bluuebunny/crossref_metadata_2025 huggingface dataset:
Efeitos de intervenção educativa no letramento em saúde e no conhecimento sobre diabetes: estudo quase-experimental
Authors: Paes Robson Giovani , Mantovani Maria de Fátima , Costa Marta Cossetin , Pereira Adriana Cordeiro Lopes , Kalinke Luciana Puchalski , Moreira Ricardo Castanho
Published: June 2022

RESUMO Objetivo Analisar os efeitos de intervenção educativa no letramento em saúde e no conhecimento sobre diabetes em adultos atendidos na atenção primária à saúde. Método Trata-se de estudo quase-experimental com intervenção educativa durante a consulta de enfermagem, atividade em grupo e acompanhamento telefônico com 33 adultos diagnosticados com diabetes mellitus tipo 2, cadastrados em uma Unidade Estratégia Saúde da Família no Sul do Brasil. Aplicaram-se instrumentos sociodemográfico e clínico (Spoken Knowledge in Low Literacy Patients with Diabetes e Eight-Item Health Literacy Assessment Tool) antes e após a intervenção. Os dados foram analisados pelos testes de Wilcoxom, coeficiente de correlação de Spearman e McNemar. Resultados A maioria dos participantes eram mulheres (69,7%), com idade média de 57,0 anos, tempo de escolaridade inferior a nove anos (69,7%) e tempo de diagnóstico ≤10 anos (51,5%). Após a intervenção, obteve-se aumento do conhecimento sobre a diabetes (p = 0,001), correlacionado ao letramento em saúde (r = 0,494; p = 0,001) e ao tempo do diagnóstico (r = 0,455; p = 0,001). Conclusão e implicação para a prática A utilização de instrumentos para mensurar o letramento em saúde e o conhecimento sobre diabetes possibilitou a construção de estratégias educativas voltadas para as lacunas existentes, promovendo aumento do conhecimento, o qual favorece o desenvolvimento das habilidades para a autogestão.
Playing to Program: Towards an Intelligent Programming Tutor for RUR-PLE
Authors: DesJardins Marie , Ciavolino Amy , Deloatch Robert , Feasley Eliana
Published: June 2022

Intelligent tutoring systems (ITSs) provide students with a one-on-one tutor, allowing them to work at their own pace, and helping them to focus on their weaker areas. The RUR1–Python Learning Environment (RUR-PLE), a game-like virtual environment to help students learn to program, provides an interface for students to write their own Python code and visualize the code execution (Roberge 2005). RUR-PLE provides a fixed sequence of learning lessons for students to explore. We are extending RUR-PLE to develop the Playing to Program (PtP) ITS, which consists of three components: (1) a Bayesian student model that tracks student competence, (2) a diagnosis module that provides tailored feedback to students, and (3) a problem selection module that guides the student’s learning process. In this paper, we summarize RUR-PLE and the PtP design, and describe an ongoing user study to evaluate the predictive accuracy of our student modeling approach.
A Mouse-Trajectory Based Model for Predicting Query-URL Relevance
Authors: Hengjie Song , Liao Ruoxue , Zhang Xiangliang , Miao Chunyan , Yang Qiang
Published: June 2022

For the learning-to-ranking algorithms used in commercial search engines, a conventional way to generate the training examples is to employ professional annotators to label the relevance of query-url pairs. Since label quality depends on the expertise of annotators to a large extent, this process is time-consuming and labor-intensive. Automatically generating labels from click-through data has been well studied to have comparable or better performance than human judges. Click-through data present users’ action and imply their satisfaction on search results, but exclude the interactions between users and search results beyond the page-view level (e.g., eye and mouse movements). This paper proposes a novel approach to comprehensively consider the information underlying mouse trajectory and click-through data so as to describe user behaviors more objectively and achieve a better understanding of the user experience. By integrating multi-sources data, the proposed approach reveals that the relevance labels of query-url pairs are related to positions of urls and users’ behavioral features. Based on their correlations, query-url pairs can be labeled more accurately and search results are more satisfactory to users. The experiments that are conducted on the most popular Chinese commercial search engine (Baidu) validated the rationality of our research motivation and proved that the proposed approach outperformed the state-of-the-art methods.
Dynamically Switching between Synergistic Workﬂows for Crowdsourcing
Authors: Lin Christopher , Mausam Mausam , Weld Daniel
Published: June 2022

To ensure quality results from unreliable crowdsourced workers, task designers often construct complex workflows and aggregate worker responses from redundant runs. Frequently, they experiment with several alternative workflows to accomplish the task, and eventually deploy the one that achieves the best performance during early trials. Surprisingly, this seemingly natural design paradigm does not achieve the full potential of crowdsourcing. In particular, using a single workflow (even the best) to accomplish a task is suboptimal. We show that alternative workflows can compose synergistically to yield much higher quality output. We formalize the insight with a novel probabilistic graphical model. Based on this model, we design and implement AGENTHUNT, a POMDP-based controller that dynamically switches between these workflows to achieve higher returns on investment. Additionally, we design offline and online methods for learning model parameters. Live experiments on Amazon Mechanical Turk demonstrate the superiority of AGENTHUNT for the task of generating NLP training data, yielding up to 50% error reduction and greater net utility compared to previous methods.
Filtering Decomposable Global Cost Functions
Authors: Allouche David , Bessiere Christian , Boizumault Patrice , De Givry Simon , Gutierrez Patricia , Loudni Samir , Métivier Jean-Philippe , Schiex Thomas
Published: June 2022

