bluuebunny/crossref_metadata_2025 huggingface dataset:
Reconsidering Mutual Information Based Feature Selection: A Statistical Significance View
Authors: Vinh Nguyen , Chan Jeffrey , Bailey James
Published: June 2022

Mutual information (MI) based approaches are a popular feature selection paradigm. Although the stated goal of MI-based feature selection is to identify a subset of features that share the highest mutual information with the class variable, most current MI-based techniques are greedy methods that make use of low dimensional MI quantities. The reason for using low dimensional approximation has been mostly attributed to the difficulty associated with estimating the high dimensional MI from limited samples. In this paper, we argue a different viewpoint that, given a very large amount of data, the high dimensional MI objective is still problematic to be employed as a meaningful optimization criterion, due to its overfitting nature: the MI almost always increases as more features are added, thus leading to a trivial solution which includes all features. We propose a novel approach to the MI-based feature selection problem, in which the overfitting phenomenon is controlled rigourously by means of a statistical test. We develop local and global optimization algorithms for this new feature selection model, and demonstrate its effectiveness in the applications of explaining variables and objects.
A Local Non-Negative Pursuit Method for Intrinsic Manifold Structure Preservation
Authors: Chen Dongdong , Lv Jian Cheng , Yi Zhang
Published: June 2022

The local neighborhood selection plays a crucial role for most representation based manifold learning algorithms. This paper reveals that an improper selection of neighborhood for learning representation will introduce negative components in the learnt representations. Importantly, the representations with negative components will affect the intrinsic manifold structure preservation. In this paper, a local non-negative pursuit (LNP) method is proposed for neighborhood selection and non-negative representations are learnt. Moreover, it is proved that the learnt representations are sparse and convex. Theoretical analysis and experimental results show that the proposed method achieves or outperforms the state-of-the-art results on various manifold learning problems.
Sparse Compositional Metric Learning
Authors: Shi Yuan , Bellet Aurélien , Sha Fei
Published: June 2022

We propose a new approach for metric learning by framing it as learning a sparse combination of locally discriminative metrics that are inexpensive to generate from the training data. This flexible framework allows us to naturally derive formulations for global, multi-task and local metric learning. The resulting algorithms have several advantages over existing methods in the literature: a much smaller number of parameters to be estimated and a principled way to generalize learned metrics to new testing data points. To analyze the approach theoretically, we derive a generalization bound that justifies the sparse combination. Empirically, we evaluate our algorithms on several datasets against state-of-the-art metric learning methods. The results are consistent with our theoretical findings and demonstrate the superiority of our approach in terms of classification performance and scalability.
Fast Multi-Instance Multi-Label Learning
Authors: Huang Sheng-Jun , Gao Wei , Zhou Zhi-Hua
Published: June 2022

In multi-instance multi-label learning (MIML), one object is represented by multiple instances and simultaneously associated with multiple labels. Existing MIML approaches have been found useful in many applications; however, most of them can only handle moderate-sized data. To efficiently handle large data sets, we propose the MIMLfast approach, which first constructs a low-dimensional subspace shared by all labels, and then trains label specific linear models to optimize approximated ranking loss via stochastic gradient descent. Although the MIML problem is complicated, MIMLfast is able to achieve excellent performance by exploiting label relations with shared space and discovering sub-concepts for complicated labels. Experiments show that the performance of MIMLfast is highly competitive to state-of-the-art techniques, whereas its time cost is much less; particularly, on a data set with 30K bags and 270K instances, where none of existing approaches can return results in 24 hours, MIMLfast takes only 12 minutes. Moreover, our approach is able to identify the most representative instance for each label, and thus providing a chance to understand the relation between input patterns and output semantics.
Partial Multi-View Clustering
Authors: Li Shao-Yuan , Jiang Yuan , Zhou Zhi-Hua
Published: June 2022

Real data are often with multiple modalities or comingfrom multiple channels, while multi-view clusteringprovides a natural formulation for generating clustersfrom such data. Previous studies assumed that each exampleappears in all views, or at least there is one viewcontaining all examples. In real tasks, however, it is oftenthe case that every view suffers from the missing ofsome data and therefore results in many partial examples,i.e., examples with some views missing. In this paper,we present possibly the first study on partial multiviewclustering. Our proposed approach, PVC, worksby establishing a latent subspace where the instancescorresponding to the same example in different viewsare close to each other, and similar instances (belongingto different examples) in the same view should bewell grouped. Experiments on two-view data demonstratethe advantages of our proposed approach.
Bagging by Design (on the Suboptimality of Bagging)
Authors: Papakonstantinou Periklis , Xu Jia , Cao Zhu
Published: June 2022

