bluuebunny/crossref_metadata_2025 huggingface dataset:
Consumption inductors of displacement modes linked to ride-hailing in Brazil
Authors: Violin Fábio Luciano
Published: June 2022

Abstract The study proposed to survey the consumption inducers related to displacement on demand in Brazil and had 3425 interviews whose data were submitted to exploratory and confirmatory factor analysis using the IBM SPSS Statistics software. In order to have robustness in the results measured, the analysis variables relating to the country's reality were designed through a review of the state of the art and the design of the supply incidence points. Six variables underlying the consumption decision-making process, based on the technological aspect of supply and marked by the utilitarian sense of use, emerged from the research. As theoretical-practical implications, the results corroborate international research linking consumption to the aspect of personal interest to the detriment of the common good and socio-environmental concerns pointed out by the first studies related to the theme. In this way, advances in the field of knowledge are indicated by highlighting the impacts of convenience, technology and individualism in consumption. The study is innovative because it outlines the indicators of consumption direction, indicating trends to be observed by public managers and private organizations, in contrast to studies on the same theme whose observation base is restricted to one location or even region.
CHARACTERIZATION OF STRICTO SENSU SCIENTIFIC PRODUCTION IN PSYCHOLOGY ON YOUNG UNIVERSITY STUDENTS (2008-2017)
Authors: Paes-Ribeiro Jaqueline Regina , Urnau Lilian Caroline
Published: June 2022

ABSTRACT This article aims to characterize and systematize the scientific production by Brazilian stricto sensu postgraduate courses in Psychology regarding young people in Higher Education, developed from 2008 to 2017. The state of knowledge was outlined by means of keyword searches in the CAPES theses and dissertations catalog. In the end of the search, 78 works were selected, and information, obtained by reading the abstracts, was tabulated in Excel spreadsheets and analyzed quantitatively, considering: a) region, b) institution, c) year of defense, d) theoretical perspective, e) types of research, f) methodological procedures and g) themes studied. Among the results, the following stand out: the studies mostly come from the Southeast region; there is a greater number of productions and qualitative research works between the years 2013 and 2017. Concerning young people’s health, themes and studies on the true meaning of policies for access and permanence in Higher Education predominate.
O IMPACTO DE OFICINAS SOBRE SEXUALIDADE: UM RELATO DE EXPERIÊNCIA COM ESTUDANTES
Authors: Magrin Nicolly Papacidero , Moraes Amanda Silva de , Paniago Cecília de Moraes , Santos Ingrid Fernandes dos , Lacerda Renata Musa , Cunha Rachel Nunes da
Published: June 2022

RESUMO O espaço para discussão da sexualidade nas escolas é limitado, mesmo com a perspectiva de prevenção a Infecções Sexualmente Transmissíveis (ISTs). Por demanda de uma escola do Ensino Médio, estudantes vinculadas ao Programa de Educação Tutorial do curso de Psicologia e ao Projeto de Extensão Polo de Prevenção de ISTs/AIDS realizaram oficinas com adolescentes. O objetivo desse projeto foi dialogar sobre a sexualidade e promover discussões para a prevenção a ISTs por meio de 9 oficinas, com participação de 18 estudantes. Essas oficinas foram avaliadas a partir de dois questionários e os dados tratados em três eixos: Avaliação das Oficinas - Metodologias e Facilitadoras; Avaliação dos Adolescentes como Multiplicadores e Avaliação da Importância da Temática para os Adolescentes. Concluiu-se que as oficinas propiciaram um meio para a promoção de comportamentos de prevenção a ISTs, para o incentivo do exercício responsável da sexualidade e possibilitou a formação de vínculo entre facilitadoras e adolescentes.
Reflexões sobre a morte e o morrer
Authors: Koenig Anne Marise , Teixeira Luciana de Almeida Silva
Published: June 2022

Resumo Este ensaio teórico tem por objetivo propor reflexões acerca da morte e do morrer sob o prisma de possíveis antagonismos, do adoecimento e das ocupações humanas envolvidas nesse processo. Os antagonismos são colocados em pauta obedecendo ao sentido de oposição mútua entre vida e morte, nascimento e morte, bem como entre sua previsão e sua negação. O adoecimento é discutido sob o prisma dos modelos dinâmico e ontológico de Laplantine, abrindo-se reflexões sobre o significado do adoecimento para o doente, muitas vezes expropriado de seu processo de morrer. Também aborda como a morte é impelida, atualmente, para os bastidores da vida social. Por último, são abordadas as ocupações da morte pautadas nos “princípios da boa morte”, sob o olhar da terapia ocupacional, sendo discorridos os preparativos e os ritos fúnebres desenvolvidos pelo morrente, seu círculo social e pelos profissionais da saúde, religiosos, agentes fúnebres e funcionários de cemitérios. Depreendemos que a morte é um processo social.
Diagnóstico das salas de vacinação em unidades básicas de saúde brasileiras participantes do projeto PlanificaSUS, 2019
Authors: Souza Evelyn Lima de , Eshriqui Ilana , Masuda Eliana Tiemi , Bonfim Daiana , Barra Rubia Pereira , Paresque Márcio Anderson Cardozo
Published: June 2022