As (Lee et al., 2012) have shown, weighted constraint satisfaction problems can benefit from the introduction of global cost functions, leading to a new Cost Function Programming paradigm. In this paper, we explore the possibility of decomposing global cost functions in such a way that enforcing soft local consistencies on the decomposition offers guarantees on the level of consistency enforced on the original global cost function. We show that directional arc consistency and virtual arc consistency offer such guarantees. We conclude by experiments on decomposable cost functions showing that decompositions may be very useful to easily integrate efficient global cost functions in solvers.
DUCT: An Upper Confidence Bound Approach to Distributed Constraint Optimization Problems
Authors: Ottens Brammert , Dimitrakakis Christos , Faltings Boi
Published: June 2022

The Upper Confidence Bounds (UCB) algorithm is a well-known near-optimal strategy for the stochastic multi-armed bandit problem. Its extensions to trees, such as the Upper Confidence Tree (UCT) algorithm, have resulted in good solutions to the problem of Go. This paper introduces DUCT, a distributed algorithm inspired by UCT, for solving Distributed Constraint Optimization Problems (DCOP). Bounds on the solution quality are provided, and experiments show that, compared to existing DCOP approaches, DUCT is able to solve very large problems much more efficiently, or to find significantly higher quality solutions.
A Dichotomy for 2-Constraint Forbidden CSP Patterns
Authors: Cooper Martin , Escamocher Guillaume
Published: June 2022

Novel tractable classes of the binary CSP (constraint satisfaction problem) have recently been discovered by studying classes of instances defined by excluding subproblems described by patterns. The complete characterisation of all tractable classes defined by forbidden patterns is a challenging problem. We demonstrate a dichotomy in the case of forbidden patterns consisting of two constraints.
Iterative Resource Allocation for Memory Intensive Parallel Search Algorithms on Clouds, Grids, and Shared Clusters
Authors: Fukunaga Alex , Kishimoto Akihiro , Botea Adi
Published: June 2022

The increasing availability of “utility computing” resources such as clouds, grids, and massively parallel shared clusters can provide practically unlimited processing and memory capacity on demand, at some cost per unit of resource usage. This requires a new perspective in the design and evaluation of parallel search algorithms. Previous work in parallel search implicitly assumed ownership of a cluster with a static amount of CPU cores and RAM, and emphasized wallclock runtime. With utility computing resources, trade-offs between performance and monetary costs must be considered. This paper considers dynamically increasing the usage of utility computing resources until a problem is solved. Efficient resource allocation policies are analyzed in comparison with an optimal allocation strategy. We evaluate our iterative allocation strategy by applying it to the HDA* parallel search algorithm. The experimental results validate our theoretical predictions. They show that, in practice, the costs incurred by iterative allocation are reasonably close to an optimal (but a priori unknown) policy, and are significantly better than the worst-case analytical bounds.
Predicting Satisfiability at the Phase Transition
Authors: Xu Lin , Hoos Holger , Leyton-Brown Kevin
Published: June 2022

Uniform random 3-SAT at the solubility phase transition is one of the most widely studied and empirically hardest distributions of SAT instances. For 20 years, this distribution has been used extensively for evaluating and comparing algorithms. In this work, we demonstrate that simple rules can predict the solubility of these instances with surprisingly high accuracy. Specifically, we show how classification accuracies of about 70% can be obtained based on cheaply (polynomial-time) computable features on a wide range of instance sizes. We argue in two ways that classification accuracy does not decrease with instance size: first, we show that our models' predictive accuracy remains roughly constant across a wide range of problem sizes; second, we show that a classifier trained on small instances is sufficient to achieve very accurate predictions across the entire range of instance sizes currently solvable by complete methods. Finally, we demonstrate that a simple decision tree based on only two features, and again trained only on the smallest instances, achieves predictive accuracies close to those of our most complex model. We conjecture that this two-feature model outperforms random guessing asymptotically; due to the model's extreme simplicity, we believe that this conjecture is a worthwhile direction for future theoretical work.
Crossing Boundaries: Multi-Level Introspection in a Complex Robotic Architecture for Automatic Performance Improvements
Authors: Krause Evan , Schermerhorn Paul , Scheutz Matthias
Published: June 2022

