Social and Behavioral Sciences Commons^™

Sonified Hudson Valley Landscapes And The Influence Of Industry, Jess Belardi

Senior Projects Fall 2021

Senior Project submitted to The Division of Arts of Bard College.

Senior Project submitted to The Division of Social Studies of Bard College.

Brave New World: Privacy, Data Sharing And Evidence Based Policy Making, Rhonda S. Binda

Open Educational Resources

The trifecta of globalization, urbanization and digitization have created new opportunities and challenges across our nation, cities, boroughs and urban centers. Cities in particular are in a unique position at the center of commerce and technology becoming hubs for innovation and practical application of emerging technology. In this rapidly changing 24/7 digitized world, governments are leveraging innovation and technology to become more effective, efficient, transparent and to be able to better plan for and anticipate the needs of its citizens, businesses and community organizations. This class will provide the framework for how cities and communities can become smarter and more …

From Protecting To Performing Privacy, Garfield Benjamin

The Journal of Sociotechnical Critique

Privacy is increasingly important in an age of facial recognition technologies, mass data collection, and algorithmic decision-making. Yet it persists as a contested term, a behavioural paradox, and often fails users in practice. This article critiques current methods of thinking privacy in protectionist terms, building on Deleuze's conception of the society of control, through its problematic relation to freedom, property and power. Instead, a new mode of understanding privacy in terms of performativity is provided, drawing on Butler and Sedgwick as well as Cohen and Nissenbaum. This new form of privacy is based on identity, consent and collective action, a …

Data Governance And The Emerging University, Michael J. Madison

Book Chapters

Knowledge and information governance questions are tractable primarily in institutional terms, rather than in terms of abstractions such as knowledge itself or individual or social interests. This chapter offers the modern research university as an example. Practices of data-intensive research by university-based researchers, sometimes reduced to the popular phrase “Big Data,” pose governance challenges for the university. The chapter situates those challenges in the traditional understanding of the university as an institution for understanding forms and flows of knowledge. At a broad level, the chapter argues that the new salience of data exposes emerging shifts in the social, cultural, and …

Tools For Data Governance, Michael J. Madison

Articles

This article describes the challenges of data governance in terms of the broader framework of knowledge commons governance, an institutional approach to governing shared knowledge, information, and data resources. Knowledge commons governance highlights the potential for effective community- and collective-based governance of knowledge resources. The article focuses on key concepts within the knowledge commons framework rather than on specific law and public policy questions, directing the attention of researchers and policymakers to critical inquiry regarding relevant social groups and relevant data “things.” Both concepts are key tools for effective data governance.

Apex2s: A Two-Layer Machine Learning Model For Discovery Of Host-Pathogen Protein-Protein Interactions On Cloud-Based Multiomics Data, Huaming Chen, Jun Shen, Lei Wang, Chi-Hung Chi

Faculty of Engineering and Information Sciences - Papers: Part B

No abstract provided.

Refinement And Augmentation For Data In Micro Learning Activity With An Evolutionary Rule Generators, Geng Sun, Jiayin Lin, Tingru Cui, Jun Shen, Dongming Xu, Mahesh Kayastha

Faculty of Engineering and Information Sciences - Papers: Part B

Improving both the quantity and quality of existing data are placed at the center of research for adaptive micro open learning. To cover this research gap, our work targets on the current scarcity of both data and rules that represent open learning activities. An evolutionary rule generator is constructed, which consists of an outer loop and an inner loop. The outer loop runs a genetic algorithm (GA) to produce association rules that can be effective in the micro open learning scenario from a small amount of available data sources; while the inner loop optimizes generated candidates by taking into account …

A New Data Driven Long-Term Solar Yield Analysis Model Of Photovoltaic Power Plants, Biplob Ray, Rakibuzzaman Shah, Md Rabiul Islam, Syed Islam

Faculty of Engineering and Information Sciences - Papers: Part B

Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV …

On Masking And Releasing Smart Meter Data At Micro-Level: The Multiplicative Noise Approach, John Brackenbury, P. Y. O'Shaughnessy, Yan-Xia Lin