Resumo Objetivo: Descrever o diagnóstico de salas de vacinação de unidades básicas de saúde do Brasil. Métodos: Estudo transversal, com dados secundários de amostra de conveniência de 25 salas. Foram utilizados resultados de checklist adaptada do Instrumento de Supervisão em Sala de Vacinação, do Programa Nacional de Imunizações em 2019, nas dimensões ‘organização geral’, ‘aspectos gerais’, ‘procedimentos técnicos’, ‘rede de frio’, ‘sistema de informação’, ‘eventos adversos pós-vacinação’, ‘imunobiológicos especiais’, ‘vigilância epidemiológica’ e ‘educação em saúde’. Percentuais de pontuação geral e das dimensões foram descritos em mediana, intervalo interquartil, valores mínimo e máximo. Resultados: A mediana geral foi de 77,1%, maior para ‘educação em saúde’ (100,0%) e ‘rede de frio’ (86,7%), e menor para ‘imunobiológicos especiais’ (50,0%) e ‘organização geral’ (58,3%). Conclusão: A utilização da checklist permitiu o diagnóstico em diferentes macrorregiões, sendo encontradas diferenças inter e intrarregionais nas dimensões; e, no plano geral, resultados positivos e oportunidades de melhoria.
Fast, Accurate, and Practical Identity Inference Using TV Remote Controls
Authors: Phielip Mariano , Galan Magdiel , Lee Richard , Kveton Branislav , Hightower Jeffrey
Published: June 2022

Non-invasive identity inference in the home environment is a very challenging problem. A practical solution to the problem could have far reaching implications in many industries, such as home entertainment. In this work, we consider the problem of identity inference using a TV remote control. In particular, we address two challenges that have so far prevented the work of Chang et al. (2009) from being applied in a home entertainment system. First, we show how to learn the patterns of TV remote controls incrementally and online. Second, we generalize our results to partially labeled data. To achieve our goal, we use state-of-the-art methods for max-margin learning and online convex programming. Our solution is efficient, runs in real time, and comes with theoretical guarantees. It performs well in practice and we demonstrate this on 4 datasets of 2 to 4 people.
A Machine Learning Approach to the Detection of Fetal Hypoxia during Labor and Delivery
Authors: Warrick Philip , Hamilton Emily , Kearney Robert , Precup Doina
Published: June 2022

Labor monitoring is crucial in modern health care, as it can be used to detect (and help avoid) significant problems with the fetus. In this paper we focus on hypoxia (or oxygen deprivation), a very serious condition that can arise from different pathologies and can lead to life-long disability and death. We present a novel approach to hypoxia detection based on recordings of the uterine pressure and fetal heart rate, which are routinely monitored during labor. The key idea is to learn models of the fetal response to signals from its environment, using time series data recorded during labor. Then, we use the parameters of these models as attributes in a binary classification problem. A majority vote over several periods is taken to provide the current label for the fetus. We use a unique database of real clinical recordings, both from normal and pathological cases. Our approach classifies correctly more than half the pathological cases, 1.5 hours before delivery. These are cases that were missed by clinicians; early detection of this type would have allowed the physician to perform a Caesarean section, possibly avoiding the negative outcome.
An Action Research Report from a Multi-Year Approach to Teaching Artificial Intelligence at the K-6 Level
Authors: Heinze Clint , Haase Janet , Higgins Helen
Published: June 2022

In Australia, the Scientists-in-Schools program partners professional scientists with teachers from K-12 schools to improve early engagement and educational outcomes in the sciences and mathematics.  An overview of the developing syllabus of a K-6 course resulting from the pairing of a senior AI researcher with teachers from a K-6 (primary) school is presented. Now entering its third year, the course introduces the basic concepts, vocabulary and history of science generally and AI specifically in a manner that emphasises student engagement and provides a challenging but age appropriate syllabus. Reflecting on the course at this time provides an action research basis for ongoing maturation of the syllabus, and the paper is presented in that light.
A Simulator for Teaching Robotics Programming Using the iRobot Create
Authors: Hettlinger Andrew , Boutell Matthew
Published: June 2022

Past educational robotics research has indicated that the use of simulators can increase students’ performance in introductory robotics programming courses. In this paper, we introduce a simulator for the iRobot Create that works on Windows PCs. It was developed to work with a Python robotics library and includes an Eclipse plugin, but can simulate any library that uses the serial Open Interface on the Create. The platform, library, and simulator are all easy to use and have been well-received initially by students.
Model AI Assignments
Authors: Neller Todd , DeNero John , Klein Dan , Koenig Sven , Yeoh William , Zheng Xiaoming , Daniel Kenny , Nash Alex , Dodds Zachary , Carenini Giuseppe , Poole David , Brooks Chris
Published: June 2022

