Computer Sciences Commons^™

Identity-Adaptive Facial Expression Recognition Through Expression Regeneration Using Conditional Generative Adversarial Networks, Huiyuan Yang, Zheng Zhang, Lijun Yin

Computer Science Faculty Research & Creative Works

Subject variation is a challenging issue for facial expression recognition, especially when handling unseen subjects with small-scale labeled facial expression databases. Although transfer learning has been widely used to tackle the problem, the performance degrades on new data. In this paper, we present a novel approach (so-called IA-gen) to alleviate the issue of subject variations by regenerating expressions from any input facial images. First of all, we train conditional generative models to generate six prototypic facial expressions from any given query face image while keeping the identity related information unchanged. Generative Adversarial Networks are employed to train the conditional generative …

Ensuring Data Confidentiality Via Plausibly Deniable Encryption And Secure Deletion – A Survey, Qionglu Zhang, Shijie Jia, Bing Chang, Bo Chen

Michigan Tech Publications, Part 1

Ensuring confidentiality of sensitive data is of paramount importance, since data leakage may not only endanger dataowners’ privacy, but also ruin reputation of businesses as well as violate various regulations like HIPPA andSarbanes-Oxley Act. To provide confidentiality guarantee, the data should be protected when they are preserved inthe personal computing devices (i.e.,confidentiality duringtheirlifetime); and also, they should be rendered irrecoverableafter they are removed from the devices (i.e.,confidentiality after their lifetime). Encryption and secure deletion are usedto ensure data confidentiality during and after their lifetime, respectively.This work aims to perform a thorough literature review on the techniques being used to protect …

Enhancing Autonomous Vehicles With Commonsense: Smart Mobility In Smart Cities, Priya Persaud, Aparna Varde, Stefan Robila

Department of Computer Science Faculty Scholarship and Creative Works

Recent advances in AI include a Law firm hiring a robot lawyer and companies developing autonomous vehicles with robot drivers. Findings from our study have gauged the current cognitive capacity of such systems, indicating areas for improvement. We focus on autonomous vehicles, i.e., those that conduct automated driving and need to make autonomous, i.e., independent decisions. We propose an approach enabled with commonsense knowledge (CSK) from worldwide repositories to simulate intuitive humanlike decision-making in autonomous vehicles. We consider the repository WebChild with a multitude of CSK concepts, properties and relations. We investigate this and related domain-specific knowledge bases (domain KBs) …

Static Analysis Of Context Leaks In Android Applications, Flavio Toffalini, Jun Sun, Martín Cohoa

Research Collection School Of Computing and Information Systems

Android native applications, written in Java and distributed in APK format, are widely used in mobile devices. Their specific pattern of use lets the operating system control the creation and destruction of key resources, such as activities and services (contexts). Programmers are not supposed to interfere with such lifecycle events. Otherwise contexts might be leaked, i.e. they will never be deallocated from memory, or be deallocated too late, leading to memory exhaustion and frozen applications. In practice, it is easy to write incorrect code, which hinders garbage collection of contexts and subsequently leads to context leakage.In this work, we present …

Towards Optimal Concolic Testing, Xinyu Wang, Jun Sun, Zhenbang Chen, Peixin Zhang, Jingyi Wang, Yun Lin

Research Collection School Of Computing and Information Systems

Concolic testing integrates concrete execution (e.g., random testing) and symbolic execution for test case generation. It is shown to be more cost-effective than random testing or symbolic execution sometimes. A concolic testing strategy is a function which decides when to apply random testing or symbolic execution, and if it is the latter case, which program path to symbolically execute. Many heuristics-based strategies have been proposed. It is still an open problem what is the optimal concolic testing strategy. In this work, we make two contributions towards solving this problem. First, we show the optimal strategy can be defined based on …

Augustana Invitational Robotics Challenge 2018, Forrest Stonedahl

Celebration of Learning

We will be hosting the 3rd Annual Augustana Invitational Robotics Challenge. This event will involve student teams from Augustana and potentially several other schools in the region bringing forth the robots that they have designed, built, and programmed, to compete against one another. This year's challenge task involves the careful relocation of soda pop cans.

Quantitative Electroencephalography For Detecting Concussions, Sara Krehbiel, Kathy Hoke, Joanna Wares

Biology and Medicine Through Mathematics Conference

No abstract provided.