Introspection mechanisms are employed in agent architectures toimprove agent performance. However, there is currently no approach tointrospection that makes automatic adjustments at multiple levels inthe implemented agent system. We introduce our novel multi-levelintrospection framework that can be used to automatically adjustarchitectural configurations based on the introspection results at theagent, infrastructure and component level. We demonstrate the utilityof such adjustments in a concrete implementation on a robot where thehigh-level goal of the robot is used to automatically configure thevision system in a way that minimizes resource consumption whileimproving overall task performance.
Lessons Learned From a Rational Reconstruction of Minstrel
Authors: Tearse Brandon , Mawhorter Peter , Mateas Michael , Wardrip-Fruin Noah
Published: June 2022

Scott Turner's 1993 Minstrel system was a high water mark in story generation, harnessing the concept of imaginative recall to generate creative stories. Using case-based reasoning and an author level planning system, Minstrel models human creative processes. However, the algorithmic and representational commitments made in Minstrel were never subject to principled and quantitative analysis. By rationally reconstructing Minstrel, we are able to investigate Turner's computational model of creativity and learn new lessons about his architecture. We find that Minstrel's original performance was tied to a well-groomed case library, but by modifying several components of the algorithm we can create a more general version which can construct stories using a sparser and less structured case library. Through a rational reconstruction of Minstrel, we both learn new architectural and algorithmic lessons about Minstrel’s computational model of creativity as well as make his architecture available to the contemporary research community for further experimentation.
Scheduling Conservation Designs via Network Cascade Optimization
Authors: Xue Shan , Fern Alan , Sheldon Daniel
Published: June 2022

We introduce the problem of scheduling land purchases to conserve an endangered species in a way that achieves maximum population spread but delays purchases as long as possible, so that conservation planners retain maximum flexibility and use available budgets in the most efficient way. We develop the problem formally as a stochastic optimization problem over a network cascade model describing the population spread, and present a solution approach that reduces the stochastic problem to a novel variant of a Steiner tree problem. We give a primal-dual algorithm for the problem that computes both a feasible solution and a bound on the quality of an optimal solution. Our experiments, using actual conservation data and a standard diffusion model, show that the approach produces near optimal results and is much more scalable than more generic off-the-shelf optimizers.
A Multi-Agent Control Architecture for a Rescue Robot
Authors: Haber Adam
Published: June 2022

Cognitive architectures investigate the components and interactions neccessary for construction of an intelligent system. Despite much progress and theory, implementations of architectures are rare. This research presents a novel cognitive architecture grounded in the design of a control system for an autonomous rescue robot. Experiments are conducted in high-fidelity 3D simulation of a rescue environment based on NISTs RoboCup Rescue.
Capabilities in Heterogeneous Multi-Robot Systems
Authors: Buehler Jennifer
Published: June 2022

Groups of robots are often able to to accomplish missions that no single robot can achieve by themselves. Teamwork is a very important factor in complex, dynamic domains. In heterogeneous teams, robustness and flexibility are increased by the diversity of the robots, each contributing different capabilities. In such heterogeneous Multi-Robot Systems it is reasonable to explicitly take the robots' capabilities into account when determining which one is best suited for a task. In this paper I present a framework that formalizes robots' capabilities and provides a means to estimate their suitability for a task. In highly unpredictable domains, accurate predictions of the outcomes of a robot's actions are virtually impossible. Approximate models and algorithms are required which help to estimate the outcome with highest possible confidence. The proposed architecture can provide estimates of task solution qualities at three levels of confidence: the lowest level only taking the mere existence of capabilities into account, the middle level considering task-specific details with approximate parameters of the capabilities, and the highest confidence level considering more elaborate planning algorithms.
Planning Under Time Pressure
Authors: Burns Ethan
Published: June 2022

Heuristic search is a technique used pervasively in the fieldsof artificial intelligence, automated planning and operations research to solve a wide range of problems from planning military deployments to planning tasks for a robot that must clean a messy kitchen. An automated agent can use heuristic search to construct a plan that, when executed, will achieve a desired task. The search algorithm explores different sequences of actions that the agent can execute, looking for a sequence that will lead it to a desired goal state. In many situations, an agent is given a task that it would like to solve as quickly as possible. The agent must allocate its time between searching for the actions that will achieve the task and actually executing them. We call this problem planning under time pressure.
Enriching Chatter Bots With Semantic Conversation Control
Authors: Chakrabarti Chayan
Published: June 2022

Businesses deploy chatter bots to engage in text-based conversations with customers that are intended resolve their issues. However, these chatter bots are only effective in exchanges consisting of question-answer pairs, where the context may switch with every pair. I am designing a semantic architecture that enables chatter bots to hold short conversations, where context is maintained throughout the exchange. I leverage specific ideas from conversation theory, speech acts theory, and knowledge representation. My architecture models a conversation as a stochastic process that flows through a set of states. The main contribution of this work is that it analyses and models the semantics of conversations as entities, instead of lower level grammatical and linguistics forms. I evaluate the performance of the architecture in accordance with Grice’s cooperative maxims, which form the central idea in the theory of pragmatics.
Table Header Detection and Classification
Authors: Fang Jing , Mitra Prasenjit , Tang Zhi , Giles C. Lee
Published: June 2022