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of eight AI assignments that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs.
On the Effectiveness of Belief State Representation in Contingent Planning
Authors: To Son , Son Tran , Pontelli Enrico
Published: June 2022

This work proposes new approaches to contingent planning using alternative belief state representations extended from those in conformant planning and a new AND/OR forward search algorithm, called PrAO, for contingent solutions. Each representation was implemented in a new contingent planner. The important role of belief state representation has been confirmed by the fact that our planners all outperform other stateof- the-art planners on most benchmarks and the comparison of their performances varies across all the benchmarks even using the same search algorithm PrAO and same unsophisticated heuristic scheme. The work identifies the properties of each representation method that affect the performance.
Commonsense Causal Reasoning Using Millions of Personal Stories
Authors: Gordon Andrew , Bejan Cosmin , Sagae Kenji
Published: June 2022

The personal stories that people write in their Internet weblogs include a substantial amount of information about the causal relationships between everyday events. In this paper we describe our efforts to use millions of these stories for automated commonsense causal reasoning. Casting the commonsense causal reasoning problem as a Choice of Plausible Alternatives, we describe four experiments that compare various statistical and information retrieval approaches to exploit causal information in story corpora. The top performing system in these experiments uses a simple co-occurrence statistic between words in the causal antecedent and consequent, calculated as the Pointwise Mutual Information between words in a corpus of millions of personal stories.
CCRank: Parallel Learning to Rank with Cooperative Coevolution
Authors: Wang Shuaiqiang , Gao Byron , Wang Ke , Lauw Hady
Published: June 2022

We propose CCRank, the first parallel algorithm for learning to rank, targeting simultaneous improvement in learning accuracy and efficiency. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures. Moreover, CC naturally allows parallelization of sub-solutions to the decomposed subproblems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. Extensive experiments on benchmarks in comparison with the state-of-the-art algorithms show that CCRank gains in both accuracy and efficiency.
A Whole Page Click Model to Better Interpret Search Engine Click Data
Authors: Chen Weizhu , Ji Zhanglong , Shen Si , Yang Qiang
Published: June 2022

Recent advances in click modeling have established it as an attractive approach to interpret search click data. These advances characterize users’ search behavior either in advertisement blocks, or within an organic search block through probabilistic models. Yet, when searching for information on a search result page, one is often interacting with the search engine via an entire page instead of a single block. Consequently, previous works that exclusively modeled user behavior in a single block may sacrifice much useful user behavior information embedded in other blocks. To solve this problem, in this paper, we put forward a novel Whole Page Click (WPC) Model to characterize user behavior in multiple blocks. Specifically, WPC uses a Markov chain to learn the user transition probabilities among different blocks in the whole page. To compare our model with the best alternatives in the Web-Search literature, we run a large-scale experiment on a real dataset and demonstrate the advantage of the WPC model in terms of both the whole page and each block in the page. Especially, we find that WPC can achieve significant gain in interpreting the advertisement data, despite of the sparsity of the advertisement click data.
Trust Transitivity in Complex Social Networks
Authors: Liu Guanfeng , Wang Yan , Orgun Mehmet
Published: June 2022

In Online Social Networks (OSNs), participants can conduct rich activities, where trust is one of the most important factors for their decision making. This necessitates the evaluation of the trustworthiness between two unknown participants along the social trust paths between them based on the trust transitivity properties (i.e., if A trusts B and B trusts C, then A can trust C to some extent). In order to compute more reasonable trust value between two unknown participants, a critical and challenging problem is to make clear how and to what extent trust is transitive along a social trust path. To address this problem, we first propose a new complex social network structure that takes, besides trust, social relationships, recommendation roles and preference similarity between participants into account. These factors have significant influence on trust transitivity. We then propose a general concept, called Quality of Trust Transitivity (QoTT), that takes any factor with impact on trust transitivity as an attribute to illustrate the ability of a trust path to guarantee a certain level of quality in trust transitivity. Finally, we propose a novel Multiple QoTT Constrained Trust Transitivity (MQCTT) model. The results of our experiments demonstrate that our proposed MQCTT model follows the properties of trust and the principles illustrated in social psychology, and thus can compute more resonable trust values than existing methods that consider neither the impact of social aspects nor the properties of trust.
Understanding User Migration Patterns in Social Media
Authors: Kumar Shamanth , Zafarani Reza , Liu Huan
Published: June 2022

