bluuebunny/crossref_metadata_2025 huggingface dataset:
SP-SVM: Large Margin Classifier for Data on Multiple Manifolds
Authors: Shen Bin , Liu Bao-Di , Wang Qifan , Fang Yi , Allebach Jan
Published: June 2022

As one of the most important state-of-the-art classification techniques, Support Vector Machine (SVM) has been widely adopted in many real-world applications, such as object detection, face recognition, text categorization, etc., due to its competitive practical performance and elegant theoretical interpretation. However, it treats all samples independently, and ignores the fact that, in many real situations especially when data are in high dimensional space, samples typically lie on low dimensional manifolds of the feature space and thus a sample can be related to its neighbors by being represented as a linear combination of other samples on the same manifold. This linear representation, which is usually sparse, reflects the structure of underlying manifolds. It has been extensively explored in the recent literature and proven to be critical for the performance of classification. To benefit from both the underlying low dimensional manifold structure and the large margin classifier, this paper proposes a novel method called Sparsity Preserving Support Vector Machine(SP-SVM), which explicitly considers the sparse representation of samples while maximizing the margin between different classes. Consequently, SP-SVM inherits both the discriminative power of support vector machine and the merits of sparsity. A set of experiments on real-world benchmark data sets show that SP-SVM achieves significantly higher precision on recognition task than various competitive baselines including the traditional SVM, the sparse representation based method and the classical nearest neighbor classifier.
Non-Linear Regression for Bag-of-Words Data via Gaussian Process Latent Variable Set Model
Authors: Yoshikawa Yuya , Iwata Tomoharu , Sawada Hiroshi
Published: June 2022

Gaussian process (GP) regression is a widely used method for non-linear prediction.The performance of the GP regression depends on whether it can properly capture the covariance structure of target variables, which is represented by kernels between input data.However, when the input is represented as a set of features, e.g. bag-of-words, it is difficult to calculate desirable kernel values because the co-occurrence of different but relevant words cannot be reflected in the kernel calculation.To overcome this problem, we propose a Gaussian process latent variable set model (GP-LVSM), which is a non-linear regression model effective for bag-of-words data.With the GP-LVSM, a latent vector is associated with each word, and each document is represented as a distribution of the latent vectors for words appearing in the document. We efficiently represent the distributions by using the framework of kernel embeddings of distributions that can hold high-order moment information of distributions without need for explicit density estimation.By learning latent vectors so as to maximize the posterior probability, kernels that reflect relations between words are obtained, and also words are visualized in a low-dimensional space.In experiments using 25 item review datasets, we demonstrate the effectiveness of the GP-LVSM in prediction and visualization.
A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing
Authors: Zhou Quan , Chen Wenlin , Song Shiji , Gardner Jacob , Weinberger Kilian , Chen Yixin
Published: June 2022

Algorithmic reductions are one of the corner stones of theoretical computer science. Surprisingly, to-date, they have only played a limited role in machine learning. In this paper we introduce a formal and practical reduction between two of the most widely used machine learning algorithms: from the Elastic Net (and the Lasso as a special case) to the Support Vector Machine. First, we derive the reduction and summarize it in only 11 lines of MATLAB. Then, we demonstrate its high impact potential by translating recent advances in parallelizing SVM solvers directly to the Elastic Net. The resulting algorithm is a parallel solver for the Elastic Net (and Lasso) that naturally utilizes GPU and multi-core CPUs. We evaluate it on twelve real world data sets, and show that it yields identical results as the popular (and highly optimized) glmnet implementation but is up-to two orders of magnitude faster.
Nystrom Approximation for Sparse Kernel Methods: Theoretical Analysis and Empirical Evaluation
Authors: Xu Zenglin , Jin Rong , Shen Bin , Zhu Shenghuo
Published: June 2022

Nystrom approximation is an effective approach to accelerate the computation of kernel matrices in many kernel methods. In this paper, we consider the Nystrom approximation for sparse kernel methods. Instead of relying on the low-rank assumption of the original kernels, which sometimes does not hold in some applications, we take advantage of the restricted eigenvalue condition, which has been proved to be robust for sparse kernel methods. Based on the restricted eigenvalue condition, we have provided not only the approximation bound for the original kernel matrix but also the recovery bound for the sparse solutions of sparse kernel regression. In addition to the theoretical analysis, we also demonstrate the good performance of the Nystrom approximation for sparse kernel regression on real world data sets.
Learning Relational Kalman Filtering
Authors: Choi Jaesik , Amir Eyal , Xu Tianfang , Valocchi Albert
Published: June 2022