Faculty of Engineering and Information Sciences - Papers: Part B

Smart meter electricity data presents privacy risks when malicious agents gain insights of private information, including residents’ lifestyle and daily habits. When allowing access to record-level data, we apply the multiplicative noise method to mask individual smart meter data, which simultaneously aims to minimise disclosure of a dwelling’s consumption signal to any third party and to enable accurate estimation of the sum of a cluster of households. Three testing criteria are introduced to measure the performance of multiplicative noise masking approach relevant to the smart meter data. We propose a novel ‘Twin Uniform’ noise distribution and derive relevant theoretical results. …

A Framework Towards Data Analysis On Host-Pathogen Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song

Faculty of Engineering and Information Sciences - Papers: Part B

With the rapid development of high-throughput technologies, systems biology is now embracing a great opportunity made possible by the increased accumulation of data available online. Biological data analytics is considered as a critical means to contribute to a better understanding on such data through extraction of the latent features, relationships and the associated mechanisms. Therefore, it is important to evaluate how to involve data analytics from both computational and biological perspectives in practice. This paper has investigated interaction relationships in the proteomics area, which provide insights of the critical molecular processes within infection mechanisms. Specifically, we focused on host–pathogen protein–protein …

What Can We Do? Puzzling Over The Interpretation Of Heredity And Variation From Galton To Genetic Engineering, Peter J. Taylor

Working Papers on Science in a Changing World

First six chapters of a book motivated as follows: When I had mentioned to colleagues that I was exploring some significant issues overlooked by both sides in nature-nurture debates, the typical response was “we know, of course, that nature and nurture are intertwined”; they never asked “which nature-nurture science are you referring to?” It occurred to me that, in the long history of nature-nurture debates, opposing sides had always assumed or implied that these different scientific approaches were speaking to the same issues. If that were the case, then the challenge—something I was already puzzling over—was how best to draw …

Data Privacy And System Security For Banking And Financial Services Industry Based On Cloud Computing Infrastructure, Abhishek Mahalle, Jianming Yong, Xiaohui Tao, Jun Shen

Faculty of Engineering and Information Sciences - Papers: Part B

No abstract provided.

Fast Multi-Resource Allocation With Patterns In Large Scale Cloud Data Center, Jiyuan Shi, Junzhou Luo, Fang Dong, Jiahui Jin, Jun Shen

Faculty of Engineering and Information Sciences - Papers: Part B

How to achieve fast and efficient resource allocation is an important optimization problem of resource management in cloud data center. On one hand, in order to ensure the user experience of resource requesting, the system has to achieve fast resource allocation to timely process resource requests; on the other hand, in order to ensure the efficiency of resource allocation, how to allocate multi-dimensional resource requests to servers needs to be optimized, such that server's resource utilization can be improved. However, most of existing approaches focus on finding out the mapping of each specific resource request to each specific server. This …

Exploring The Potential Of Big Data On The Health Care Delivery Value Chain (Cdvc): A Preliminary Literature And Research Agenda, William J. Tibben, Samuel Fosso Wamba

Faculty of Engineering and Information Sciences - Papers: Part B

Big data analytics (BDA) is emerging as a game changer in healthcare. While the practitioner literature has been speculating on the high potential of BDA in transforming the healthcare sector, few rigorous empirical studies have been conducted by scholars to assess the real potential of BDA. Drawing on the health care delivery value chain (CDVC) and an extensive literature review, this exploratory study aims to discuss current peer-reviewed articles dealing with BDA across the CDVC and discuss future research directions.

Data Fusion For Maas: Opportunities And Challenges, Jianqing Wu, Luping Zhou, Chen Cai, Jun Shen, S K. Lau, Jianming Yong

Faculty of Engineering and Information Sciences - Papers: Part B

No abstract provided.

Are You Really Anonymous Online? Your Friends On Twitter May Give You Away, Jessica T. Su

FDLA Journal

As you browse the internet, online advertisers track nearly every site you visit, amassing a trove of information on your habits and preferences. When you visit a news site, they might see you’re a fan of basketball, opera and mystery novels, and accordingly select ads tailored to your tastes.

Advertisers use this information to create highly personalized experiences, but they typically don’t know exactly who you are. They observe only your digital trail, not your identity itself, and so you might feel that you’ve retained a degree of anonymity.

