Stevens Institute of Technology

About: Stevens Institute of Technology is a education organization based out in Hoboken, New Jersey, United States. It is known for research contribution in the topics: Cognitive radio & Wireless network. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.

Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning

Abstract: Deep Learning has recently become hugely popular in machine learning for its ability to solve end-to-end learning systems, in which the features and the classifiers are learned simultaneously, providing significant improvements in classification accuracy in the presence of highly-structured and large databases. Its success is due to a combination of recent algorithmic breakthroughs, increasingly powerful computers, and access to significant amounts of data. Researchers have also considered privacy implications of deep learning. Models are typically trained in a centralized manner with all the data being processed by the same training algorithm. If the data is a collection of users' private data, including habits, personal pictures, geographical positions, interests, and more, the centralized server will have access to sensitive information that could potentially be mishandled. To tackle this problem, collaborative deep learning models have recently been proposed where parties locally train their deep learning structures and only share a subset of the parameters in the attempt to keep their respective training sets private. Parameters can also be obfuscated via differential privacy (DP) to make information extraction even more challenging, as proposed by Shokri and Shmatikov at CCS'15. Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper. In particular, we show that a distributed, federated, or decentralized deep learning approach is fundamentally broken and does not protect the training sets of honest participants. The attack we developed exploits the real-time nature of the learning process that allows the adversary to train a Generative Adversarial Network (GAN) that generates prototypical samples of the targeted training set that was meant to be private (the samples generated by the GAN are intended to come from the same distribution as the training data). Interestingly, we show that record-level differential privacy applied to the shared parameters of the model, as suggested in previous work, is ineffective (i.e., record-level DP is not designed to address our attack).

1.6 V Nanogenerator for Mechanical Energy Harvesting Using PZT Nanofibers

Abstract: Energy harvesting technologies that are engineered to miniature sizes, while still increasing the power delivered to wireless electronics,(1, 2) portable devices, stretchable electronics,(3) and implantable biosensors,(4, 5) are strongly desired. Piezoelectric nanowire- and nanofiber-based generators have potential uses for powering such devices through a conversion of mechanical energy into electrical energy.(6) However, the piezoelectric voltage constant of the semiconductor piezoelectric nanowires in the recently reported piezoelectric nanogenerators(7-12) is lower than that of lead zirconate titanate (PZT) nanomaterials. Here we report a piezoelectric nanogenerator based on PZT nanofibers. The PZT nanofibers, with a diameter and length of approximately 60 nm and 500 μm, were aligned on interdigitated electrodes of platinum fine wires and packaged using a soft polymer on a silicon substrate. The measured output voltage and power under periodic stress application to the soft polymer was 1.63 V and 0.03 ...

The Future of Crowd Work

Abstract: Paid crowd work offers remarkable opportunities for improving productivity, social mobility, and the global economy by engaging a geographically distributed workforce to complete complex tasks on demand and at scale. But it is also possible that crowd work will fail to achieve its potential, focusing on assembly-line piecework. Can we foresee a future crowd workplace in which we would want our children to participate? This paper frames the major challenges that stand in the way of this goal. Drawing on theory from organizational behavior and distributed computing, as well as direct feedback from workers, we outline a framework that will enable crowd work that is complex, collaborative, and sustainable. The framework lays out research challenges in twelve major areas: workflow, task assignment, hierarchy, real-time response, synchronous collaboration, quality control, crowds guiding AIs, AIs guiding crowds, platforms, job design, reputation, and motivation.

One Size Does Not Fit All Projects: Exploring Classical Contingency Domains

Abstract: Not many authors have attempted to classify projects according to any specific scheme, and those who have tried rarely offered extensive empirical evidence. From a theoretical perspective, a traditional distinction between radical and incremental innovation has often been used in the literature of innovation, and has created the basis for many classical contingency studies. Similar concepts, however, did not become standard in the literature of projects, and it seems that theory development in project management is still in its early years. As a result, most project management literature still assumes that all projects are fundamentally similar and that "one size fits all." The purpose of this exploratory research is to show how different types of projects are managed in different ways, and to explore the domain of traditional contingency theory in the more modern world of projects. This two-step research is using a combination of qualitative and quantitative methods and two data sets to suggest a conceptual, two-dimensional construct model for the classification of technical projects and for the investigation of project contingencies. Within this framework, projects are classified into four levels of technological uncertainty, and into three levels of system complexity, according to a hierarchy of systems and subsystems. The study provides two types of implications. For project leadership it shows why and how management should adapt a more project-specific style. For theory development, it offers a collection of insights that seem relevant to the world of projects as temporary organizations, but are, at times, different from classical structural contingency theory paradigms in enduring organizations. While still exploratory in nature, this study attempts to suggest new inroads to the future study of modern project domains.