Bagging (Breiman 1996) and its variants is one of the most popular methods in aggregating classifiers and regressors. Originally, its analysis assumed that the bootstraps are built from an unlimited, independent source of samples, therefore we call this form of bagging ideal-bagging. However in the real world, base predictors are trained on data subsampled from a limited number of training samples and thus they behave very differently. We analyze the effect of intersections between bootstraps, obtained by subsampling, to train different base predictors. Most importantly, we provide an alternative subsampling method called design-bagging based on a new construction of combinatorial designs, and prove it universally better than bagging. Methodologically, we succeed at this level of generality because we compare the prediction accuracy of bagging and design-bagging relative to the accuracy ideal-bagging. This finds potential applications in more involved bagging-based methods. Our analytical results are backed up by experiments on classification and regression settings.
A Relevance-Based Compilation Method for Conformant Probabilistic Planning
Authors: Taig Ran , Brafman Ronen
Published: June 2022

Conformant probabilistic planning (CPP) differs from conformant planning (CP) by two key elements: the initial belief state is probabilistic,and the conformant plan must achieve the goal with probability $\geq\theta$, for some $0<\theta\leq 1$. In earlier work we observed that one can reduce CPP to CP by finding a set of initial states whose probability $\geq\theta$, for whicha conformant plan exists. In previous solvers we used the underlying planner to select this set of states and to plan for them simultaneously. Here we suggest an alternative approach: start with relevance analysis to determine a promising set of initial states on which to focus. Then, call an off-the-shelf conformant planner to solve the resulting problem. This approach has a number of advantages. First, instead of depending on the heuristic function to select the set of initial states,we can introduce specific, efficient relevance reasoning techniques. Second, we can benefit from optimizations used by conformant planners that are unsound when applied to the original CPP. Finally, we are free to use any existing (or new) CP solver. Consequently, the new planner dominates previous solvers on almost all domains and scales to instances that were not solved before.
Tightening Bounds for Bayesian Network Structure Learning
Authors: Fan Xiannian , Yuan Changhe , Malone Brandon
Published: June 2022

A recent breadth-first branch and bound algorithm (BFBnB)for learning Bayesian network structures (Maloneet al. 2011) uses two bounds to prune the searchspace for better efficiency; one is a lower bound calculatedfrom pattern database heuristics, and the otheris an upper bound obtained by a hill climbing search.Whenever the lower bound of a search path exceeds theupper bound, the path is guaranteed to lead to suboptimalsolutions and is discarded immediately. This paperintroduces methods for tightening the bounds. Thelower bound is tightened by using more informed variablegroupings when creating the pattern databases, andthe upper bound is tightened using an anytime learningalgorithm. Empirical results show that these boundsimprove the efficiency of Bayesian network learning bytwo to three orders of magnitude.
Crowdsourcing for Multiple-Choice Question Answering
Authors: Aydin Bahadir , Yavuz Selim Yilmaz Yavuz Selim Yilmaz , Li Yaliang , Li Qi , Gao Jing , Demirbas Murat
Published: June 2022

We leverage crowd wisdom for multiple-choice question answering, and employ lightweight machine learning techniques to improve the aggregation accuracy of crowdsourced answers to these questions. In order to develop more effective aggregation methods and evaluate them empirically, we developed and deployed a crowdsourced system for playing the “Who wants to be a millionaire?” quiz show. Analyzing our data (which consist of more than 200,000 answers), we find that by just going with the most selected answer in the aggregation, we can answer over 90% of the questions correctly, but the success rate of this technique plunges to 60% for the later/harder questions in the quiz show. To improve the success rates of these later/harder questions, we investigate novel weighted aggregation schemes for aggregating the answers obtained from the crowd. By using weights optimized for reliability of participants (derived from the participants’ confidence), we show that we can pull up the accuracy rate for the harder questions by 15%, and to overall 95% average accuracy. Our results provide a good case for the benefits of applying machine learning techniques for building more accurate crowdsourced question answering systems.
A Smart Range Helping Cognitively-Impaired Persons Cooking
Authors: Bouchard Bruno , Bouchard Kevin , Bouzouane Abdenour
Published: June 2022

People suffering from a loss of autonomy caused by a cognitive deficit generally have to perform important daily tasks (such as cooking) using devices and appliances designed for healthy people, which do not take into consideration their cognitive impairment. Using these devices is risky and may lead to a tragedy (e.g. fire). A potential solution to this issue is to provide automated systems, which perform tasks on behalf of the patient. However, clinical studies have shown that encouraging users to maintain their autonomy greatly help to preserve health, dignity, and motivation. Therefore, we present in this paper a new smart range prototype allowing monitoring and guiding a cognitively-impaired user in the activity of preparing a meal. This new original prototype is capable of giving adapted prompting to the user in the completion of several recipes by exploiting load cells, heat sensors and electromagnetic contacts embedded in the range. We currently own a provisional patent on this new invention, and we completed a first experimental phase.
Multi-Document Summarization Based on Two-Level Sparse Representation Model
Authors: Liu He , Yu Hongliang , Deng Zhi-Hong
Published: June 2022