Identity-Based Remote Data Integrity Checking With Perfect Data Privacy Preserving For Cloud Storage, Yong Yu, Man Ho Au, Giuseppe Ateniese, Xinyi Huang, Willy Susilo, Yuanshun Dai, Geyong Min

Faculty of Engineering and Information Sciences - Papers: Part A

Remote data integrity checking (RDIC) enables a data storage server, say a cloud server, to prove to a verifier that it is actually storing a data owner's data honestly. To date, a number of RDIC protocols have been proposed in the literature. However, most of the constructions suffer from the issue of requiring complex key management. That is, they rely on the expensive public key infrastructure (PKI), which might hinder the deployment of RDIC in practice. In this paper, we propose a new construction of identity-based (ID-based) RDIC protocol by making use of key-homomorphic cryptographic primitive to reduce the system …

Sharing Social Network Data: Differentially Private Estimation Of Exponential Family Random-Graph Models, Vishesh Karwa, Pavel N. Krivitsky, Aleksandra B. Slavkovic

Faculty of Engineering and Information Sciences - Papers: Part A

Motivated by a real life problem of sharing social network data that contain sensitive personal information, we propose a novel approach to release and analyse synthetic graphs to protect privacy of individual relationships captured by the social network while maintaining the validity of statistical results. A case-study using a version of the Enron e-mail corpus data set demonstrates the application and usefulness of the proposed techniques in solving the challenging problem of maintaining privacy and supporting open access to network data to ensure reproducibility of existing studies and discovering new scientific insights that can be obtained by analysing such data. …

Collaborative Data Analytics Towards Prediction On Pathogen-Host Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song

Faculty of Engineering and Information Sciences - Papers: Part B

Nowadays more and more data are being sequenced and accumulated in system biology, which bring the data analytics researchers to a brand new era, namely 'big data', to extract the inner relationship and knowledge from the huge amount of data.

Towards Massive Data And Sparse Data In Adaptive Micro Open Educational Resource Recommendation: A Study On Semantic Knowledge Base Construction And Cold Start Problem, Geng Sun, Tingru Cui, Ghassan Beydoun, Shiping Chen, Fang Dong, Dongming Xu, Jun Shen

Faculty of Engineering and Information Sciences - Papers: Part B

Micro Learning through open educational resources (OERs) is becoming increasingly popular. However, adaptive micro learning support remains inadequate by current OER platforms. To address this, our smart system, Micro Learning as a Service (MLaaS), aims to deliver personalized OER with micro learning to satisfy their real-time needs.

Cost-Effective Big Data Mining In The Cloud: A Case Study With K-Means, Qiang He, Xiaodong Zhu, Dongwei Li, Shuliang Wang, Jun Shen, Yun Yang

Faculty of Engineering and Information Sciences - Papers: Part B

Mining big data often requires tremendous computationalresources. This has become a major obstacle to broad applicationsof big data analytics. Cloud computing allows data scientists to access computationalresources on-demand for building their big data analytics solutions in the cloud.

Metamorphic Testing For Adobe Data Analytics Software, Darryl C. Jarman, Zhiquan Zhou, Tsong Yueh Chen

Faculty of Engineering and Information Sciences - Papers: Part B

It is challenging to test data analytics software because a test oracle might not be available. This study reports our experience of applying metamorphic testing to Adobe's data analytics software that is used for anomaly detection in a set of time series data. We make use of geometric transformations to build metamorphic relations and generate simple time series data as the source test cases. The results of this study show that metamorphic testing is highly effective for both verification and validation purposes. An investigation of the issues detected during metamorphic testing revealed three bugs in the software under test.

Towards Cost Reduction In Cloud-Based Workflow Management Through Data Replication, Fei Xie, Jun Yan, Jun Shen

Faculty of Engineering and Information Sciences - Papers: Part B

No abstract provided.

Text Data Mining Of Aged Care Accreditation Reports To Identify Risk Factors In Medication Management In Australian Residential Aged Care Homes, Tao Jiang, Siyu Qian, David M. Hailey, Jun Ma, Ping Yu

Faculty of Engineering and Information Sciences - Papers: Part B

This study aimed to identify risk factors in medication management in Australian residential aged care (RAC) homes. Only 18 out of 3,607 RAC homes failed aged care accreditation standard in medication management between 7th March 2011 and 25th March 2015. Text data mining methods were used to analyse the reasons for failure. This led to the identification of 21 risk indicators for an RAC home to fail in medication management. These indicators were further grouped into ten themes. They are overall medication management, medication assessment, ordering, dispensing, storage, stock and disposal, administration, incident report, monitoring, staff and resident satisfaction. The …