In digital libraries, a table, as a specific document component as well as a condensed way to present structured and relational data, contains rich information and often the only source of .that information. In order to explore, retrieve, and reuse that data, tables should be identified and the data extracted. Table recognition is an old field of research. However, due to the diversity of table styles, the results are still far from satisfactory, and not a single algorithm performs well on all different types of tables. In this paper, we randomly take samples from the CiteSeerX to investigate diverse table styles for automatic table extraction. We find that table headers are one of the main characteristics of complex table styles. We identify a set of features that can be used to segregate headers from tabular data and build a classifier to detect table headers. Our empirical evaluation on PDF documents shows that using a Random Forest classifier achieves an accuracy of 92%.
Generating Pictorial Storylines Via Minimum-Weight Connected Dominating Set Approximation in Multi-View Graphs
Authors: Wang Dingding , Li Tao , Ogihara Mitsunori
Published: June 2022

This paper introduces a novel framework for generating pictorial storylines for given topics from text and image data on the Internet. Unlike traditional text summarization and timeline generation systems, the proposed framework combines text and image analysis and delivers a storyline containing textual, pictorial, and structural information to provide a sketch of the topic evolution. A key idea in the framework is the use of an approximate solution for the dominating set problem. Given a collection of topic-related objects consisting of images and their text descriptions, a weighted multi-view graph is first constructed to capture the contextual and temporal relationships among these objects. Then the objects are selected by solving the minimum-weighted connected dominating set problem defined on this graph. Comprehensive experiments on real-world data sets demonstrate the effectiveness of the proposed framework.
On Finding Optimal Polytrees
Authors: Gaspers Serge , Koivisto Mikko , Liedloff Mathieu , Ordyniak Sebastian , Szeider Stefan
Published: June 2022

Inferring probabilistic networks from data is a notoriously difficult task. Under various goodness-of-fit measures, finding an optimal network is NP-hard, even if restricted to polytrees of bounded in-degree. Polynomial-time algorithms are known only for rare special cases, perhaps most notably for branchings, that is, polytrees in which the in-degree of every node is at most one. Here, we study the complexity of finding an optimal polytree that can be turned into a branching by deleting some number of arcs or nodes, treated as a parameter. We show that the problem can be solved via a matroid intersection formulation in polynomial time if the number of deleted arcs is bounded by a constant. The order of the polynomial time bound depends on this constant, hence the algorithm does not establish fixed-parameter tractability when parameterized by the number of deleted arcs. We show that a restricted version of the problem allows fixed-parameter tractability and hence scales well with the parameter. We contrast this positive result by showing that if we parameterize by the number of deleted nodes, a somewhat more powerful parameter, the problem is not fixed-parameter tractable, subject to a complexity-theoretic assumption.
Exploring the Duality in Conflict-Directed Model-Based Diagnosis
Authors: Stern Roni , Kalech Meir , Feldman Alexander , Provan Gregory
Published: June 2022

A model-based diagnosis problem occurs when an observation is inconsistent with the assumption that the diagnosed system is not faulty. The task of a diagnosis engine is to compute diagnoses, which are assumptions on the health of components in the diagnosed system that explain the observation. In this paper, we extend Reiter's well-known theory of diagnosis by exploiting the duality of the relation between conflicts and diagnoses. This duality means that a diagnosis is a hitting set of conflicts, but a conflict is also a hitting set of diagnoses. We use this property to interleave the search for diagnoses and conflicts: a set of conflicts can guide the search for diagnosis, and the computed diagnoses can guide the search for more conflicts. We provide the formal basis for this dual conflict-diagnosis relation, and propose a novel diagnosis algorithm that exploits this duality. Experimental results show that the new algorithm is able to find a minimal cardinality diagnosis faster than the well-known Conflict-Directed A*.
A Scalable Message-Passing Algorithm for Supply Chain Formation
Authors: Penya-Alba Toni , Vinyals Meritxell , Cerquides Jesus , Rodriguez-Aguilar Juan
Published: June 2022

Supply Chain Formation (SCF) is the process of determining the participants in a supply chain, who will exchange what with whom, and the terms of the exchanges. Decentralized SCF appears as a highly intricate task because agents only possess local information and have limited knowledge about the capabilities of other agents. The decentralized SCF problem has been recently cast as an optimization problem that can be efficiently approximated using max-sum loopy belief propagation. Along this direction, in this paper we propose a novel encoding of the problem into a binary factor graph (containing only binary variables) as well as an alternative algorithm. We empirically show that our approach allows to significantly increase scalability, hence allowing to form supply chains in market scenarios with a large number of participants and high competition.
Finding Optimal Abstract Strategies in Extensive-Form Games
Authors: Johanson Michael , Bard Nolan , Burch Neil , Bowling Michael
Published: June 2022

