Physical Sciences and Mathematics Commons^™

Enhancing Classroom Instruction With Online News, Michael D. Ekstrand, Katherine Landau Wright, Maria Soledad Pera

Computer Science Faculty Publications and Presentations

Purpose

Investigate how school teachers look for informational texts for their classrooms. Access to current, varied, and authentic informational texts improves learning outcomes for K-12 students, but many teachers lack resources to expand and update readings. The Web offers freely-available resources, but finding suitable ones is time-consuming. This research lays the groundwork for building tools to ease that burden.

Methodology

This paper reports qualitative findings from a study in two stages: (1) a set of semi-structured interviews, based on the Critical Incident Technique, eliciting teachers’ information-seeking practices and challenges; and (2) observations of teachers using a prototype teaching-oriented news search …

Different Approximation Algorithms For Channel Scheduling In Wireless Networks, Qiufen Ni, Chuanhe Huang, Panos M. Pardalos, Jia Ye, Bin Fu

Computer Science Faculty Publications and Presentations

We introduce a new two-side approximation method for the channel scheduling problem, which controls the accuracy of approximation in two sides by a pair of parameters . We present a series of simple and practical-for-implementation greedy algorithms which give constant factor approximation in both sides. First, we propose four approximation algorithms for the weighted channel allocation problem: 1. a greedy algorithm for the multichannel with fixed interference radius scheduling problem is proposed and an one side -IS-approximation is obtained; 2. a greedy -approximation algorithm for single channel with fixed interference radius scheduling problem is presented; 3. we improve the existing …

Relocating Units In Robot Swarms With Uniform Control Signals Is Pspace-Complete, David Caballero, Angel A. Cantu, Timothy Gomez, Austin Luchsinger, Robert Schweller, Tim Wylie

Computer Science Faculty Publications and Presentations

This paper investigates a restricted version of robot motion planning, in which particles on a board uniformly respond to global signals that cause them to move one unit distance in a particular direction on a 2D grid board with geometric obstacles. We show that the problem of deciding if a particular particle can be relocated to a specified location on the board is PSPACE-complete when only allowing 1x1 particles. This shows a separation between this problem, called the relocation problem, and the occupancy problem in which we ask whether a particular location can be occupied by any particle on the …

Evaluating Stochastic Rankings With Expected Exposure, Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, Ben Carterette

Computer Science Faculty Publications and Presentations

We introduce the concept of expected exposure as the average attention ranked items receive from users over repeated samples of the same query. Furthermore, we advocate for the adoption of the principle of equal expected exposure: given a fixed information need, no item should receive more or less expected exposure than any other item of the same relevance grade. We argue that this principle is desirable for many retrieval objectives and scenarios, including topical diversity and fair ranking. Leveraging user models from existing retrieval metrics, we propose a general evaluation methodology based on expected exposure and draw connections to related …

The Majority Rule: A General Protection On Recommender System, Lei Xu, Lin Chen, Martin Flores, Hansheng Lei, Liyu Zhang, Mahmoud K. Quweider, Fitratullah Khan, Weidong Shi

Computer Science Faculty Publications and Presentations

Recommender systems are widely used in a variety of scenarios, including online shopping, social network, and contents distribution. As users rely more on recommender systems for information retrieval, they also become attractive targets for cyber-attacks. The high-level idea of attacking a recommender system is straightforward. An adversary selects a strategy to inject manipulated data into the database of the recommender system to influence the recommendation results, which is also known as a profile injection attack. Most existing works treat attacking and protection in a static manner, i.e., they only consider the adversary’s behavior when analyzing the influence without considering normal …

Lenskit For Python: Next-Generation Software For Recommender Systems Experiments, Michael D. Ekstrand

Computer Science Faculty Publications and Presentations

LensKit is an open-source toolkit for building, researching, and learning about recommender systems. First released in 2010 as a Java framework, it has supported diverse published research, small-scale production deployments, and education in both MOOC and traditional classroom settings. In this paper, I present the next generation of the LensKit project, re-envisioning the original tool's objectives as flexible Python package for supporting recommender systems research and development. LensKit for Python (LKPY) enables researchers and students to build robust, flexible, and reproducible experiments that make use of the large and growing PyData and Scientific Python ecosystem, including scikit-learn, and TensorFlow. To …