The incredible growth of the social web over the last decade has ushered in a flurry of new social media sites. On one hand, users have an inordinate number of choices; on the other hand, users are constrained by limited time and resources and have to choose sites in order to remain social and active. Hence, dynamic social media entails user migration, a well studied phenomenon in fields such as sociology and psychology. Users are valuable assets for social media sites as they help contribute to the growth of a site and generate revenue by increased traffic. We are intrigued to know if social media user migration can be studied, and what migration patterns are. In particular, we investigate whether people migrate, and if they do, how they migrate. We formalize site and attention migration to help identify the migration between popular social media sites and determine clear patterns of migration between sites. This work suggests a feasible way to study migration patterns in social media. The discovered patterns can help understand social media sites and gauge their popularity to improve business intelligence and revenue generation through the retention of users.
NewsFinder: Automating an Artificial Intelligence News Service
Authors: Dong Liang , Smith Reid , Buchanan Bruce
Published: June 2022

NewsFinder automates the steps involved in finding, select- ing and publishing news stories that meet subjective judgments of relevance and interest to the Artificial Intelligence community. NewsFinder combines a broad search with AI-specific filters and incorporates a learning program whose judgment of interestingness of stories can be trained by feedback from readers. Since August, 2010, the program has been used to operate the AI in the News service that is part of the AAAI AI Topics site.
Online Planning to Control a Packaging Infeed System
Authors: Do Minh , Lee Lawrence , Zhou Rong , Crawford Lara , Uckun Serdar
Published: June 2022

In this paper, we investigate a novel application of online planning and scheduling: controlling an automated infeeder for a packaging line of food and consumer packaged goods. In this system, products arrive continuously at high-speed from the end of the production line and need to be arranged into a specific configuration for downstream primary and secondary packaging machines. In collaboration with a domain expert from the packaging industry, we developed an innovative design for a reconfigurable parallel infeed system using a matrix of interchangeable smart belts. We also adapted our online model-based Plantrol planner to this domain. Our planner can control various configurations of the new in-feed system through simulation both in nominal planning and when runtime failures occur. We are also building a small physical prototype to validate the new design and our sofeware framework.
Designing Resilient Long-Reach Passive Optical Networks
Authors: Mehta Deepak , O’Sullivan Barry , Quesada Luis , Ruffini Marco , Payne David , Doyle Linda
Published: June 2022

We report on an emerging application focused on the design of resilient long reach passive optical networks using combinatorial optimisation techniques. The objective of the application is to determine the optimal position and capacity of a set of metro nodes. We specifically consider dual parented networks whereby each customer must be associated with two metro nodes. An important property of such a placement is resilience to single node failure. Therefore excess capacity should be provided at each metro node in order to ensure that customers can be redistributed amongst the metro sites. Our application, as well as finding optimal node placements, can compute the minimum level of excess capacity on all metro nodes. In this paper we present three alternative approaches to optimal metro node placement. We present a detailed analysis of the impact of different placement approaches on the distribution of excess capacity throughout the network. We show that preferential distributions occur in practice, based on a case-study in Ireland. Finally we show that load and excess capacity provision are independent of each other.
TRUSTS: Scheduling Randomized Patrols for Fare Inspection in Transit Systems
Authors: Yin Zhengyu , Jiang Albert , Johnson Matthew , Tambe Milind , Kiekintveld Christopher , Leyton-Brown Kevin , Sandholm Tuomas , Sullivan John
Published: June 2022

In proof-of-payment transit systems, passengers are legally required to purchase tickets before entering but are not physically forced to do so. Instead, patrol units move about the transit system, inspecting the tickets of passengers, who face fines if caught fare evading. The deterrence of such fines depends on the unpredictability and effectiveness of the patrols. In this paper, we present TRUSTS, an application for scheduling randomized patrols for fare inspection in transit systems. TRUSTS models the problem of computing patrol strategies as a leader-follower Stackelberg game where the objective is to deter fare evasion and hence maximize revenue. This problem differs from previously studied Stackelberg settings in that the leader strategies must satisfy massive temporal and spatial constraints; moreover, unlike in these counterterrorism-motivated Stackelberg applications, a large fraction of the ridership might realistically consider fare evasion, and so the number of followers is potentially huge. A third key novelty in our work is deliberate simplification of leader strategies to make patrols easier to be executed. We present an efficient algorithm for computing such patrol strategies and present experimental results using real-world ridership data from the Los Angeles Metro Rail system. The Los Angeles County Sheriff’s department has begun trials of TRUSTS.
Resource Sharing for Control of Wildland Fires
Authors: Tsang Alan , Larson Kate , McAlpine Rob
Published: June 2022

Wildland fires (or wildfires) occur on all continents except for Antarctica. These fires threaten communities, change ecosystems, destroy vast quantities of natural resources and the cost estimates of the damage done annually is in the billions of dollars. Controlling wildland fires is resource-intensive and there are numerous examples where the resource demand has outstripped resource availability. Trends in changing climates, fire occurrence and the expansion of the wildland-urban interface all point to increased resource shortages in the future. One approach for coping with these shortages has been the sharing of resources across different wildland-fire agencies. This introduces new issues as agencies have to balance their own needs and risk-management with their desire to help fellow agencies in need. Using ideas from the field of multiagent systems, we conduct the first analysis of strategic issues arising in resource-sharing for wildland-fire control. We also argue that the wildland-fire domain has numerous features that make it attractive to researchers in artificial intelligence and computational sustainability.
Adaptive Spatio-Temporal Exploratory Models: Hemisphere-wide species distributions from massively crowdsourced eBird data
Authors: Fink Daniel , Damoulas Theodoros , Dave Jaimin
Published: June 2022