Multi-document summarization is of great value to many real world applications since it can help people get the main ideas within a short time.In this paper, we tackle the problem of extracting summary sentences from multi-document sets by applying sparse coding techniques and present a novel framework to this challenging problem. Based on the data reconstruction and sentence denoising assumption, we present a two-level sparse representation model to depict the process of multi-document summarization. Three requisite properties is proposed to form an ideal reconstructable summary: Coverage, Sparsity and Diversity. We then formalize the task of multi-document summarization as an optimization problem according to the above properties, and use simulated annealing algorithm to solve it.Extensive experiments on summarization benchmark data sets DUC2006 and DUC2007 show that our proposed model is effective and outperforms the state-of-the-art algorithms.
Trust Models for RDF Data: Semantics and Complexity
Authors: Fionda Valeria , Greco Gianluigi
Published: June 2022

Due to the openness and decentralization of the Web, mechanisms to represent and reason about the reliability of RDF data become essential. This paper embarks on a formal analysis of RDF data enriched with trust information by focusing on the characterization of its model-theoretic semantics and on the study of relevant reasoning problems. The impact of trust values on the computational complexity of well-known concepts related to the entailment of RDF graphs is studied. In particular, islands of tractability are identified for classes of acyclic and nearly-acyclic graphs. Moreover, an implementation of the framework and an experimental evaluation on real data are discussed.
Representation Learning for Aspect Category Detection in Online Reviews
Authors: Zhou Xinjie , Wan Xiaojun , Xiao Jianguo
Published: June 2022

User-generated reviews are valuable resources for decision making. Identifying the aspect categories discussed in a given review sentence (e.g., “food” and “service” in restaurant reviews) is an important task of sentiment analysis and opinion mining. Given a predefined aspect category set, most previous researches leverage hand-crafted features and a classification algorithm to accomplish the task. The crucial step to achieve better performance is feature engineering which consumes much human effort and may be unstable when the product domain changes. In this paper, we propose a representation learning approach to automatically learn useful features for aspect category detection. Specifically, a semi-supervised word embedding algorithm is first proposed to obtain continuous word representations on a large set of reviews with noisy labels. Afterwards, we propose to generate deeper and hybrid features through neural networks stacked on the word vectors. A logistic regression classifier is finally trained with the hybrid features to predict the aspect category. The experiments are carried out on a benchmark dataset released by SemEval-2014. Our approach achieves the state-of-the-art performance and outperforms the best participating team as well as a few strong baselines.
A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data
Authors: Ghassemi Marzyeh , Pimentel Marco , Naumann Tristan , Brennan Thomas , Clifton David , Szolovits Peter , Feng Mengling
Published: June 2022

The ability to determine patient acuity (or severity of illness) has immediate practical use for clinicians. We evaluate the use of multivariate timeseries modeling with the multi-task Gaussian process (GP) models using noisy, incomplete, sparse, heterogeneous and unevenly-sampled clinical data, including both physiological signals and clinical notes. The learned multi-task GP (MTGP) hyperparameters are then used to assess and forecast patient acuity. Experiments were conducted with two real clinical data sets acquired from ICU patients: firstly, estimating cerebrovascular pressure reactivity, an important indicator of secondary damage for traumatic brain injury patients, by learning the interactions between intracranial pressure and mean arterial blood pressure signals, and secondly, mortality prediction using clinical progress notes. In both cases, MTGPs provided improved results: an MTGP model provided better results than single-task GP models for signal interpolation and forecasting (0.91 vs 0.69 RMSE), and the use of MTGP hyperparameters obtained improved results when used as additional classification features (0.812 vs 0.788 AUC).
Bayesian Affect Control Theory of Self
Authors: Hoey Jesse , Schroeder Tobias
Published: June 2022

Notions of identity and of the self have long been studied in social psychology and sociology as key guiding elements of social interaction and coordination. In the AI of the future, these notions will also play a role in producing natural, socially appropriate artificially intelligent agents that encompass subtle and complex human social and affective skills. We propose here a Bayesian generalization of the sociological affect control theory of self as a theoretical foundation for socio-affectively skilled artificial agents. This theory posits that each human maintains an internal model of his or her deep sense of "self" that captures their emotional, psychological, and socio-cultural sense of being in the world. The "self" is then externalised as an identity within any given interpersonal and institutional situation, and this situational identity is the person's local (in space and time) representation of the self. Situational identities govern the actions of humans according to affect control theory. Humans will seek situations that allow them to enact identities consistent with their sense of self. This consistency is cumulative over time: if some parts of a person's self are not actualized regularly, the person will have a growing feeling of inauthenticity that they will seek to resolve. In our present generalisation, the self is represented as a probability distribution, allowing it to be multi-modal (a person can maintain multiple different identities), uncertain (a person can be unsure about who they really are), and learnable (agents can learn the identities and selves of other agents). We show how the Bayesian affect control theory of self can underpin artificial agents that are socially intelligent.
Moral Decision-Making by Analogy: Generalizations versus Exemplars
Authors: Blass Joseph , Forbus Kenneth
Published: June 2022

