bluuebunny/crossref_metadata_2025 huggingface dataset:
THE VITAL VALUE OF BREASTFEEDING FOR IMPRISONED WOMEN
Authors: Santos Márcia Vieira dos , Alves Valdecyr Herdy , Pereira Audrey Vidal , Vieira Bianca Dargam Gomes , Rodrigues Diego Pereira , Tavares Marli Rodrigues , Calandrini Tatiana Socorro dos Santos , Ferreira Ediane de Andrade
Published: June 2022

ABSTRACT Objective: to understand the perceptions about the values of breastfeeding for nursing mothers under custody of the penitentiary system. Method: a qualitative and phenomenological research study based on the Schelerian values, with seven phenomenological interviews conducted with nursing mothers deprived of their freedom in a women's prison unit from Rio de Janeiro, Brazil. The interviews took place between November 2020 and May 2021. Content analysis for treatment of the results was performed after transcribing the testimonies. Results: two categories were identified for the vital value: The value of bonding as a breastfeeding expansion process; and The nursing mother as a safety and protection value for the infant. Through the perception of the nursing mothers deprived of their freedom, the vital value identified acts as protection for breastfeeding within the prison setting, strengthening the woman's and child's health. Conclusion: by unveiling the vital value, it is observed that it is related to the mother's and infant's everyday life in prison. However, the prison institution does not meet the real vital needs of imprisoned nursing mothers, with the consequent need to redirect the breastfeeding practice in the prison setting, transforming it into a legitimate environment that values the meaning of breastfeeding.
Internações por condições sensíveis à atenção primária (ICSAP): uma análise segundo características sociodemográficas, Brasil e regiões, 2010 a 2019
Authors: Santos Filipe Malta dos , Macieira César , Machado Antônio Thomaz Gonzaga da Matta , Borde Elis Mina Seraya , Santos Alaneir Fátima dos
Published: June 2022

RESUMO: Objetivo: Analisar a tendência temporal das internações por condições sensíveis à atenção primária (ICSAP) no Brasil, por sexo, regiões, causas e faixas etárias, no período de 2010 a 2019. Métodos: Trata-se de estudo ecológico baseado na tendência temporal das taxas de ICSAP. Foram analisadas as taxas padronizadas segundo regressão linear simples e modelo linear generalizado (MLG) gama. Observou-se também a variação percentual em três períodos: 2010–2014, 2015–2019 e 2010–2019. Resultados: Ocorreu redução nas taxas de internação entre 2010 e 2019 para Brasil: 124,3/10.000 hab. para 88,2/10.000 hab. em mulheres (-29,0%) e 119,0/10.000 hab. para 88,2/10.000 hab. (-25,9%) em homens, em todas as regiões. A queda foi maior entre 2010 e 2014 (-17,7% e -17,8%) do que entre 2015 e 2019 (-9,2% e -5,9%) — valores para sexo feminino e masculino, respectivamente. Esse declínio menor no período de 2015 a 2019 foi mais perceptível entre as faixas de zero a quatro anos e de cinco a 19 anos em todas as regiões. Quanto às causas, foi verificada redução para maioria dos códigos da Classificação Internacional de Doenças (CID-10), mais expressiva para gastroenterites (-60%), entretanto tiveram aumento doenças cerebrovasculares entre mulheres (11,2%) e homens (17,1%) e angina (15%%) e infecções da pele (56,1%) entre homens. Conclusão: Ocorreu importante queda das taxas de ICSAP no período analisado, especialmente para as faixas etárias de zero a quatro e de cinco a 19. As taxas tiveram menor queda entre 2015 e 2019, período de austeridade e crise econômica.
Identidade profissional de assistentes sociais no IFSC: entre o assalariamento e o direcionamento social
Authors: Garcia Shirlei , Silva Adriano Larentes da
Published: June 2022

Resumo Este artigo apresenta uma análise sobre o trabalho dos/as assistentes sociais no Instituto Federal de Educação de Santa Catarina, em sua relação com o processo de construção da identidade profissional. A partir do final dos anos 2000, observa-se uma expansão no número de assistentes sociais atuando na política educacional, com a constituição do Programa Nacional de Assistência Estudantil e da Rede Federal de Educação Profissional, Científica e Tecnológica, requisitado, principalmente, para a execução das ações de assistência estudantil. As demandas, requisições e condições de trabalho são elementos que corroboram a construção do projeto ético-político da profissão, e refletir sobre tais aspectos possibilita compreender a realidade para além da sua aparência imediata. Este estudo, fundamentado no materialismo histórico-dialético, desenvolveu-se com base na pesquisa bibliográfica e documental e na pesquisa de campo, utilizando-se do questionário para o levantamento de dados. Os resultados apontam limites e possibilidades ao trabalho dos/as assistentes sociais no Instituto, demonstrando que a construção da identidade profissional ocorre em meio à contradição posta entre as determinações do trabalho assalariado, ao qual os/as assistentes sociais estão sujeitos, e o direcionamento social que se busca imprimir ao exercício profissional, tendo em vista a materialização do projeto profissional.
Perfis profissionais de planos de cursos técnicos em saúde: mercado, SUS e formação humana
Authors: Corrêa Adriana Katia , Clapis Maria José , Moraes Sílvia Helena Mendonça de
Published: June 2022