Broad-scale spatiotemporal processes in conservation and sustainability science, such as continent-wide animal movement, occur across a range of spatial and temporal scales. Understanding these processes at multiple scales is crucial for developing and coordinating conservation strategies across national boundaries. In this paper we propose a general class of models we call AdaSTEM, for Adaptive Spatio-Temporal Exploratory Models, that are able to exploit variation in the density of observations while adapting to multiple scales in space and time. We show that this framework is able to efficiently discover multiscale structure when it is present, while retaining predictive performance when absent. We provide an empirical comparison and analysis, offer theoretical insights from the ensemble loss decomposition, and deploy AdaSTEM to estimate the spatiotemporal distribution of Barn Swallow (Hirundo rustica) across the Western Hemisphere using massively crowdsourced eBird data.
A Temporal Motif Mining Approach to Unsupervised Energy Disaggregation: Applications to Residential and Commercial Buildings
Authors: Shao Huijuan , Marwah Manish , Ramakrishnan Naren
Published: June 2022

Non-intrusive appliance load monitoring has emerged as an attractive approach to study energy consumption patterns without instrumenting every device in a building. The ensuing computational problem is to disaggregate total energy usage into usage by specific devices, to gain insight into consumption patterns. We exploit the temporal ordering implicit in on/off events of devices to uncover motifs (episodes) corresponding to the operation of individual devices. Extracted motifs are then subjected to a sequence of constraint checks to ensure that the resulting episodes are interpretable. Our results reveal that motif mining is adept at distinguishing devices with multiple power levels and at disentangling the combinatorial operation of devices. With suitably configured processing steps, we demonstrate the applicability of our method to both residential and commercial buildings.
IAAI-13 Preface
Authors: Munoz-Avila Hector , Stracuzzi David
Published: June 2022

Welcome to the Twenty-Fifth Annual Conference on the Innovative Applications of Artificial Intelligence (IAAI-13), the premier conference focusing on applied AI research ranging from exciting new potential ap- plications to innovative deployments of AI technology.
Data-Efficient Generalization of Robot Skills with Contextual Policy Search
Authors: Kupcsik Andras , Deisenroth Marc , Peters Jan , Neumann Gerhard
Published: June 2022

In robotics, controllers make the robot solve a task within a specific context. The context can describe the objectives of the robot or physical properties of the environment and is always specified before task execution. To generalize the controller to multiple contexts, we follow a hierarchical approach for policy learning: A lower-level policy controls the robot for a given context and an upper-level policy generalizes among contexts. Current approaches for learning such upper-level policies are based on model-free policy search, which require an excessive number of interactions of the robot with its environment. More data-efficient policy search approaches are model based but, thus far, without the capability of learning hierarchical policies. We propose a new model-based policy search approach that can also learn contextual upper-level policies. Our approach is based on learning probabilistic forward models for long-term predictions. Using these predictions, we use information-theoretic insights to improve the upper-level policy. Our method achieves a substantial improvement in learning speed compared to existing methods on simulated and real robotic tasks.
Compact RGBD Surface Models Based on Sparse Coding
Authors: Ruhnke Michael , Bo Liefeng , Fox Dieter , Burgard Wolfram
Published: June 2022

In this paper, we describe a novel approach to construct compact colored 3D environment models representing local surface attributes via sparse coding. Our method decomposes a set of colored point clouds into local surface patches and encodes them based on an overcomplete dictionary. Instead of storing the entire point cloud, we store a dictionary, surface patch positions, and a sparse code description of the depth and RGB attributes for every patch. The dictionary is learned in an unsupervised way from surface patches sampled from indoor maps. We show that better dictionaries can be learned by extending the K-SVD method with a binary weighting scheme that ignores undefined surface cells. Through experimental evaluation on real world laser and RGBD datasets we demonstrate that our method produces compact and accurate models. Furthermore, we clearly outperform an existing state of the art method in terms of compactness, accuracy, and computation time. Additionally, we demonstrate that our sparse code descriptions can be utilized for other important tasks such as object detection.
Guiding Scientific Discovery with Explanations Using DEMUD
Authors: Wagstaff Kiri , Lanza Nina , Thompson David , Dietterich Thomas , Gilmore Martha
Published: June 2022

