bluuebunny/crossref_metadata_2025 huggingface dataset:
Uncertainty Propagation in Long-Term Structured Regression on Evolving Networks
Authors: Gligorijevic Djordje , Stojanovic Jelena , Obradovic Zoran
Published: June 2022

In long-term forecasting it is important to estimate the confidence of predictions, as they are often affected by errors that are accumulated over the prediction horizon. To address this problem, an effective novel iterative method is developed for Gaussian structured learning models in this study for propagating uncertainty in temporal graphs by modeling noisy inputs. The proposed method is applied for three long-term (up to 8 years ahead) structured regression problems on real-world evolving networks from the health and climate domains. The obtained empirical results and use case analysis provide evidence that the new approach allows better uncertainty propagation as compared to published alternatives.
Finding One's Best Crowd: Online Learning By Exploiting Source Similarity
Authors: Liu Yang , Liu Mingyan
Published: June 2022

We consider an online learning problem (classification or prediction) involving disparate sources of sequentially arriving data, whereby a user over time learns the best set of data sources to use in constructing the classifier by exploiting their similarity. We first show that, when (1) the similarity information among data sources is known, and (2) data from different sources can be acquired without cost, then a judicious selection of data from different sources can effectively enlarge the training sample size compared to using a single data source, thereby improving the rate and performance of learning; this is achieved by bounding the classification error of the resulting classifier. We then relax assumption (1) and characterize the loss in learning performance when the similarity information must also be acquired through repeated sampling. We further relax both (1) and (2) and present a cost-efficient algorithm that identifies a best crowd from a potentially large set of data sources in terms of both classifier performance and data acquisition cost. This problem has various applications, including online prediction systems with time series data of various forms, such as financial markets, advertisement and network measurement.
The l2,1-Norm Stacked Robust Autoencoders for Domain Adaptation
Authors: Jiang Wenhao , Gao Hongchang , Chung Fu-lai , Huang Heng
Published: June 2022

Recently, deep learning methods that employ stacked denoising autoencoders (SDAs) have been successfully applied in domain adaptation. Remarkable performance in multi-domain sentiment analysis datasets has been reported, making deep learning a promising approach to domain adaptation problems. SDAs are distinguished by learning robust data representations for recovering the original features that have been artificially corrupted with noise. The idea has been further exploited to marginalize out the random corruptions by a state-of-the-art method called mSDA. In this paper, a deep learning method for domain adaptation called l2,1-norm stacked robust autoencoders (l2,1-SRA) is proposed to learn useful representations for domain adaptation tasks. Each layer of l2,1-SRA contains two steps: a robust linear reconstruction step which is based on l2,1 robust regression and a non-linear squashing transformation step. The experimental results demonstrate that the proposed method is very effective in multiple cross domain classification datasets which include Amazon review dataset, spam dataset from ECML/PKDD discovery challenge 2006 and 20 newsgroups dataset.
The Hidden Convexity of Spectral Clustering
Authors: Voss James , Belkin Mikhail , Rademacher Luis
Published: June 2022

In recent years, spectral clustering has become a standard method for data analysis used in a broad range of applications. In this paper we propose a new class of algorithms for multiway spectral clustering based on optimization of a certain "contrast function" over the unit sphere. These algorithms, partly inspired by certain Indepenent Component Analysis techniques, are simple, easy to implement and efficient. Geometrically, the proposed algorithms can be interpreted as hidden basis recovery by means of function optimization. We give a complete characterization of the contrast functions admissible for provable basis recovery. We show how these conditions can be interpreted as a "hidden convexity" of our optimization problem on the sphere; interestingly, we use efficient convex maximization rather than the more common convex minimization. We also show encouraging experimental results on real and simulated data.
Analogical Generalization of Linguistic Constructions
Authors: McFate Clifton
Published: June 2022

Human language is extraordinarily creative in form and function, and adapting to this ever-shifting linguistic landscape is a daunting task for interactive cognitive systems. Recently, construction grammar has emerged as a linguistic theory for representing these complex and often idiomatic linguistic forms. Furthermore, analogical generalization has been proposed as a learning mechanism for extracting linguistic constructions from input. I propose an account that uses a computational model of analogy to learn and generalize argument structure constructions.
Integrating Planning and Recognition to Close the Interaction Loop
Authors: Freedman Richard
Published: June 2022