Real-Time Road Hazard Information System, Carlos Pena-Caballero, Dong-Chul Kim, Adolfo Gonzalez, Osvaldo Castellanos, Angel A. Cantu, Jungseok Ho

Computer Science Faculty Publications and Presentations

Infrastructure is a significant factor in economic growth for systems of government. In order to increase economic productivity, maintaining infrastructure quality is essential. One of the elements of infrastructure is roads. Roads are means which help local and national economies be more productive. Furthermore, road damage such as potholes, debris, or cracks is the cause of many on-road accidents that have cost the lives of many drivers. In this paper, we propose a system that uses Convolutional Neural Networks to detect road degradations without data pre-processing. We utilize the state-of-the-art object detection algorithm, YOLO detector for the system. First, we …

Analyzing Sensor-Based Individual And Population Behavior Patterns Via Inverse Reinforcement Learning, Beiyu Lin, Diane J. Cook

Computer Science Faculty Publications and Presentations

Digital markers of behavior can be continuously created, in everyday settings, using time series data collected by ambient sensors. The goal of this work was to perform individual- and population-level behavior analysis from such time series sensor data. In this paper, we introduce a novel algorithm-Resident Relative Entropy-Inverse Reinforcement Learning (RRE-IRL)-to perform an analysis of a single smart home resident or a group of residents, using inverse reinforcement learning. By employing this method, we learnt an individual's behavioral routine preferences. We then analyzed daily routines for an individual and for eight smart home residents grouped by health diagnoses. We observed …

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

Computer Science Faculty Publications and Presentations

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 …

Falcon: Framework For Anomaly Detection In Industrial Control Systems, Subin Sapkota, A.K.M. Nuhil Mehdy, Stephen Reese, Hoda Mehrpouyan

Computer Science Faculty Publications and Presentations

Industrial Control Systems (ICS) are used to control physical processes in critical infrastructure. These systems are used in a wide variety of operations such as water treatment, power generation and distribution, and manufacturing. While the safety and security of these systems are of serious concern, recent reports have shown an increase in targeted attacks aimed at manipulating physical processes to cause catastrophic consequences. This trend emphasizes the need for algorithms and tools that provide resilient and smart attack detection mechanisms to protect ICS. In this paper, we propose an anomaly detection framework for ICS based on a deep neural network. …

Polyhedra Circuits And Their Applications, Bin Fu, Pengfei Gu, Yuming Zhao

Computer Science Faculty Publications and Presentations

To better compute the volume and count the lattice points in geometric objects, we propose polyhedral circuits. Each polyhedral circuit characterizes a geometric region in Rd . They can be applied to represent a rich class of geometric objects, which include all polyhedra and the union of a finite number of polyhedron. They can be also used to approximate a large class of d-dimensional manifolds in Rd . Barvinok [3] developed polynomial time algorithms to compute the volume of a rational polyhedron, and to count the number of lattice points in a rational polyhedron in Rd with a fixed dimensional …

Computational Complexity Characterization Of Protecting Elections From Bribery, Lin Chen, Ahmed Sunny, Lei Xu, Shouhuai Xu, Zhimin Gao, Yang Lu, Weidong Shi, Nolan Shah

Computer Science Faculty Publications and Presentations

The bribery problem in election has received considerable attention in the literature, upon which various algorithmic and complexity results have been obtained. It is thus natural to ask whether we can protect an election from potential bribery. We assume that the protector can protect a voter with some cost (e.g., by isolating the voter from potential bribers). A protected voter cannot be bribed. Under this setting, we consider the following bi-level decision problem: Is it possible for the protector to protect a proper subset of voters such that no briber with a fixed budget on bribery can alter the election …

Hardness Of Sparse Sets And Minimal Circuit Size Problem, Bin Fu

Computer Science Faculty Publications and Presentations

We study the magnification of hardness of sparse sets in nondeterministic time complexity classes on a randomized streaming model. One of our results shows that if there exists a 2no(1) -sparse set in NDTIME(2no(1)) that does not have any randomized streaming algorithm with no(1) updating time, and no(1) space, then NEXP≠BPP , where a f(n)-sparse set is a language that has at most f(n) strings of length n. We also show that if MCSP is ZPP -hard under polynomial time truth-table reductions, then EXP≠ZPP .