In the era of large scientific data sets, there is an urgent need for methods to automatically prioritize data for review. At the same time, for any automated method to be adopted by scientists, it must make decisions that they can understand and trust. In this paper, we propose Discovery through Eigenbasis Modeling of Uninteresting Data (DEMUD), which uses principal components modeling and reconstruction error to prioritize data. DEMUD’s major advance is to offer domain-specific explanations for its prioritizations. We evaluated DEMUD’s ability to quickly identify diverse items of interest and the value of the explanations it provides. We found that DEMUD performs as well or better than existing class discovery methods and provides, uniquely, the first explanations for why those items are of interest. Further, in collaborations with planetary scientists, we found that DEMUD (1) quickly identifies very rare items of scientific value, (2) maintains high diversity in its selections, and (3) provides explanations that greatly improve human classification accuracy.
A Tensor-Variate Gaussian Process for Classification of Multidimensional Structured Data
Authors: Zhao Qibin , Zhang Liqing , Cichocki Andrzej
Published: June 2022

As tensors provide a natural and efficient representation of multidimensional structured data, in this paper, we consider probabilistic multinomial probit classification for tensor-variate inputs with Gaussian processes (GP) priors placed over the latent function. In order to take into account the underlying multimodes structure information within the model, we propose a framework of probabilistic product kernels for tensorial data based on a generative model assumption. More specifically, it can be interpreted as mapping tensors to probability density function space and measuring similarity by an information divergence. Since tensor kernels enable us to model input tensor observations, the proposed tensor-variate GP is considered as both a generative and discriminative model. Furthermore, a fully variational Bayesian treatment for multiclass GP classification with multinomial probit likelihood is employed to estimate the hyperparameters and infer the predictive distributions. Simulation results on both synthetic data and a real world application of human action recognition in videos demonstrate the effectiveness and advantages of the proposed approach for classification of multiway tensor data, especially in the case that the underlying structure information among multimodes is discriminative for the classification task.
Information Sharing Under Costly Communication in Joint Exploration
Authors: Rochlin Igor , Sarne David
Published: June 2022

This paper studies distributed cooperative multi-agent exploration methods in settings where the exploration is costly and the overall performance measure is determined by the minimum performance achieved by any of the individual agents. Such an exploration setting is applicable to various multi-agent systems, e.g., in Dynamic Spectrum Access exploration. The goal in such problems is to optimize the process as a whole, considering the tradeoffs between the quality of the solution obtained and the cost associated with the exploration and coordination between the agents. Through the analysis of the two extreme cases where coordination is completely free and when entirely disabled, we manage to extract the solution for the general case where coordination is taken to be costly, modeled as a fee that needs to be paid for each additional coordinated agent. The strategy structure for the general case is shown to be threshold-based, and the thresholds which are analytically derived in this paper can be calculated offline, resulting in a very low online computational load.
Sparse Multi-Task Learning for Detecting Influential Nodes in an Implicit Diffusion Network
Authors: Wang Yingze , Xiang Guang , Chang Shi-Kuo
Published: June 2022

How to identify influential nodes is a central research topic in information diffusion analysis. Many existing methods rely on the assumption that the network structure is completely known by the model. However, in many applications, such a network is either unavailable or insufficient to explain the underlying information diffusion phenomena. To address this challenge, we develop a multi-task sparse linear influence model (MSLIM), which can simultaneously predict the volume for each contagion and automatically identify sets of the most influential nodes for different contagions. Our method is based on the linear influence model with two main advantages: 1) it does not require the network structure; 2) it can detect different sets of the most influential nodes for different contagions. To solve the corresponding convex optimization problem for learning the model, we adopt the accelerated gradient method (AGM) framework and show that there is an exact closed-form solution for the proximal mapping. Therefore, the optimization procedure achieves the optimal first-order convergence rate and can be scaled to very large datasets. The proposed model is validated on a set of 2.6 millions tweets from 1000 users of Twitter. We show that MSLIM can efficiently select the most influential users for specific contagions. We also present several interesting patterns of the selected influential users.
A Generalized Student-t Based Approach to Mixed-Type Anomaly Detection
Authors: Lu Yen-Cheng , Chen Feng , Chen Yang , Lu Chang-Tien
Published: June 2022

Anomaly detection for mixed-type data is an important problem that has not been well addressed in the machine learning field. There are two challenging issues for mixed-type datasets, namely modeling mutual correlations between mixed-type attributes and capturing large variations due to anomalies. This paper presents BuffDetect, a robust error buffering approach for anomaly detection in mixed-type datasets. A new variant of the generalized linear model is proposed to model the dependency between mixed-type attributes. The model incorporates an error buffering component based on Student-t distribution to absorb the variations caused by anomalies. However, because of the non- Gaussian design, the problem becomes analytically intractable. We propose a novel Bayesian inference approach, which integrates Laplace approximation and several computational optimizations, and is able to efficiently approximate the posterior of high dimensional latent variables by iteratively updating the latent variables in groups. Extensive experimental evaluations based on 13 benchmark datasets demonstrate the effectiveness and efficiency of BuffDetect.
A General Formal Framework for Pathfinding Problems with Multiple Agents
Authors: Erdem Esra , Kisa Doga , Oztok Umut , Schüller Peter
Published: June 2022