In many real-world domains, the presence of machines is becoming more ubiquitous to the point that they are usually more than simple automation tools. As part of the environment amongst human users, it is necessary for these computers and robots to be able to interact with them reasonably by either working independently around them or participating in a task, especially one with which a person needs help. This interactive procedure requires several steps: recognizing the user and environment from sensor data, interpreting the user’s activity and motives, determining a responsive behavior, performing the behavior, and then recognizing everything again to confirm the behavior choice and replan if necessary. At the moment, the research areas addressing these steps, activity recognition, plan recognition, intent recognition, and planning, have all been primarily studied independently. However, pipelining each independent process can be risky in real-time situations where there may be enough time to only run a few steps. This leads to a critical question: how do we perform everything under time constraints? In this thesis summary, I propose a framework that integrates these processes by taking advantage of features shared between them.
A Fraud Resilient Medical Insurance Claim System
Authors: Shi Yuliang , Sun Chenfei , Li Qingzhong , Cui Lizhen , Yu Han , Miao Chunyan
Published: June 2022

As many countries in the world start to experience population aging, there are an increasing number of people relying on medical insurance to access healthcare resources. Medical insurance frauds are causing billions of dollars in losses for public healthcare funds. The detection of medical insurance frauds is an important and difficult challenge for the artificial intelligence (AI) research community. This paper outlines HFDA, a hybrid AI approach to effectively and efficiently identify fraudulent medical insurance claims which has been tested in an online medical insurance claim system in China.
DECT: Distributed Evolving Context Tree for Understanding User Behavior Pattern Evolution
Authors: Shu Xiaokui , Laptev Nikolay , Yao Danfeng
Published: June 2022

Internet user behavior models characterize user browsing dynamics or the transitions among web pages. The models help Internet companies improve their services by accurately targeting customers and providing them the information they want. For instance, specific web pages can be customized and prefetched for individuals based on sequences of web pages they have visited. Existing user behavior models abstracted as time-homogeneous Markov models cannot efficiently model user behavior variation through time. This demo presents DECT, a scalable time-variant variable-order Markov model. DECT digests terabytes of user session data and yields user behavior patterns through time. We realize DECT using Apache Spark and deploy it on top of Yahoo! infrastructure. We demonstrate the benefits of DECT with anomaly detection and ad click rate prediction applications. DECT enables the detection of higher-order path anomalies and provides deep insights into ad click rates with respect to user visiting paths.
WWDS APIs: Application Programming Interfaces for Efficient Manipulation of World WordNet Database Structure
Authors: Redkar Hanumant , Bhingardive Sudha , Patel Kevin , Bhattacharyya Pushpak , Prabhugaonkar Neha , Nagvenkar Apurva , Karmali Ramdas
Published: June 2022

WordNets are useful resources for natural language processing. Various WordNets for different languages have been developed by different groups. Recently, World WordNet Database Structure (WWDS) was proposed by Redkar et. al (2015) as a common platform to store these different WordNets. However, it is underutilized due to lack of programming interface. In this paper, we present WWDS APIs, which are designed to address this shortcoming. These WWDS APIs, in conjunction with WWDS, act as a wrapper that enables developers to utilize WordNets without worrying about the underlying storage structure. The APIs are developed in PHP, Java, and Python, as they are the preferred programming languages of most developers and researchers working in language technologies. These APIs can help in various applications like machine translation, word sense disambiguation, multilingual information retrieval, etc.
Conceptualizing Curse of Dimensionality with Parallel Coordinates
Authors: Devi G. , Chauhan Charu , Chakraborti Sutanu
Published: June 2022

We report on a novel use of parallel coordinates as a pedagogical tool for illustrating the non-intuitive properties of high dimensional spaces with special emphasis on the phenomenon of Curse of Dimensionality. Also, we have collated what we believe to be a representative sample of diverse approaches that exist in literature to conceptualize the Curse of Dimensionality. We envisage that the paper will have pedagogical value in structuring the way Curse of Dimensionality is presented in classrooms and associated lab sessions.
What's Hot in Heuristic Search
Authors: Stern Roni , Lelis Levi
Published: June 2022

Search in general, and heuristic search in particular, is at the heart of many Artificial Intelligence algorithms and applications. There is now a growing and active community devoted to the empirical and theoretical study of heuristic search algorithms, thanks to the successful application of search-based algorithms to areas such as robotics, domain-independent planning, optimization, and computer games. In this extended abstract we highlight recent efforts in understanding suboptimal search algorithms, as well as ensembles of heuristics and algorithms. The result of these efforts are meta-reasoning methods which are applied to orchestrate the different components of modern search algorithms. Finally, we mention recent innovative applications of search that demonstrate the relevance of the field to general AI.
Angry Birds as a Challenge for Artificial Intelligence
Authors: Renz Jochen , Ge XiaoYu , Verma Rohan , Zhang Peng
Published: June 2022