Moral reasoning is important to accurately model as AI systems become ever more integrated into our lives. Moral reasoning is rapid and unconscious; analogical reasoning, which can be unconscious, is a promising approach to model moral reasoning. This paper explores the use of analogical generalizations to improve moral reasoning. Analogical reasoning has already been used to successfully model moral reasoning in the MoralDM model, but it exhaustively matches across all known cases, which is computationally intractable and cognitively implausible for human-scale knowledge bases. We investigate the performance of an extension of MoralDM to use the MAC/FAC model of analogical retrieval over three conditions, across a set of highly confusable moral scenarios.
Power System Restoration With Transient Stability
Authors: Hijazi Hassan , Mak Terrence W.K. , Van Hentenryck Pascal
Published: June 2022

We address the problem of power system restoration after a significant blackout. Prior work focus on optimization methods for finding high-quality restoration plans. Optimal solutions consist in a sequence of grid repairs and corresponding steady states. However, such approaches lack formal guarantees on the transient stability of restoration actions, a key property to avoid additional grid damage and cascading failures. In this paper, we show how to integrate transient stability in the optimization procedure by capturing the rotor dynamics of power generators. Our approach reasons about the differential equations describing the dynamics and their underlying transient states. The key contribution lies in modeling and solving optimization problems that return stable generators dispatch minimizing the difference with respect to steady states solutions. Computational efficiency is increased using preprocessing procedures along with traditional reduction techniques. Experimental results on existing benchmarks confirm the feasibility of the new approach.
Towards Optimal Solar Tracking: A Dynamic Programming Approach
Authors: Panagopoulos Athanasios Aris , Chalkiadakis Georgios , Jennings Nicholas
Published: June 2022

The power output of photovoltaic systems (PVS) increases with the use of effective and efficient solar tracking techniques. However, current techniques suffer from several drawbacks in their tracking policy: (i) they usually do not consider the forecasted or prevailing weather conditions; even when they do, they (ii) rely on complex closed-loop controllers and sophisticated instruments; and (iii) typically, they do not take the energy consumption of the trackers into account. In this paper, we propose a policy iteration method (along with specialized variants), which is able to calculate near-optimal trajectories for effective and efficient day-ahead solar tracking, based on weather forecasts coming from on-line providers. To account for the energy needs of the tracking system, the technique employs a novel and generic consumption model. Our simulations show that the proposed methods can increase the power output of a PVS considerably, when compared to standard solar tracking techniques.
Entity Resolution in a Big Data Framework
Authors: Kejriwal Mayank
Published: June 2022

Entity Resolution (ER) concerns identifying logically equivalent pairs of entities that may be syntactically disparate. Although ER is a long-standing problem in the artificial intelligence community, the growth of Linked Open Data, a collection of semi-structured datasets published and inter-connected on the Web, mandates a new approach. The thesis is that building a viable Entity Resolution solution for serving Big Data needs requires simultaneously resolving challenges of automation, heterogeneity, scalability and domain independence. The dissertation aims to build such a system and evaluate it on real-world datasets published already as Linked Open Data.
Computing Nash Equilibrium in Interdependent Defense Games
Authors: Chan Hau , Ortiz Luis
Published: June 2022

Roughly speaking, Interdependent Defense (IDD) games, previously proposed, model the situation where an attacker wants to cause as much damage as possible to a network by attacking one of the sites in the network. Each site must make an investment decision regarding security to protect itself against a direct or indirect attack, the latter due to potential transfer-risk from an unprotected neighboring site. The work introducing IDD games discusses potential applications to model the essence of real-world scenarios such as the 2006 transatlantic aircraft plot. In this paper, our focus is the study of the problem of computing a Nash Equilibrium (NE) in IDD games. We show that an efficient algorithm to determine whether some attacker’s strategy can be a part of a NE in an instance of IDD games is unlikely to exist. Yet, we provide a dynamic programming algorithm to compute an approximate NE when the graph/network structure of the game is a directed tree with a single source, and show that it is an FPTAS. We also introduce an improved heuristic to compute an approximate NE on arbitrary graph structures. Our experiments show that our heuristic is more efficient, and provides better approximations, than best-response-gradient dynamics for the case of Internet games, a class of games introduced and studied in the original work on IDD games.
Sequence-Form Algorithm for Computing Stackelberg Equilibria in Extensive-Form Games
Authors: Bosansky Branislav , Cermak Jiri
Published: June 2022

