derhan/semantic-scholar-manajemen-proyek huggingface dataset:
Sigmah free software for humanitarian project management
Authors: Olivier Sarrat; V. Geoffroy
Year: 2011

Sigmah is free software for the integrated management of humanitarian projects. It provides international aid organization with the following software features: - Monitoring project progress and funding; - Creating, sharing, analysing and mapping indicators, for monitoring and assessment; - Centralising project documents; - Improving schedule management and early-warning alarms for events; - Implementing a quality assurance approach, by defining criteria and critical points linked to projects. The Sigmah project is led by a group of humanitarian organizations, which has, at the time of writing, 12 members: Action Against Hunger (France), Aide Médicale Internationale, Comité d’Aide Médicale, Comité de Secours Internationaux, French Red Cross, Groupe Urgence Réhabilitation Développement (Groupe URD), Handicap International (France), Islamic Relief (France), Médecins du Monde, Première Urgence, Solidarités International and Triangle Génération Humanitaire. Sigmah is developed as free software because it aims to be available to the humanitarian community as a whole.
Method of Optimal Planning of Cyberprotection Actions for a Corporate Information System
Authors: A. Litvinenko; Boris Maslovsky; Oleksiy Glazok; A. Petrov
Year: 2019

. The modern concept of cybersecurity management involves its consideration as a process of implementing a set of measures and activities organized into projects. This approach makes it possible to use the mathematical apparatus developed in the theory of cybernetic systems and project management for developing managerial decisions in the cybersecurity field. Based on this concept, a mathematical model is built that allows to create a schedule for performing the cyber-protection measures, aimed at maximizing the profit of the organization in conditions of limited resources. It is shown that in the above statement, the task of planning the process for implementing cyberprotection measures may be transformed to the canonical form of combinatorial optimization problems with a linear structure belonging to the NP-class. For its solution, it is proposed to use an al-gorithm based on the improved method improved that implements the idea of directed search of variants. The directed search method uses sequential fragmentation of the full set of solutions to the problem, until either the optimal plan is found, or the fact of the incompatibility of the system of restrictions is established. The resulting new subsets of the variants are subjected to formal analysis aimed at minimization of the solution process duration. A further development of the described approach to planning cyberprotection activities can be transition to stochastic models in which all financial and time indicators that relate to the implementation of cyberprotection measures are random variables with predetermined distribution laws. The proposed method is optimization-focused, so its application can provide increased competitiveness, efficiency and financial performance of companies in the context of modern cyber threats.
An analysis of sustainable change management for quality 4.0: Evidence from hybrid project management adoption in the Malaysian FinTech context
Authors: Tan Chi Xiang; Zunirah Mohd Talib; M. Johar
Year: 2023

In this paper, the authors aim to analyse organisational intention and focus on hybrid project management (HPM) methodology adoption in FinTech system software development. It is important to ascertain the internal and external factors that affect organisational decision-makers’ intentions towards HPM adoption. This study aims to apply a theoretical approach integrating Technology-Organisation-Environment (TOE), which examines the factors that impact FinTech organisations’ decisions to adopt HPM into their software development projects, together with the Theory of Planned Behaviour (TPB) which examines the behavioural intention. It addresses those factors that form organisational decision-makers’ readiness for HPM implementation and enable their intention to use it. When combining the independent, dependent, and moderating variables, the results show that the effect of relative advantage, top management support, and industry pressure have a positive influence on individual’s attitude towards HPM adoption in FinTech Malaysia and sustainability in Quality 4.0. The authors also considered the influence of attitudes and perceived behavioural control variables having a positive influence on sustainable intention of HPM adoption in the FinTech industry. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to verify the proposed hypotheses, with the exception of the direct influence of top management support or attitude on intention to adopt.
MT-WAVE: profiling multi-tier web applications
Authors: A. Arkles; D. Makaroff
Year: 2011

