Science and Technology Studies Commons^™

Comparing Political Implications Of Punitive Paradigms In Digital Surveillance And Data Driven Algorithms Between The Polities Of The United States Of America And The People's Republic Of China, Shedelande Lily Carpenter

Senior Projects Spring 2022

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

Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels

SMU Data Science Review

In this paper, we present an analysis of features influencing Yelp's proprietary review filtering algorithm. Classifying or misclassifying reviews as recommended or non-recommended affects average ratings, consumer decisions, and ultimately, business revenue. Our analysis involves systematically sampling and scraping Yelp restaurant reviews. Features are extracted from review metadata and engineered from metrics and scores generated using text classifiers and sentiment analysis. The coefficients of a multivariate logistic regression model were interpreted as quantifications of the relative importance of features in classifying reviews as recommended or non-recommended. The model classified review recommendations with an accuracy of 78%. We found that reviews …

Networks Of Isolation: The Case Of Donald J. Trump, Facebook, And The Limits Of Social Movement Theory, Carol L. Stimmel

International Development, Community and Environment (IDCE)

The 2016 election that catapulted Donald J. Trump to the U.S. presidency has raised questions for how Facebook may have enabled the emergence and coalescence of a social movement among traditionally improbable voters. The research in this paper engages with contemporary social movement theory, assessing its adequacy for explaining the role of Facebook as a primary method for facilitating a social movement among the civically-alienated, who are the most unlikely of all Americans to join an organized collective for change. From a methodological perspective, the exploration takes up the case as a strategy of inquiry to explore social movement theory …

Formulation Of A Model Predictive Control Algorithm To Enhance The Performance Of A Latent Heat Solar Thermal System, Gianluca Serale, Massimo Fiorentini, Alfonso Capozzoli, Paul Cooper, Marco Perino

Faculty of Engineering and Information Sciences - Papers: Part B

Model predictive control has proved to be a promising control strategy for improving the operational performance of multi-source thermal energy generation systems with the aim of maximising the exploitation of on-site renewable resources. This paper presents the formulation and implementation of a model predictive control strategy for the management of a latent heat thermal energy storage unit coupled with a solar thermal collector and a backup electric heater. The system uses an innovative Phase Change Material slurry for both the heat transfer fluid and storage media. The formulation of a model predictive controller of such a closed-loop solar system is …

Obfuscating Re-Encryption Algorithm With Flexible And Controllable Multihop On Untrusted Outsourcing Server, Mingwu Zhang, Yan Jiang, Yi Mu, Willy Susilo

Faculty of Engineering and Information Sciences - Papers: Part B

An outsourcing re-encryption program can help a ciphertext owner (delegator) transform his/her ciphertext into another ciphertext of delegatee. For example, an e-mail receiver can re-transfer an encrypted e-mail to his secretary while allowing the e-mail to be readable for her. For a multi-hop re-encryption, the delegatee can re-encrypt the ciphertext to another user in delegation chain, repeatedly. Traditionally, this transformation is usually conducted by a proxy or an outsourcing server. However, the proxy or outsourcing server needs a re-encryption key (i.e., re-key) and the re-encryption program must execute in a black-box manner (cannot trace into or debug and monitor the …

Renewable Energy Management In A Remote Area Using Modified Gravitational Search Algorithm, Sahand Ghavidel, Jamshid Aghaei, Kashem M. Muttaqi, Alireza Heidari

Faculty of Engineering and Information Sciences - Papers: Part A

In this paper, a small remote area which is located in Nigeria has been considered as a model to be tested by a managing scheme for providing both electricity and water. In this strategy, the groundwater is pumped into a water tank which can be later used for supplying required irrigation and drinking water. A PAT (Pump as Turbine) is used as a hybrid system for supplying electricity and water as well as storing water in the water tank. Also, a PV (photovoltaic plant), a package of batteries (BAT) in addition to a diesel ICE (internal combustion engine) are used …

A New Algorithm For Protecting Aggregate Business Microdata Via A Remote System, Yue Ma, Yan-Xia Lin, James O. Chipperfield, John Newman, Victoria Leaver