Building Patterned Shapes In Robot Swarms With Uniform Control Signals, David Caballero, Angel A. Cantu, Timothy Gomez, Austin Luchsinger, Robert Schweller, Tim Wylie

Computer Science Faculty Publications and Presentations

This paper investigates a restricted version of robot motion planning, in which particles on a board uniformly respond to global signals that cause them to move one unit distance in a particular direction. We look at the problem of assembling patterns within this model. We first derive upper and lower bounds on the worst-case number of steps needed to reconfigure a general purpose board into a target pattern. We then show that the construction of k-colored patterns of size-n requires Ω(n log k) steps in general, and Ω(n log k + √ k) steps if the constructed shape must always …

Ieee Access Special Section Editorial: Machine Learning Designs, Implementations And Techniques, Shadi A. Aljawarneh, Oguz Bayat, Juan A. Lara, Robert P. Schumaker

Computer Science Faculty Publications and Presentations

IEEE access special section editorial.

Rrsds: Towards A Robot-Ready Spoken Dialogue System, Casey Kennington, Daniele Moro, Lucas Marchand, Jake Carns, David Mcneill

Computer Science Faculty Publications and Presentations

Spoken interaction with a physical robot requires a dialogue system that is modular, multimodal, distributive, incremental and temporally aligned. In this demo paper, we make significant contributions towards fulfilling these requirements by expanding upon the ReTiCo incremental framework. We outline the incremental and multimodal modules and how their computation can be distributed. We demonstrate the power and flexibility of our robot-ready spoken dialogue system to be integrated with almost any robot.

Learning Word Groundings From Humans Facilitated By Robot Emotional Displays, David Mcneill, Casey Kennington

Computer Science Faculty Publications and Presentations

In working towards accomplishing a human-level acquisition and understanding of language, a robot must meet two requirements: the ability to learn words from interactions with its physical environment, and the ability to learn language from people in settings for language use, such as spoken dialogue. In a live interactive study, we test the hypothesis that emotional displays are a viable solution to the cold-start problem of how to communicate without relying on language the robot does not–indeed, cannot–yet know. We explain our modular system that can autonomously learn word groundings through interaction and show through a user study with 21 …

Semantics With Feeling: Emotions For Abstract Embedding, Affect For Concrete Grounding, Daniele Moro, Gerardo Caracas, David Mcneill, Casey Kennington

Computer Science Faculty Publications and Presentations

An important yet underexplored aspect of meaning in both distributional and grounded models of semantics is emotion. In this paper, we explore how emotion can be predicted from descriptions of robot behaviors represented with embeddings. We then compare this approach with a grounded model that maps corresponding robot behaviors represented as internal states to the same emotion labels and discover comparable results. We then take the predictions from the second model and use them as a proxy for concrete affect (as opposed to abstract emotion) and use this derived affect to ground a semantic classifier in a retrieval task and …

Weak Mitoticity Of Bounded Disjunctive And Conjunctive Truth-Table Autoreducible Sets, Liyu Zhang, Mahmoud K. Quweider, Hangsheng Lei, Fitratullah Khan

Computer Science Faculty Publications and Presentations

Glaßer et al. (SIAMJCOMP 2008 and TCS 2009)2 proved existence of two sparse sets A and B in EXP, where A is 3-tt (truth-table) polynomial-time autoreducible but not weakly polynomial-time Turing mitotic and B is polynomial-time 2-tt autoreducible but not weakly polynomial-time 2-tt mitotic. We unify and strengthen both of those results by showing that there is a sparse set in EXP that is polynomial-time 2-tt autoreducible but not even weakly polynomial-time Turing mitotic. All these results indicate that polynomial-time autoreducibilities in general do not imply polynomial-time mitoticity at all with the only exceptions of the many-one and 1-tt reductions. …

