Computer Sciences Commons^™

Systems For Free Parking Assignment, Abeer M. Hakeem

Dissertations

Finding a free, curbside parking spaces in metropolitan areas, especially during rush hours, is difficult for drivers. The difficulty arises from not knowing where the available spaces may be at that time; and, even if the spaces are known, many vehicles may pursue the same spaces, causing serious parking contention and traffic congestion. This dissertation presents three cost-effective and easily deployable free parking assignment systems that optimize the travel time of the drivers.

The first contribution is the Free Parking System (FPS), a centralized solution that solves the curbside parking problem. Unlike existing solutions, FPS is cost-effective, as it does …

Ranking Volatility In Building Energy Consumption Using Ensemble Learning And Information Entropy, Kunal Sharma, Jung-Ho Lewe

Georgia Journal of Science

Given the rise in building energy consumption and demand worldwide, energy inefficiency detection has become extremely important. A significant portion of the energy used in commercial buildings is wasted as a result of poor maintenance, degradation or improperly controlled equipment. Most facilities employ sensors to track energy consumption across multiple buildings. Smart fault detection and diagnostic systems use various anomaly detection techniques to discover point anomalies in consumption. While these systems work reasonably well in detecting equipment anomalies over short-term intervals, further exploration is needed in finding methods that consider long-term consumption to detect anomalous buildings. This paper presents a …

An Effective Method For Attribute Subset Selection, Considering The Resource In Pattern Recognition, Bakhtiyorjon Bakirovich Akbaraliev

Chemical Technology, Control and Management

An analytical method for determining informative sets of features (INP) is developed, taking into account the resource for criteria based on the use of a measure of dispersion of classified objects. The areas of existence of the solution are defined. The statements and properties for the Fischer-type information criterion are proved, using which the proposed analytical method for determining the INP guarantees optimal results in the sense of maximizing the selected functional. The appropriateness of choosing this type of informative criterion is justified. A method for transforming attributes is proposed. The universality of the method in relation to the type …

Prerequisite Course Recommendation Based On Course Description And Students’ Grades, Haozhe Zhou

The Journal of Purdue Undergraduate Research

No abstract provided.

Estimating Vehicular Traffic Intensity With Deep Learning And Semantic Segmentation, Logan Bradley-Trietsch

The Journal of Purdue Undergraduate Research

No abstract provided.

Comparison Of Machine Learning Models: Gesture Recognition Using A Multimodal Wrist Orthosis For Tetraplegics, Charlie Martin

The Journal of Purdue Undergraduate Research

Many tetraplegics must wear wrist braces to support paralyzed wrists and hands. However, current wrist orthoses have limited functionality to assist a person’s ability to perform typical activities of daily living other than a small pocket to hold utensils. To enhance the functionality of wrist orthoses, gesture recognition technology can be applied to control mechatronic tools attached to a novel fabricated wrist brace. Gesture recognition is a growing technology for providing touchless human-computer interaction that can be particularly useful for tetraplegics with limited upper-extremity mobility. In this study, three gesture recognition models were compared—two dynamic time-warping models and a hidden …

Find Me If You Can: Aligning Users In Different Social Networks, Priyanka Kasbekar, Katerina Potika, Chris Pollett

Faculty Publications, Computer Science

Online Social Networks allow users to share experiences with friends and relatives, make announcements, find news and jobs, and more. Several have user bases that number in the hundred of millions and even billions. Very often many users belong to multiple social networks at the same time under possibly different user names. Identifying a user from one social network on another social network gives information about a user's behavior on each platform, which in turn can help companies perform graph mining tasks, such as community detection and link prediction. The process of identifying or aligning users in multiple networks is …

Big Data Energy Management, Analytics And Visualization For Residential Areas, Ragini Gupta, A. R. Al-Ali, Imran A. Zualkernan, Sajal K. Das

Computer Science Faculty Research & Creative Works

With the rapid development of IoT based home appliances, it has become a possibility that home owners share with Utilities in the management of home appliances energy consumption. Thus, the proposed work empowers home owners to manage their home appliances energy consumption and allow them to compare their consumption with respect to their local community total consumption. This serves as a nudge in consumer's behavior to schedule their home appliances operation according to their local community consumption profile and trend. Utilizing the same common communication infrastructure, it also allows the utilities on different consumption levels (community, state, country) to monitor …

