebKit collaboration network over time. With our findings, we show the explanation power from network visualizations capturing the collaborative dynamics of a high-networked software project over time; and highlight the power of the open-source fork concept as a nexus enabling both features of competition and collaboration. We also reveal the WebKit project as a valuable research site manifesting the novel notion of open-coopetition, where rival firms collaborate with competitors in the open-source community., Comment: As submitted to ACM SIG MIS Computers and People Research (CPR) May 29 - 31, 2014 Singapore

Federations among sets of Cloud Providers (CPs), whereby a set of CPs agree to mutually use their own resources to run the VMs of other CPs, are considered a promising solution to the problem of reducing the energy cost. In this paper, we address the problem of federation formation for a set of CPs, whose solution is necessary to exploit the potential of cloud federations for the reduction of the energy bill. We devise a distributed algorithm, based on cooperative game theory, that allows a set of CPs to cooperatively set up their federations in such a way that their individual profit is increased with respect to the case in which they work in isolation, and we show that, by using our algorithm and the proposed CPs' utility function, they are able to self-organize into Nash-stable federations and, by means of iterated executions, to adapt themselves to environmental changes. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm., Comment: Added publisher info and citation notice

This report exists to provide high-level guidance for the strategic and engineering development of Data Management and Preservation plans for 'Big Science' data. Although the report's nominal audience is therefore rather narrow, we intend the document to be of use to other planners and data architects who wish to implement good practice in this area. For the purposes of this report, we presume that the reader is broadly persuaded (by external fiat if nothing else) of the need to preserve research data appropriately, and that they have both sophisticated technical support and the budget to support developments. The goal of the document is not to provide mechanically applicable recipes, but to allow the user to develop and lead a high-level plan which is appropriate to their organisation. Throughout, the report is informed where appropriate by the OAIS reference model., Comment: Project final report, 47 pages; see http://purl.org/nxg/projects/mardi-gross/report for other document versions

The project which led to this report was funded by JISC in 2010--2011 as part of its 'Managing Research Data' programme, to examine the way in which Big Science data is managed, and produce any recommendations which may be appropriate. Big science data is different: it comes in large volumes, and it is shared and exploited in ways which may differ from other disciplines. This project has explored these differences using as a case-study Gravitational Wave data generated by the LSC, and has produced recommendations intended to be useful variously to JISC, the funding council (STFC) and the LSC community. In Sect. 1 we define what we mean by 'big science', describe the overall data culture there, laying stress on how it necessarily or contingently differs from other disciplines. In Sect. 2 we discuss the benefits of a formal data-preservation strategy, and the cases for open data and for well-preserved data that follow from that. This leads to our recommendations that, in essence, funders should adopt rather light-touch prescriptions regarding data preservation planning: normal data management practice, in the areas under study, corresponds to notably good practice in most other areas, so that the only change we suggest is to make this planning more formal, which makes it more easily auditable, and more amenable to constructive criticism. In Sect. 3 we briefly discuss the LIGO data management plan, and pull together whatever information is available on the estimation of digital preservation costs. The report is informed, throughout, by the OAIS reference model for an open archive., Comment: Project final report, 45 pages: see http://purl.org/nxg/projects/mrd-gw for project details, and http://purl.org/nxg/projects/mrd-gw/report for other document versions

In modern organizations, Information and Communication Technologies are used to support the organizations' activities. To manage the quality of the organization processes, audit processes are implemented. Also, the audit processes can aim the quality of ICT systems themselves because their involvement in organization processes. The paper investigates the ways in which a quality management can be applied for audit processes in order to obtain a high level of quality for the audit recommendations., Comment: 9 pages, 2 figures

This paper examines the current system development processes of three major Turkish banks in terms of compliance to internationally accepted system development and software engineering standards to determine the common process problems of banks. After an in-depth investigation into system development and software engineering standards, related process-based standards were selected. Questions were then prepared covering the whole system development process by applying the classical Waterfall life cycle model. Each question is made up of guidance and suggestions from the international system development standards. To collect data, people from the information technology departments of three major banks in Turkey were interviewed. Results have been aggregated by examining the current process status of the three banks together. Problematic issues were identified using the international system development standards., Comment: 12 pages; International Journal of Software Engineering & Applications (IJSEA), Vol.2, No.2, April 2011

In this paper, we discuss the problem of the software engineering of a class of business spreadsheet models. A methodology for structured software development is proposed, which is based on structured analysis of data, represented as Jackson diagrams. It is shown that this analysis allows a straightforward modularisation, and that individual modules may be represented with indentation in the block-structured form of structured programs. The benefits of structured format are discussed, in terms of comprehensibility, ease of maintenance, and reduction in errors. The capability of the methodology to provide a modular overview in the model is described, and examples are given. The potential for a reverse-engineering tool, to transform existing spreadsheet models is discussed., Comment: 8 Pages, 7 Figures