Stackelberg equilibrium is a solution concept prescribing for a player an optimal strategy to commit to, assuming the opponent knows this commitment and plays the best response. Although this solution concept is a cornerstone of many security applications, the existing works typically do not consider situations where the players can observe and react to the actions of the opponent during the course of the game. We extend the existing algorithmic work to extensive-form games and introduce novel algorithm for computing Stackelberg equilibria that exploits the compact sequence-form representation of strategies. Our algorithm reduces the size of the linear programs from exponential in the baseline approach to linear in the size of the game tree. Experimental evaluation on randomly generated games and a security-inspired search game demonstrates significant improvement in the scalability compared to the baseline approach.
On the Convergence of Iterative Voting: How Restrictive Should Restricted Dynamics Be?
Authors: Obraztsova Svetlana , Markakis Evangelos , Polukarov Maria , Rabinovich Zinovi , Jennings Nicholas
Published: June 2022

We study convergence properties of iterative voting procedures. Such procedures are defined by a voting rule and a (restricted) iterative process, where at each step one agent can modify his vote towards a better outcome for himself. It is already known that if the iteration dynamics (the manner in which voters are allowed to modify their votes) are unrestricted, then the voting process may not converge. For most common voting rules this may be observed even under the best response dynamics limitation. It is therefore important to investigate whether and which natural restrictions on the dynamics of iterative voting procedures can guarantee convergence. To this end, we provide two general conditions on the dynamics based on iterative myopic improvements, each of which is sufficient for convergence. We then identify several classes of voting rules (including Positional Scoring Rules, Maximin, Copeland and Bucklin), along with their corresponding iterative processes, for which at least one of these conditions hold.
Providing Arguments in Discussions Based on the Prediction of Human Argumentative Behavior
Authors: Rosenfeld Ariel , Kraus Sarit
Published: June 2022

Argumentative discussion is a highly demanding task. In order to help people in such situations, this paper provides an innovative methodology for developing an agent that can support people in argumentative discussions by proposing possible arguments to them. By analyzing more than 130 human discussions and 140 questionnaires, answered by people, we show that the well-established Argumentation Theory is not a good predictor of people's choice of arguments. Then, we present a model that has 76% accuracy when predicting people’s top three argument choices given a partial deliberation. We present the Predictive and Relevance based Heuristic agent (PRH), which uses this model with a heuristic that estimates the relevance of possible arguments to the last argument given in order to propose possible arguments. Through extensive human studies with over 200 human subjects, we show that people’s satisfaction from the PRH agent is significantly higher than from other agents that propose arguments based on Argumentation Theory, predict arguments without the heuristics or only the heuristics. People also use the PRH agent's proposed arguments significantly more often than those proposed by the other agents.
When Suboptimal Rules
Authors: Elmalech Avshalom , Sarne David , Rosenfeld Avi , Erez Eden
Published: June 2022

This paper represents a paradigm shift in what advice agents should provide people. Contrary to what was previously thought, we empirically show that agents that dispense optimal advice will not necessary facilitate the best improvement in people's strategies. Instead, we claim that agents should at times suboptimally advise. We provide results demonstrating the effectiveness of a suboptimal advising approach in extensive experiments in two canonical mixed agent-human advice-giving domains. Our proposed guideline for suboptimal advising is to rely on the level of intuitiveness of the optimal advice as a measure for how much the suboptimal advice presented to the user should drift from the optimal value.
Reusing Previously Found A* Paths for Fast Goal-Directed Navigation in Dynamic Terrain
Authors: Hernandez Carlos , Asin Roberto , Baier Jorge
Published: June 2022

Generalized Adaptive A* (GAA*) is an incremental algorithm that replans using A* when solving goal-directed navigation problems in dynamic terrain. Immediately after each A* search, it runs an efficient procedure that updates the heuristic values of states that were just expanded by A*, making them more informed. Those updates allow GAA* to speed up subsequent A* searches. Being based on A*, it is simple to describe and communicate; however, it is outperformed by other incremental algorithms like the state-of-the-art D*Lite algorithm at goal-directed navigation. In this paper we show how GAA* can be modified to exploit more information from a previous search in addition to the updated heuristic function. Specifically, we show how GAA* can be modified to utilize the paths found by a previous A* search. Our algorithm — Multipath Generalized Adaptive A* (MPGAA*) — has the same theoretical properties of GAA* and differs from it by only a few lines of pseudocode. Arguably, MPGAA* is simpler to understand than D*Lite. We evaluate MPGAA* over various realistic dynamic terrain settings, and observed that it generally outperforms the state-of-the-art algorithm D*Lite in scenarios resembling outdoor and indoor navigation.
Complexity Results for Compressing Optimal Paths
Authors: Botea Adi , Strasser Ben , Harabor Daniel
Published: June 2022