The Kalman Filter (KF) is pervasively used to control a vast array of consumer, health and defense products. By grouping sets of symmetric state variables, the Relational Kalman Filter (RKF) enables us to scale the exact KF for large-scale dynamic systems. In this paper, we provide a parameter learning algorithm for RKF, and a regrouping algorithm that prevents the degeneration of the relational structure for efficient filtering. The proposed algorithms significantly expand the applicability of the RKFs by solving the following questions: (1) how to learn parameters for RKF from partial observations; and (2) how to regroup the degenerated state variables by noisy real-world observations. To our knowledge, this is the first paper on learning parameters in relational continuous probabilistic models. We show that our new algorithms significantly improve the accuracy and the efficiency of filtering large-scale dynamic systems.
Tractable Cost-Optimal Planning over Restricted Polytree Causal Graphs
Authors: Aghighi Meysam , Jonsson Peter , Ståhlberg Simon
Published: June 2022

Causal graphs are widely used to analyze the complexity of planning problems. Many tractable classes have been identified with their aid and state-of-the-art heuristics have been derived by exploiting such classes. In particular, Katz and Keyder have studied causal graphs that are hourglasses (which is a generalization of forks and inverted-forks) and shown that the corresponding cost-optimal planning problem is tractable under certain restrictions. We continue this work by studying polytrees (which is a generalization of hourglasses) under similar restrictions. We prove tractability of cost-optimal planning by providing an algorithm based on a novel notion of variable isomorphism. Our algorithm also sheds light on the k-consistency procedure for identifying unsolvable planning instances. We speculate that this may, at least partially, explain why merge-and-shrink heuristics have been successful for recognizing unsolvable instances.
Preference Planning for Markov Decision Processes
Authors: Li Meilun , She Zhikun , Turrini Andrea , Zhang Lijun
Published: June 2022

The classical planning problem can be enriched with quantitative and qualitative user-defined preferences on how the system behaves on achieving the goal. In this paper, we propose the probabilistic preference planning problem for Markov decision processes, where the preferences are based on an enriched probabilistic LTL-style logic. We develop P4Solver, an SMT-based planner computing the preferred plan by reducing the problem to quadratic programming problem, which can be solved using SMT solvers such as Z3. We illustrate the framework by applying our approach on two selected case studies.
Approximate Linear Programming for Constrained Partially Observable Markov Decision Processes
Authors: Poupart Pascal , Malhotra Aarti , Pei Pei , Kim Kee-Eung , Goh Bongseok , Bowling Michael
Published: June 2022

In many situations, it is desirable to optimize a sequence of decisions by maximizing a primary objective while respecting some constraints with respect to secondary objectives. Such problems can be naturally modeled as constrained partially observable Markov decision processes (CPOMDPs) when the environment is partially observable. In this work, we describe a technique based on approximate linear programming to optimize policies in CPOMDPs. The optimization is performed offline and produces a finite state controller with desirable performance guarantees. The approach outperforms a constrained version of point-based value iteration on a suite of benchmark problems.
Information Gathering and Reward Exploitation of Subgoals for POMDPs
Authors: Ma Hang , Pineau Joelle
Published: June 2022

Planning in large partially observable Markov decision processes (POMDPs) is challenging especially when a long planning horizon is required. A few recent algorithms successfully tackle this case but at the expense of a weaker information-gathering capacity. In this paper, we propose Information Gathering and Reward Exploitation of Subgoals (IGRES), a randomized POMDP planning algorithm that leverages information in the state space to automatically generate "macro-actions" to tackle tasks with long planning horizons, while locally exploring the belief space to allow effective information gathering. Experimental results show that IGRES is an effective multi-purpose POMDP solver, providing state-of-the-art performance for both long horizon planning tasks and information-gathering tasks on benchmark domains. Additional experiments with an ecological adaptive management problem indicate that IGRES is a promising tool for POMDP planning in real-world settings.
Spatio-Spectral Exploration Combining In Situ and Remote Measurements
Authors: Thompson David , Wettergreen David , Foil Greydon , Furlong Michael , Kiran Anatha
Published: June 2022

Adaptive exploration uses active learning principles to improve the efficiency of autonomous robotic surveys. This work considers an important and understudied aspect of autonomous exploration: in situ validation of remote sensing measurements. We focus on high- dimensional sensor data with a specific case study of spectroscopic mapping. A field robot refines an orbital image by measuring the surface at many wavelengths. We introduce a new objective function based on spectral unmixing that seeks pure spectral signatures to accurately model diluted remote signals. This objective reflects physical properties of the multi-wavelength data. The rover visits locations that jointly improve its model of the environment while satisfying time and energy constraints. We simulate exploration using alternative planning approaches, and show proof of concept results with the canonical spectroscopic map of a mining district in Cuprite, Nevada.
Lifting Model Sampling for General Game Playing to Incomplete-Information Models
Authors: Schofield Michael , Thielscher Michael
Published: June 2022