Soft Computing Ideas Can Help Earthquake Geophysics, Solymar Ayala Cortez, Aaron A. Velasco, Vladik Kreinovich

Departmental Technical Reports (CS)

Earthquakes can be devastating, thus it is important to gain a good understanding of the corresponding geophysical processing. One of the challenges in geophysics is that we cannot directly measure the corresponding deep-earth quantities, we have to rely on expert knowledge, knowledge which often comes in terms of imprecise ("fuzzy") words from natural language. To formalize this knowledge, it is reasonable to use techniques that were specifically designed for such a formalization -- namely, fuzzy techniques, In this paper, we formulate the problem of optimally representing such knowledge. By solving the corresponding optimization problem, we conclude that the optimal representation …

Fuzzy Ideas Explain A Complex Heuristic Algorithm For Gauging Pavement Conditions, Edgar Daniel Rodriguez Velasquez, Carlos M. Chang Albitres, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

To gauge pavement conditions, researchers have come up with a complex heuristic algorithm that combines several expert estimates of pavement characteristics into a single index -- which is well correlated with the pavement's durability and other physical characteristics. While empirically, this algorithm works well, it lacks physical or mathematical justification beyond being a good fit for the available data. This lack of justification decreases our confidence in the algorithm's results -- since it is known that often, empirically successful heuristic algorithms need change when the conditions change. To increase the practitioners' confidence in the resulting pavement condition estimates, it is …

When Is Propagation Of Interval And Fuzzy Uncertainty Feasible?, Vladik Kreinovich, Andrzej Pownuk, Olga Kosheleva, Aleksandra Belina

Departmental Technical Reports (CS)

In many engineering problems, to estimate the desired quantity, we process measurement results and expert estimates. Uncertainty in inputs leads to the uncertainty in the result of data processing. In this paper, we show how the existing feasible methods for propagating the corresponding interval and fuzzy uncertainty can be extended to new cases of potential practical importance.

Robotic Laundry Folding, Evan Honnold

Dartmouth College Undergraduate Theses

We designed and implemented laundry-folding strategies for a six-jointed robotic arm. Our two main contributions are a "bar-folding" method for folding shirts that have already been unwrinkled and positioned on a table, and a planner for generating new sequences of folds that this method can use. Our "bar-folding" method is quick, accurate, and simple compared to other folding methods, and our fold planner provides an automated alternative to the pre-programmed fold sequences used in other research.

Securing, Standardizing, And Simplifying Electronic Health Record Audit Logs Through Permissioned Blockchain Technology, Jessie Anderson

Dartmouth College Undergraduate Theses

Audit logs perform critical functions in electronic health record (EHR) systems. They provide a chronological record of all operations performed in an EHR, allowing health care organizations to track EHR usage, hold system users accountable for their interactions with patient records, detect anomalous and potentially malicious behavior in the system, protect patient privacy, and develop insight into workflows and interactions among system users. However, several problems exist with the way that current state-of-the-art EHR technology handles audit data. Specifically, current systems complicate the collection and analysis of audit logs because they lack an interoperable audit log structure, spread audit log …

Balancing Patient Control And Practical Access Policy For Electronic Health Records Via Blockchain Technology, Elena Horton

Dartmouth College Undergraduate Theses

Electronic health records (EHRs) have revolutionized the health information technology domain, as patient data can be easily stored and accessed within and among medical institutions. However, in working towards nationwide patient engagement and interoperability goals, recent literature adopts a very patient-centric model---patients own their universal, holistic medical records and control exactly who can access their health data. I contend that this approach is largely impractical for healthcare workflows, where many separate providers require access to health records for care delivery. My work investigates the potential of a blockchain network to balance patient control and provider accessibility with a two-fold approach. …

The Next Generation Of Empress: A Metadata Management System For Accelerated Scientific Discovery At Exascale, Margaret R. Lawson

Dartmouth College Undergraduate Theses

Scientific data sets have grown rapidly in recent years, outpacing the growth in memory and network bandwidths. This I/O bottleneck has made it increasingly difficult for scientists to read and search outputted datasets in an attempt to find features of interest. In this paper, we will present the next generation of EMPRESS, a scalable metadata management service that offers the following solution: users can "tag" features of interest and search these tags without having to read in the associated datasets. EMPRESS provides, in essence, a digital scientific notebook where scientists can write down observations and highlight interesting results, and an …

Navigating Virtual Reality Using Only Your Gazes And Mind, Christopher J. Kymn

Dartmouth College Undergraduate Theses

We present a novel brain-computer interface that allows users to control virtual reality using only their brain waves and eye gazes. The interface allows users to control multiple objects with two dimensions of control. The system is portable, non-invasive, and runs on commercial-grade hardware. It thus provides a high-transmission and user-adaptive interface for users to engage in virtual reality. In addition, we present a training procedure that allows the user to increase control over the brain-computer interface by engaging with the program in an intuitive manner. We explain this procedure and demonstrate its effectiveness in formulating more readily interpreted commands.