In this work we give a first tractability analysis of Compressed Path Databases, space efficient oracles used to very quickly identify the first arc on a shortest path. We study the complexity of computing an optimal compressed path database for general directed and undirected graphs. We find that in both cases the problem is NP-complete. We also show that, for graphs which can be decomposed along articulalion points, the problem can be decomposed into independent parts, with a corresponding reduction in its level of difficulty. In particular, this leads to simple and tractable algorithms which yield optimal compression results for trees.
Stochastic Local Search for Satisfiability Modulo Theories
Authors: Fröhlich Andreas , Biere Armin , Wintersteiger Christoph , Hamadi Youssef
Published: June 2022

Satisfiability Modulo Theories (SMT) is essential for many practical applications, e.g., in hard- and software verification, and increasingly also in other scientific areas like computational biology. A large number of applications in these areas benefit from bit-precise reasoning over finite-domain variables. Current approaches in this area translate a formula over bit-vectors to an equisatisfiable propositional formula, which is then given to a SAT solver. In this paper, we present a novel stochastic local search (SLS) algorithm to solve SMT problems, especially those in the theory of bit-vectors, directly on the theory level. We explain how several successful techniques used in modern SLS solvers for SAT can be lifted to the SMT level. Experimental results show that our approach can compete with state-of-the-art bit-vector solvers on many practical instances and, sometimes, outperform existing solvers. This offers interesting possibilities in combining our approach with existing techniques, and, moreover, new insights into the importance of exploiting problem structure in SLS solvers for SAT. Our approach is modular and, therefore, extensible to support other theories, potentially allowing SLS to become part of the more general SMT framework.
RANSAC versus CS-RANSAC
Authors: Jo Geun , Lee Kee-Sung , Chandra Devy , Jang Chol-Hee , Ga Myung-Hyun
Published: June 2022

A homography matrix is used in computer vision field to solve the correspondence problem between a pair of stereo images. RANSAC algorithm is often used to calculate the homography matrix by randomly selecting a set of features iteratively. CS-RANSAC algorithm in this paper converts RANSAC algorithm into two-layers. The first layer is addressing sampling problem which we can describe our knowledge about degenerate features by mean of Constraint Satisfaction Problems (CSP). By dividing the input image into a N X N grid and making feature points into discrete domains, we can model the image into the CSP model to efficiently filter out degenerate feature samples using CSP in the first layer, so that computer has knowledge about how to skip computing the homography matrix in the model estimation step for the second layer. The experimental results show that the proposed CS-RANSAC algorithm can outperform the most of variants of RANSAC without sacrificing its execution time.
Tractable Interval Temporal Propositional and Description Logics
Authors: Artale Alessandro , Kontchakov Roman , Ryzhikov Vladislav , Zakharyaschev Michael
Published: June 2022

We design a tractable Horn fragment of the Halpern-Shoham temporal logic and extend it to interval-based temporal description logics, instance checking in which is P-complete for both combined and data complexity.
Instance-Driven Ontology Evolution in DL-Lite
Authors: Wang Zhe , Wang Kewen , Zhuang Zhiqiang , Qi Guilin
Published: June 2022

The development and maintenance of large and complex ontologies are often time-consuming and error-prone. Thus, automated ontology learning and evolution have attracted intensive research interest. In data-centric applications where ontologies are designed from the data or automatically learnt from it, when new data instances are added that contradict the ontology, it is often desirable to incrementally revise the ontology according to the added data. In description logics, this problem can be intuitively formulated as the operation of TBox contraction, i.e., rational elimination of certain axioms from the logical consequences of a TBox, and it is w.r.t. an ABox. In this paper we introduce a model-theoretic approach to such a contraction problem by using an alternative semantic characterisation of DL-Lite TBoxes. We show that entailment checking (without necessarily first computing the contraction result) is in coNP, which does not shift the corresponding complexity in propositional logic, and the problem is tractable when the size of the new data is bounded.
Cupid: Commitments in Relational Algebra
Authors: Chopra Amit , Singh Munindar
Published: June 2022

We propose Cupid, a language for specifying commitments that supports their information-centric aspects, and offers crucial benefits. One, Cupid is first-order, enabling a systematic treatment of commitment instances. Two, Cupid supports features needed for real-world scenarios such as deadlines, nested commitments, and complex event expressions for capturing the lifecycle of commitment instances. Three, Cupid maps to relational database queries and thus provides a set-based semantics for retrieving commitment instances in states such as being violated, discharged, and so on. We prove that Cupid queries are safe. Four, to aid commitment modelers, we propose the notion of well-identified commitments, and finitely violable and finitely expirable commitments. We give syntactic restrictions for obtaining such commitments.
Identifying At-Risk Students in Massive Open Online Courses
Authors: He Jiazhen , Bailey James , Rubinstein Benjamin , Zhang Rui
Published: June 2022