General Game Playing is the design of AI systems able to understand the rules of new games and to use such descriptions to play those games effectively. Games with incomplete information have recently been added as anew challenge for general game-playing systems. The only published solutions to this challenge are based on sampling complete information models. In doing so they ground all of the unknown information, thereby making information gathering moves of no value; a well-known criticism of such sampling based systems. We present and analyse a method for escalating reasoning from complete information models to incomplete information models and show how this enables a general game player to correctly value information in incomplete information games. Experimental results demonstrate the success of this technique over standard model sampling.
Representing Aggregators in Relational Probabilistic Models
Authors: Buchman David , Poole David
Published: June 2022

We consider the problem of, given a probabilistic model on a set of random variables, how to add a new variable that depends on the other variables, without changing the original distribution. In particular, we consider relational models (such as Markov logic networks (MLNs)), where we cannot directly define conditional probabilities. In relational models, there may be an unbounded number of parents in the grounding, and conditional distributions need to be defined in terms of aggregators. The question we ask is whether and when it is possible to represent conditional probabilities at all in various relational models. Some aggregators have been shown to be representable by MLNs, by adding auxiliary variables; however it was unknown whether they could be defined without auxiliary variables. For other aggregators, it was not known whether they can be represented by MLNs at all. We obtained surprisingly strong negative results on the capability of flexible undirected relational models such as MLNs to represent aggregators without affecting the original model's distribution. We provide a map of what aspects of the models, including the use of auxiliary variables and quantifiers, result in the ability to represent various aggregators. In addition, we provide proof techniques which can be used to facilitate future theoretic results on relational models, and demonstrate them on relational logistic regression (RLR).
Recovering Causal Effects from Selection Bias
Authors: Bareinboim Elias , Tian Jin
Published: June 2022

Controlling for selection and confounding biases are two of the most challenging problems that appear in data analysis in the empirical sciences as well as in artificial intelligence tasks. The combination of previously studied methods for each of these biases in isolation is not directly applicable to certain non-trivial cases in which selection and confounding biases are simultaneously present. In this paper, we tackle these instances non-parametrically and in full generality. We provide graphical and algorithmic conditions for recoverability of interventional distributions for when selection and confounding biases are both present. Our treatment completely characterizes the class of causal effects that are recoverable in Markovian models, and is suffi- cient for Semi-Markovian models.
Linear-Time Gibbs Sampling in Piecewise Graphical Models
Authors: Afshar Hadi , Sanner Scott , Abbasnejad Ehsan
Published: June 2022

Many real-world Bayesian inference problems such as preference learning or trader valuation modeling in financial markets naturally use piecewise likelihoods. Unfortunately, exact closed-form inference in the underlying Bayesian graphical models is intractable in the general case and existing approximation techniques provide few guarantees on both approximation quality and efficiency. While (Markov Chain) Monte Carlo methods provide an attractive asymptotically unbiased approximation approach, rejection sampling and Metropolis-Hastings both prove inefficient in practice, and analytical derivation of Gibbs samplers require exponential space and time in the amount of data. In this work, we show how to transform problematic piecewise likelihoods into equivalent mixture models and then provide a blocked Gibbs sampling approach for this transformed model that achieves an exponential-to-linear reduction in space and time compared to a conventional Gibbs sampler. This enables fast, asymptotically unbiased Bayesian inference in a new expressive class of piecewise graphical models and empirically requires orders of magnitude less time than rejection, Metropolis-Hastings, and conventional Gibbs sampling methods to achieve the same level of accuracy.
On the Decreasing Power of Kernel and Distance Based Nonparametric Hypothesis Tests in High Dimensions
Authors: Ramdas Aaditya , Jakkam Reddi Sashank , Poczos Barnabas , Singh Aarti , Wasserman Larry
Published: June 2022

This paper is about two related decision theoretic problems, nonparametric two-sample testing and independence testing. There is a belief that two recently proposed solutions, based on kernels and distances between pairs of points, behave well in high-dimensional settings. We identify different sources of misconception that give rise to the above belief. Specifically, we differentiate the hardness of estimation of test statistics from the hardness of testing whether these statistics are zero or not, and explicitly discuss a notion of "fair" alternative hypotheses for these problems as dimension increases. We then demonstrate that the power of these tests actually drops polynomially with increasing dimension against fair alternatives. We end with some theoretical insights and shed light on the median heuristic for kernel bandwidth selection. Our work advances the current understanding of the power of modern nonparametric hypothesis tests in high dimensions.
Tighter Value Function Bounds for Bayesian Reinforcement Learning
Authors: Lee Kanghoon , Kim Kee-Eung
Published: June 2022