Infusing Raspberry Pi In The Computer Science Curriculum For Enhanced Learning, Fitratullah Khan, Mahmoud K. Quweider, Ala Qubbaj, Emmett Tomai, Lei Xu, Liyu Zhang, Hansheng Lei

Computer Science Faculty Publications and Presentations

With the advent of cloud computing, the Internet of Things (IoT), and mobile computing, CS faculty are continuously revamping the curriculum material to address such burgeoning set of technologies in practical and relatable ways. Raspberry Pi (RPi) devices represent an ideal hardware/software framework that embodies all these technologies through its simple architecture, small form factor (that minimizes the volume and footprint of a desktop computer), and ability to integrate various sensors that network together and connect to the Cloud. Therefore, one of the strategies of Computer Science Department, to enhance depth of learning concepts, has been to infuse Raspberry Pi …

Cybersecurity, Digital Forensics, And Mobile Computing: Building The Pipeline Of Next-Generation University Graduates Through Focused High School Summer Camps, Mahmoud K. Quweider, Fitratullah Khan, Liyu Zhang, Lei Xu, Yessica Rodriguez, Yessenia Rodriguez

Computer Science Faculty Publications and Presentations

To prepare the next generation of skilled university graduates that would help in filling the national need for cybersecurity, digital forensics, and mobile computing professionals, a team of minority/under-represented graduate students, the University Upward Bound Program (a federally funded program and part of the U.S. Department of Education; one of 967 programs nationwide) staff, and faculty from the Computer Science (CS) department got together and proposed a focused 10-week long funded summer camp for two local high schools with the following objectives:

1. Provide graduate students to instruct in the areas of` mobile application development, forensics and cyber Security.

2. …

Visual Attention Consistency Under Image Transforms For Multi-Label Image Classification, Hao Guo, Kang Zheng, Xiaochuan Fan, Hongkai Yu, Song Wang

Computer Science Faculty Publications and Presentations

Human visual perception shows good consistency for many multi-label image classification tasks under certain spatial transforms, such as scaling, rotation, flipping and translation. This has motivated the data augmentation strategy widely used in CNN classifier training -- transformed images are included for training by assuming the same class labels as their original images. In this paper, we further propose the assumption of perceptual consistency of visual attention regions for classification under such transforms, i.e., the attention region for a classification follows the same transform if the input image is spatially transformed. While the attention regions of CNN classifiers can be …

Using Continuous Sensor Data To Formalize A Model Of In-Home Activity Patterns, Beiyu Lin, Diane J. Cook, Maureen Schmitter-Edgecombe

Computer Science Faculty Publications and Presentations

Formal modeling and analysis of human behavior can properly advance disciplines ranging from psychology to economics. The ability to perform such modeling has been limited by a lack of ecologically-valid data collected regarding human daily activity. We propose a formal model of indoor routine behavior based on data from automatically-sensed and recognized activities. A mechanistic description of behavior patterns for identical activity is offered to both investigate behavioral norms with 99 smart homes and compare these norms between subgroups. We identify and model the patterns of human behaviors based on inter-arrival times, the time interval between two successive activities, for …

Verification And Computation In Restricted Tile Automata, David Caballero, Timothy Gomez, Robert Schweller, Tim Wylie

Computer Science Faculty Publications and Presentations

Many models of self-assembly have been shown to be capable of performing computation. Tile Automata was recently introduced combining features of both Celluar Automata and the 2-Handed Model of self-assembly both capable of universal computation. In this work we study the complexity of Tile Automata utilizing features inherited from the two models mentioned above. We first present a construction for simulating Turing Machines that performs both covert and fuel efficient computation. We then explore the capabilities of limited Tile Automata systems such as 1-Dimensional systems (all assemblies are of height 1) and freezing Systems (tiles may not repeat states). Using …

Estimating Error And Bias In Offline Evaluation Results, Mucun Tian, Michael D. Ekstrand

Computer Science Faculty Publications and Presentations

Offline evaluations of recommender systems attempt to estimate users’ satisfaction with recommendations using static data from prior user interactions. These evaluations provide researchers and developers with first approximations of the likely performance of a new system and help weed out bad ideas before presenting them to users. However, offline evaluation cannot accurately assess novel, relevant recommendations, because the most novel items were previously unknown to the user, so they are missing from the historical data and cannot be judged as relevant.