Massive Open Online Courses (MOOCs) have received widespread attention for their potential to scale higher education, with multiple platforms such as Coursera, edX and Udacity recently appearing. Despite their successes, a major problem faced by MOOCs is low completion rates. In this paper, we explore the accurate early identification of students who are at risk of not completing courses. We build predictive models weekly, over multiple offerings of a course. Furthermore, we envision student interventions that present meaningful probabilities of failure, enacted only for marginal students.To be effective, predicted probabilities must be both well-calibrated and smoothed across weeks.Based on logistic regression, we propose two transfer learning algorithms to trade-off smoothness and accuracy by adding a regularization term to minimize the difference of failure probabilities between consecutive weeks. Experimental results on two offerings of a Coursera MOOC establish the effectiveness of our algorithms.
Exploiting Task-Feature Co-Clusters in Multi-Task Learning
Authors: Xu Linli , Huang Aiqing , Chen Jianhui , Chen Enhong
Published: June 2022

In multi-task learning, multiple related tasks are considered simultaneously, with the goal to improve the generalization performance by utilizing the intrinsic sharing of information across tasks. This paper presents a multi-task learning approach by modeling the task-feature relationships. Specifically, instead of assuming that similar tasks have similar weights on all the features, we start with the motivation that the tasks should be related in terms of subsets of features, which implies a co-cluster structure. We design a novel regularization term to capture this task-feature co-cluster structure. A proximal algorithm is adopted to solve the optimization problem. Convincing experimental results demonstrate the effectiveness of the proposed algorithm and justify the idea of exploiting the task-feature relationships.
A Novel Neural Topic Model and Its Supervised Extension
Authors: Cao Ziqiang , Li Sujian , Liu Yang , Li Wenjie , Ji Heng
Published: June 2022

Topic modeling techniques have the benefits of modeling words and documents uniformly under a probabilistic framework. However, they also suffer from the limitations of sensitivity to initialization and unigram topic distribution, which can be remedied by deep learning techniques. To explore the combination of topic modeling and deep learning techniques, we first explain the standard topic modelfrom the perspective of a neural network. Based on this, we propose a novel neural topic model (NTM) where the representation of words and documents are efficiently and naturally combined into a uniform framework. Extending from NTM, we can easily add a label layer and propose the supervised neural topic model (sNTM) to tackle supervised tasks. Experiments show that our models are competitive in both topic discovery and classification/regression tasks.
Local Context Sparse Coding
Authors: Kim Seungyeon , Lee Joonseok , Lebanon Guy , Park Haesun
Published: June 2022

The n-gram model has been widely used to capture the local ordering of words, yet its exploding feature space often causes an estimation issue. This paper presents local context sparse coding (LCSC), a non-probabilistic topic model that effectively handles large feature spaces using sparse coding. In addition, it introduces a new concept of locality, local contexts, which provides a representation that can generate locally coherent topics and document representations. Our model efficiently finds topics and representations by applying greedy coordinate descent updates. The model is useful for discovering local topics and the semantic flow of a document, as well as constructing predictive models.
Gazetteer-Independent Toponym Resolution Using Geographic Word Profiles
Authors: DeLozier Grant , Baldridge Jason , London Loretta
Published: June 2022

Toponym resolution, or grounding names of places to their actual locations, is an important problem in analysis of both historical corpora and present-day news and web content. Recent approaches have shifted from rule-based spatial minimization methods to machine learned classifiers that use features of the text surrounding a toponym. Such methods have been shown to be highly effective, but they crucially rely on gazetteers and are unable to handle unknown place names or locations. We address this limitation by modeling the geographic distributions of words over the earth's surface: we calculate the geographic profile of each word based on local spatial statistics over a set of geo-referenced language models. These geo-profiles can be further refined by combining in-domain data with background statistics from Wikipedia. Our resolver computes the overlap of all geo-profiles in a given text span; without using a gazetteer, it performs on par with existing classifiers. When combined with a gazetteer, it achieves state-of-the-art performance for two standard toponym resolution corpora (TR-CoNLL and Civil War). Furthermore, it dramatically improves recall when toponyms are identified by named entity recognizers, which often (correctly) find non-standard variants of toponyms.
Target-Dependent Churn Classification in Microblogs
Authors: Amiri Hadi , Daume III Hal
Published: June 2022

We consider the problem of classifying micro-posts as churny or non-churny with respect to a given brand. Using Twitter data about three brands, we find that standard machine learning techniques clearly outperform keyword based approaches. However, the three machine learning techniques we employed (linear classification, support vector machines, and logistic regression) do not perform as well on churn classification as on other text classification problems. We investigate demographic, content, and context churn indicators in microblogs and examine factors that make this problem more challenging. Experimental results show an average F1 performance of 75% for target-dependent churn classification in microblogs.
English Light Verb Construction Identification Using Lexical Knowledge
Authors: Chen Wei-Te , Bonial Claire , Palmer Martha
Published: June 2022

