Bringing Global Sourcing into the Classroom: Experiential Learning via Software Development Project

Global sourcing of software development has imposed new skill requirements on Information Technology (IT) personnel. In the U.S., this has resulted in a paradigm shift from technical to softer skills such as communications and virtual team management. Higher education institutions must, consequently, initiate innovative curriculum transformations to better prepare students for these emerging workforce needs. This paper describes one such venture between Marquette University (MU), U.S.A. and Management Development Institute (MDI), India, wherein IT students at MU collaborated with Management Information Systems (MIS) students at MDI on an offshore software development project. The class environment replicated an offshore client/vendor relationship in a fully virtual setting while integrating communications and virtual team management with traditional IT project management principles. Course measures indicated that students benefited from this project, gained first-hand experience in the process of software offshoring, and learned skills critical for conduct of global business. For faculty considering such initiatives, we describe the design and administration of this class over two semesters, lessons learned from our engagement, and factors critical to success of such initiatives and those detrimental to their sustenance

Bringing Global Sourcing into The Classroom: Experiential Learning Via a Global Software Development Project

The growing trend in offshore software development has imposed new skills requirements on collaborating global partners. In the U.S. this has translated into skill sets that include communications, project management, business analysis, and team management. In a virtual setting, these skills take on a complex proportion. This paper describes an educational initiative in offshore software development between undergraduate students enrolled in a project management course at Marquette University, USA and graduate business students enrolled in an Information Systems Analysis and Design course at Management Development Institute, India. The course replicated an offshore client/vendor relationship in a virtual setting. For faculty considering such initiatives, this paper describes the setting and factors critical to success of this initiative and cautions against others that can be detrimental to such an effort

Project Quality of Offshore Virtual Teams Engaged in Software Requirements Analysis: An Exploratory Comparative Study

The off-shore software development companies in countries such as India use a global delivery model in which initial requirement analysis phase of software projects get executed at client locations to leverage frequent and deep interaction between user and developer teams. Subsequent phases such as design, coding and testing are completed at off-shore locations. Emerging trends indicate an increasing interest in off-shoring even requirements analysis phase using computer mediated communication. We conducted an exploratory research study involving students from Management Development Institute (MDI), India and Marquette University (MU), USA to determine quality of such off-shored requirements analysis projects. Our findings suggest that project quality of teams engaged in pure off-shore mode is comparable to that of teams engaged in collocated mode. However, the effect of controls such as user project monitoring on the quality of off-shored projects needs to be studied further

How Does Adaptive Optimization Impact Local Neural Network Geometry?

Adaptive optimization methods are well known to achieve superior convergence relative to vanilla gradient methods. The traditional viewpoint in optimization, particularly in convex optimization, explains this improved performance by arguing that, unlike vanilla gradient schemes, adaptive algorithms mimic the behavior of a second-order method by adapting to the global geometry of the loss function. We argue that in the context of neural network optimization, this traditional viewpoint is insufficient. Instead, we advocate for a local trajectory analysis. For iterate trajectories produced by running a generic optimization algorithm OPT, we introduce $R^{\text{OPT}}_{\text{med}}$, a statistic that is analogous to the condition number of the loss Hessian evaluated at the iterates. Through extensive experiments, we show that adaptive methods such as Adam bias the trajectories towards regions where $R^{\text{Adam}}_{\text{med}}$ is small, where one might expect faster convergence. By contrast, vanilla gradient methods like SGD bias the trajectories towards regions where $R^{\text{SGD}}_{\text{med}}$ is comparatively large. We complement these empirical observations with a theoretical result that provably demonstrates this phenomenon in the simplified setting of a two-layer linear network. We view our findings as evidence for the need of a new explanation of the success of adaptive methods, one that is different than the conventional wisdom

Bringing Global Sourcing into the Classroom: Lessons from an Experiential Software Development Project

Global sourcing of software development has imposed new skill requirements on Information Technology (IT) personnel. In the U.S., this has resulted in a paradigm shift from technical to softer skills such as communications and virtual team management. Higher education institutions must, consequently, initiate innovative curriculum transformations to better prepare students for these emerging workforce needs. This paper describes one such venture between MU, U.S.A. and MDI, India, wherein IT students at MU collaborated with Management Information Systems (MIS) students at MDI on an offshore software development project. The class environment replicated an offshore client/vendor relationship in a fully virtual setting while integrating communications and virtual team management with traditional IT project management principles. Course measures indicated that students benefited from this project, gained first-hand experience in the process of software offshoring, and learned skills critical for conduct of global business. For faculty considering such initiatives, we describe the design and administration of this class over two semesters, lessons learned from our engagement, and factors critical to success of such initiatives and those detrimental to their sustenance

PTPerf: On the performance evaluation of Tor Pluggable Transports

Tor, one of the most popular censorship circumvention systems, faces regular blocking attempts by censors. Thus, to facilitate access, it relies on "pluggable transports" (PTs) that disguise Tor's traffic and make it hard for the adversary to block Tor. However, these are not yet well studied and compared for the performance they provide to the users. Thus, we conduct a first comparative performance evaluation of a total of 12 PTs -- the ones currently supported by the Tor project and those that can be integrated in the future. Our results reveal multiple facets of the PT ecosystem. (1) PTs' download time significantly varies even under similar network conditions. (2) All PTs are not equally reliable. Thus, clients who regularly suffer censorship may falsely believe that such PTs are blocked. (3) PT performance depends on the underlying communication primitive. (4) PTs performance significantly depends on the website access method (browser or command-line). Surprisingly, for some PTs, website access time was even less than vanilla Tor. Based on our findings from more than 1.25M measurements, we provide recommendations about selecting PTs and believe that our study can facilitate access for users who face censorship.Comment: 25 pages, 12 figure

Specifying and Solving Robust Empirical Risk Minimization Problems Using CVXPY

We consider robust empirical risk minimization (ERM), where model parameters are chosen to minimize the worst-case empirical loss when each data point varies over a given convex uncertainty set. In some simple cases, such problems can be expressed in an analytical form. In general the problem can be made tractable via dualization, which turns a min-max problem into a min-min problem. Dualization requires expertise and is tedious and error-prone. We demonstrate how CVXPY can be used to automate this dualization procedure in a user-friendly manner. Our framework allows practitioners to specify and solve robust ERM problems with a general class of convex losses, capturing many standard regression and classification problems. Users can easily specify any complex uncertainty set that is representable via disciplined convex programming (DCP) constraints