Thinking Inside The Box: Converting Encapsulated Postscript To Scalable Vector Graphics, Trevor L. Davis

Dartmouth College Undergraduate Theses

Following the deprecation of the MacDraw graphics application, no extant application arose as a suitable substitute. A team of of Dartmouth undergraduates that included myself set out to rectify this by creating DartDraw, a graphics app that mimics MacDraw along with a few improvements. This was our combined effort over the past year. My role was to convert the Scalable Vector Graphics (SVG) used within the application to an exportable Encapsulated PostScript File. This process relied on an in-depth understanding of both the PostScript language and the React-Redux framework. Computing the bounding boxes of figures proved to be the largest …

Applied Computing For Behavioral And Social Sciences (Acbss) Minor, Farshid Marbouti, Valerie Carr, Belle Wei, Morris Jones, Amy Strage

Faculty Publications

The growing digital economy creates unprecedented demand for technical workers, especially those with both domain knowledge and technical skills. To meet this need, an ACBSS (Applied Computing for Behavioral and Social Sciences) minor degree has been developed by an interdisciplinary team of faculty at San José State University (SJSU). The minor degree comprises four courses: Python programming, algorithms and data structures, R programming, and culminating projects. The first ACBSS cohort started in Fall 2016 with 32 students, and the second cohort in Fall 2017 reached its capacity of 40 students, 62% of whom are female and 35% are underrepresented minority …

Cuoricino Thermal Pulse Classification By Machine Learning Algorithms, Joshua Mann

Physics

Many of the various properties of neutrinos are still a mystery. One unknown is whether neutrinos are Majorana fermions or Dirac fermions. Cuoricino and CUORE are experiments that aim to solve this mystery. Noise reduction in these experiments hinges on the ability to discern among alpha, beta and gamma particle detections using the thermal pulses they create. In this paper, we look at Cuoricino data and attempt to classify pulses, not as alpha, beta or gamma particles, but rather as signal, noise or calibration data. We will use this preliminary testing ground to examine various machine learning algorithms' abilities in …

What Is The Economically Optimal Way To Guarantee Interval Bounds On Control?, Alfredo Vaccaro, Martine Ceberio, Vladik Kreinovich

Departmental Technical Reports (CS)

For control under uncertainty, interval methods enable us to find a box B=[u⁻₁,u⁺₁] X ... X [u⁻_n,u⁺_n] for which any control u from B has the desired properties -- such as stability. Thus, in real-life control, we need to make sure that u_i is in [u⁻_i,u⁺_i] for all parameters u_i describing control. In this paper, we describe the economically optimal way of guaranteeing these bounds.

Analysing Popularity Of Software Testing Careers In Canada, Luiz Fernando Capretz, Pradeep Waychal, Sachin Pardeshi

Electrical and Computer Engineering Publications

Software testing is critical to prevent software failures. Therefore, research has been carried out in testing but that is largely limited to the processand technology dimensions and has not sufficiently addressed the human dimension. Even though there are reports about inadequacies of testing professionals and their skills, only a few studies have tackled the problem. Therefore, we decided to explore the human dimension. We started with the basic problem that plagues the testing profession, the shortage of talent, by asking why do students and professionals are reluctant to consider testing careers, what can be done about that, and is the …

Deaddrop: Message Passing Without Metadata Leakage, Davis Mike Arndt

Computer Science and Software Engineering

Even when network data is encrypted, observers can make inferences about content based on collected metadata. DeadDrop is an exploratory API designed to protect the metadata of a conversation from both outside observers and the facilitating server. To do so, DeadDrop servers are passed no recipient address, instead relying upon the recipient to check for messages of their own volition. In addition, the recipient downloads a copy of every encrypted message on the server to prevent even the server from knowing to whom each message is intended. To these purposes, DeadDrop is mostly successful. However, it does not obscure all …

Finding Spanning Trees In Strongly Connected Graphs With Per-Vertex Degree Constraints, Samuel Benjamin Chase

Computer Science and Software Engineering

In this project, I sought to develop and prove new algorithms to create spanning trees on general graphs with per-vertex degree constraints. This means that each vertex in the graph would have some additional value, a degree constraint d. For a spanning tree to be correct, every vertex v_i in the spanning tree must have a degree exactly equal to a degree constraint d_i. This poses an additional constraint on what would otherwise be a trivial spanning tree problem. In this paper, two proofs related to my studies will be discussed and analyzed, leading to my algorithm …

Extractive Text Summarization With Deep Learning, Garrett G. Chan

Computer Engineering

This project explores extractive text summarization using the capabilities of Deep Learning. The goal of this project is to create an application with a neural network to take in text as its input, and create a summary that is a shorter, condensed version of the input text. This has been implemented in Python by configuring and training a neural network that takes in a vector of features that are extracted from the text using various Natural Language Processing libraries. The implementation demonstrates that we can train simple deep neural networks to successfully summarize text.