Extensive-form games are a powerful model for representing interactions between agents. Nash equilibrium strategies are a common solution concept for extensive-form games and, in two-player zero-sum games, there are efficient algorithms for calculating such strategies. In large games, this computation may require too much memory and time to be tractable. A standard approach in such cases is to apply a lossy state-space abstraction technique to produce a smaller abstract game that can be tractably solved, while hoping that the resulting abstract game equilibrium is close to an equilibrium strategy in the unabstracted game. Recent work has shown that this assumption is unreliable, and an arbitrary Nash equilibrium in the abstract game is unlikely to be even near the least suboptimal strategy that can be represented in that space. In this work, we present for the first time an algorithm which efficiently finds optimal abstract strategies --- strategies with minimal exploitability in the unabstracted game. We use this technique to find the least exploitable strategy ever reported for two-player limit Texas hold'em.
Efficient Multi-Stage Conjugate Gradient for Trust Region Step
Authors: Gong Pinghua , Zhang Changshui
Published: June 2022

The trust region step problem, by solving a sphere constrained quadratic programming, plays a critical role in the trust region Newton method. In this paper, we propose an efficient Multi-Stage Conjugate Gradient (MSCG) algorithm to compute the trust region step in a multi-stage manner. Specifically, when the iterative solution is in the interior of the sphere, we perform the conjugate gradient procedure. Otherwise, we perform a gradient descent procedure which points to the inner of the sphere and can make the next iterative solution be a interior point. Subsequently, we proceed with the conjugate gradient procedure again. We repeat the above procedures until convergence. We also present a theoretical analysis which shows that the MSCG algorithm converges. Moreover, the proposed MSCG algorithm can generate a solution in any prescribed precision controlled by a tolerance parameter which is the only parameter we need. Experimental results on large-scale text data sets demonstrate our proposed MSCG algorithm has a faster convergence speed compared with the state-of-the-art algorithms.
Knapsack Based Optimal Policies for Budget–Limited Multi–Armed Bandits
Authors: Tran-Thanh Long , Chapman Archie , Rogers Alex , Jennings Nicholas
Published: June 2022

In budget–limited multi–armed bandit (MAB) problems, thelearner’s actions are costly and constrained by a fixed budget.Consequently, an optimal exploitation policy may not be topull the optimal arm repeatedly, as is the case in other variantsof MAB, but rather to pull the sequence of different arms thatmaximises the agent’s total reward within the budget. Thisdifference from existing MABs means that new approachesto maximising the total reward are required. Given this, wedevelop two pulling policies, namely: (i) KUBE; and (ii)fractional KUBE. Whereas the former provides better performanceup to 40% in our experimental settings, the latteris computationally less expensive. We also prove logarithmicupper bounds for the regret of both policies, and show thatthese bounds are asymptotically optimal (i.e. they only differfrom the best possible regret by a constant factor).
Convex Matching Pursuit for Large-Scale Sparse Coding and Subset Selection
Authors: Tan Mingkui , Tsang Ivor , Wang Li , Zhang Xinming
Published: June 2022

In this paper, a new convex matching pursuit scheme is proposed for tackling large-scale sparse coding and subset selection problems. In contrast with current matching pursuit algorithms such as subspace pursuit (SP), the proposed algorithm has a convex formulation and guarantees that the objective value can be monotonically decreased. Moreover, theoretical analysis and experimental results show that the proposed method achieves better scalability while maintaining similar or better decoding ability compared with state-of-the-art methods on large-scale problems.
Supervised Probabilistic Robust Embedding with Sparse Noise
Authors: Zhang Yu , Yeung Dit-Yan , Xing Eric
Published: June 2022

Many noise models do not faithfully reflect the noise processes introduced during data collection in many real-world applications. In particular, we argue that a type of noise referred to as sparse noise is quite commonly found in many applications and many existing works have been proposed to model such sparse noise. However, all the existing works only focus on unsupervised learning without considering the supervised information, i.e., label information. In this paper, we consider how to model and handle sparse noise in the context of embedding high-dimensional data under a probabilistic formulation for supervised learning. We propose a supervised probabilistic robust embedding (SPRE) model in which data are corrupted either by sparse noise or by a combination of Gaussian and sparse noises. By using the Laplace distribution as a prior to model sparse noise, we devise a two-fold variational EM learning algorithm in which the update of model parameters has analytical solution. We report some classification experiments to compare SPRE with several related models.
Unsupervised Detection of Music Boundaries by Time Series Structure Features
Authors: Serrà Joan , Müller Meinard , Grosche Peter , Arcos Josep Lluis
Published: June 2022

Locating boundaries between coherent and/or repetitive segments of a time series is a challenging problem pervading many scientific domains. In this paper we propose an unsupervised method for boundary detection, combining three basic principles: novelty, homogeneity, and repetition. In particular, the method uses what we call structure features, a representation encapsulating both local and global properties of a time series. We demonstrate the usefulness of our approach in detecting music structure boundaries, a task that has received much attention in recent years and for which exist several benchmark datasets and publicly available annotations. We find our method to significantly outperform the best accuracies published so far. Importantly, our boundary approach is generic, thus being applicable to a wide range of time series beyond the music and audio domains.
Identifying Adverse Drug Events by Relational Learning
Authors: Page David , Santos Costa Vitor , Natarajan Sriraam , Barnard Aubrey , Peissig Peggy , Caldwell Michael
Published: June 2022

