Computer Sciences Commons^™

Understanding Intention For Machine Theory Of Mind: A Position Paper, Casey Kennington

Computer Science Faculty Publications and Presentations

Theory of Mind is often characterized as the ability to recognize desires, beliefs, and intentions of others. In this position paper, I look at the literature on modeling Theory of Mind in machines and find that, to date, intention is not usually a focus. I define what I mean by intention—choice with commitment—following prior work. Intention has a long history of research in some communities, and I offer one theoretical framework for modeling intention as a starting point. I take inspiration from how children learn intention through joint attention with others and how that leads to Theory of Mind. I …

Measuring Fairness In Ranked Results: An Analytical And Empirical Comparison, Amifa Raj, Michael D. Ekstrand

Computer Science Faculty Publications and Presentations

Information access systems, such as search and recommender systems, often use ranked lists to present results believed to be relevant to the user's information need. Evaluating these lists for their fairness along with other traditional metrics provides a more complete understanding of an information access system's behavior beyond accuracy or utility constructs. To measure the (un)fairness of rankings, particularly with respect to the protected group(s) of producers or providers, several metrics have been proposed in the last several years. However, an empirical and comparative analyses of these metrics showing the applicability to specific scenario or real data, conceptual similarities, and …

Incremental Unit Networks For Distributed, Symbolic Multimodal Processing And Representation, Mir Tahsin Imtiaz, Casey Kennington

Computer Science Faculty Publications and Presentations

Incremental dialogue processing has been an important topic in spoken dialogue systems research, but the broader research community that makes use of language interaction (e.g., chatbots, conversational AI, spoken interaction with robots) have not adopted incremental processing despite research showing that humans perceive incremental dialogue as more natural. In this paper, we extend prior work that identifies the requirements for making spoken interaction with a system natural with the goal that our framework will be generalizable to many domains where speech is the primary method of communication. The Incremental Unit framework offers a model of incremental processing that has been …

Symbol And Communicative Grounding Through Object Permanence With A Mobile Robot, Josue Torres-Fonseca, Catherine Henry, Casey Kennington

Computer Science Faculty Publications and Presentations

Object permanence is the ability to form and recall mental representations of objects even when they are not in view. Despite being a crucial developmental step for children, object permanence has had only some exploration as it relates to symbol and communicative grounding in spoken dialogue systems. In this paper, we leverage SLAM as a module for tracking object permanence and use a robot platform to move around a scene where it discovers objects and learns how they are denoted. We evaluated by comparing our system’s effectiveness at learning words from human dialogue partners both with and without object permanence. …

Workflow Critical Path: A Data-Oriented Critical Path Metric For Holistic Hpc Workflows, Daniel D. Nguyen, Karen L. Karavanic

Computer Science Faculty Publications and Presentations

Current trends in HPC, such as the push to exascale, convergence with Big Data, and growing complexity of HPC applications, have created gaps that traditional performance tools do not cover. One example is Holistic HPC Workflows — HPC workflows comprising multiple codes, paradigms, or platforms that are not developed using a workflow management system. To diagnose the performance of these applications, we define a new metric called Workflow Critical Path (WCP), a data-oriented metric for Holistic HPC Workflows. WCP constructs graphs that span across the workflow codes and platforms, using data states as vertices and data mutations as edges. …

Cuts: Scaling Subgraph Isomorphism On Distributed Multi-Gpu Systems Using Trie Based Data Structure, Lizhi Xiang, Arif Khan, Edoardo Serra, Mahantesh Halappanavar, Aravind Sukumaran-Rajam

Computer Science Faculty Publications and Presentations

Subgraph isomorphism is a pattern-matching algorithm widely used in many domains such as chem-informatics, bioinformatics, databases, and social network analysis. It is computationally expensive and is a proven NP-hard problem. The massive parallelism in GPUs is well suited for solving subgraph isomorphism. However, current GPU implementations are far from the achievable performance. Moreover, the enormous memory requirement of current approaches limits the problem size that can be handled. This work analyzes the fundamental challenges associated with processing subgraph isomorphism on GPUs and develops an efficient GPU implementation. We also develop a GPU-friendly trie-based data structure to drastically reduce the intermediate …