A Fortran-Keras Deep Learning Bridge For Scientific Computing, Jordan Ott, Mike Pritchard, Natalie Best, Erik Linstead, Milan Curcic, Pierre Baldi

Engineering Faculty Articles and Research

Implementing artificial neural networks is commonly achieved via high-level programming languages such as Python and easy-to-use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way …

An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja

Publications and Research

Machine Learning (ML), including Deep Learning (DL), systems, i.e., those with ML capabilities, are pervasive in today's data-driven society. Such systems are complex; they are comprised of ML models and many subsystems that support learning processes. As with other complex systems, ML systems are prone to classic technical debt issues, especially when such systems are long-lived, but they also exhibit debt specific to these systems. Unfortunately, there is a gap of knowledge in how ML systems actually evolve and are maintained. In this paper, we fill this gap by studying refactorings, i.e., source-to-source semantics-preserving program transformations, performed in real-world, open-source …

Analysis Of Distributed And Autonomous Scheduling Functions For 6tisch Networks, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

The 6TiSCH architecture is expected to play a significant role to enable the Internet of Things paradigm also in industrial environments, where reliability and timeliness are of paramount importance to support critical applications. Many research activities have focused on the Scheduling Function (SF) used for managing the allocation of communication resources in order to guarantee the application requirements. Two different approaches have mainly attracted the interest of researchers, namely distributed and autonomous scheduling. Although many different (both distributed and autonomous) SFs have been proposed and analyzed, a direct comparison of these two approaches is still missing. In this work, we …

Machine Learning Corrected Quantum Dynamics Calculations, A. Jasinski, J. Montaner, R. C. Forrey, B. H. Yang, P. C. Stancil, Naduvalath Balakrishnan, J. Dai, A. Vargas-Hernandez, R. V. Krems

Chemistry and Biochemistry Faculty Research

Quantum scattering calculations for all but low-dimensional systems at low energies must rely on approximations. All approximations introduce errors. The impact of these errors is often difficult to assess because they depend on the Hamiltonian parameters and the particular observable under study. Here, we illustrate a general, system- and approximation-independent, approach to improve the accuracy of quantum dynamics approximations. The method is based on a Bayesian machine learning (BML) algorithm that is trained by a small number of exact results and a large number of approximate calculations, resulting in ML models that can generalize exact quantum results to different dynamical …

Cryptography, Passwords, Privacy, And The Fifth Amendment, Gary C. Kessler, Ann M. Phillips

Journal of Digital Forensics, Security and Law

Military-grade cryptography has been widely available at no cost for personal and commercial use since the early 1990s. Since the introduction of Pretty Good Privacy (PGP), more and more people encrypt files and devices, and we are now at the point where our smartphones are encrypted by default. While this ostensibly provides users with a high degree of privacy, compelling a user to provide a password has been interpreted by some courts as a violation of our Fifth Amendment protections, becoming an often insurmountable hurdle to law enforcement lawfully executing a search warrant. This paper will explore some of the …

Evaluation Of Standard And Semantically-Augmented Distance Metrics For Neurology Patients, Daniel B. Hier, Jonathan Kopel, Steven U. Brint, Donald C. Wunsch, Gayla R. Olbricht, Sima Azizi, Blaine Allen

Electrical and Computer Engineering Faculty Research & Creative Works

Background: Patient distances can be calculated based on signs and symptoms derived from an ontological hierarchy. There is controversy as to whether patient distance metrics that consider the semantic similarity between concepts can outperform standard patient distance metrics that are agnostic to concept similarity. The choice of distance metric can dominate the performance of classification or clustering algorithms. Our objective was to determine if semantically augmented distance metrics would outperform standard metrics on machine learning tasks.

Methods: We converted the neurological findings from 382 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated patient distances by …

New Bounds On Augmenting Steps Of Block-Structured Integer Programs, Lin Chen, Martin Koutecký, Lei Xu, Weidong Shi

Computer Science Faculty Publications

Iterative augmentation has recently emerged as an overarching method for solving Integer Programs (IP) in variable dimension, in stark contrast with the volume and flatness techniques of IP in fixed dimension. Here we consider 4-block n-fold integer programs, which are the most general class considered so far. A 4-block n-fold IP has a constraint matrix which consists of n copies of small matrices A, B, and D, and one copy of C, in a specific block structure. Iterative augmentation methods rely on the so-called Graver basis of the constraint matrix, which constitutes a set of fundamental augmenting steps. All existing …