Resumo O estudo que originou este artigo é do tipo qualitativo, documental e de perspectiva histórico-dialética, cujo objetivo é refletir sobre aproximações e afastamentos da formação de técnicos da área da saúde em relação ao Sistema Único de Saúde e à formação humana, mediante a análise de sete planos de cursos, com foco no perfil profissional de seis escolas (quatro públicas e dois estabelecimentos de ensino privados) do estado de São Paulo. A análise foi feita com base nas relações estabelecidas entre perfis profissionais e alguns dispositivos político-legais. A explicitação do Sistema Único de Saúde na descrição dos perfis é fragilizada, não havendo referência, na maioria dos planos, exceto em dois da enfermagem (um deles pertencente à Escola Técnica do Sistema Único de Saúde). Essa fragilidade vai ao encontro dos catálogos nacionais dos cursos técnicos nas versões utilizadas das áreas profissionais em foco. Nos planos, com ênfase nos cursos técnicos em farmácia e nutrição e dietética, sobressai nas justificativas e tendências o enfoque explícito ao mercado. Há apropriação de conceitos potencializadores da lógica neoliberal, mesmo com indicação de ‘formação crítica’. É desafiante a proposição de formação humana e a favor do Sistema Único de Saúde na educação profissional técnica de nível médio em saúde considerando-se os avanços da lógica neoliberal.
Desafios da reabilitação profissional desenvolvida pelo Instituto Nacional do Seguro Social (INSS): uma perspectiva histórico-cultural
Authors: Tessarro Melina Trento Vendrameto , Querol Marco Antonio Pereira , Almeida Ildeberto Muniz de
Published: June 2022

Resumo Introdução: transformações ocorridas na prestação do serviço de Reabilitação Profissional (RP) pelo Instituto Nacional do Seguro Social (INSS) fizeram emergir contradições entre seus resultados e o que se espera de uma política pública de inclusão social. Objetivo: compreender como tais transformações afetaram a atividade de trabalho e como o serviço de RP está lidando com as mudanças e problemas decorrentes. Métodos: realizada análise histórica das contradições entre elementos do sistema de atividade da RP em serviço do INSS localizado no interior do estado de São Paulo. Foram utilizados dados etnográficos e discursivos de intervenção baseada no método Laboratório de Mudança. Resultados: evidenciou-se que o objeto da RP foi historicamente reduzido à orientação e capacitação profissional. Foram identificadas dificuldades do sistema de atividade da RP em lidar com determinações judiciais, além da diminuição da capacidade instalada e acúmulo de tarefas que impactam no atendimento aos trabalhadores. Conclusão: a redução administrativa do objeto da RP, bem como o sistema de atividades apoiado em ações de atores externos à Previdência Social, sem articulação institucional e protocolos de cooperação entre os serviços, demonstra o descompasso entre o desmonte histórico da RP no INSS e a demanda social dos trabalhadores com incapacidades para o trabalho
REWOrD: Semantic Relatedness in the Web of Data
Authors: Pirró Giuseppe
Published: June 2022

This paper presents REWOrD, an approach to compute semantic relatedness between entities in the Web of Data representing real word concepts. REWOrD exploits the graph nature of RDF data and the SPARQL query language to access this data. Through simple queries, REWOrD constructs weighted vectors keeping the informativeness of RDF predicates used to make statements about the entities being compared. The most informative path is also considered to further refine informativeness. Relatedness is then computed by the cosine of the weighted vectors. Differently from previous approaches based on Wikipedia, REWOrD does not require any prepro- cessing or custom data transformation. Indeed, it can lever- age whatever RDF knowledge base as a source of background knowledge. We evaluated REWOrD in different settings by using a new dataset of real word entities and investigate its flexibility. As compared to related work on classical datasets, REWOrD obtains comparable results while, on one side, it avoids the burden of preprocessing and data transformation and, on the other side, it provides more flexibility and applicability in a broad range of domains.
Social Context-Aware Trust Network Discovery in Complex Contextual Social Networks
Authors: Liu Guanfeng , Wang Yan , Orgun Mehmet
Published: June 2022