Bayesian reinforcement learning (BRL) provides a principled framework for optimal exploration-exploitation tradeoff in reinforcement learning. We focus on model based BRL, which involves a compact formulation of the optimal tradeoff from the Bayesian perspective. However, it still remains a computational challenge to compute the Bayes-optimal policy. In this paper, we propose a novel approach to compute tighter value function bounds of the Bayes-optimal value function, which is crucial for improving the performance of many model-based BRL algorithms. We then present how our bounds can be integrated into real-time AO* heuristic search, and provide a theoretical analysis on the impact of improved bounds on the search efficiency. We also provide empirical results on standard BRL domains that demonstrate the effectiveness of our approach.
A New Computational Intelligence Model for Long-Term Prediction of Solar and Geomagnetic Activity
Authors: Parsapoor Mahboobeh , Brooke John , Svensson Bertil
Published: June 2022

This paper briefly describes how the neural structure of fear conditioning has inspired to develop a computational intelligence model that is referred to as the brain emotional learning-inspired model (BELIM). The model is applied to predict long step ahead of solar activity and geomagnetic storms.
Dealing with Trouble: A Data-Driven Model of a Repair Type for a Conversational Agent
Authors: Höhn Sviatlana
Published: June 2022

Troubles in hearing, comprehension or speech production are common in human conversations, especially if participants of the conversation communicate in a foreign language that they have not yet fully mastered. Here I describe a data-driven model for simulation of dialogue sequences where the learner user does not understand the talk of a conversational agent in chat and asks for clarification.
Multimedia Data for the Visually Impaired
Authors: Tandon Niket , Sharma Shekhar , Makkad Tanima
Published: June 2022

The Web contains a large amount of information in the form of videos that remains inaccessible to the visually impaired people. We identify a class of videos whose information content can be approximately encoded as an audio, thereby increasing the amount of accessible videos. We propose a model to automatically identify such videos. Our model jointly relies on the textual metadata and visual content of the video. We use this model to re-rank Youtube video search results based on accessibility of the video. We present preliminary results by conducting a user study with visually impaired people to measure the effectiveness of our system.
Predicting the Quality of User Experiences to Improve Productivity and Wellness
Authors: Donti Priya , Rosenbloom Jacob , Gruver Alex , Boerkoel James Jr
Published: June 2022

College students often struggle to balance their work with personal wellness. In part, this occurs because students work when they are unable to focus. We hypothesize that we can adapt the Experience Sampling Method (ESM) to build a model of users’ efficacy and predict when they will be most likely to experience flow, a state of motivation and immersion. We also hypothesize that we can present this information effectively to users, allowing them to understand when they are most likely to achieve flow. In order to test these hypotheses, we introduce the Productivity and Wellness Pal (PaWPal), a smartphone-based application that seeks to make users aware of their efficacy at various tasks as well as which courses of action are likely to lead to immersive experiences.
Placing Influencing Agents in a Flock
Authors: Genter Katie , Stone Peter
Published: June 2022

Flocking is a emergent behavior exhibited by many different animal species, including birds and fish. In our work we consider adding a small set of influencing agents, that are under our control, into a flock. Following ad hoc teamwork methodology, we assume that we are given knowledge of, but no direct control over, the rest of the flock. In our ongoing work highlighted in this abstract, we are specifically considering the problem of where to initially place influencing agents that we add to such a flock. We use these influencing agents to influence the flock to behave in a particular way - for example, to fly in a particular orientation or fly in a particular pattern such as to avoid an obstacle.
Leveraging Common Structure to Improve Prediction across Related Datasets
Authors: Barnes Matt , Gisolfi Nick , Fiterau Madalina , Dubrawski Artur
Published: June 2022

In many applications, training data is provided in the form of related datasets obtained from several sources, which typically affects the sample distribution. The learned classification models, which are expected to perform well on similar data coming from new sources, often suffer due to bias introduced by what we call `spurious' samples -- those due to source characteristics and not representative of any other part of the data. As standard outlier detection and robust classification usually fall short of determining groups of spurious samples, we propose a procedure which identifies the common structure across datasets by minimizing a multi-dataset divergence metric, increasing accuracy for new datasets.
On Computing Maximal Subsets of Clauses that Must Be Satisfiable with Possibly Mutually-Contradictory Assumptive Contexts
Authors: Besnard Philippe , Grégoire Eric , Lagniez Jean-Marie
Published: June 2022

An original method for the extraction of one maximal subset of a set of Boolean clauses that must be satisfiable with possibly mutually contradictory assumptive contexts is motivated and experimented. Noticeably, it performs a direct computation and avoids the enumeration of all subsets that are satisfiable with at least one of the contexts. The method applies for subsets that are maximal with respect to inclusion or cardinality.
SAT Modulo Monotonic Theories
Authors: Bayless Sam , Bayless Noah , Hoos Holger , Hu Alan
Published: June 2022