Faculty of Engineering and Information Sciences - Papers: Part A

Releasing business microdata is a challenging problem for many statistical agencies. Businesses with distinct continuous characteristics such as extremely high income could easily be identified while these businesses are normally included in surveys representing the population. In order to provide data users with useful statistics while maintaining confidentiality, some statistical agencies have developed online based tools to allow users to specify and request tables created from microdata. These tools only release perturbed cell values generated from automatic output perturbation algorithms in order to protect each underlying observation against various attacks, such as differencing attacks. An example of the perturbation algorithms …

Algo-Ritmo: More-Than-Human Performative Acts And The Racializing Assemblages Of Algorithmic Architectures, Ezekiel J. Dixon-Román

Ezekiel J Dixon-Román

Vehicle Routing Problem With Stochastic Demand (Vrpsd): Optimisation By Neighbourhood Search Embedded Adaptive Ant Algorithm (Ns-Aaa), M Nagalakshmi, Mukul Tripathi, Nagesh Shukla, Manoj Tiwari

Nagesh Shukla

Taking into account the real world applications, this paper considers a vehicle routing problem with stochastic demand (VRPSD) in which the customer demand has been modelled as a stochastic variable. Considering the computational complexity of the problem and to enhance the algorithm performance, a neighbourhood search embedded adaptive ant algorithm (ns-AAA) is proposed as an improvement to the existing ant colony optimisation. The proposed metaheuristic adapts itself to maintain an adequate balance between exploitation and exploration throughout the run of the algorithm. The performance of the proposed methodology is benchmarked against a set of test instances that were generated using …

A Distributed Broadcast Algorithm For Duty-Cycled Networks With Physical Interference Model, Dianbo Zhao, Kwan-Wu Chin

Faculty of Engineering and Information Sciences - Papers: Part A

Broadcast is a fundamental operation in multi-hop wireless networks. Given a source node with a message to broadcast, the objective is to propagate the message to all nodes in an interferencefree manner while incurring minimum latency. This problem, called Minimum-Latency Broadcast Scheduling (MLBS), has been studied extensively in wireless networks whereby nodes remain on all times and has been shown to be NP-hard. However, only a few studies have addressed this problem in the context of duty-cycled wireless networks, which unfortunately, remains NP-hard. In these networks, nodes do not wake up simultaneously, and hence, not all neighbors of a transmitting …

Evaluating Distributed Generations In Utility Operation And Planning Issues Using A Novel Fusion Pso-Sfla Algorithm, Esmaeil Mahboubi-Moghaddam, Jamshid Aghaei, Kashem M. Muttaqi, Behrouz Zoghdar-Moghadam-Shahrekohne, Mohammad Rasoul Narimani

Faculty of Engineering and Information Sciences - Papers: Part A

Increasing demand of electrical energy has leaded to utilization of more and more Distributed generation (DG) sources in distribution systems. Since the locations and capacities of the DG sources connected to the distribution system profoundly impact on reducing system loss and improving system reliability, so placement and sizing indication of DGs is the most substantial process in distribution systems. By adding the reliability objective to this problem, it becomes more complicated than before and it needs to be solved with an accurate algorithm. To this reason, to solve the proposed problem a new approach based on the mixture of two …

Target Tracking Algorithm Using Angular Point Matching Combined With Compressive Tracking, Jing Luo, Tingting Dong, Chunyuan Zi, Chunbo Xiu, Huixin Tian, Jiangtao Xi

Faculty of Engineering and Information Sciences - Papers: Part A

To solve the problems of tracking errors such as target missing that emerged in compressive tracking (CT) algorithm due to factors such as pose variation, illumination change, and occlusion, a novel tracking algorithm combined angular point matching with compressive tracking (APMCCT) was proposed. A sparse measurement matrix was adopted to extract the Haar-like features. The offset of the predicted target position was integrated into the angular point matching, and the new target position was calculated. Furthermore, the updating mechanism of the template was optimized. Experiments on different video sequences have shown that the proposed APMCCT performs better than CT algorithm …

Application Of The Largest Lyapunov Exponent Algorithm For Feature Extraction In Low Speed Slew Bearing Condition Monitoring, Wahyu Caesarendra, Prabuono Buyung Kosasih, A Kiet Tieu, Craig A. S Moodie