The Angry Birds AI Competition (aibirds.org) has been held annually since 2012 in conjunction with some of the major AI conferences, most recently with IJCAI 2015. The goal of the competition is to build AI agents that can play new Angry Birds levels as good as or better than the best human players. Successful agents should be able to quickly analyze new levels and to predict physical consequences of possible actions in order to select actions that solve a given level with a high score. Agents have no access to the game internal physics, but only receive screenshots of the live game. In this paper we describe why this problem is a challenge for AI, and why it is an important step towards building AI that can successfully interact with the real world. We also summarise some highlights of past competitions, including a new competition track we introduced recently.
"8 Amazing Secrets for Getting More Clicks": Detecting Clickbaits in News Streams Using Article Informality
Authors: Biyani Prakhar , Tsioutsiouliklis Kostas , Blackmer John
Published: June 2022

Clickbaits are articles with misleading titles, exaggerating the content on the landing page. Their goal is to entice users to click on the title in order to monetize the landing page. The content on the landing page is usually of low quality. Their presence in user homepage stream of news aggregator sites (e.g., Yahoo news, Google news) may adversely impact user experience. Hence, it is important to identify and demote or block them on homepages. In this paper, we present a machine-learning model to detect clickbaits. We use a variety of features and show that the degree of informality of a webpage (as measured by different metrics) is a strong indicator of it being a clickbait. We conduct extensive experiments to evaluate our approach and analyze properties of clickbait and non-clickbait articles. Our model achieves high performance (74.9% F-1 score) in predicting clickbaits.
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
Authors: He Ruining , McAuley Julian
Published: June 2022

Modern recommender systems model people and items by discovering or `teasing apart' the underlying dimensions that encode the properties of items and users' preferences toward them. Critically, such dimensions are uncovered based on user feedback, often in implicit form (such as purchase histories, browsing logs, etc.); in addition, some recommender systems make use of side information, such as product attributes, temporal information, or review text.However one important feature that is typically ignored by existing personalized recommendation and ranking methods is the visual appearance of the items being considered. In this paper we propose a scalable factorization model to incorporate visual signals into predictors of people's opinions, which we apply to a selection of large, real-world datasets. We make use of visual features extracted from product images using (pre-trained) deep networks, on top of which we learn an additional layer that uncovers the visual dimensions that best explain the variation in people's feedback. This not only leads to significantly more accurate personalized ranking methods, but also helps to alleviate cold start issues, and qualitatively to analyze the visual dimensions that influence people's opinions.
Business-Aware Visual Concept Discovery from Social Media for Multimodal Business Venue Recognition
Authors: Chen Bor-Chun , Chen Yan-Ying , Chen Francine , Joshi Dhiraj
Published: June 2022

Image localization is important for marketing and recommendation of local business; however, the level of granularity is still a critical issue. Given a consumer photo and its rough GPS information, we are interested in extracting the fine-grained location information, i.e. business venues, of the image. To this end, we propose a novel framework for business venue recognition. The framework mainly contains three parts. First, business-aware visual concept discovery: we mine a set of concepts that are useful for business venue recognition based on three guidelines including business awareness, visually detectable, and discriminative power. We define concepts that satisfy all of these three criteria as business-aware visual concept. Second, business-aware concept detection by convolutional neural networks (BA-CNN): we propose a new network configuration that can incorporate semantic signals mined from business reviews for extracting semantic concept features from a query image. Third, multimodal business venue recognition: we extend visually detected concepts to multimodal feature representations that allow a test image to be associated with business reviews and images from social media for business venue recognition. The experiments results show the visual concepts detected by BA-CNN can achieve up to 22.5% relative improvement for business venue recognition compared to the state-of-the-art convolutional neural network features. Experiments also show that by leveraging multimodal information from social media we can further boost the performance, especially when the database images belonging to each business venue are scarce.
STELLAR: Spatial-Temporal Latent Ranking for Successive Point-of-Interest Recommendation
Authors: Zhao Shenglin , Zhao Tong , Yang Haiqin , Lyu Michael , King Irwin
Published: June 2022

Successive point-of-interest (POI) recommendation in location-based social networks (LBSNs) becomes a significant task since it helps users to navigate a number of candidate POIs and provides the best POI recommendations based on users’ most recent check-in knowledge. However, all existing methods for successive POI recommendation only focus on modeling the correlation between POIs based on users’ check-in sequences, but ignore an important fact that successive POI recommendation is a time-subtle recommendation task. In fact, even with the same previous check-in information, users would prefer different successive POIs at different time. To capture the impact of time on successive POI recommendation, in this paper, we propose a spatial-temporal latent ranking (STELLAR) method to explicitly model the interactions among user, POI, and time. In particular, the proposed STELLAR model is built upon a ranking-based pairwise tensor factorization framework with a fine-grained modeling of user-POI, POI-time, and POI-POI interactions for successive POI recommendation. Moreover, we propose a new interval-aware weight utility function to differentiate successive check-ins’ correlations, which breaks the time interval constraint in prior work. Evaluations on two real-world datasets demonstrate that the STELLAR model outperforms state-of-the-art successive POI recommendation model about 20% in Precision@5 and Recall@5.
Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark
Authors: You Quanzeng , Luo Jiebo , Jin Hailin , Yang Jianchao
Published: June 2022