Boolean satisfiability (SAT) solvers have been successfully applied to a wide variety of difficult combinatorial problems. Many further problems can be solved by SAT Modulo Theory (SMT) solvers, which extend SAT solvers to handle additional types of constraints. However, building efficient SMT solvers is often very difficult. In this paper, we define the concept of a Boolean monotonic theory and show how to easily build efficient SMT solvers, including effective theory propagation and clause learning, for such theories. We present examples showing useful constraints that are monotonic, including many graph properties (e.g., shortest paths), and geometric properties (e.g., convex hulls). These constraints arise in problems that are otherwise difficult for SAT solvers to handle, such as procedural content generation. We have implemented several monotonic theory solvers using the techniques we present in this paper and applied these to content generation problems, demonstrating major speed-ups over SAT, SMT, and Answer Set Programming solvers, easily solving instances that were previously out of reach.
Abstraction for Solving Large Incomplete-Information Games
Authors: Sandholm Tuomas
Published: June 2022

Most real-world games and many recreational games are games of incomplete information. Over the last dozen years, abstraction has emerged as a key enabler for solving large incomplete-information games. First, the game is abstracted to generate a smaller, abstract game that is strategically similar to the original game. Second, an approximate equilibrium is computed in the abstract game. Third, the strategy from the abstract game is mapped back to the original game. In this paper, I will review key developments in the field. I present reasons for abstracting games, and point out the issue of abstraction pathology. I then review the practical algorithms for information abstraction and action abstraction. I then cover recent theoretical breakthroughs that beget bounds on the quality of the strategy from the abstract game, when measured in the original game. I then discuss how to reverse map the opponent's action into the abstraction if the opponent makes a move that is not in the abstraction. Finally, I discuss other topics of current and future research.
Steering Evolution Strategically: Computational Game Theory and Opponent Exploitation for Treatment Planning, Drug Design, and Synthetic Biology
Authors: Sandholm Tuomas
Published: June 2022

Living organisms adapt to challenges through evolution. This has proven to be a key difficulty in developing therapies, since the organisms evolve resistance.I propose the wild idea of steering evolution strategically — using computational game theory for (typically incomplete-information) multistage games and opponent exploitation techniques. A sequential contingency plan for steering evolution is constructed computationally for the setting at hand. In the biological context, the opponent (e.g., a disease) has a systematic handicap because it evolves myopically. This can be exploited by computing trapping strategies that cause the opponent to evolve into states where it can be handled effectively. Potential application classes include therapeutics at the population, individual, and molecular levels (drug design), as well as cell repurposing and synthetic biology.
Machine Teaching: An Inverse Problem to Machine Learning and an Approach Toward Optimal Education
Authors: Zhu Xiaojin
Published: June 2022

I draw the reader's attention to machine teaching, the problem of finding an optimal training set given a machine learning algorithm and a target model. In addition to generating fascinating mathematical questions for computer scientists to ponder, machine teaching holds the promise of enhancing education and personnel training. The Socratic dialogue style aims to stimulate critical thinking.
Mechanism Learning with Mechanism Induced Data
Authors: Liu Tie-Yan , Chen Wei , Qin Tao
Published: June 2022

Machine learning and game theory are two important directions of AI. The former usually assumes data is independent of the models to be learned; the latter usually assumes agents are fully rational. In many modern Internet applications, like sponsored search and crowdsourcing, the two basic assumptions are violated and new challenges are posed to both machine learning and game theory. To better model and study such applications, we need to go beyond conventional machine learning and game theory (mechanism design), and adopt a new approach called mechanism learning with mechanism induced data. Specifically, we propose to learn a behavior model from data to describe how real agents play the complicated game, instead of making the full-rationality assumption. Then we propose to optimize the mechanism by using the learned behavior models to predict the future behaviors of agents in response to the new mechanism. Because the above process couples mechanism learning and behavior learning in a loop, new algorithms and theories are needed to perform the task and guarantee the asymptotical performance. As shown in this paper, there are many interesting research topics along this direction, many of which are still open problems, waiting for researchers in our community to deeply investigate.
SimSensei Demonstration: A Perceptive Virtual Human Interviewer for Healthcare Applications
Authors: Morency Louis-Philippe , Stratou Giota , DeVault David , Hartholt Arno , Lhommet Margo , Lucas Gale , Morbini Fabrizio , Georgila Kallirroi , Scherer Stefan , Gratch Jonathan , Marsella Stacy , Traum David , Rizzo Albert
Published: June 2022

We present the SimSensei system, a fully automatic virtual agent that conducts interviews to assess indicators of psychological distress. We emphasize on the perception part of the system, a multimodal framework which captures and analyzes user state for both behavioral understanding and interactional purposes.
Scheherazade: Crowd-Powered Interactive Narrative Generation
Authors: Li Boyang , Riedl Mark
Published: June 2022