Modern web applications consist of many distinct services that collaborate to provide the full application functionality. To improve application performance, developers need to be able to identify the root cause of performance problems; identifying and fixing performance problems in these distributed, heterogeneous applications can be very difficult. As web applications become more complicated, the number of systems involved will continue to grow and full-system performance tuning will become more difficult.
 We postulate that multi-tier profiling, starting at the web browser, is the appropriate way to solve this problem. Instrumenting from the web browser, as the user experiences it, ensures that we can tell what each service in the application is contributing to overall page-load time; thus, each tier must provide instrumentation data that developers can use to quickly identify the root cause of performance problems.
 We have built MT-WAVE, a system that integrates with the different tiers of a web application (including a browser extension) and collects light-weight instrumentation to a central location via X-Trace facilities. The collected data is presented with our visualization system that provides varying levels of detail.
 To validate our approach, we performed case studies of two applications, both showing performance insight. In particular, we identified and fixed a significant and unintuitive bottleneck in an open-source project management application and verified caching behaviour in a cloud-hosted commercial product. While specific technologies are used in our case study, we believe that most web technologies in common use today would require straightforward modifications to be able to utilize MT-WAVE tracing facilities.
 This tool is designed to be used by application developers and system administrators while testing new software, or after deployment when it becomes clear that existing performance is not meeting user needs.
Teaching in a High-Tech Conference Room:
Authors: L. Rehling
Year: 2005

As a response to research about both the work space of professional writers and the pedagogy using workplace simulations, a professional writing course was adapted for a high-tech conference room equipped with electronic meeting tools. This experiment improved students’ learning of course content, which included collaborative writing strategies, project management, and teamwork; research methods; presentation and design skills; and organizational culture and professional development. Students also better understood workplace realities and distinctions between academic and workplace environments. In addition, the experiment facilitated students’idea sharing and communication as well as their preparation for transitioning to the workplace. The teaching experience was more creative and rewarding, too.
Implementing New Management Initiatives in Project-Based Environments: The Role of Organizational Practices and Power Dynamics
Authors: A. Goussevskaia; H. Scarbrough; J. Swan; Mike Bresnen
Year: 2006

This paper aims to contribute to understanding the relationship between project-based working and wider organizational processes through a multiple case study of the implementation of new management initiatives in the construction sector. The comparative analysis of the cases allows exploring the factors associated with project-based working which enabled, and/or inhibited, the introduction of change initiatives. It reveals two major dimensions that influence the extent to which new organizational practices are accepted and embedded across projects. First, the degree to which the change initiative interferes with (or is seen as interfering with) project management practices and, second, the degree to which the implementation of the initiative disrupts existing power/knowledge conditions within the organization.
Project and Process Management of Public-Private Partnerships: Managerial Tensions and Coping Strategies
Authors: A. Krogh; Svante Aasbjerg Thygesen
Year: 2022

Western governments increasingly use public-private partnerships (PPPs) to solve complex social problems. This article examines how public managers handle conflicting demands for classical project management and collaborative process management in PPPs for preventing negative social control. Building on theories of public governance, PPP management, and paradox management, it develops an analytical framework for studying managerial tensions in PPPs. Applying the framework in a case study of three Danish PPPs, it shows how partnership managers handle managerial tensions through strategies of opposition, separation, and synthesis. The study demonstrates a promising path of integrating paradox management theory in public governance hybridity research.
Application research of genetic algorithm in construction resource optimization and progress impact prediction
Authors: Chengyue Zhang; Ronggui Liu; Xiaoqian Zhang
Year: 2024

With the acceleration of urbanization and the continuous advancement of infrastructure construction, the optimization of construction resources and the prediction of progress impact have become important issues in the field of engineering construction. This is one of the important contents in engineering project management, aiming to ensure the reasonable satisfaction of resource demand during construction and maximize the utilization of resources. The types and quantities of resources required are vast, making scheduling and allocation difficult, with waste being a common phenomenon. In the complex and ever-changing construction environment, it is difficult to achieve the maximization of resource utilization and precise control of progress. The accuracy of progress prediction directly affects the allocation of construction resources and cost control. Most construction enterprises still use traditional, experience-based estimation methods for progress prediction, which are easily influenced by various factors. At the same time, due to the numerous uncertainties in the construction process, progress prediction faces great difficulties. This requires the use of artificial intelligence, big data analysis, etc., to improve the accuracy of prediction. At the same time, strengthening project management and team coordination capabilities to ensure the smooth progress of construction work. This paper mainly utilizes principal component analysis and qualitative and quantitative analysis to experimentally analyze genetic algorithms, machine learning models, regression analysis, time series analysis, and construction resource allocation. The experimental results show that the prediction results of machine learning models and regression analysis are 0.54, which is 0.01 higher than the actual results.
Pareto Efficiency in Robust Optimization
Authors: D. Iancu; Nikolaos Trichakis
Year: 2014

This paper formalizes and adapts the well-known concept of Pareto efficiency in the context of the popular robust optimization RO methodology for linear optimization problems. We argue that the classical RO paradigm need not produce solutions that possess the associated property of Pareto optimality, and we illustrate via examples how this could lead to inefficiencies and suboptimal performance in practice. We provide a basic theoretical characterization of Pareto robustly optimal PRO solutions and extend the RO framework by proposing practical methods that verify Pareto optimality and generate solutions that are PRO. Critically important, our methodology involves solving optimization problems that are of the same complexity as the underlying robust problems; hence, the potential improvements from our framework come at essentially limited extra computational cost. We perform numerical experiments drawn from three different application areas portfolio optimization, inventory management, and project management, which demonstrate that PRO solutions have a significant potential upside compared with solutions obtained using classical RO methods. 
 