Predicting Human–Pathogen Protein–Protein Interactions Using Natural Language Processing Methods, Nikhil Mathews, Tuan Tran, Banafsheh Rekabdar, Chinwe Ekenna

Computer Science Faculty Publications and Presentations

In this paper, we predict the interaction of proteins between Humans and Yersinia pestis via amino acid sequences. We utilize multiple Natural Language Processing (NLP) methods available in deep learning in a unique format and produce promising results. Our developed model gives a cross-validation AUC score of 0.92 and is comparable with other work that utilizes extensive biochemical properties i.e, network and sequence in conjunction. We achieve this by combining advanced tools in neural machine translation into an integrated end-to-end deep learning framework as well as methods of preprocessing that are novel to the field of bioinformatics. We show that …

Rotten Green Tests In Java, Pharo And Python, Vincent Aranega, Julien Delplanque, Matias Martinez, Andrew P. Black, Stéphane Ducasse, Anne Etien, Christopher Fuhrman, Guillermo Polito

Computer Science Faculty Publications and Presentations

Rotten Green Tests are tests that pass, but not because the assertions they contain are true: a rotten test passes because some or all of its assertions are not actually executed. The presence of a rotten green test is a test smell, and a bad one, because the existence of a test gives us false confidence that the code under test is valid, when in fact that code may not have been tested at all. This article reports on an empirical evaluation of the tests in a corpus of projects found in the wild. We selected approximately one hundred mature …

In-Game Social Interactions To Facilitate Esl Students' Morphological Awareness, Language And Literacy Skills, Yolanda A. Rankin, Sana Tibi, Casey Kennington, Na-Eun Han

Computer Science Faculty Publications and Presentations

Video games that require players to utilize a target or second language to complete tasks have emerged as alternative pedagogical tools for Second Language Acquisition (SLA). With the exception of vocabulary acquisition, much of the prior research in game-based SLA fails to gauge students' literacy skills, specifically their morphological awareness or understanding of the smallest meaningful linguistic units (e.g., prefixes, suffixes, and roots). Given this shortcoming, we utilize a two-player online game to facilitate social interactions between Native English Speakers (NES) and English as a Second Language (ESL) students as a mechanism to generate ESL students' written output in the …

Raising Algorithm Bias Awareness Among Computer Science Students Through Library And Computer Science Instruction, Shalini Ramachandran, Steven Matthew Cutchin, Sheree Fu

Computer Science Faculty Publications and Presentations

We are a computer science professor and two librarians who work closely with computer science students. In this paper, we outline the development of an introductory algorithm bias instruction session. As part of our lesson development, we analyzed the results of a survey we conducted of computer science students at three universities on their perceptions about search-engine and big-data algorithms. We examined whether an information literacy component focused on algorithmic bias was beneficial to offer to students in the computational sciences and designed an instructional prototype. We studied qualitative data, including feedback from students and colleagues on our initial instruction …

Hierarchical Mapping For Crosslingual Word Embedding Alignment, Ion Madrazo Azpiazu, Maria Soledad Pera

Computer Science Faculty Publications and Presentations

The alignment of word embedding spaces in different languages into a common crosslingual space has recently been in vogue. Strategies that do so compute pairwise alignments and then map multiple languages to a single pivot language (most often English). These strategies, however, are biased towards the choice of the pivot language, given that language proximity and the linguistic characteristics of the target language can strongly impact the resultant crosslingual space in detriment of topologically distant languages. We present a strategy that eliminates the need for a pivot language by learning the mappings across languages in a hierarchicalway. Experiments demonstrate that …

Exploring Author Gender In Book Rating And Recommendation, Michael D. Ekstrand, Daniel Kluver

Computer Science Faculty Publications and Presentations

Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items. Many of these patterns reflect important real-world phenomena driving interactions between the various users and items; other patterns may be irrelevant or reflect undesired discrimination, such as discrimination in publishing or purchasing against authors who are women or ethnic minorities. In this work, we examine the response of collaborative filtering recommender algorithms to the distribution of their input data with respect to one dimension of social concern, namely content creator gender. Using publicly available book ratings data, we measure …