Interactive narrative is a form of storytelling in which users affect a dramatic storyline through actions by assuming the role of characters in a virtual world.This extended abstract outlines the Scheherazade-IF system, which uses crowdsourcing and artificial intelligence to automatically construct text-based interactive narrative experiences.
Sparse Deep Stacking Network for Image Classification
Authors: Li Jun , Chang Heyou , Yang Jian
Published: June 2022

Sparse coding can learn good robust representation to noise and model more higher-order representation for image classification. However, the inference algorithm is computationally expensive even though the supervised signals are used to learn compact and discriminative dictionaries in sparse coding techniques. Luckily, a simplified neural network module (SNNM) has been proposed to directly learn the discriminative dictionaries for avoiding the expensive inference. But the SNNM module ignores the sparse representations. Therefore, we propose a sparse SNNM module by adding the mixed-norm regularization (l1/l2 norm). The sparse SNNM modules are further stacked to build a sparse deep stacking network (S-DSN). In the experiments, we evaluate S-DSN with four databases, including Extended YaleB, AR, 15 scene and Caltech101. Experimental results show that our model outperforms related classification methods with only a linear classifier. It is worth noting that we reach 98.8% recognition accuracy on 15 scene.
Complex Event Detection via Event Oriented Dictionary Learning
Authors: Yan Yan , Yang Yi , Shen Haoquan , Meng Deyu , Liu Gaowen , Hauptmann Alex , Sebe Nicu
Published: June 2022

Complex event detection is a retrieval task with the goal of finding videos of a particular event in a large-scale unconstrained internet video archive, given example videos and text descriptions. Nowadays, different multimodal fusion schemes of low-level and high-level features are extensively investigated and evaluated for the complex event detection task. However, how to effectively select the high-level semantic meaningful concepts from a large pool to assist complex event detection is rarely studied in the literature. In this paper, we propose two novel strategies to automatically select semantic meaningful concepts for the event detection task based on both the events-kit text descriptions and the concepts high-level feature descriptions. Moreover, we introduce a novel event oriented dictionary representation based on the selected semantic concepts. Towards this goal, we leverage training samples of selected concepts from the Semantic Indexing (SIN) dataset with a pool of 346 concepts, into a novel supervised multi-task dictionary learning framework. Extensive experimental results on TRECVID Multimedia Event Detection (MED) dataset demonstrate the efficacy of our proposed method.
Robust Subspace Clustering via Thresholding Ridge Regression
Authors: Peng Xi , Yi Zhang , Tang Huajin
Published: June 2022

Given a data set from a union of multiple linear subspaces, a robust subspace clustering algorithm fits each group of data points with a low-dimensional subspace and then clusters these data even though they are grossly corrupted or sampled from the union of dependent subspaces. Under the framework of spectral clustering, recent works using sparse representation, low rank representation and their extensions achieve robust clustering results by formulating the errors (e.g., corruptions) into their objective functions so that the errors can be removed from the inputs. However, these approaches have suffered from the limitation that the structure of the errors should be known as the prior knowledge. In this paper, we present a new method of robust subspace clustering by eliminating the effect of the errors from the projection space (representation) rather than from the input space. We firstly prove that ell_1-, ell_2-, and ell_infty-norm-based linear projection spaces share the property of intra-subspace projection dominance, i.e., the coefficients over intra-subspace data points are larger than those over inter-subspace data points. Based on this property, we propose a robust and efficient subspace clustering algorithm, called Thresholding Ridge Regression (TRR). TRR calculates the ell2-norm-based coefficients of a given data set and performs a hard thresholding operator; and then the coefficients are used to build a similarity graph for clustering. Experimental studies show that TRR outperforms the state-of-the-art methods with respect to clustering quality, robustness, and time-saving.
Exploring Semantic Inter-Class Relationships (SIR) for Zero-Shot Action Recognition
Authors: Gan Chuang , Lin Ming , Yang Yi , Zhuang Yueting , G.Hauptmann Alexander
Published: June 2022

Automatically recognizing a large number of action categories from videos is of significant importance for video understanding. Most existing works focused on the design of more discriminative feature representation, and have achieved promising results when the positive samples are enough. However, very limited efforts were spent on recognizing a novel action without any positive exemplars, which is often the case in the real settings due to the large amount of action classes and the users' queries dramatic variations. To address this issue, we propose to perform action recognition when no positive exemplars of that class are provided, which is often known as the zero-shot learning. Different from other zero-shot learning approaches, which exploit attributes as the intermediate layer for the knowledge transfer, our main contribution is SIR, which directly leverages the semantic inter-class relationships between the known and unknown actions followed by label transfer learning. The inter-class semantic relationships are automatically measured by continuous word vectors, which learned by the skip-gram model using the large-scale text corpus. Extensive experiments on the UCF101 dataset validate the superiority of our method over fully-supervised approaches using few positive exemplars.
Planned Protest Modeling in News and Social Media
Authors: Muthiah Sathappan , Huang Bert , Arredondo Jaime , Mares David , Getoor Lise , Katz Graham , Ramakrishnan Naren
Published: June 2022