Pathfinding for a single agent is the problem of planning a route from an initial location to a goal location in an environment, going around obstacles. Pathfinding for multiple agents also aims to plan such routes for each agent, subject to different constraints, such as restrictions on the length of each path or on the total length of paths, no self-intersecting paths, no intersection of paths/plans, no crossing/meeting each other. It also has variations for finding optimal solutions, e.g., with respect to the maximum path length, or the sum of plan lengths. These problems are important for many real-life applications, such as motion planning, vehicle routing, environmental monitoring, patrolling, computer games. Motivated by such applications, we introduce a formal framework that is general enough to address all these problems: we use the expressive high-level representation formalism and efficient solvers of the declarative programming paradigm Answer Set Programming. We also introduce heuristics to improve the computational efficiency and/or solution quality. We show the applicability and usefulness of our framework by experiments, with randomly generated problem instances on a grid, on a real-world road network, and on a real computer game terrain.
Abstract Preference Frameworks — a Unifying Perspective on Separability and Strong Equivalence
Authors: Faber Wolfgang , Truszczyński Mirosław , Woltran Stefan
Published: June 2022

We introduce abstract preference frameworks to study general properties common across a variety of preference formalisms. In particular, we study strong equivalence in preference formalisms and their separability. We identify abstract postulates on preference frameworks, satisfied by most of the currently studied preference formalisms, that lead to characterizations of both properties of interest.
Efficient Evolutionary Dynamics with Extensive-Form Games
Authors: Gatti Nicola , Panozzo Fabio , Restelli Marcello
Published: June 2022

Evolutionary game theory combines game theory and dynamical systems and is customarily adopted to describe evolutionary dynamics in multi-agent systems. In particular, it has been proven to be a successful tool to describe multi-agent learning dynamics. To the best of our knowledge, we provide in this paper the first replicator dynamics applicable to the sequence form of an extensive-form game, allowing an exponential reduction of time and space w.r.t. the currently adopted replicator dynamics for normal form. Furthermore, our replicator dynamics is realization equivalent to the standard replicator dynamics for normal form. We prove our results for both discrete-time and continuous-time cases. Finally, we extend standard tools to study the stability of a strategy profile to our replicator dynamics.
Mixed Heuristic Local Search for Protein Structure Prediction
Authors: Shatabda Swakkhar , Newton M. A. , Sattar Abdul
Published: June 2022

Protein structure prediction is an unsolved problem in computational biology. One great difficulty is due to the unknown factors in the actual energy function. Moreover, the energy models available are often not very informative particularly when spatially similar structures are compared during search. We introduce several novel heuristics to augment the energy model and present a new local search algorithm that exploits these heuristics in a mixed fashion. Although the heuristics individually are weaker in performance than the energy function, their combination interestingly produces stronger results. For standard benchmark proteins on the face centered cubic lattice and a realistic 20x20 energy model, we obtain structures with significantly lower energy than those obtained by the state-of-the-art algorithms. We also report results for these proteins using the same energy model on the cubic lattice.
Search More, Disclose Less
Authors: Hajaj Chen , Hazon Noam , Sarne David , Elmalech Avshalom
Published: June 2022

The blooming of comparison shopping agents (CSAs) in recent years enables buyers in today's markets to query more than a single CSA while shopping, thus substantially expanding the list of sellers whose prices they obtain. From the individual CSA point of view, however, the multi-CSAs querying is definitely non-favorable as most of today's CSAs benefit depends on payments they receive from sellers upon transferring buyers to their websites (and making a purchase). The most straightforward way for the CSA to improve its competence is through spending more resources on getting more sellers' prices, potentially resulting in a more attractive ``best price''. In this paper we suggest a complementary approach that improves the attractiveness of the best price returned to the buyer without having to extend the CSAs' price database. This approach, which we term ``selective price disclosure'' relies on removing some of the prices known to the CSA from the list of results returned to the buyer. The advantage of this approach is in the ability to affect the buyer's beliefs regarding the probability of obtaining more attractive prices if querying additional CSAs. The paper presents two methods for choosing the subset of prices to be presented to a fully-rational buyer, attempting to overcome the computational complexity associated with evaluating all possible subsets. The effectiveness and efficiency of the methods are demonstrated using real data, collected from five CSAs for four products. Furthermore, since people are known to have an inherently bounded rationality, the two methods are also evaluated with human buyers, demonstrating that selective price-disclosing can be highly effective with people, however the subset of prices that needs to be used should be extracted in a different (and more simplistic) manner.
Computational Aspects of Nearly Single-Peaked Electorates
Authors: Erdélyi Gábor , Lackner Martin , Pfandler Andreas
Published: June 2022