A Coprocessor-Based Introspection Framework Via Intel Management Engine, Lei Zhou, Fengwei Zhang, Jidong Xiao, Kevin Leach, Westley Weimer, Xuhua Ding, Guojun Wang

Computer Science Faculty Publications and Presentations

During the past decade, virtualization-based (e.g., virtual machine introspection) and hardware-assisted approaches (e.g., x86 SMM and ARM TrustZone) have been used to defend against low-level malware such as rootkits. However, these approaches either require a large Trusted Computing Base (TCB) or they must share CPU time with the operating system, disrupting normal execution. In this article, we propose an introspection framework called Nighthawk that transparently checks system integrity and monitor the runtime state of target system. Nighthawk leverages the Intel Management Engine (IME), a co-processor that runs in isolation from the main CPU. By using the IME, our approach has …

Distributing Participation In Design: Addressing Challenges Of A Global Pandemic, Jerry Alan Fails

Computer Science Faculty Publications and Presentations

Participatory Design (PD) – whose inclusive benefits are broadly recognised in design – can be very challenging, especially when involving children. The recent COVID-19 pandemic has given rise to further barriers to PD with such groups. One key barrier is the advent of social distancing and government-imposed social restrictions due to the additional risks posed for e.g. children and families vulnerable to COVID-19. This disrupts traditional in-person PD (which involves close socio-emotional and often physical collaboration between participants and researchers). However, alongside such barriers, we have identified opportunities for new and augmented approaches to PD across distributed geographies, backgrounds, ages …

Engage!: Co-Designing Search Engine Result Pages To Foster Interactions, Garrett Allen, Ben Peterson, Dhanush Kumar Ratakonda, Mostofa Najmus Sakib, Jerry Alan Fails, Casey Kennington, Katherine Landau Wright, Maria Soledad Pera

Computer Science Faculty Publications and Presentations

In this paper, we take a step towards understanding how to design search engine results pages (SERP) that encourage children’s engagement as they seek for online resources. For this, we conducted a participatory design session to enable us to elicit children’s preferences and determine what children (ages 6–12) find lacking in more traditional SERP. We learned that children want more dynamic means of navigating results and additional ways to interact with results via icons. We use these findings to inform the design of a new SERP interface, which we denoted CHIRP. To gauge the type of engagement that a SERP …

5Th Kidrec Workshop: Search And Recommendation Technology Through The Lens Of A Teacher, Monica Landoni, Theo Huibers, Maria Soledad Pera, Jerry Alan Fails

Computer Science Faculty Publications and Presentations

In this past year, the role of technology to support education has been more prominent than ever. This has prompted us to focus the 5^th Edition of the International and Interdisciplinary Perspectives on Children & Recommender and Information Retrieval Systems (KidRec) around a major stakeholder when it comes to technology adoption for the classroom: the teacher. Much like in the previous editions of the workshop, our priority remains understanding what is good when it comes to information retrieval systems for children, this time from the perspectives of teachers. In order to control scope of our discussion and …

Using Service-Learning In Graduate Curriculum To Address Teenagers' Vulnerability To Web Misinformation, Francesca Spezzano

Computer Science Faculty Publications and Presentations

We report on how we implemented service-learning (S-L) in a CS graduate class to improve student understanding of the class materials and provide a service to the community, i.e., addressing teenagers’ vulnerability to Web misinformation. We show how S-L benefits CS students in their course theory understanding and personal skills development, while teenagers’ news media literacy and misinformation detection accuracy were positively impacted.