Civil unrest (protests, strikes, and “occupy” events) is a common occurrence in both democracies and authoritarian regimes. The study of civil unrest is a key topic for political scientists as it helps capture an important mechanism by which citizenry express themselves. In countries where civil unrest is lawful, qualitative analysis has revealed that more than 75% of the protests are planned, organized, and/or announced in advance; therefore detecting future time mentions in relevant news and social media is a direct way to develop a protest forecasting system. We develop such a system in this paper, using a combination of key phrase learning to identify what to look for, probabilistic soft logic to reason about location occurrences in extracted results, and time normalization to resolve future tense mentions. We illustrate the application of our system to 10 countries in Latin America, viz. Argentina, Brazil, Chile, Colombia, Ecuador, El Salvador, Mexico, Paraguay, Uruguay, and Venezuela. Results demonstrate our successes in capturing significant societal unrest in these countries with an average lead time of 4.08 days. We also study the selective superiorities of news media versus social media (Twitter, Facebook) to identify relevant tradeoffs.
Day-Ahead Hail Prediction Integrating Machine Learning with Storm-Scale Numerical Weather Models
Authors: Gagne David , McGovern Amy , Jerald Jerald , Coniglio Michael , Correia James , Xue Ming
Published: June 2022

Hail causes billions of dollars in losses by damaging buildings, vehicles, and crops. Improving the spatial and temporal accuracy of hail forecasts would allow people to mitigate hail damage. We have developed an approach to forecasting hail that identifies potential hail storms in storm-scale numerical weather prediction models and matches them with observed hailstorms. Machine learning models, including random forests, gradient boosting trees, and linear regression, are used to predict the expected hail size from each forecast storm. The individual hail size forecasts are merged with a spatial neighborhood ensemble probability technique to produce a consensus probability of hail at least 25.4 mm in diameter. The system was evaluated during the 2014 National Oceanic and Atmospheric Administration Hazardous Weather Testbed Experimental Forecast Program and compared with a physics-based hail size model. The machine-learning-based technique shows advantages in producing smaller size errors and more reliable probability forecasts. The machine learning approaches correctly predicted the location and extent of a significant hail event in eastern Nebraska and a marginal severe hail event in Colorado.
A Robust and Extensible Tool for Data Integration Using Data Type Models
Authors: Quiroz Andres , Huang Eric , Ceriani Luca
Published: June 2022

Integrating heterogeneous data sets has been a significant barrier to many analytics tasks, due to the variety in structure and level of cleanliness of raw data sets requiring one-off ETL code. We propose HiperFuse, which significantly automates the data integration process by providing a declarative interface, robust type inference, extensible domain-specific data models, and a data integration planner which optimizes for plan completion time. The proposed tool is designed for schema-less data querying, code reuse within specific domains, and robustness in the face of messy unstructured data. To demonstrate the tool and its reference implementation, we show the requirements and execution steps for a use case in which IP addresses from a web clickstream log are joined with census data to obtain average income for particular site visitors (IPs), and offer preliminary performance results and qualitative comparisons to existing data integration and ETL tools.
Maestoso: An Intelligent Educational Sketching Tool for Learning Music Theory
Authors: Taele Paul , Barreto Laura , Hammond Tracy
Published: June 2022

Learning music theory not only has practical benefits for musicians to write, perform, understand, and express music better, but also for both non-musicians to improve critical thinking, math analytical skills, and music appreciation. However, current external tools applicable for learning music theory through writing when human instruction is unavailable are either limited in feedback, lacking a written modality, or assuming already strong familiarity of music theory concepts. In this paper, we describe Maestoso, an educational tool for novice learners to learn music theory through sketching practice of quizzed music structures. Maestoso first automatically recognizes students’ sketched input of quizzed concepts, then relies on existing sketch and gesture recognition techniques to automatically recognize the input, and finally generates instructor-emulated feedback. From our evaluations, we demonstrate that Maestoso performs reasonably well on recognizing music structure elements and that novice students can comfortably grasp introductory music theory in a single session.
Dependency Tree Representations of Predicate-Argument Structures
Authors: Qiu Likun , Zhang Yue , Zhang Meishan
Published: June 2022

We present a novel annotation framework for representing predicate-argument structures, which uses dependency trees to encode the syntactic and semantic roles of a sentence simultaneously. The main contribution is a semantic role transmission model, which eliminates the structural gap between syntax and shallow semantics, making them compatible. A Chinese semantic treebank was built under the proposed framework, and the first release containing about 14K sentences is made freely available. The proposed framework enables semantic role labeling to be solved as a sequence labeling task, and experiments show that standard sequence labelers can give competitive performance on the new treebank compared with state-of-the-art graph structure models.
Hashtag-Based Sub-Event Discovery Using Mutually Generative LDA in Twitter
Authors: Xing Chen , Wang Yuan , Liu Jie , Huang Yalou , Ma Wei-Ying
Published: June 2022