Faculty of Engineering and Information Sciences - Papers: Part A

This paper presents a new application of the largest Lyapunov exponent (LLE) algorithm for feature extraction method in low speed slew bearing condition monitoring. The LLE algorithm is employed to measure the degree of non-linearity of the vibration signal which is not easily monitored by existing methods. The method is able to detect changes in the condition of the bearing and demonstrates better tracking of the progressive deterioration of the bearing during the 139 measurement days than comparable methods such as the time domain feature methods based on root mean square (RMS), skewness and kurtosis extraction from the raw vibration …

The Optimisation Of The Secondary Cooling Water Distribution With Improved Genetic Algorithm In Continuous Casting Of Steels, Yu-Chun Zhai, Ying Li, Beiyue Ma, C Yan, Zhengyi Jiang

Faculty of Engineering and Information Sciences - Papers: Part A

An improved genetic algorithm is presented for the water consumption of the secondary cooling zone based on the heat transfer model of the off-line bloom caster. This study is to control the existing cooling systems and the steel casting practises in order to produce steel with best possible quality. The fitness function of improved genetic algorithm is founded according to the metallurgical criteria. This algorithm coupled with heat transfer model and metallurgical criteria, added dynamic coding method and self-adapting mutation on the original genetic algorithm can increase water distribution adaptively and improve the process efficiency. The simulation results of T91 …

An Effective Asexual Genetic Algorithm For Solving The Job Shop Scheduling Problem, Mehrdad Amirghasemi, Reza R. Zamani

Faculty of Engineering and Information Sciences - Papers: Part A

All rights reserved. Abstract By using the notion of elite pool, this paper presents an effective asexual genetic algorithm for solving the job shop scheduling problem. Based on mutation operations, the algorithm selectively picks the solution with the highest quality from the pool and after its modification, it can replace the solution with the lowest quality with such a modified solution. The elite pool is initially filled with a number of non-delay schedules, and then, in each iteration, the best solution of the elite pool is removed and mutated in a biased fashion through running a limited tabu search procedure. …

A Sparsity-Based Training Algorithm For Least Squares Svm, Jie Yang, Jun Ma

Faculty of Engineering and Information Sciences - Papers: Part A

We address the training problem of the sparse Least Squares Support Vector Machines (SVM) using compressed sensing. The proposed algorithm regards the support vectors as a dictionary and selects the important ones that minimize the residual output error iteratively. A measurement matrix is also introduced to reduce the computational cost. The main advantage is that the proposed algorithm performs model training and support vector selection simultaneously. The performance of the proposed algorithm is tested with several benchmark classification problems in terms of number of selected support vectors and size of the measurement matrix. Simulation results show that the proposed algorithm …

A Structure Optimization Algorithm Of Neural Networks For Large-Scale Data Sets, Jie Yang, Jun Ma, Matthew J. Berryman, Pascal Perez

Faculty of Engineering and Information Sciences - Papers: Part A

Over the past several decades, neural networks have evolved into powerful computation systems, which are able to learn complex nonlinear input-output relationship from data. However, the structure optimization problem of neural network is a big challenge for processing huge-volumed, diversified and uncertain data. This paper focuses on this problem and introduces a network pruning algorithm based on sparse representation, termed SRP. The proposed approach starts with a large network, then selects important hidden neurons from the original structure using a forward selection criterion that minimizes the residual output error. Furthermore, the presented algorithm has no constraints on the network type. …

An Improved Genetic Algorithm For Cost-Effective Data-Intensive Service Composition, Lijuan Wang, Jun Shen, Junzhou Luo, Fang Dong

Faculty of Engineering and Information Sciences - Papers: Part A

The explosion of digital data and the dependence on data-intensive services have been recognized as the most significant characteristics of IT trends in the current decade. Designing workflow of data-intensive services requires data analysis from multiple sources to get required composite services. Composing such services requires effective transfer of large data. Thus many new challenges are posed to control the cost and revenue of the whole composition. This paper addresses the data-intensive service composition and presents an innovative data-intensive service selection algorithm based on a modified genetic algorithm. The performance of this new algorithm is also tested by simulations and …

A Multiple Wavelength Unwrapping Algorithm For Digital Fringe Profilometry Based On Spatial Shift Estimation, Pu Cao, Jiangtao Xi, Yanguang Yu, Qinghua Guo