Learned Dual-View Reflection Removal, Simon Niklaus, Xuaner Cecilia Zhang, Jonathan T. Barron, Neal Wadhwa, Rahul Garg, Feng Liu, Tianfan Xue

Computer Science Faculty Publications and Presentations

Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconvenient for users. We instead propose a learning-based dereflection algorithm that uses stereo images as input. This is an effective trade-off between the two extremes: the parallax between two views provides cues to remove reflections, and two views are easy to capture due to the adoption of stereo cameras in smartphones. Our model consists of a learning-based reflection-invariant flow model for dual-view registration, and a learned synthesis model for combining aligned image pairs. Because …

Somewhere Over The Rainbow: Exploring The Sense For Relevance In Children, Monica Landoni, Theo Huibers, Emiliana Murgia, Mohammad Aliannejadi, Maria Soledad Pera

Computer Science Faculty Publications and Presentations

We explore the facets of relevance that guide children when assessing materials retrieved by search engines when looking for information in the classroom. We involved children in a collaborative exercise and asked them to design innovative icons to point their peers towards useful results. We also asked them to complete a survey meant to capture explicit motivators guiding their design. This resulted in a rich set of metaphors. Analysis of the emerging metaphors is what allowed us to identify and discuss the many interpretations of relevance children naturally assign to resources they find in response to school-related information discovery …

Estimation Of Fair Ranking Metrics With Incomplete Judgments, Ömer Kırnap, Fernando Diaz, Asia Biega, Michael Ekstrand, Ben Carterette, Emine Yilmaz

Computer Science Faculty Publications and Presentations

There is increasing attention to evaluating the fairness of search system ranking decisions. These metrics often consider the membership of items to particular groups, often identified using protected attributes such as gender or ethnicity. To date, these metrics typically assume the availability and completeness of protected attribute labels of items. However, the protected attributes of individuals are rarely present, limiting the application of fair ranking metrics in large scale systems. In order to address this problem, we propose a sampling strategy and estimation technique for four fair ranking metrics. We formulate a robust and unbiased estimator which can operate even …

Spellchecking For Children In Web Search: A Natural Language Interface Case-Study, Casey Kennington, Jerry Alan Fails, Katherine Landau Wright, Maria Soledad Pera

Computer Science Faculty Publications and Presentations

Given the more widespread nature of natural language interfaces, it is increasingly important to understand who are accessing those interfaces, and how those interfaces are being used. In this paper, we explore spellchecking in the context of web search with children as the target audience. In particular, via a literature review we show that, while widely used, popular search tools are ill-designed for children. We then use spellcheckers as a case study to highlight the need for an interdisciplinary approach that brings together natural language processing, education, human-computer interaction to address a known information retrieval problem: query misspelling. We conclude …

The Role Of Steps And Game Elements In Gamified Fitness Tracker Apps: A Systematic Review, Aatish Neupane, Derek Hansen, Jerry Alan Fails, Anud Sharma

Computer Science Faculty Publications and Presentations

This article reviews 103 gamified fitness tracker apps (Android and iOS) that incorporate step count data into gameplay. Games are labeled with a set of 13 game elements as well as meta-data from the app stores (e.g., avg rating, number of reviews). Network clustering and visualizations are used to identify the relationship between game elements that occur in the same games. A taxonomy of how steps are used as rewards is provided, along with example games. An existing taxonomy of how games use currency is also mapped to step-based games. We show that many games use the triad of Social …

Statistical Inference: The Missing Piece Of Recsys Experiment Reliability Discourse, Ngozi Ihemelandu, Michael D. Ekstrand

Computer Science Faculty Publications and Presentations

This paper calls attention to the missing component of the recommender system evaluation process: Statistical Inference. There is active research in several components of the recommender system evaluation process: selecting baselines, standardizing benchmarks, and target item sampling. However, there has not yet been significant work on the role and use of statistical inference for analyzing recommender system evaluation results.

In this paper, we argue that the use of statistical inference is a key component of the evaluation process that has not been given sufficient attention. We support this argument with systematic review of recent RecSys papers to understand how statistical …

Incremental Unit Networks For Multimodal, Fine-Grained Information State Representation, Casey Kennington, David Schlangen

Computer Science Faculty Publications and Presentations

We offer a sketch of a fine-grained information state annotation scheme that follows directly from the Incremental Unit abstract model of dialogue processing when used within a multimodal, co-located, interactive setting. We explain the Incremental Unit model and give an example application using the Localized Narratives dataset, then offer avenues for future research.