This paper was accepted by Gerard P. Cachon, optimization.
SETAP: Software engineering teamwork assessment and prediction using machine learning
Authors: D. Petkovic; Marc Sosnick-Pérez; Shihong Huang; Rainer Todtenhoefer; K. Okada; Swati Arora; Ramasubramanian Sreenivasen; Lorenzo Flores; Sonai Dubey
Year: 2014

Effective teaching of teamwork skills in local and globally distributed Software Engineering (SE) teams is recognized as an important part of the education of current and future software engineers. Effective methods for assessment and early prediction of learning effectiveness in SE teamwork are not only a critical part of teaching but also of value in industrial training and project management. This paper presents a novel analytical approach to the assessment and, most importantly, the prediction of learning outcomes in SE teamwork based on data from our joint software engineering class concurrently taught at San Francisco State University (SFSU), Florida Atlantic University (FAU) and Fulda University, Germany (Fulda). Our approach focuses on assessment and prediction of SE teamwork in terms of ability of student teams to apply best SE processes and develop SE products. It differs from existing work in the following aspects: a) it develops and uses only objective and quantitative measures of team activity from multiple sources, such as statistics of student time use, software engineering tool use, and instructor observations; b) it leverages powerful machine learning (ML) techniques applied to team activity measurements to identify quantitative and objective factors which can assess and predict learning of software engineering teamwork skills at the team level. In this paper we provide the following contributions: a) we present in detail for the first time the full team activity measurement data set we developed, consisting of over 40 objective and quantitative measures extracted from student teams working on class projects; b) we present a ML framework which applies the Random Forest (RF) algorithm to the team activity measurements and team outcomes, focusing on predicting teams that are likely to fail; c) we describe in detail our now fully implemented and operational data processing pipeline, consisting of data collection methods from multiple sources, ML training database creation, and ML analysis subsystems; and finally d) we present very preliminary results of ML analysis results based on the data from our joint software engineering classes in Fall 2012, and Spring 2013, with the data from 17 student teams. While our ML training database is currently small, it continuously grows. Our preliminary results, verified with two independent accuracy measures, show that RF is able to predict SE Process and SE Product team performance in intuitively explainable manner.
Particle Swarm Optimization-based Linear Regression for Software Effort Estimation
Authors: Puguh Jayadi; Khairul Adilah binti Ahmad; Rayhan Zulfitri; Dwi Cahyo; Jofanza Denis
Year: 2024

In the context of software effort estimation, this study investigates the use of Particle Swarm Optimization (PSO)-based Linear Regression to improve estimation accuracy. The main problem faced is the limitations of standard Linear Regression models in accurately estimating the effort required for software development projects. This research aims to improve the quality of estimation of software efforts to optimize resource management and project schedules. The method used was the integration of PSOs in Linear Regression, which was evaluated using three different COCOMO datasets. Experimental results show that LR+PSO models consistently outperform standard Linear Regression with lower MAE, MSE, and RMSE, as well as higher R-squared. In conclusion, integrating PSOs in Linear Regression effectively improves the estimation accuracy of software efforts, demonstrating great potential for improving estimation quality in software project management practices.
Distributionally Robust Optimization Based on Kernel Density Estimation and Mean-Entropic Value-at-Risk
Authors: Wei Liu; Li Yang; Bo Yu
Year: 2022

In this paper, a distributionally robust optimization model based on kernel density estimation (KDE) and mean entropic value-at-risk (EVaR) is proposed, where the ambiguity set is defined as a KDE-[Formula: see text]-divergence “ball” centered at the empirical distribution in the weighted KDE distribution function family, which is a finite-dimensional set. Instead of the joint probability distribution of the random vector, the one-dimensional probability distribution of the random loss function is approximated by the univariate weighted KDE for dimensionality reduction. Under the mild conditions of the kernel and [Formula: see text]-divergence function, the computationally tractable reformulation of the corresponding distributionally robust mean-EVaR optimization model is derived by Fenchel’s duality theory. Convergence of the optimal value and the solution set of the distributionally robust optimization problem based on KDE and mean-EVaR to those of the corresponding stochastic programming problem with the true distribution is proved. For some special cases, including portfolio selection, newsvendor problem, and linear two-stage stochastic programming problem, concrete tractable reformulations are given. Primary empirical test results for portfolio selection and project management problems show that the proposed model is promising.
Setting a Research Agenda for IT Project Management Offices
Authors: J. McKay; P. Marshall; S. Arumugam; N. Grainger
Year: 2013