The pharmaceutical industry, consumer protection groups, users of medications and government oversight agencies are all strongly interested in identifying adverse reactions to drugs. While a clinical trial of a drug may use only a thousand patients, once a drug is released on the market it may be taken by millions of patients. As a result, in many cases adverse drug events (ADEs) are observed in the broader population that were not identiﬁed during clinical trials. Therefore, there is a need for continued, postmarketing surveillance of drugs to identify previously-unanticipated ADEs. This paper casts this problem as a reverse machine learning task, related to relational subgroup discovery and provides an initial evaluation of this approach based on experiments with an actual EMR/EHR and known adverse drug events.
Three Controversial Hypotheses Concerning Computation in the Primate Cortex
Authors: Dean Thomas , Corrado Greg , Shlens Jonathon
Published: June 2022

We consider three hypotheses concerning the primate neocortex which have influenced computational neuroscience in recent years. Is the mind modular in terms of its being profitably described as a collection of relatively independent functional units? Does the regular structure of the cortex imply a single algorithm at work, operating on many different inputs in parallel? Can the cognitive differences between humans and our closest primate relatives be explained in terms of a scalable cortical architecture? We bring to bear diverse sources of evidence to argue that the answers to each of these questions — with some judicious qualifications — are in the affirmative. In particular, we argue that while our higher cognitive functions may interact in a complicated fashion, many of the component functions operate through well-defined interfaces and, perhaps more important, are built on a neural substrate that scales easily under the control of a modular genetic architecture. Processing in the primary sensory cortices seem amenable to similar algorithmic principles, and, even for those cases where alternative principles are at play, the regular structure of cortex allows the same or greater advantages as the architecture scales. Similar genetic machinery to that used by nature to scale body plans has apparently been applied to scale cortical computations. The resulting replicated computing units can be used to build larger working memory and support deeper recursions needed to qualitatively improve our abilities to handle language, abstraction and social interaction.
Identifying Bullies with a Computer Game
Authors: Mancilla-Caceres Juan , Pu Wen , Amir Eyal , Espelage Dorothy
Published: June 2022

Current computer involvement in adolescent social networks (youth between the ages of 11 and 17) provides new opportunities to study group dynamics, interactions amongst peers, and individual preferences. Nevertheless, most of the research in this area focuses on efficiently retrieving information that is explicit in large social networks (e.g., properties of the graph structure), but not on how to use the dynamics of the virtual social network to discover latent characteristics of the real-world social network. In this paper, we present the analysis of a game designed to take advantage of the familiarity of adolescents with online social networks, and describe how the data generated by the game can be used to identify bullies in 5th grade classrooms. We present a probabilistic model of the game and using the in-game interactions of the players (i.e., content of chat messages) infer their social role within their classroom (either a bully or non-bully). The evaluation of our model is done by using previously collected data from psychological surveys on the same 5th grade population and by comparing the performance of the new model with off-the-shelf classifiers.
Generating Chinese Classical Poems with Statistical Machine Translation Models
Authors: He Jing , Zhou Ming , Jiang Long
Published: June 2022

This paper describes a statistical approach to generation of Chinese classical poetry and proposes a novel method to automatically evaluate poems. The system accepts a set of keywords representing the writing intents from a writer and generates sentences one by one to form a completed poem. A statistical machine translation (SMT) system is applied to generate new sentences, given the sentences generated previously. For each line of sentence a specific model specially trained for that line is used, as opposed to using a single model for all sentences. To enhance the coherence of sentences on every line, a coherence model using mutual information is applied to select candidates with better consistency with previous sentences. In addition, we demonstrate the effectiveness of the BLEU metric for evaluation with a novel method of generating diverse references.
Using First-Order Logic to Compress Sentences
Authors: Huang Minlie , Shi Xing , Jin Feng , Zhu Xiaoyan
Published: June 2022

Sentence compression is one of the most challenging tasks in natural language processing,which may be of increasing interest to many applicationssuch as abstractive summarization and text simplification for mobile devices.In this paper, we present a novel sentence compression model based on first-order logic, using Markov Logic Network.Sentence compression is formulated as a word/phrase deletion problem in this model.By taking advantage of first-order logic, the proposed method is able to incorporate local linguistic features and to capture global dependencies between word deletion operations. Experiments on both written and spoken corpora show that our approach produces competitive performance against the state-of-the-art methods in terms of manual evaluation measures such as importance, grammaticality, and overall quality.
Exacting Social Events for Tweets Using a Factor Graph
Authors: Liu Xiaohua , Zhou Xiangyang , Fu Zhongyang , Wei Furu , Zhou Ming
Published: June 2022