Network Architecture For Generating A Labeled Overhead Image, Nathan Jacobs, Scott Workman

Computer Science Faculty Patents

A computer-implemented process is disclosed for generating a labeled overhead image of a geographical area. A plurality of ground level images of the geographical area is retrieved. A ground level feature map is generated, via a ground level convolutional neural network, based on features extracted from the plurality of ground level images. An overhead image of the geographical area is also retrieved. A joint feature map is generated, via an overhead convolutional neural network based on the ground level feature map and features extracted from the plurality of ground level images. Geospatial function values at a plurality of pixels of …

Benchmarking Deep Learning Robustness On Images With Two-Factor Corruption, Wei Dai

Theses and Dissertations

Deep learning is an increasingly popular technology used for such tasks as image classification, speech recognition, and language translation. Deep learning technology is under active development, so that many innovative chipsets, useful frameworks, creative algorithms, and big data sets are emerging. Previous research using image data for measuring deep learning classifiers has usually focused on high quality image datasets. Thus, scientists usually have not tried to benchmark deep learning robustness with imperfect images. However, high quality images are not always available, and people also expect image classifier robustness in applications where image quality is not high. Therefore, in this research …

An Effective Method For Synthesizing The Abbreviated Disjunctive Normal Form Of A Boolean Function, Erkin Urunbaev

Scientific Journal of Samarkand University

In discrete mathematics, minimizing Boolean functions in the class of disjunctive normal forms is one of the necessary tasks. This paper presents an effective method for synthesizing the reduced disjunctive normal form of a Boolean function.

Self-Stabilizing Token Distribution On Trees With Constant Space, Yuichi Sudo, Ajoy K. Datta, Lawrence L. Larmore, Toshimitsu Masuzawa

Computer Science Faculty Research

Self-stabilizing and silent distributed algorithms for token distribution in rooted tree networks are given. Initially, each process of a graph holds at most l tokens. Our goal is to distribute the tokens uniformly in the whole network so that every process holds exactly k tokens. In the initial configuration, the total number of tokens in the network may not be nk where n is the number of processes in the network. The root process is given the ability to create a new token or remove a token from the network. We aim to minimize the convergence time, the number of …

Misogyny Detection In Social Media On The Twitter Platform, Elena Shushkevich

Doctoral

The thesis is devoted to the problem of misogyny detection in social media. In the work we analyse the difference between all offensive language and misogyny language in social media, and review the best existing approaches to detect offensive and misogynistic language, which are based on classical machine learning and neural networks. We also review recent shared tasks aimed to detect misogyny in social media, several of which we have participated in. We propose an approach to the detection and classification of misogyny in texts, based on the construction of an ensemble of models of classical machine learning: Logistic Regression, …

A First Look At Forensic Analysis Of Sailfishos, Krassimir Tzvetanov, Umit Karabiyik

Faculty Publications

SailfishOS is a Linux kernel-based embedded device operation system, mostly deployed on cell phones. Currently, there is no sufficient research in this space, and at the same time, this operating system is gaining popularity, so it is likely for investigators to encounter it in the field. This paper focuses on mapping the digital artifacts pertinent to an investigation, which can be found on the filesystem of a phone running SailfishOS 3.2. Currently, there is no other known publicly available research and no commercially available solutions for the acquisition and analysis of this platform. This is a major gap, as the …

Blockchain Technology And Freight Forwarder Exploration Of Implications Focused On Practitioners In Shanghai, Johannes Van Bohemen

World Maritime University Dissertations

No abstract provided.

A Two-Stage Model For Social Network Investigations In Digital Forensics, Anne David, Sarah Morris, Gareth Appleby-Thomas

Journal of Digital Forensics, Security and Law

This paper proposes a two-stage model for identifying and contextualizing features from artefacts created as a result of social networking activity. This technique can be useful in digital investigations and is based on understanding and the deconstruction of the processes that take place prior to, during and after user activity; this includes corroborating artefacts. Digital Investigations are becoming more complex due to factors such as, the volume of data to be examined; different data formats; a wide range of sources for digital evidence; the volatility of data and the limitations of some of the standard digital forensic tools. This paper …

Paralleling Simulation Of Operation Plan Based On Decision Point Controlling, Zhanguang Cao, Pinggang Yu, Kuo Wang