Manipulation, bribery, and control are well-studied ways of changing the outcome of an election. Many voting systems are, in the general case, computationally resistant to some of these manipulative actions. However when restricted to single-peaked electorates, these systems suddenly become easy to manipulate. Recently, Faliszewski, Hemaspaandra, and Hemaspaandra studied the complexity of dishonest behavior in nearly single-peaked electorates. These are electorates that are not single-peaked but close to it according to some distance measure. In this paper we introduce several new distance measures regarding single-peakedness. We prove that determining whether a given profile is nearly single-peaked is NP-complete in many cases. For one case we present a polynomial-time algorithm. Furthermore, we explore the relations between several notions of nearly single-peakedness.
Bounding the Cost of Stability in Games over Interaction Networks
Authors: Meir Reshef , Zick Yair , Elkind Edith , Rosenschein Jeffrey
Published: June 2022

We study the stability of cooperative games played over an interaction network, in a model that was introduced by Myerson ['77]. We show that the cost of stability of such games (i.e., the subsidy required to stabilize the game) can be bounded in terms  of natural parameters of their underlying interaction networks. Specifically, we prove that if the treewidth of the interaction network H is k, then the relative cost of stability of any game played over H is at most k + 1, and if the pathwidth of H is k', then the relative cost of stability is at most k'. We show that these bounds are tight for all k≥ 2and all k' ≥ 1, respectively.
Effective Bilingual Constraints for Semi-Supervised Learning of Named Entity Recognizers
Authors: Wang Mengqiu , Che Wanxiang , Manning Christopher
Published: June 2022

Most semi-supervised methods in Natural Language Processing capitalize on unannotated resources in a single language; however, information can be gained from using parallel resources in more than one language, since translations of the same utterance in different languages can help to disambiguate each other. We demonstrate a method that makes effective use of vast amounts of bilingual text (a.k.a. bitext) to improve monolingual systems. We propose a factored probabilistic sequence model that encourages both crosslanguage and intra-document consistency. A simple Gibbs sampling algorithm is introduced for performing approximate inference. Experiments on English-Chinese Named Entity Recognition (NER) using the OntoNotes dataset demonstrate that our method is significantly more accurate than state-ofthe- art monolingual CRF models in a bilingual test setting. Our model also improves on previous work by Burkett et al. (2010), achieving a relative error reduction of 10.8% and 4.5% in Chinese and English, respectively. Furthermore, by annotating a moderate amount of unlabeled bi-text with our bilingual model, and using the tagged data for uptraining, we achieve a 9.2% error reduction in Chinese over the state-ofthe- art Stanford monolingual NER system.
Discovering Hierarchical Structure for Sources and Entities
Authors: Pal Aditya , Dalvi Nilesh , Bellare Kedar
Published: June 2022

In this paper, we consider the problem of jointly learning hierarchies over a set of sources and entities based on their containment relationship. We model the concept of hierarchy using a set of latent binary features and propose a generative model that assigns those latent features to sources and entities in order to maximize the probability of the observed containment. To avoid fixing the number of features beforehand, we consider a non-parametric approach based on the Indian Buffet Process. The hierarchies produced by our algorithm can be used for completing missing associations and discovering structural bindings in the data. Using simulated and real datasets we provide empirical evidence of the effectiveness of the proposed approach in comparison to the existing hierarchy agnostic approaches.
Equilibria of Online Scheduling Algorithms
Authors: Ashlagi Itai , Lucier Brendan , Tennenholtz Moshe
Published: June 2022

We describe a model for competitive online scheduling algorithms. Two servers, each with a single observable queue, compete for customers. Upon arrival, each customer strategically chooses the queue with minimal expected wait time. Each scheduler wishes to maximize its number of customers, and can strategically select which scheduling algorithm, such as First-Come-First-Served (FCFS), to use for its queue. This induces a game played by the servers and the customers. We consider a non-Bayesian setting, where servers and customers play to maximize worst-case payoffs. We show that there is a unique subgame perfect safety-level equilibrium and we describe the associated scheduling algorithm (which is not FCFS). The uniqueness result holds for both randomized and deterministic algorithms, with a different equilibrium algorithm in each case. When the goal of the servers is to minimize competitive ratio, we prove that it is an equilibrium for each server to apply FCFS: each server obtains the optimal competitive ratio of 2.
The Deployment of a Constraint-Based Dental School Timetabling System
Authors: Cambazard Hadrien , O’Sullivan Barry , Simonis Helmut
Published: June 2022

We describe a constraint-based timetabling system that was developed for the dental school based at Cork University Hospital in Ireland. This system has been deployed since 2010. Dental school timetabling differs from other university course scheduling in that certain clinic sessions can be used by multiple courses at the same time, provided a limit on room capacity is satisfied. Starting from a constraint programming solution using a web interface, we have moved to a mixed integer programming-based solver to deal with multiple objective functions, along with a dedicated Java application, which provides a rich user interface. Solutions for the years 2010, 2011 and 2012 have been used in the dental school, replacing a manual timetabling process, which could no longer cope with increasing student numbers and resulting resource bottlenecks. The use of the automated system allowed the dental school to increase student numbers to the maximum possible given the available resources. It also provides the school with a valuable “what-if” analysis tool.