Social events are events that occur between people where at least one person is aware of the other and of the event taking place. Extracting social events can play an important role in a wide range of applications, such as the construction of social network. In this paper, we introduce the task of social event extraction for tweets, an important source of fresh events. One main challenge is the lack of information in a single tweet, which is rooted in the short and noise-prone nature of tweets. We propose to collectively extract social events from multiple similar tweets using a novel factor graph, to harvest the redundance in tweets, i.e., the repeated occurrences of a social event in several tweets. We evaluate our method on a human annotated data set, and show that it outperforms all baselines, with an absolute gain of 21% in F1.
Improving Hierarchical Planning Performance by the Use of Landmarks
Authors: Elkawkagy Mohamed , Bercher Pascal , Schattenberg Bernd , Biundo Susanne
Published: June 2022

In hierarchical planning, landmarks are tasks that occur on any search path leading from the initial plan to a solution. In this work, we present novel domain-independent planning strategies based on such hierarchical landmarks. Our empirical evaluation on four benchmark domains shows that these landmark-aware strategies outperform established search strategies in many cases.
Repeated Sequential Auctions with Dynamic Task Clusters
Authors: Heap Bradford , Pagnucco Maurice
Published: June 2022

Sequential auctions can be used to provide solutions to the multi-robot task-allocation problem. In this paper we extend previous work on sequential auctions and propose an algorithm that clusters and auctions uninitiated task clusters repeatedly upon the completion of individual tasks. We demonstrate empirically that our algorithm results in lower overall team costs than other sequential auction algorithms that only assign tasks once.
Mobile Robot Planning to Seek Help with Spatially-Situated Tasks
Authors: Rosenthal Stephanie , Veloso Manuela
Published: June 2022

Indoor autonomous mobile service robots can overcome their hardware and potential algorithmic limitations by asking humans for help. In this work, we focus on mobile robots that need human assistance at specific spatially-situated locations (e.g., to push buttons in an elevator or to make coffee in the kitchen). We address the problem of what the robot should do when there are no humans present at such help locations. As the robots are mobile, we argue that they should plan to proactively seek help and travel to offices or occupied locations to bring people to the help locations. Such planning involves many trade-offs, including the wait time at the help location before seeking help, and the time and potential interruption to find and displace someone in an office. In order to choose appropriate parameters to represent such decisions, we first conduct a survey to understand potential helpers' travel preferences in terms of distance, interruptibility, and frequency of providing help. We then use these results to contribute a decision-theoretic algorithm to evaluate the possible choices in offices and plan where to proactively seek help. We demonstrate that our algorithm aims to minimize the number of office interruptions as well as task completion time.
Using the Web to Interactively Learn to Find Objects
Authors: Samadi Mehdi , Kollar Thomas , Veloso Manuela
Published: June 2022

In order for robots to intelligently perform tasks with humans, they must be able to access a broad set of background knowledge about the environments in which they operate. Unlike other approaches, which tend to manually define the knowledge of the robot, our approach enables robots to actively query the World Wide Web (WWW) to learn background knowledge about the physical environment. We show that our approach is able to search the Web to infer the probability that an object, such as a "coffee,'' can be found in a location, such as a "kitchen.'' Our approach, called ObjectEval, is able to dynamically instantiate a utility function using this probability, enabling robots to find arbitrary objects in indoor environments. Our experimental results show that the interactive version of ObjectEval visits 28% fewer locations than the version trained offline and 71% fewer locations than a baseline approach which uses no background knowledge.
Advances in Lifted Importance Sampling
Authors: Gogate Vibhav , Jha Abhay , Venugopal Deepak
Published: June 2022

We consider lifted importance sampling (LIS), a previously proposed approximate inference algorithm for statistical relational learning (SRL) models. LIS achieves substantial variance reduction over conventional importance sampling by using various lifting rules that take advantage of the symmetry in the relational representation. However, it suffers from two drawbacks. First, it does not take advantage of some important symmetries in the relational representation and may exhibit needlessly high variance on models having these symmetries. Second, it uses an uninformative proposal distribution which adversely affects its accuracy. We propose two improvements to LIS that address these limitations. First, we identify a new symmetry in SRL models and define a lifting rule for taking advantage of this symmetry. The lifting rule reduces the variance of LIS. Second, we propose a new, structured approach for constructing and dynamically updating the proposal distribution via adaptive sampling. We demonstrate experimentally that our new, improved LIS algorithm is substantially more accurate than the LIS algorithm.
I'm Doing as Well as I Can: Modeling People as Rational Finite Automata
Authors: Halpern Joe , Pass Rafael , Seeman Lior
Published: June 2022