Trust is one of the most important factors for participants' decision-making in Online Social Networks (OSNs). The trust network from a source to a target without any prior interaction contains some important intermediate participants, the trust relations between the participants, and the social context, each of which has an important influence on trust evaluation. Thus, before performing any trust evaluation, the contextual trust network from a given source to a target needs to be extracted first, where constraints on the social context should also be considered to guarantee the quality of extracted networks. However, this problem has been proved to be NP-Complete. Towards solving this challenging problem, we first propose a complex contextual social network structure which considers social contextual impact factors. These factors have significant influences on both social interaction between participants and trust evaluation. Then, we propose a new concept called QoTN (Quality of Trust Network) and a social context-aware trust network discovery model. Finally, we propose a Social Context-Aware trust Network discovery algorithm (SCAN) by adopting the Monte Carlo method and our proposed optimization strategies. The experimental results illustrate that our proposed model and algorithm outperform the existing methods in both algorithm efficiency and the quality of the extracted trust network.
Music-Inspired Texture Representation
Authors: Horsburgh Ben , Craw Susan , Massie Stewart
Published: June 2022

Techniques for music recommendation are increasingly relying on hybrid representations to retrieve new and exciting music. A key component of these representations is musical content, with texture being the most widely used feature. Current techniques for representing texture however are inspired by speech, not music, therefore music representations are not capturing the correct nature of musical texture. In this paper we investigate two parts of the well-established mel-frequency cepstral coefficients (MFCC) representation: the resolution of mel-frequencies related to the resolution of musical notes; and how best to describe the shape of texture. Through contextualizing these parts, and their relationship to music, a novel music-inspired texture representation is developed. We evaluate this new texture representation by applying it to the task of music recommendation. We use the representation to build three recommendation models, based on current state-of-the-art methods. Our results show that by understanding two key parts of texture representation, it is possible to achieve a significant recommendation improvement. This contribution of a music-inspired texture representation will not only improve content-based representation, but will allow hybrid systems to take advantage of a stronger content component.
Fine-Grained Entity Recognition
Authors: Ling Xiao , Weld Daniel
Published: June 2022

Entity Recognition (ER) is a key component of relation extraction systems and many other natural-language processing applications. Unfortunately, most ER systems are restricted to produce labels from to a small set of entity classes, e.g., person, organization, location or miscellaneous. In order to intelligently understand text and extract a wide range of information, it is useful to more precisely determine the semantic classes of entities mentioned in unstructured text. This paper defines a fine-grained set of 112 tags, formulates the tagging problem as multi-class, multi-label classification, describes an unsupervised method for collecting training data, and presents the FIGER implementation. Experiments show that the system accurately predicts the tags for entities. Moreover, it provides useful information for a relation extraction system, increasing the F1 score by 93%. We make FIGER and its data available as a resource for future work.
Two New Local Search Strategies for Minimum Vertex Cover
Authors: Cai Shaowei , Su Kaile , Sattar Abdul
Published: June 2022

In this paper, we propose two new strategies to design efficient local search algorithms for the minimum vertex cover (MVC) problem. There are two main drawbacks in state-of-the-art MVC local search algorithms: First, they select a pair of vertices to be exchanged simultaneously, which is time consuming; Second, although they use edge weighting techniques, they do not have a strategy to decrease the weights. To address these drawbacks, we propose two new strategies: two stage exchange and edge weighting with forgetting. The two stage exchange strategy selects two vertices to be exchanged separately and performs the exchange in two stages. The strategy of edge weighting with forgetting not only increases weights of uncovered edges, but also decreases some weights for each edge periodically. We utilize these two strategies to design a new algorithm dubbed NuMVC. The experimental results show that NuMVC significantly outperforms existing state-of-the-art heuristic algorithms on most of the hard DIMACS instances and all instances in the hard random BHOSLIB benchmark.
Configuration Checking with Aspiration in Local Search for SAT
Authors: Cai Shaowei , Su Kaile
Published: June 2022