Psychological research results have confirmed that people can have different emotional reactions to different visual stimuli. Several papers have been published on the problem of visual emotion analysis. In particular, attempts have been made to analyze and predict people's emotional reaction towards images. To this end, different kinds of hand-tuned features are proposed. The results reported on several carefully selected and labeled small image data sets have confirmed the promise of such features. While the recent successes of many computer vision related tasks are due to the adoption of Convolutional Neural Networks (CNNs), visual emotion analysis has not achieved the same level of success. This may be primarily due to the unavailability of confidently labeled and relatively large image data sets for visual emotion analysis. In this work, we introduce a new data set, which started from 3+ million weakly labeled images of different emotions and ended up 30 times as large as the current largest publicly available visual emotion data set. We hope that this data set encourages further research on visual emotion analysis. We also perform extensive benchmarking analyses on this large data set using the state of the art methods including CNNs.
On the Effectiveness of Linear Models for One-Class Collaborative Filtering
Authors: Sedhain Suvash , Menon Aditya , Sanner Scott , Braziunas Darius
Published: June 2022

In many personalised recommendation problems, there are examples of items users prefer or like, but no examples of items they dislike. A state-of-the-art method for such implicit feedback, or one-class collaborative filtering (OC-CF), problems is SLIM, which makes recommendations based on a learned item-item similarity matrix. While SLIM has been shown to perform well on implicit feedback tasks, we argue that it is hindered by two limitations: first, it does not produce user-personalised predictions, which hampers recommendation performance; second, it involves solving a constrained optimisation problem, which impedes fast training. In this paper, we propose LRec, a variant of SLIM that overcomes these limitations without sacrificing any of SLIM's strengths.At its core, LRec employs linear logistic regression; despite this simplicity, LRec consistently and significantly outperforms all existing methods on a range of datasets. Our results thus illustrate that the OC-CF problem can be effectively tackled via linear classification models.
Survival Prediction by an Integrated Learning Criterion on Intermittently Varying Healthcare Data
Authors: Zhang Jianfei , Chen Lifei , Vanasse Alain , Courteau Josiane , Wang Shengrui
Published: June 2022

Survival prediction is crucial to healthcare research, but is confined primarily to specific types of data involving only the present measurements. This paper considers the more general class of healthcare data found in practice, which includes a wealth of intermittently varying historical measurements in addition to the present measurements. Making survival predictions on such data bristles with challenges to the existing prediction models. For this reason, we propose a new semi-proportional hazards model using locally time-varying coefficients, and a novel complete-data model learning criterion for coefficient optimization. Experiments on the healthcare data demonstrate the effectiveness and generalizability of our model and its promise in practical applications.
Learning Visual Sentiment Distributions via Augmented Conditional Probability Neural Network
Authors: Yang Jufeng , Sun Ming , Sun Xiaoxiao
Published: June 2022

Visual sentiment analysis is raising more and more attention with the increasing tendency to express emotions through images. While most existing works assign a single dominant emotion to each image, we address the sentiment ambiguity by label distribution learning (LDL), which is motivated by the fact that image usually evokes multiple emotions. Two new algorithms are developed based on conditional probability neural network (CPNN). First, we proposed BCPNN which encodes image label into a binary representation to replace the signless integers used in CPNN, and employ it as a part of input for the neural network. Then, we train our ACPNN model by adding noises to ground truth label and augmenting affective distributions. Since current datasets are mostly annotated for single-label learning, we build two new datasets, one of which is relabeled on the popular Flickr dataset and the other is collected from Twitter. These datasets contain 20,745 images with multiple affective labels, which are over ten times larger than the existing ones. Experimental results show that the proposed methods outperform the state-of-the-art works on our large-scale datasets and other publicly available benchmarks.
Community Preserving Network Embedding
Authors: Wang Xiao , Cui Peng , Wang Jing , Pei Jian , Zhu Wenwu , Yang Shiqiang
Published: June 2022