Journal of System Simulation

Abstract: Operation plan simulation traditionally uses serial and static method. It can't be fulfilled for the military need of swift simulation and real-time decision in the modern complicated and dynamically battle field. So, the higher complexity and dynamical decision according to situation of the operation plan bring new challenge to the simulation. Operation Plan Paralleling Simulation Based on Decision Point Controlling (P2SDPC) can realize the operation plan's dynamic adjusting and cutting impossible branch based on decision point controlling technology. From then on, the efficiency of operation plan's simulation could be improved by the way of paralleling simulation. Thus the problem …

Model And Simulation Of Virtual Character Based On Real-Time Sensor Data-Driven, Lufeng Luo, Xiangjun Zou, Zhang Cong, Xie Lei

Journal of System Simulation

Abstract: The real-time control of virtual character behavior is a challenge in simulating virtual exercise. A kind of virtual character running behavior control method based on real-time data-driven was proposed, the real-time data acquisition with the help of sensor was expounded, the time-series law of human body running was analyzed, the human body posture mathematics expression was modeled, and a limb movement parts and rotation degrees of freedom computing model was proposed. By use of rotation angle method, the space and time relationship of each part of human movement chains was established and the movement process frame when moving was …

Method For Auto-Generating Cartoon Based On A Set Of Portrait Dictionary, Jingjing Sun, Jiajun Yu, Li Bei, Zhifeng Xie, Youdong Ding

Journal of System Simulation

Abstract: An efficient method of auto-generating portrait cartoon based on true portrait pictures was proposed. The first step was to establish a set of portrait dictionary. As a result, the portrait was divided into six components including eyebrows, eyes, nose, mouth, face, hair, so that the material library of cartoon corresponding to each component could be established. The next was to collect some real portrait images and synthesize them using the component material, meanwhile to calculate the facial shape features and the texture features of hair corresponding to each portrait. The second step was to input a real portrait image …

Neural Network Model Of Information Fusion For Coal Storage And Kinetic Energy Of Ball Mill, Bai Yan, He Fang

Journal of System Simulation

Abstract: A dynamic mathematical model of coal pulverizing system was analyzed. Simulation experiments on mill operation process were conducted by PFC3D software platform based on discrete element method. The associated data between different coal quality, coal storage and balls' motion were obtained under certain quantitative optimized operating parameters configuration. Neural network model of information fusion for coal storage and kinetic energy of ball mill was established by using an adaptive combination learning algorithm. Coal storage in mill cylinder was predicted from the energy point of view. The results indicate that there is a close relationship between coal storage, pulverizing efficiency …

Modeling Of High-Speed Railway Operation Informational Interaction Process Using Oopn, Liping Feng, Qiyuan Peng, Wen Chao

Journal of System Simulation

Abstract: High-speed railway transportation is an informational interaction process involving multi-levels and multiple subsystems. The majority of the present study focuses on the local system, causing some limitations to the overall control of high-speed rail traffic command. The high-speed railway operation process covering traffic control subsystem, train control subsystem, computer interlocking subsystem were researched with the key point of information interaction. Then the future directions were proposed, modeling and application respectively, which could remind of the deepen research on high-speed railway operation. Based on OOPN, the HTOIIP model with the formalized definition was built.

Application Of Domain Knowledge Representation In Simulation Tutoring For Weapon Utilization, Zhang Chi, Danhua Peng, Kedi Huang

Journal of System Simulation

Abstract: According to the requirements and characteristics of the simulation tutoring system for weapon utilization, a knowledge base depending on the domain ontology was designed. The knowledge for weapon utilization, the knowledge representation, and the method of knowledge representation were introduced. Through a combination of the ontology technology and various existing models, the key points of using specific weapon could be mastered by processing the date, information, and experience from expert. The conceptual model of knowledge was built. A knowledge base for weapon utilization was designed and implemented. The structure and function of the simulation tutoring system and rule-based expert …

Method For Auto-Detection Of Tracking Moving Objects In Complicated Dynamic Environment, Yanling Wang, Guanglun Li, Lin Xiao

Journal of System Simulation

Abstract: An approach was proposed to solve the problem of not accurately identifying the moving targets in the complex dynamic environment. The moving objects were detected by utilizing the method based on the HSV color space and auto-updating the background. The background subtraction method based on the HSV color space was used to solve the problem that the objects could not be accurately identified for the color similarity of objects and background and the influence of the shadow of the moving targets. The background change was divided into the temporary stability and the rapid change, and an automatically updating double …