Faculty of Engineering and Information Sciences - Papers: Part A

In this paper, a new approach is presented for solving the problem of spatial shift wrapping associated with Spatial Shift Estimation (SSE)-based Fringe Pattern Profilometry (FPP). The problem arises as the result of fringe reuse (that is, fringes periodic light intensity variance), and the spatial shift can only be identified without ambiguity with the range of a fringe width. It is demonstrated that the problem is similar to the phase unwrapping problem associated with the phase detection based FPP, and the proposed method is inspired by the existing ideas of using multiple images with different wavelengths proposed for phase unwrapping. …

Shape Optimization Of Thin-Walled Steel Sections Using Graph Theory And Aco Algorithm, Pezhman Sharafi, Lip H. Teh, Muhammad N. S Hadi

Faculty of Engineering and Information Sciences - Papers: Part A

This paper presents an intuitive procedure for the shape and sizing optimizations of open and closed thin-walled steel sections using the graph theory. The goal is to find shapes of optimum mass and strength (bi-objectives). The shape optimization of open sections is treated as a multi-objective all-pairs shortest path problem, while that of closed sections is treated as a multi-objective minimum mean cycle problem. The sizing optimization of a predetermined shape is treated as a multi-objective single-pair shortest path problem. Multi-colony ant algorithms are formulated for solving the optimization problems. The verification and numerical examples involving the shape optimizations of …

Distribution Expansion Planning Considering Reliability And Security Of Energy Using Modified Pso (Particle Swarm Optimization) Algorithm, Jamshid Aghaei, Kashem M. Muttaqi, Ali Azizivahed, Mohsen Gitizadeh

Faculty of Engineering and Information Sciences - Papers: Part A

Distribution feeders and substations need to provide additional capacity to serve the growing electrical demand of customers without compromising the reliability of the electrical networks. Also, more control devices, such as DG (Distributed Generation) units are being integrated into distribution feeders. Distribution networks were not planned to host these intermittent generation units before construction of the systems. Therefore, additional distribution facilities are needed to be planned and prepared for the future growth of the electrical demand as well as the increase of network hosting capacity by DG units. This paper presents a multiobjective optimization algorithm for the MDEP (Multi-Stage Distribution …

Optimal Design Of Semi Active Control For Adjacent Buildings Connected By Mr Damper Based On Integrated Fuzzy Logic And Multi-Objective Genetic Algorithm, Mehmet Uz, Muhammad N. S Hadi

Faculty of Engineering and Information Sciences - Papers: Part A

An optimal design strategy based on genetic algorithms (GA) is proposed for nonlinear hysteretic control devices that prevent pounding damage and achieve the best results in seismic response mitigation of two adjacent structures. An integrated fuzzy controller is used in order to provide the interactive relationships between damper forces and input voltages for MR dampers based on the modified Bouc-Wen model. Furthermore, Linear Quadratic Regulator (LQR) and H2/LQG (Linear Quadratic Gaussian) controllers based on clipped voltage law (CVL) are also used to compare the results obtained by fuzzy controller. This study employs the main objectives of the optimal design that …

Power Allocation Algorithm In Ofdm-Based Cognitive Radio Systems, Van Vinh Nguyen, Yang Shouyi, Le Chung Tran

Faculty of Engineering and Information Sciences - Papers: Part A

In orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems, the optimized algorithms for sub-carrier power allocation face the problems of complex iterative calculation and difficult realization. In this paper, we propose an exponential power distribution function and derive a sub-optimal power allocation algorithm. This algorithm aims to allocate power of in-band subcarriers of cognitive users according to the numerical characteristics of the power distribution function by using a convex optimization numerical method under linear constraints. This algorithm has the advantages of fast calculation speed and easy realization, and reduces the interference to the authorized users, which is caused by …

Cost And Time Aware Ant Colony Algorithm For Data Replica In Alpha Magnetic Spectrometer Experiment, Lijuan Wang, Junzhou Luo, Jun Shen, Fang Dong

Faculty of Engineering and Information Sciences - Papers: Part A

Huge collections of data have been created in recent years. Cloud computing provides a way to enable massive amounts of data to work together as data-intensive services. Considering Big Data and the cloud together, which is a practical and economical way to deal with Big Data, will accelerate the availability and acceptability of analysis of the data. Providing an efficient mechanism for optimized data-intensive services will become critical to meet the expected growth in demand. Because the competition is an extremely important factor in the marketplace, the cost model for data-intensive service provision is the key to provide a sustainable …