Predictive Inference For Big, Spatial, Non-Gaussian Data: Modis Cloud Data And Its Change-Of-Support, Aritra Sengupta, Noel A. Cressie, Brian H. Kahn, Richard Frey

Faculty of Engineering and Information Sciences - Papers: Part A

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non-Gaussian in distribution. To overcome computational challenges, we use the reduced-rank spatial random effects (SRE) model in a statistical analysis of cloud-mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models' future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel-scale clear-sky-probability process, is needed to establish reliable estimates …

Public Cloud Data Auditing With Practical Key Update And Zero Knowledge Privacy, Yong Yu, Yannan Li, Man Ho Au, Willy Susilo, Kim-Kwang Raymond Choo, Xinpeng Zhang

Faculty of Engineering and Information Sciences - Papers: Part A

Data integrity is extremely important for cloud based storage services, where cloud users no longer have physical possession of their outsourced files. A number of data auditing mechanisms have been proposed to solve this problem. However, how to update a cloud user's private auditing key (as well as the authenticators those keys are associated with) without the user's re-possession of the data remains an open problem. In this paper, we propose a key-updating and authenticator-evolving mechanism with zero-knowledge privacy of the stored files for secure cloud data auditing, which incorporates zero knowledge proof systems, proxy re-signatures and homomorphic linear authenticators. …

Two-Factor Data Security Protection Mechanism For Cloud Storage System, Joseph K. Liu, Kaitai Liang, Willy Susilo, Jianghua Liu, Yang Xiang

Faculty of Engineering and Information Sciences - Papers: Part A

In this paper, we propose a two-factor data security protection mechanism with factor revocability for cloud storage system. Our system allows a sender to send an encrypted message to a receiver through a cloud storage server. The sender only needs to know the identity of the receiver but no other information (such as its public key or its certificate). The receiver needs to possess two things in order to decrypt the ciphertext. The first thing is his/her secret key stored in the computer. The second thing is a unique personal security device which connects to the computer. It is impossible …

A Data-Driven Predictive Model For Residential Mobility In Australia - A Generalised Linear Mixed Model For Repeated Measured Binary Data, Mohammad-Reza Namazi-Rad, Payam Mokhtarian, Nagesh Shukla, Albert Munoz

Faculty of Engineering and Information Sciences - Papers: Part A

Household relocation modelling is an integral part of the Government planning process as residential movements influence the demand for community facilities and services. This study will address the problem of modelling residential relocation choice by estimating a logit-link class model. The proposed model estimates the probability of an event which triggers household relocation. The attributes considered in this study are: requirement for bedrooms, employment status, income status, household characteristics, and tenure (i.e. duration living at the current location). Accurate prediction of household relocations for population units should rely on real world observations. In this study, a longitudinal survey data gathered …

Towards Data Analytics Of Pathogen-Host Protein-Protein Interaction: A Survey, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song

Faculty of Engineering and Information Sciences - Papers: Part A

"Big Data" is immersed in many disciplines, including computer vision, economics, online resources, bioinformatics and so on. Increasing researches are conducted on data mining and machine learning for uncovering and predicting related domain knowledge. Protein-protein interaction is one of the main areas in bioinformatics as it is the basis of the biological functions. However, most pathogen-host protein-protein interactions, which would be able to reveal much more infectious mechanisms between pathogen and host, are still up for further investigation. Considering a decent feature representation of pathogen-host protein-protein interactions (PHPPI), currently there is not a well structured database for research purposes, not …

A Bottom-Up Data Collection Methodology For Characterising The Residential Building Stock In Australia, Clayton Mcdowell, Georgios Kokogiannakis, Paul Cooper, Michael P. Tibbs

Faculty of Engineering and Information Sciences - Papers: Part B

In Australia the majority of the current residential building stock has been constructed with little regard to energy consumption or thermal comfort. With only 1-2 % of Australia's building stock being replaced each year retrofitting solutions are necessary if residential energy consumption is to be reduced. Australia's records of the characteristics of its current building stock are minimal and outdated and thus these need to be renewed to enable the evaluation of retrofit upgrade strategies. Thus this paper presents a methodology and results of a bottom-up data collection tool that captured building and occupant characteristics from 200 elderly low income …