Errors in spreadsheet applications and models are alarmingly common (some authorities, with justification cite spreadsheets containing errors as the norm rather than the exception). Faced with this body of evidence, the auditor can be faced with a huge task - the temptation may be to launch code inspections for every spreadsheet in an organisation. This can be very expensive and time-consuming. This paper describes risk assessment based on the "SpACE" audit methodology used by H M Customs & Excise's tax inspectors. This allows the auditor to target resources on the spreadsheets posing the highest risk of error, and justify the deployment of those resources to managers and clients. Since the opposite of audit risk is audit assurance the paper also offers an overview of some elements of good practice in the use of spreadsheets in business., Comment: 11 Pages, 1 Figure

The use of uncontrolled financial spreadsheets can expose organizations to unacceptable business and compliance risks, including errors in the financial reporting process, spreadsheet misuse and fraud, or even significant operational errors. These risks have been well documented and thoroughly researched. With the advent of regulatory mandates such as SOX 404 and FDICIA in the U.S., and MiFID, Basel II and Combined Code in the UK and Europe, leading tax and audit firms are now recommending that organizations automate their internal controls over critical spreadsheets and other end-user computing applications, including Microsoft Access databases. At a minimum, auditors mandate version control, change control and access control for operational spreadsheets, with more advanced controls for critical financial spreadsheets. This paper summarises the key issues regarding the establishment and maintenance of control of Business Critical spreadsheets., Comment: 4 Pages

Amongst the large number of write-and-throw-away spreadsheets developed for one-time use there is a rather neglected proportion of spreadsheets that are huge, periodically used, and submitted to regular update-cycles like any conventionally evolving valuable legacy application software. However, due to the very nature of spreadsheets, their evolution is particularly tricky and therefore error-prone. In our strive to develop tools and methodologies to improve spreadsheet quality, we analysed consolidation spreadsheets of an internationally operating company for the errors they contain. The paper presents the results of the field audit, involving 78 spreadsheets with 60,446 non-empty cells. As a by-product, the study performed was also to validate our analysis tools in an industrial context. The evaluated auditing tool offers the auditor a new view on the formula structure of the spreadsheet by grouping similar formulas into equivalence classes. Our auditing approach defines three similarity criteria between formulae, namely copy, logical and structural equivalence. To improve the visualization of large spreadsheets, equivalences and data dependencies are displayed in separated windows that are interlinked with the spreadsheet. The auditing approach helps to find irregularities in the geometrical pattern of similar formulas., Comment: 12 Pages, 2 Figures, 4 Tables

[Spreadsheet] Models are invaluable tools for strategic planning. Models help key decision makers develop a shared conceptual understanding of complex decisions, identify sensitivity factors and test management scenarios. Different modelling approaches are specialist areas in themselves. Model development can be onerous, expensive, time consuming, and often bewildering. It is also an iterative process where the true magnitude of the effort, time and data required is often not fully understood until well into the process. This paper explores the traditional approaches to strategic planning modelling commonly used in organisations and considers the application of a real-options approach to match and benefit from the increasing uncertainty in today's rapidly changing world., Comment: 7 Pages

The Cell Error Rate in simple spreadsheets averages about 2% to 5%. This CER has been measured in domain free environments. This paper compares the CERs occurring in domain free and applied domain tasks. The applied domain task requires the application of simple linear algebra to a costing problem. The results show that domain referent knowledge influences participants' approaches to spreadsheet creation and spreadsheet usage. The conclusion is that spreadsheet error making is influenced by domain knowledge and domain perception. Qualitative findings also suggest that spreadsheet error making is a part of overall human behaviour, and ought to be analyzed against this wider canvas., Comment: 15 pages

Despite strong evidence of widespread errors, spreadsheet developers rarely subject their spreadsheets to post-development testing to reduce errors. This may be because spreadsheet developers are overconfident in the accuracy of their spreadsheets. This conjecture is plausible because overconfidence is present in a wide variety of human cognitive domains, even among experts. This paper describes two experiments in overconfidence in spreadsheet development. The first is a pilot study to determine the existence of overconfidence. The second tests a manipulation to reduce overconfidence and errors. The manipulation is modestly successful, indicating that overconfidence reduction is a promising avenue to pursue.

The Sarbanes-Oxley Act of 2002 has finally forced corporations to examine the validity of their spreadsheets. They are beginning to understand the spreadsheet error literature, including what it tells them about the need for comprehensive spreadsheet testing. However, controlling for fraud will require a completely new set of capabilities, and a great deal of new research will be needed to develop fraud control capabilities. This paper discusses the riskiness of spreadsheets, which can now be quantified to a considerable degree. It then discusses how to use control frameworks to reduce the dangers created by spreadsheets. It focuses especially on testing, which appears to be the most crucial element in spreadsheet controls., Comment: 45 pages, 7 figures