This paper recommends a number of research directions and themes to IS researchers concerning the planning, implementation, operations, and delivery of business value from IT Project Management Offices (IT PMOs). The recommendations are based on a review and analysis of academic literature on PMOs in which the authors noted that there were many challenges and tensions regarding PMOs that seemed unresolved. These formed the basis for a recommended research agenda that is presented in this paper. The research agenda is organized under three main themes: Up front planning and preparation for an IT PMO implementation, implementing and operating an IT PMO, and finally, delivering value from an IT PMO. These themes were then discussed with IT PMO managers in the context of their organizational environment, to establish whether they represented issues and challenges that were of genuine concern. It is intended that the research agenda will motivate and guide future research into IT PMOs.
Pitfalls in managing a simulation project
Authors: E. Williams; Onur M. Ülgen
Year: 2012

When simulation analyses first became at least somewhat commonplace (as opposed to theoretical and research endeavors often considered esoteric or exploratory), simulation studies were usually not considered “projects” in the usual corporate-management context. When the evolution from “special research investigation” to “analytical project intended to improve corporate profitability” began in the 1970s (both authors' career work in simulation began that decade), corporate managers naturally and sensibly expected to apply the tools and techniques of project management to the guidance and supervision of simulation projects. Intelligent application of these tools is typically a necessary but not a sufficient condition to assure simulation project success. Based on various experiences culled from several decades (sometimes the most valuable lessons come from the least successful projects), we offer advisories on the pitfalls which loom at various places on the typical simulation project path.
Software Cost Estimation Based on Dolphin Algorithm
Authors: Anfal A. Fadhil; R. Alsarraj; A. Altaie
Year: 2020

Accurate Software Effort Estimation is of high importance with regard to Software Project Management. It can be specified as the process for predicting Effort regarding costs, needed for developing software products. A lot of techniques related to software effort estimation were carried out for developing models that are generating optimal estimation accuracy. Swarm intelligence is one such technique. The process-related in selecting the optimum estimation algorithm is expert dependent and complex. The presented study optimizes the estimation using the COCOMO II models by two models: the first model applied the dolphin algorithm, the second model applied suggested hybrid dolphin and bat algorithm (DolBat). By applying the two models on two data set and evaluate with the use of Magnitude of Relative Error(MRE) and Mean Magnitude of Relative Error(MMRE). The results indicate that the dolphin algorithm has better than previous algorithms but the (DolBat) is the best to get the coefficient value of the COCOMO II model.
Graduate Student's Guide to Necessary Skills for Nonacademic Conservation Careers
Authors: Jessica L. Blickley; Kristy Deiner; K. Garbach; Iara Lacher; Mariah H. Meek; Lauren M. Porensky; Marit L. Wilkerson; Eric M. Winford; M. Schwartz
Year: 2012

Graduate education programs in conservation science generally focus on disciplinary training and discipline‐specific research skills. However, nonacademic conservation professionals often require an additional suite of skills. This discrepancy between academic training and professional needs can make it difficult for graduate students to identify the skills and experiences that will best prepare them for the conservation job market. We analyzed job advertisements for conservation‐science positions and interviewed conservation professionals with experience hiring early‐career conservation scientists to determine what skills employers of conservation professionals seek; whether the relative importance of skills varies by job sector (government, nonprofit, and private); and how graduate students interested in careers in conservation science might signal competency in key skills to potential employers. In job advertisements, disciplinary, interpersonal, and project‐management skills were in the top 5 skills mentioned across all job sectors. Employers’ needs for additional skills, like program leadership, conflict resolution and negotiation, and technical and information technology skills, varied across sectors. Our interview results demonstrated that some skills are best signaled to employers via experiences obtained outside thesis or dissertation work. Our findings suggest that graduate students who wish to be competitive in the conservation job market can benefit by gaining skills identified as important to the job sector in which they hope to work and should not necessarily expect to be competent in these skills simply by completing their chosen degree path.