A Practical And Secure Stateless Order Preserving Encryption For Outsourced Databases, Ning Shen, Jyh-Haw Yeh, Hung-Min Sun, Chien-Ming Chen

Computer Science Faculty Publications and Presentations

Order-preserving encryption (OPE) plays an important role in securing outsourced databases. OPE schemes can be either Stateless or Stateful. Stateful schemes can achieve the ideal security of order-preserving encryption, i.e., “reveal no information about the plaintexts besides order.” However, comparing to stateless schemes, stateful schemes require maintaining some state information locally besides encryption keys and the ciphertexts are mutable. On the other hand, stateless schemes only require remembering encryption keys and thus is more efficient. It is a common belief that stateless schemes cannot provide the same level of security as stateful ones because stateless schemes reveal the relative distance …

View Synthesis Of Dynamic Scenes Based On Deep 3d Mask Volume, Kai-En Lin, Guowei Yang, Lei Xiao, Feng Liu, Ravi Ramamoorthi

Computer Science Faculty Publications and Presentations

Image view synthesis has seen great success in reconstructing photorealistic visuals, thanks to deep learning and various novel representations. The next key step in immersive virtual experiences is view synthesis of dynamic scenes. However, several challenges exist due to the lack of high-quality training datasets, and the additional time dimension for videos of dynamic scenes. To address this issue, we introduce a multi-view video dataset, captured with a custom 10-camera rig in 120FPS. The dataset contains 96 high-quality scenes showing various visual effects and human interactions in outdoor scenes. We develop a new algorithm, Deep 3D Mask Volume, which enables …

Towards Formally Verified Compilation Of Tag-Based Policy Enforcement, Chr Chhak, Andrew Tolmach, Sean Anderson

Computer Science Faculty Publications and Presentations

Hardware-assisted reference monitoring is receiving increasing attention as a way to improve the security of existing software. One example is the PIPE architecture extension, which attaches metadata tags to register and memory values and executes tag-based rules at each machine instruction to enforce a software-defined security policy. To use PIPE effectively, engineers should be able to write security policies in terms of source-level concepts like functions, local variables, and structured control operators, which are not visible at machine level. It is the job of the compiler to generate PIPE-aware machine code that enforces these source-level policies. The compiler thus becomes …

An Effective And Efficient Graph Representation Learning Approach For Big Graphs, Edoardo Serra, Mikel Joaristi, Alfredo Cuzzocrea, Selim Soufargi, Carson K. Leung

Computer Science Faculty Publications and Presentations

In the Big Data era, large graph datasets are becoming increasingly popular due to their capability to integrate and interconnect large sources of data in many fields, e.g., social media, biology, communication networks, etc. Graph representation learning is a flexible tool that automatically extracts features from a graph node. These features can be directly used for machine learning tasks. Graph representation learning approaches producing features preserving the structural information of the graphs are still an open problem, especially in the context of large-scale graphs. In this paper, we propose a new fast and scalable structural representation learning approach called SparseStruct. …

An Analysis Of People’S Reasoning For Sharing Real And Fake News, Anu Shrestha, Francesca Spezzano

Computer Science Faculty Publications and Presentations

The problem of the increase in the volume of fake news and its widespread over social media has gained massive attention as most of the population seeks social media for daily news diet. Humans are equally responsible for the surge of fake news spread. Thus, it is imperative to understand people’s behavior when they decide to share real and fake news items on social media. In an attempt to do so, we performed an analysis on data collected through a survey where participants (n= 363) were asked whether they were willing to share the given news item on their social …

An Empirical Analysis Of Collaborative Recommender Systems Robustness To Shilling Attacks, Anu Shrestha, Francesca Spezzano, Maria Soledad Pera

Computer Science Faculty Publications and Presentations

Recommender systems play an essential role in our digital society as they suggest products to purchase, restaurants to visit, and even resources to support education. Recommender systems based on collaborative filtering are the most popular among the ones used in e-commerce platforms to improve user experience. Given the collaborative environment, these recommenders are more vulnerable to shilling attacks, i.e., malicious users creating fake profiles to provide fraudulent reviews, which are deliberately written to sound authentic and aim to manipulate the recommender system to promote or demote target products or simply to sabotage the system. Therefore, understanding the effects of shilling …