Network embedding, aiming to learn the low-dimensional representations of nodes in networks, is of paramount importance in many real applications. One basic requirement of network embedding is to preserve the structure and inherent properties of the networks. While previous network embedding methods primarily preserve the microscopic structure, such as the first- and second-order proximities of nodes, the mesoscopic community structure, which is one of the most prominent feature of networks, is largely ignored. In this paper, we propose a novel Modularized Nonnegative Matrix Factorization (M-NMF) model to incorporate the community structure into network embedding. We exploit the consensus relationship between the representations of nodes and community structure, and then jointly optimize NMF based representation learning model and modularity based community detection model in a unified framework, which enables the learned representations of nodes to preserve both of the microscopic and community structures. We also provide efficient updating rules to infer the parameters of our model, together with the correctness and convergence guarantees. Extensive experimental results on a variety of real-world networks show the superior performance of the proposed method over the state-of-the-arts.
Joint Identification of Network Communities and Semantics via Integrative Modeling of Network Topologies and Node Contents
Authors: He Dongxiao , Feng Zhiyong , Jin Di , Wang Xiaobao , Zhang Weixiong
Published: June 2022

The objective of discovering network communities, an essential step in complex systems analysis, is two-fold: identification of functional modules and their semantics at the same time. However, most existing community-finding methods have focused on finding communities using network topologies, and the problem of extracting module semantics has not been well studied and node contents, which often contain semantic information of nodes and networks, have not been fully utilized. We considered the problem of identifying network communities and module semantics at the same time. We introduced a novel generative model with two closely correlated parts, one for communities and the other for semantics. We developed a co-learning strategy to jointly train the two parts of the model by combining a nested EM algorithm and belief propagation. By extracting the latent correlation between the two parts, our new method is not only robust for finding communities and semantics, but also able to provide more than one semantic explanation to a community. We evaluated the new method on artificial benchmarks and analyzed the semantic interpretability by a case study. We compared the new method with eight state-of-the-art methods on ten real-world networks, showing its superior performance over the existing methods.
Gated Neural Networks for Option Pricing: Rationality by Design
Authors: Yang Yongxin , Zheng Yu , Hospedales Timothy
Published: June 2022

We propose a neural network approach to price EU call options that significantly outperforms some existing pricing models and comes with guarantees that its predictions are economically reasonable. To achieve this, we introduce a class of gated neural networks that automatically learn to divide-and-conquer the problem space for robust and accurate pricing. We then derive instantiations of these networks that are 'rational by design' in terms of naturally encoding a valid call option surface that enforces no arbitrage principles. This integration of human insight within data-driven learning provides significantly better generalisation in pricing performance due to the encoded inductive bias in the learning, guarantees sanity in the model's predictions, and provides econometrically useful byproduct such as risk neutral density.
Problems in Large-Scale Image Classification
Authors: Guo Yuchen
Published: June 2022

The number of images is growing rapidly in recent years because of development of Internet, especially the social networks like Facebook, and the popularization of portable image capture devices like smart phone. Annotating them with semantically meaningful words to describe them, i.e., classification, is a useful way to manage these images. However, the huge number of images and classes brings several challenges to classification, of which two are 1) how to measure the similarity efficiently between large-scale images, for example, measuring similarity between samples is the building block for SVM and kNN classifiers, and 2) how to train supervised classification models for newly emerging classes with only a few or even no labeled samples because new concepts appear every day in the Web, like Tesla's Model S. The research of my Ph. D. thesis focuses on the two problems in large-scale image classification mentioned above. Formally, these two problems are termed as large-scale similarity search which focuses on the large scale of samples/images and zero-shot/few-shots learning which focuses on the large scale of classes. Specifically, my research considers the following three aspects: 1) hashing based large-scale similarity search which adopts hashing to improve the efficiency; 2) cross-class transfer active learning which simultaneously transfers knowledge from the abundant labeled samples in the Web and selects the most informative samples for expert labeling such that we can construct effective classifiers for novel classes with only a few labeled samples; and 3) zero-shot learning which utilizes no labeled samples for novel classes at all to build supervised classifiers for them by transferring knowledge from the related classes.
Problem Formulation for Accommodation Support in Plan-Based Interactive Narratives
Authors: Amos-Binks Adam
Published: June 2022

Branching story games have gained popularity for adapting to user actions within a story world. An active area of Interactive Narrative (IN) research uses automated planning to generate story plans as it can lighten the authorial burden of writing a branching story. Branches can be generated from a declarative representation rather than hand-crafted. A goal of an Experience Manager (EM) is to guide a user through a space of desirable narrative trajectories, or story branches, in an IN. However, in the cases when an EM must accommodate user actions and mediate them from a desired narrative trajectory to a new narrative trajectory, automated planning’s authorial advantage becomes a liability as the available narrative trajectories are not known apriori. This limitation can lead to the EM choosing a new narrative trajectory that is not coherent with the previous one and may result in a negative user experience. The goal of my research is to develop a problem formulation methodology for story planning problems that elicits the available narrative trajectories enabling an EM to execute more coherent accommodations.
Representations for Continuous Learning
Authors: Isele David
Published: June 2022