Overfitting In Automated Program Repair: Challenges And Solutions, Dinh Xuan Bach Le

Dissertations and Theses Collection (Open Access)

This chapter discusses the main problem and motivation of this dissertation. It also discusses a quantification of various research issues directly related to the dissertation. A summary of works done will also be presented along with the structure of the dissertation.

Algorithm: Simplified Data Encription Standart, Flon Llapashtica

Theses and Dissertations

Duke pasur parasysh se ne po jetojmë në Epokën e Teknologjisë ku çdo gjë po digjitalizohet, vlera e informacionit është duke u bërë më e rëndësishme dhe mendohet qe në një të ardhme të afërt të shndërrohet në valutën e re, ku për këtë qëllim duhet ti kushtohet një rëndësi e veçantë shifrimit të të dhënave në mënyrë që të ruhet privatësia dhe siguria e tyre.

Me rritjen e vlerës së informacionit është rritur edhe rreziku për keqpërdorimin e tij nga spiunët apo hajdutët e internetit, të cilët mundohen që të përfitojnë në mënyra të ndryshme.

Shifrimi është mënyra e …

Data Mining Ancient Script Image Data Using Convolutional Neural Networks, Shruti Daggumati, Peter Revesz

School of Computing: Conference and Workshop Papers

The recent surge in ancient scripts has resulted in huge image libraries of ancient texts. Data mining of the collected images enables the study of the evolution of these ancient scripts. In particular, the origin of the Indus Valley script is highly debated. We use convolutional neural networks to test which Phoenician alphabet letters and Brahmi symbols are closest to the Indus Valley script symbols. Surprisingly, our analysis shows that overall the Phoenician alphabet is much closer than the Brahmi script to the Indus Valley script symbols.

Raymarching The Mandelbulb Fractal In Vr, Timotheus Alexander Letz

Computer Engineering

Elaborate 3D fractals, such as the mandelbulb, offer fascinating depths and structures that bear self-similarity as one zooms in closer and closer. Traditional rendering techniques focus on pre-rendering the fractal, to bypass the need for real-time display. To display and explore the mandelbulb in VR, this real-time display is needed, and can be provided through the use of “Raymarching”, a technique that allows for the rendering of scenes within the GPU. This paper explores various techniques and systems used to provide, augment, and accelerate this process.

Design Of Robust And Efficient Topology Using Enhanced Gene Regulatory Networks, Satyaki Roy, Vijay K. Shah, Sajal K. Das

Computer Science Faculty Research & Creative Works

Biological networks are characterized by their inherent robustness against component failures. Gene regulatory networks (GRNs) are biological networks with graph properties contributing to their innate functional robustness. In this paper, we first propose a three-tier topological characterization to study the graph properties of GRN, namely scale free out-degree distribution, low graph density, and abundance of subgraphs, called motifs. We then present a novel edge rewiring mechanism, consisting of edge addition and deletion algorithms, to remedy its vulnerability against failure of well-connected nodes while preserving its graph properties. We discuss the preferential attachment growth-based edge addition and greedy edge deletion. We …

Augmented Personalized Health: Using Semantically Integrated Multimodal Data For Patient Empowered Health Management Strategies, Amit P. Sheth, Hong Y. Yip, Utkarshani Jaimini, Dipesh Kadariya, Vaikunth Sridharan, R. Venkataramanan, Tanvi Banerjee, Krishnaprasad Thirunarayan, Maninder Kalra

Kno.e.sis Publications

Healthcare as we know it is in the process of going through a massive change from:

1. Episodic to continuous

2. Disease-focused to wellness and quality of life focused

3. Clinic-centric to anywhere a patient is

4. Clinician controlled to patient empowered

5. Being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven URL: https://mhealth.md2k.org/2018-tech-showcase-home

From 2,772 Segments To Five Personas: Summarizing A Diverse Online Audience By Generating Culturally Adapted Personas, Joni Salminen, Sercan Sengun, Haewoon Kwak, Bernard J. Jansen, Jisun An, Soon-Gyu Jung, Sarah Vieweg, D. Fox Harrell

Research Collection School Of Computing and Information Systems

Understanding users in the era of social media is challenging, requiring organizations to adopt novel computation-aided approaches. To exemplify such an approach, we retrieved information on millions of interactions with YouTube video content from a major Middle Eastern media outlet, to automatically generate personas that capture how different audience segments interact with thousands of individual content pieces. Then, we used qualitative data to provide additional insights into the automatically generated persona profiles. Our findings provide insights into social media usage in the Middle East and demonstrate the application of a novel methodology that generates culturally adapted personas of social media …