We present a simulation study to estimate the error that such missing data causes in commonly-used evaluation metrics in …

Privateex: Privacy Preserving Exchange Of Crypto-Assets On Blockchain, Lei Xu, Lin Chen, Zhimin Gao, Keshav Kasichainula, Miguel Fernandez, Bogdan Carbunar, Weidong Shi

Computer Science Faculty Publications and Presentations

Bitcoin introduces a new type of cryptocurrency that does not rely on a central system to maintain transactions. Inspired by the success of Bitcoin, all types of alt cryptocurrencies were invented in recent years. Some of the new cryptocurrencies focus on privacy enhancement, where transaction information such as value and sender/receiver identity can be hidden, such as Zcash and Monero. However, there are few schemes to support multiple types of cryptocurrencies/assets and offer privacy enhancement at the same time. The major challenge for a multiple asset system is that it needs to support two-way assets exchange between participants besides one-way …

Domain Adaptation For Vehicle Detection In Traffic Surveillance Images From Daytime To Nighttime, Jinlong Ji, Zhigang Xu, Hongkai Yu, Lan Fu, Xuesong Zhou

Computer Science Faculty Publications and Presentations

Vehicle detection in traffic surveillance images is an important approach to obtain vehicle data and rich traffic flow parameters. Recently, deep learning based methods have been widely used in vehicle detection with high accuracy and efficiency. However, deep learning based methods require a large number of manually labeled ground truths (bounding box of each vehicle in each image) to train the Convolutional Neural Networks (CNN). In the modern urban surveillance cameras, there are already many manually labeled ground truths in daytime images for training CNN, while there are little or much less manually labeled ground truths in nighttime images. In …

Developing Big Data Projects In Open University Engineering Courses: Lessons Learned, Juan A. Lara, Aurea Anguera De Sojo, Shadi Aljawarneh, Robert P. Schumaker, Bassam Al-Shargabi

Computer Science Faculty Publications and Presentations

Big Data courses in which students are asked to carry out Big Data projects are becoming more frequent as a part of University Engineering curriculum. In these courses, instructors and students must face a series of special characteristics, difficulties and challenges that it is important to know about beforehand, so the lecturer can better plan the subject and manage the teaching methods in order to prevent students' academic dropout and low performance. The goal of this research is to approach this problem by sharing the lessons learned in the process of teaching e-learning courses where students are required to develop …

Supporting Blockchain-Based Cryptocurrency Mobile Payment With Smart Devices, Lei Xu, Lin Chen, Zhimin Gao, Larry Carranco, Xinxin Fan, Nolah Shah

Computer Science Faculty Publications and Presentations

The smart device owning rate such as smart phone and smart watch is higher than ever before and mobile payment has become one of the major payment methods in many different areas. At the same time, blockchain-based cryptocurrency is becoming a nonnegligible type of currency and the total value of all types of cryptocurrency has reached USD 200 billion. Therefore, it is a natural demand to support cryptocurrency payment on mobile devices. Considering the poor infrastructure and low penetration of financial service in developing countries, this combination is especially attractive. The high storage cost and payment processing latency are the …

Spanning Properties Of Theta-Theta-6, Mirela Damian, John Iacono, Andrew Winslow

Computer Science Faculty Publications and Presentations

We show that, unlike the Yao–Yao graph YY6, the Theta–Theta graph ΘΘ6 defined by six cones is a spanner for sets of points in convex position. We also show that, for sets of points in non-convex position, the spanning ratio of ΘΘ6 is unbounded.