Systems deployed in unstructured environments must be able to adapt to novel situations. This requires the ability to perform in domains that may be vastly different from training domains. My dissertation focuses on the representations used in lifelong learning and how these representations enable predictions and knowledge sharing over time, allowing an agent to continuously learn and adapt in changing environments. Specifically, my contributions will enable lifelong learning systems to efficiently accumulate data, use prior knowledge to predict models for novel tasks, and alter existing models to account for changes in the environment.
SenseRun: Real-Time Running Routes Recommendation towards Providing Pleasant Running Experiences
Authors: Long Jiayu , Jia Jia , Xu Han
Published: June 2022

In this demo, we develop a mobile running application, SenseRun, to involve landscape experiences for routes recommendation. We firstly define landscape experiences, perceived enjoyment from landscape as motivators for running, by public natural area and traffic density. Based on landscape experiences, we categorize locations into 3 types (natural, leisure, traffic space) and set them with different basic weight. Real-time context factors (weather, season and hour of the day) are involved to adjust the weight. We propose a multi-attributes method to recommend routes with weight based on MVT (The Marginal Value Theorem) k-shortest-paths algorithm. We also use a landscape-awareness sounds algorithm as supplementary of landscape experiences. Experimental results improve that SenseRun can enhance running experiences and is helpful to promote regular physical activities.
From Semantic Models to Cognitive Buildings
Authors: Ploennigs Joern , Schumann Anika
Published: June 2022

Today's operation of buildings is either based on simple dashboards that are not scalable to thousands of sensor data or on rules that provide very limited fault information only. In either case considerable manual effort is required for diagnosing building operation problems related to energy usage or occupant comfort. We present a Cognitive Building demo that uses (i) semantic reasoning to model physical relationships of sensors and systems, (ii) machine learning to predict and detect anomalies in energy flow, occupancy and user comfort, and (iii) speech-enabled Augmented Reality interfaces for immersive interaction with thousands of devices. Our demo analyzes data from more than 3,300 sensors and shows how we can automatically diagnose building operation problems.
Natural Language Dialogue for Building and Learning Models and Structures
Authors: Perera Ian , Allen James , Galescu Lucian , Teng Choh Man , Burstein Mark , Friedman Scott , McDonald David , Rye Jeffrey
Published: June 2022

We demonstrate an integrated system for building and learning models and structures in both a real and virtual environment. The system combines natural language understanding, planning, and methods for composition of basic concepts into more complicated concepts. The user and the system interact via natural language to jointly plan and execute tasks involving building structures, with clarifications and demonstrations to teach the system along the way. We use the same architecture for building and simulating models of biology, demonstrating the general-purpose nature of the system where domain-specific knowledge is concentrated in sub-modules with the basic interaction remaining domain-independent. These capabilities are supported by our work on semantic parsing, which generates knowledge structures to be grounded in a physical representation, and composed with existing knowledge to create a dynamic plan for completing goals. Prior work on learning from natural language demonstrations enables learning of models from very few demonstrations, and features are extracted from definitions in natural language. We believe this architecture for interaction opens up a wide possibility of human-computer interaction and knowledge transfer through natural language.
Open-Ended Robotics Exploration Projects for Budding Researchers
Authors: Musicant David , Laddha Abha , Choi Tom
Published: June 2022

There are many benefits to introducing students to the idea of doing projects where the outcome is unknown or unsure. Some have proposed that engaging students in research can help with retention of underrepresented groups. In this paper, we report on a particular approach we have used to introduce high school students to open-ended robotics projects in a three-week summer program. We describe the structure of our summer program, how we ramp the students up to speed, and we summarize the five open-ended "research" projects that the students work on. These projects can be adopted for open-ended work elsewhere by high school students or undergraduates.
Creating Serious Robots That Improve Society
Authors: Imberman Susan , McManus Jean , Otts Gina
Published: June 2022

The Grace Hopper conference has many lectures/activities for participants. Tech Node presentations at this conference are two hours and focus on encouraging open discussion around a topic. This "not so grand" challenge, originally created for this conference, requires participants to brainstorm a robot creation that could somehow improve society in one of four societal areas: Elder Care (non- medical), Search and Rescue, Environment, and Affordable Home Health Care. This project format also can be used as an unplugged activity for a CS0/CS1 class or as a more advanced project that employs image processing and AI techniques such as machine learning.
A Summer Research Experience in Robotics
Authors: Grimm Cindy , Lyman-Holt Alicia , Smart William
Published: June 2022