A Composite Quality-Guided Phase Unwrapping Algorithm For Fast 3d Profile Measurement, Ke Chen, Jiangtao Xi, Yanguang Yu, Limei Song

Faculty of Engineering and Information Sciences - Papers: Part A

Fringe pattern profilometry (FPP) is one of the most promising 3D profile measurement techniques, which has been widely applied in many areas. A challenge problem associated with FPP is the unwrapping of wrapped phase maps resulted from complex object surface shapes. Although existing quality-guided phase unwrapping algorithms are able to solve such a problem, they are usually extensively computational expensive and not able to be applied to fast 3D measurement scenarios. This paper proposes a new quality-guided phase unwrapping algorithm with higher computational efficiency than the conventional ones. In the proposed method, a threshold of quality value is used to …

Approximation Algorithm For Data Broadcasting In Duty Cycled Multi-Hop Wireless Networks, Dianbo Zhao, Kwan-Wu Chin

Faculty of Engineering and Information Sciences - Papers: Part A

Broadcast is a fundamental operation in wireless networks. To this end, many past studies have studied the NP-hard, broadcast problem for always-on multi-hop networks. However, in wireless sensor networks, nodes are powered by batteries, meaning, they have finite energy. Consequently, nodes are required to have a low duty cycle, whereby they switch between active and sleep state periodically. This means that a transmission from a node may not reach all of its neighbors simultaneously. Consequently, any developed broadcast protocols must consider collisions and the wake-up times of neighboring nodes. Henceforth, this paper studies the minimum latency broadcast scheduling problem in …

Path Planning With A Lazy Significant Edge Algorithm (Lsea), Joseph Polden, Zengxi Pan, Nathan Larkin, Stephen Van Duin

Faculty of Engineering and Information Sciences - Papers: Part A

Probabilistic methods have been proven to be effective for robotic path planning in a geometrically complex environment. In this paper, we propose a novel approach, which utilizes a specialized roadmap expansion phase, to improve lazy probabilistic path planning. This expansion phase analyses roadmap connectivity information to bias sampling towards objects in the workspace that have not yet been navigated by the robot. A new method to reduce the number of samples required to navigate narrow passages is also proposed and tested. Experimental results show that the new algorithm is more efficient than the traditional path planning methodologies. It was able …

Gmc ['Gimik]: A One-Variable Monte Carlo Dose Algorithm For Proton Therapy, N Depauw, B Clasie, T Madden, Anatoly B. Rosenfeld, H Kooy

Faculty of Engineering and Information Sciences - Papers: Part A

Presentation made at the International Conference on the Use of Computers in Radiation Therapy, 6-9 May 2013, Melbourne Convention and Exhibition Centre, Australia

Condition Monitoring Of Slow Speed Slewing Bearing Based On Largest Lyapunov Exponent Algorithm And Circular-Domain Feature Extractions, Wahyu Caesarendra, Prabuono Buyung Kosasih, A Kiet Tieu, Craig A. S Moodie

Faculty of Engineering and Information Sciences - Papers: Part A

This paper presents a combined nonlinear and circular features extraction-based condition monitoring method for low speed slewing bearing. The proposed method employs the largest Lyapunov exponent (LLE) algorithm as a signal processing method based on vibration data. LLE is used to detect chaos existence in vibration data in discrete angular positions of the shaft. From the processed data, circular features such as mean, skewness and kurtosis are calculated and monitored. It is shown that the onset and the progressively deteriorating bearing condition can be detected more clearly in circular-domain features compared to time-domain features. The application of the method is …

Economical Data-Intensive Service Provision Supported With A Modified Genetic Algorithm, Lijuan Wang, Jun Shen

Faculty of Engineering and Information Sciences - Papers: Part A

The explosion of digital data and the dependence on data-intensive services have been recognized as the most significant characteristics of the decade. Providing efficient mechanisms for optimized data-intensive services will become critical to meet the expected growing demand. In order to create a cost minimizing data-intensive service composition solution, we design two steps and two negotiation processes over the lifetime of a data-intensive service composition. The solution for the first step is presented in this paper. The proposed service selection algorithm is based on a modified genetic algorithm, which some modifications of crossover and mutation operators are adopted in order …