Sub-event discovery is an effective method for social event analysis in Twitter. It can discover sub-events from large amount of noisy event-related information in Twitter and semantically represent them. The task is challenging because tweets are short, informal and noisy. To solve this problem, we consider leveraging event-related hashtags that contain many locations, dates and concise sub-event related descriptions to enhance sub-event discovery. To this end, we propose a hashtag-based mutually generative Latent Dirichlet Allocation model(MGe-LDA). In MGe-LDA, hashtags and topics of a tweet are mutually generated by each other. The mutually generative process models the relationship between hashtags and topics of tweets, and highlights the role of hashtags as a semantic representation of the corresponding tweets. Experimental results show that MGe-LDA can significantly outperform state-of-the-art methods for sub-event discovery.
Representation Learning of Knowledge Graphs with Entity Descriptions
Authors: Xie Ruobing , Liu Zhiyuan , Jia Jia , Luan Huanbo , Sun Maosong
Published: June 2022

Representation learning (RL) of knowledge graphs aims to project both entities and relations into a continuous low-dimensional space. Most methods concentrate on learning representations with knowledge triples indicating relations between entities. In fact, in most knowledge graphs there are usually concise descriptions for entities, which cannot be well utilized by existing methods. In this paper, we propose a novel RL method for knowledge graphs taking advantages of entity descriptions. More specifically, we explore two encoders, including continuous bag-of-words and deep convolutional neural models to encode semantics of entity descriptions. We further learn knowledge representations with both triples and descriptions. We evaluate our method on two tasks, including knowledge graph completion and entity classification. Experimental results on real-world datasets show that, our method outperforms other baselines on the two tasks, especially under the zero-shot setting, which indicates that our method is capable of building representations for novel entities according to their descriptions. The source code of this paper can be obtained from https://github.com/xrb92/DKRL.
Implicit Discourse Relation Classification via Multi-Task Neural Networks
Authors: Liu Yang , Li Sujian , Zhang Xiaodong , Sui Zhifang
Published: June 2022

Without discourse connectives, classifying implicit discourse relations is a challenging task and a bottleneck for building a practical discourse parser. Previous research usually makes use of one kind of discourse framework such as PDTB or RST to improve the classification performance on discourse relations. Actually, under different discourse annotation frameworks, there exist multiple corpora which have internal connections. To exploit the combination of different discourse corpora, we design related discourse classification tasks specific to a corpus, and propose a novel Convolutional Neural Network embedded multi-task learning system to synthesize these tasks by learning both unique and shared representations for each task. The experimental results on the PDTB implicit discourse relation classification task demonstrate that our model achieves significant gains over baseline systems.
A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations
Authors: Wan Shengxian , Lan Yanyan , Guo Jiafeng , Xu Jun , Pang Liang , Cheng Xueqi
Published: June 2022

Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, such methods cannot well capture the contextualized local information in the matching process. To tackle this problem, we present a new deep architecture to match two sentences with multiple positional sentence representations. Specifically, each positional sentence representation is a sentence representation at this position, generated by a bidirectional long short term memory (Bi-LSTM). The matching score is finally produced by aggregating interactions between these different positional sentence representations, through k-Max pooling and a multi-layer perceptron. Our model has several advantages: (1) By using Bi-LSTM, rich context of the whole sentence is leveraged to capture the contextualized local information in each positional sentence representation; (2) By matching with multiple positional sentence representations, it is flexible to aggregate different important contextualized local information in a sentence to support the matching; (3) Experiments on different tasks such as question answering and sentence completion demonstrate the superiority of our model.
A Representation Learning Framework for Multi-Source Transfer Parsing
Authors: Guo Jiang , Che Wanxiang , Yarowsky David , Wang Haifeng , Liu Ting
Published: June 2022

Cross-lingual model transfer has been a promising approach for inducing dependency parsers for low-resource languages where annotated treebanks are not available. The major obstacles for the model transfer approach are two-fold: 1. Lexical features are not directly transferable across languages; 2. Target language-specific syntactic structures are difficult to be recovered. To address these two challenges, we present a novel representation learning framework for multi-source transfer parsing. Our framework allows multi-source transfer parsing using full lexical features straightforwardly. By evaluating on the Google universal dependency treebanks (v2.0), our best models yield an absolute improvement of 6.53% in averaged labeled attachment score, as compared with delexicalized multi-source transfer models. We also significantly outperform the state-of-the-art transfer system proposed most recently.