The Robotics Program at Oregon State University has beenrunning an NSF-funded summer Research Experiences forUndergraduates (REU) site since 2014. Over twenty studentsper year (on average) have participated in the site, spendingten weeks embedded in the OSU Robotics Program. Our mainfocus with this REU Site is to give the participants a com-plete research experience, from problem definition to the fi-nal presentation of results, "in miniature". Our secondary ed-ucational objectives are: 1) Teach basic non-technical skillsneeded for graduate work, such as time management and lit-erature review, 2) Provide details on how to apply to gradu-ate school and for funding, 3) Clarify what we look for in agraduate student, and 4) Detail what to expect from the grad-uate student experience. In this paper, we describe the over-all structure of the participants’ summer experience, outlinesome of the training materials that we use, describe the moti-vations for our approach, and discuss the lessons that we havelearned after running the program for a number of years.
Dude, Where's My Robot?: A Localization Challenge for Undergraduate Robotics
Authors: Ruvolo Paul
Published: June 2022

I present a robotics localization challenge based on the inexpensive Neato XV robotic vacuum cleaner platform. The challenge teaches skills such as computational modeling, probabilistic inference, efficiency vs. accuracy tradeoffs, debugging, parameter tuning, and benchmarking of algorithmic performance. Rather than allowing students to pursue any localization algorithm of their choosing, here, I propose a challenge structured around the particle filter family of algorithms. This additional scaffolding allows students at all levels to successfully implement one approach to the challenge, while providing enough flexibility and richness to enable students to pursue their own creative ideas. Additionally, I provide infrastructure for automatic evaluation of systems through the collection of ground truth robot location data via ceiling-mounted location tags that are automatically scanned using an upward facing camera attached to the robot. The robot and supporting hardware can be purchased for under $400 dollars, and the challenge can even be run without any robots at all using a set of recorded sensor traces.
Model AI Assignments 2017
Authors: Neller Todd , Eckroth Joshua , Reddy Sravana , Ziegler Joshua , Bindewald Jason , Peterson Gilbert , Way Thomas , Matuszek Paula , Cassel Lillian , Papalaskari Mary-Angela , Weiss Carol , Anders Ariel , Karaman Sertac
Published: June 2022

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2017 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs.
Bounded Rationality of Restricted Turing Machines
Authors: Chen Lijie , Tang Pingzhong , Wang Ruosong
Published: June 2022

Bounded rationality aims to understand the effects of how limited rationality affects decision-making. The traditional models in game theory and multiagent system research, such as finite automata or unrestricted Turing machine, fall short of capturing how intelligent agents make decision in realistic applications. To address this problem, we model bounded rational agents as restricted Turing machines: restrictions on running time and on storage space. We study our model under the context of two-person repeated games. In the case where the running time of Turing machines is restricted, we show that computing the best response of a given strategy is much harder than the strategy itself. In the case where the storage space of the Turing machines is restricted, we show the best response of a space restricted strategy can not be implemented by machines within the same size (up to a constant factor). Finally, we study how these restrictions affect the set of Nash equilibria in infinitely repeated games.We show restricting the agent’s computational resources will give rise to new Nash equilibria.
On Covering Codes and Upper Bounds for the Dimension of Simple Games
Authors: Olsen Martin
Published: June 2022

Consider a situation with n agents or players, where some of the players form a coalition with a certain collective objective. Simple games are used to model systems that can decide whether coalitions are successful (winning) or not (losing). A simple game can be viewed as a monotone boolean function. The dimension of a simple game is the smallest positive integer d such that the simple game can be expressed as the intersection of d threshold functions, where each threshold function uses a threshold and n weights. Taylor and Zwicker have shown that d is bounded from above by the number of maximal losing coalitions. We present two new upper bounds both containing the Taylor-Zwicker bound as a special case. The Taylor-Zwicker bound implies an upper bound of (n choose n/2). We improve this upper bound significantly by showing constructively that d is bounded from above by the cardinality of any binary covering code with length n and covering radius 1. This result supplements a recent result where Olsen et al. showed how to construct simple games with dimension |C| for any binary constant weight SECDED code C with length n. Our result represents a major step in the attempt to close the dimensionality gap for simple games.
Vote Until Two of You Agree: Mechanisms with Small Distortion and Sample Complexity
Authors: Gross Stephen , Anshelevich Elliot , Xia Lirong
Published: June 2022

To design social choice mechanisms with desirable utility properties, normative properties, and low sample complexity, we propose a new randomized mechanism called 2-Agree. This mechanism asks random voters for their top alternatives until at least two voters agree, at which point it selects that alternative as the winner. We prove that, despite its simplicity and low sample complexity, 2-Agree achieves almost optimal distortion on a metric space when the number of alternatives is not large, and satisfies anonymity, neutrality, ex-post Pareto efficiency, very strong SD-participation, and is approximately truthful. We further show that 2-Agree works well for larger number of alternatives with decisive agents.
Mechanism Design for Multi-Type Housing Markets
Authors: Sikdar Sujoy , Adali Sibel , Xia Lirong
Published: June 2022