This research describes the development of a supervised classifier of English light verb constructions, for example, "take a walk" and "make a speech." This classifier relies on features from dependency parses, OntoNotes sense tags, WordNet hypernyms and WordNet lexical file information. Evaluation shows that this system achieves an 89% F1 score (four points above the state of the art) on the BNC test set used by Tu & Roth (2011), and an F1 score of 80.68 on the OntoNotes test set, which is significantly more challenging. We attribute the superior F1 score to the use of our rich linguistic features, including the use of WordNet synset and hypernym relations for the detection of previously unattested light verb constructions. We describe the classifier and its features, as well as the characteristics of the OntoNotes light verb construction test set, which relies on linguistically motivated PropBank annotation.
Multi-Task Learning and Algorithmic Stability
Authors: Zhang Yu
Published: June 2022

In this paper, we study multi-task algorithms from the perspective of the algorithmic stability. We give a definition of the multi-task uniform stability, a generalization of the conventional uniform stability, which measures the maximum difference between the loss of a multi-task algorithm trained on a data set and that of the multi-task algorithm trained on the same data set but with a data point removed in each task. In order to analyze multi-task algorithms based on multi-task uniform stability, we prove a generalized McDiarmid's inequality which assumes the difference bound condition holds by changing multiple input arguments instead of only one in the conventional McDiarmid's inequality. By using the generalized McDiarmid's inequality as a tool, we can analyze the generalization performance of general multi-task algorithms in terms of the multi-task uniform stability. Moreover, as applications, we prove generalization bounds of several representative regularized multi-task algorithms.
Pareto Ensemble Pruning
Authors: Qian Chao , Yu Yang , Zhou Zhi-Hua
Published: June 2022

Ensemble learning is among the state-of-the-art learning techniques, which trains and combines many base learners. Ensemble pruning removes some of the base learners of an ensemble, and has been shown to be able to further improve the generalization performance. However, the two goals of ensemble pruning, i.e., maximizing the generalization performance and minimizing the number of base learners, can conflict when being pushed to the limit. Most previous ensemble pruning approaches solve objectives that mix the two goals. In this paper, motivated by the recent theoretical advance of evolutionary optimization, we investigate solving the two goals explicitly in a bi-objective formulation and propose the PEP (Pareto Ensemble Pruning) approach. We disclose that PEP does not only achieve significantly better performance than the state-of-the-art approaches, and also gains theoretical support.
Obtaining Well Calibrated Probabilities Using Bayesian Binning
Authors: Pakdaman Naeini Mahdi , Cooper Gregory , Hauskrecht Milos
Published: June 2022

Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in artificial intelligence. In this paper we present a new non-parametric calibration method called Bayesian Binning into Quantiles (BBQ) which addresses key limitations of existing calibration methods. The method post processes the output of a binary classification algorithm; thus, it can be readily combined with many existing classification algorithms. The method is computationally tractable, and empirically accurate, as evidenced by the set of experiments reported here on both real and simulated datasets.
Tractability of Planning with Loops
Authors: Srivastava Siddharth , Zilberstein Shlomo , Gupta Abhishek , Abbeel Pieter , Russell Stuart
Published: June 2022

We create a unified framework for analyzing and synthesizing plans with loops for solving problems with non-deterministic numeric effects and a limited form of partial observability. Three different action models---with deterministic, qualitative non-deterministic and Boolean non-deterministic semantics---are handled using a single abstract representation. We establish the conditions under which the correctness and termination of solutions, represented as abstract policies, can be verified. We also examine the feasibility of learning abstract policies from examples. We demonstrate our techniques on several planning problems and show that they apply to challenging real-world tasks such as doing the laundry with a PR2 robot. These results resolve a number of open questions about planning with loops and facilitate the development of new algorithms and applications.
Robot Learning Manipulation Action Plans by "Watching" Unconstrained Videos from the World Wide Web
Authors: Yang Yezhou , Li Yi , Fermuller Cornelia , Aloimonos Yiannis
Published: June 2022

In order to advance action generation and creation in robots beyond simple learned schemas we need computational tools that allow us to automatically interpret and represent human actions. This paper presents a system that learns manipulation action plans by processing unconstrained videos from the World Wide Web. Its goal is to robustly generate the sequence of atomic actions of seen longer actions in video in order to acquire knowledge for robots. The lower level of the system consists of two convolutional neural network (CNN) based recognition modules, one for classifying the hand grasp type and the other for object recognition. The higher level is a probabilistic manipulation action grammar based parsing module that aims at generating visual sentences for robot manipulation. Experiments conducted on a publicly available unconstrained video dataset show that the system is able to learn manipulation actions by ``watching'' unconstrained videos with high accuracy.