We show that by modeling people as bounded finite automata, we can capture at a qualitative level the behavior observed in experiments. We consider a decision problem with incomplete information and a dynamically changing world, which can be viewed as an abstraction of many real-world settings. We provide a simple strategy for a finite automaton in this setting, and show that it does quite well, both through theoretical analysis and simulation. We show that, if the probability of nature changing state goes to 0 and the number of states in the automaton increases, then this strategy performs optimally (as well as if it were omniscient and knew when nature was making its state changes). Thus, although simple, the strategy is a sensible strategy for a resource-bounded agent to use. Moreover, at a qualitative level, the strategy does exactly what people have been observed to do in experiments.
Search Algorithms for m Best Solutions for Graphical Models
Authors: Dechter Rina , Flerova Natalia , Marinescu Radu
Published: June 2022

The paper focuses on finding the m best solutions to combinatorial optimization problems using Best-First or Branchand- Bound search. Specifically, we present m-A*, extending the well-known A* to the m-best task, and prove that all its desirable properties, including soundness, completeness and optimal efficiency, are maintained. Since Best-First algorithms have memory problems, we also extend the memoryefficient Depth-First Branch-and-Bound to the m-best task. We extend both algorithms to optimization tasks over graphical models (e.g., Weighted CSP and MPE in Bayesian networks), provide complexity analysis and an empirical evaluation. Our experiments with 5 variants of Best-First and Branch-and-Bound confirm that Best-First is largely superior when memory is available, but Branch-and-Bound is more robust, while both styles of search benefit greatly when the heuristic evaluation function has increased accuracy.
Learning Transformation Rules by Examples
Authors: Wu Bo , Szekely Pedro , Knoblock Craig
Published: June 2022

This paper presents an abstract for a general data transformation approach. Using programming by demonstration technique, we learn the transformation rules through user given examples. These transformation rules are automatically generated from a predefined grammar. Due to the grammar space is huge, we propose a grammar space reduction method to reduce the search space and a sketch of search algorithm is adopted to identify the rules that are consistent with the examples. The final experimental results show our approach achieves promising results on different transformation scenarios.
Combining Probabilistic Planning and Logic Programming on Mobile Robots
Authors: Zhang Shiqi , Bao Forrest , Sridharan Mohan
Published: June 2022

Key challenges to widespread deployment of mobile robots to interact with humans in real-world domains include the ability to: (a) robustly represent and revise domain knowledge; (b) autonomously adapt sensing and processing to the task at hand; and (c) learn from unreliable high-level human feedback. Partially observable Markov decision processes (POMDPs) have been used to plan sensing and navigation in different application domains. It is however a challenge to include common sense knowledge obtained from sensory or human inputs in POMDPs. In addition, information extracted from sensory and human inputs may have varying levels of relevance to current and future tasks. On the other hand, although a non-monotonic logic programming paradigm such as Answer Set Programming (ASP) is wellsuited for common sense reasoning, it is unable to model the uncertainty in real-world sensing and navigation (Gelfond 2008). This paper presents a hybrid framework that integrates ASP, hierarchical POMDPs (Zhang and Sridharan 2012) and psychophysics principles to address the challenges stated above. Experimental results in simulation and on mobile robots deployed in indoor domains show that the framework results in reliable and efficient operation.
Active Learning from Oracle with Knowledge Blind Spot
Authors: Fang Meng , Zhu Xingquan , Zhang Chengqi
Published: June 2022

Active learning traditionally assumes that an oracle is capable of providing labeling information for each query instance. This paper formulates a new research problem which allows an oracle admit that he/she is incapable of labeling some query instances or simply answer "I don't know the label." We define a unified objectivefunction to ensure that each query instance submitted to the oracleis the one mostly needed for labeling and the oracle should also hasthe knowledge to label. Experiments based on different types of knowledge blind spot (KBS) models demonstrate the effectiveness of theproposed design.
Learning Names for RFID-Tagged Objects in Activity Videos
Authors: Perera Ian , Allen James
Published: June 2022

We describe a method for determining the names of RFID-tagged objects in activity videos using descriptions which have been parsed to provide anaphoric reference resolution and ontological categorization.
Frugal Coordinate Descent for Large-Scale NNLS
Authors: Potluru Vamsi
Published: June 2022

The Nonnegative Least Squares (NNLS) formulation arises in many important regression problems. We present a novel coordinate descent method which differs from previous approaches in that we do not explicitly maintain complete gradient information. Empirical evidence shows that our approach outperforms a state-of-the-art NNLS solver in computation time for calculating radiation dosage for cancer treatment problems.
Estimation of Suitable Action to Realize Given Novel Effect with Given Tool Using Bayesian Tool Affordances
Authors: Jain Raghvendra , Inamura Tetsunari
Published: June 2022

We present the concept of Bayesian Tool Affordances as a solution to estimate the suitable action for the given tool to realize the given novel effects to the robot. We define Tool affordances as the “awareness within robot about the different kind of effects it can create in the environment using a tool”. It incorporates understanding the bi-directional association of executed Action, functionally relevant features of the Tool and the resulting effects. We propose Bayesian leaning of Tool Affordances for prediction, inference and planning capabilities while dealing with uncertainty, redundancy and irrelevant information using limited learning samples. The estimation results are presented in this paper to validate the proposed concept of Bayesian Tool Affordances.