We study multi-type housing markets, where there are p ≥ 2 types of items, each agent is initially endowed one item of each type, and the goal is to design mechanisms without monetary transfer to (re)allocate items to the agents based on their preferences over bundles of items, such that each agent gets one item of each type. In sharp contrast to classical housing markets, previous studies in multi-type housing markets have been hindered by the lack of natural solution concepts, because the strict core might be empty. We break the barrier in the literature by leveraging AI techniques and making natural assumptions on agents’ preferences. We show that when agents’ preferences are lexicographic, even with different importance orders, the classical top-trading-cycles mechanism can be extended while preserving most of its nice properties. We also investigate computational complexity of checking whether an allocation is in the strict core and checking whether the strict core is empty. Our results convey an encouragingly positive message: it is possible to design good mechanisms for multi-type housing markets under natural assumptions on preferences.
Probably Approximately Efficient Combinatorial Auctions via Machine Learning
Authors: Brero Gianluca , Lubin Benjamin , Seuken Sven
Published: June 2022

A well-known problem in combinatorial auctions (CAs) is that the value space grows exponentially in the number of goods, which often puts a large burden on the bidders and on the auctioneer. In this paper, we introduce a new design paradigm for CAs based on machine learning (ML). Bidders report their values (bids) to a proxy agent by answering a small number of value queries. The proxy agent then uses an ML algorithm to generalize from those bids to the whole value space, and the efficient allocation is computed based on the generalized valuations. We introduce the concept of "probably approximate efficiency (PAE)" to measure the efficiency of the new ML-based auctions, and we formally show how the generelizability of an ML algorithm relates to the efficiency loss incurred by the corresponding ML-based auction. To instantiate our paradigm, we use support vector regression (SVR) as our ML algorithm, which enables us to keep the winner determination problem of the CA tractable. Different parameters of the SVR algorithm allow us to trade off the expressiveness, economic efficiency, and computational efficiency of the CA. Finally, we demonstrate experimentally that, even with a small number of bids, our ML-based auctions are highly efficient with high probability.
Pairwise HITS: Quality Estimation from Pairwise Comparisons in Creator-Evaluator Crowdsourcing Process
Authors: Sunahase Takeru , Baba Yukino , Kashima Hisashi
Published: June 2022

A common technique for improving the quality of crowdsourcing results is to assign a same task to multiple workers redundantly, and then to aggregate the results to obtain a higher-quality result; however, this technique is not applicable to complex tasks such as article writing since there is no obvious way to aggregate the results. Instead, we can use a two-stage procedure consisting of a creation stage and an evaluation stage, where we first ask workers to create artifacts, and then ask other workers to evaluate the artifacts to estimate their quality. In this study, we propose a novel quality estimation method for the two-stage procedure where pairwise comparison results for pairs of artifacts are collected at the evaluation stage. Our method is based on an extension of Kleinberg's HITS algorithm to pairwise comparison, which takes into account the ability of evaluators as well as the ability of creators. Experiments using actual crowdsourcing tasks show that our methods outperform baseline methods especially when the number of evaluators per artifact is small.
The State of the AIIDE Conference in 2017
Authors: Sturtevant Nathan , Magerko Brian
Published: June 2022

This abstract looks at the state of the Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE), describing some of the changes in the field and areas of focus for current work.
Efficient Hyperparameter Optimization for Deep Learning Algorithms Using Deterministic RBF Surrogates
Authors: Ilievski Ilija , Akhtar Taimoor , Feng Jiashi , Shoemaker Christine
Published: June 2022

Automatically searching for optimal hyperparameter configurations is of crucial importance for applying deep learning algorithms in practice. Recently, Bayesian optimization has been proposed for optimizing hyperparameters of various machine learning algorithms. Those methods adopt probabilistic surrogate models like Gaussian processes to approximate and minimize the validation error function of hyperparameter values. However, probabilistic surrogates require accurate estimates of sufficient statistics (e.g., covariance) of the error distribution and thus need many function evaluations with a sizeable number of hyperparameters. This makes them inefficient for optimizing hyperparameters of deep learning algorithms, which are highly expensive to evaluate. In this work, we propose a new deterministic and efficient hyperparameter optimization method that employs radial basis functions as error surrogates. The proposed mixed integer algorithm, called HORD, searches the surrogate for the most promising hyperparameter values through dynamic coordinate search and requires many fewer function evaluations. HORD does well in low dimensions but it is exceptionally better in higher dimensions. Extensive evaluations on MNIST and CIFAR-10 for four deep neural networks demonstrate HORD significantly outperforms the well-established Bayesian optimization methods such as GP, SMAC, and TPE. For instance, on average, HORD is more than 6 times faster than GP-EI in obtaining the best configuration of 19 hyperparameters.