An interesting strategy called configuration checking (CC) was recently proposed to handle the cycling problem in local search for Minimum Vertex Cover. A natural question is whether this CC strategy also works for SAT. The direct application of CC did not result in stochastic local search (SLS) algorithms that can compete with the current best SLS algorithms for SAT. In this paper, we propose a new heuristic based on CC for SLS algorithms for SAT, which is called configuration checking with aspiration (CCA). It is used to develop a new SLS algorithm called Swcca. The experiments on random 3-SAT instances show that Swcca significantly outperforms Sparrow2011, the winner of the random satisfiable category of the SAT Competition 2011, which is considered to be the best local search solver for random 3-SAT instances. Moreover, the experiments on structured instances show that Swcca is competitive with Sattime, the best local search solver for the crafted benchmark in the SAT Competition 2011.
An Efficient Higher-Order Consistency Algorithm for Table Constraints
Authors: Paparrizou Anastasia , Stergiou Kostas
Published: June 2022

Table constraints are very important in constraint programming as they are present in many real problems from areas such as configuration and databases. As a result, numerous specialized algorithms that achieve generalized arc consistency (GAC) on table constraints have been proposed. Since these algorithms achieve GAC, they operate on one constraint at a time. In this paper we propose an efficient algorithm for table constraints that achieves a stronger local consistency than GAC. This algorithm, called maxRPWC+, is based on the local consistency maxRPWC and allows the efficient handling of intersecting table constraints. Experimental results from benchmark problems demonstrate that maxRPWC+ is clearly more robust than a state-of-the-art GAC algorithm in classes of problems with interleaved table constraints, being orders of magnitude faster in some of these classes.
Partial-Expansion A* with Selective Node Generation
Authors: Felner Ariel , Goldenberg Meir , Sharon Guni , Stern Roni , Beja Tal , Sturtevant Nathan , Schaeffer Jonathan , Holte Robert
Published: June 2022

A* is often described as being `optimal', in that it expands the minimum number of unique nodes. But, A* may generate many extra nodes which are never expanded. This is a performance loss, especially when the branching factor is large. Partial Expansion A* addresses this problem when expanding a node, n, by generating all the children of n but only storing children with the same f-cost as n. n is re-inserted into the OPEN list, but with the f-cost of the next best child. This paper introduces an enhanced version of PEA* (EPEA*). Given a priori domain knowledge, EPEA* generates only the children with the same f-cost as the parent. EPEA* is generalized to its iterative-deepening variant, EPE-IDA*. For some domains, these algorithms yield substantial performance improvements. State-of-the-art results were obtained for the pancake puzzle and for some multi-agent pathfinding instances. Drawbacks of EPEA* are also discussed.
Optimization and Controlled Systems: A Case Study on Thermal Aware Workload Dispatching
Authors: Bartolini Andrea , Lombardi Michele , Milano Michela , Benini Luca
Published: June 2022

Although successfully employed on many industrial problems, Combinatorial Optimization still has limited applicability on several real-world domains, often due to modeling difficulties. This is typically the case for systems under the control of an on-line policy: even when the policy itself is well known, capturing its effect on the system in a declarative model is often impossible by conventional means. Such a difficulty is at the root of the classical, sharp separation between off- line and on-line approaches. In this paper, we investigate a general method to model controlled systems, based on the integration of Machine Learning and Constraint Programming (CP). Specifically, we use an Artificial Neural Network (ANN) to learn the behavior of a controlled system (a multicore CPU with thermal con- trollers) and plug it into a CP model by means of Neuron Constraints. The method obtains significantly better results compared to an approach with no ANN guidance. Neuron Constraints were first introduced in [Bartolini et al., 2011b] as a mean to model complex systems: providing evidence of their applicability to controlled systems is a significant step forward, broadening the application field of combinatorial methods and disclosing opportunities for hybrid off-line/on-line optimization.
Discovering Constraints for Inductive Process Modeling
Authors: Todorovski Ljupco , Bridewell Will , Langley Pat
Published: June 2022

Scientists use two forms of knowledge in the construction ofexplanatory models: generalized entities and processes that relatethem; and constraints that specify acceptable combinations of thesecomponents. Previous research on inductive process modeling, whichconstructs models from knowledge and time-series data, has relied onhandcrafted constraints. In this paper, we report an approach todiscovering such constraints from a set of models that have beenranked according to their error on observations. Our approach adaptsinductive techniques for supervised learning to identify processcombinations that characterize accurate models. We evaluate themethod's ability to reconstruct known constraints and to generalizewell to other modeling tasks in the same domain. Experiments with synthetic data indicate that the approach can successfully reconstructknown modeling constraints. Another study using natural data suggests that transferring constraints acquired from one modeling scenario to another within the same domain considerably reduces the amount of search for candidate model structures while retaining the most accurate ones.
Large-Scale Mapping and Navigation in VirtualWorlds: Thesis Summary
Authors: Samperi Katrina
Published: June 2022

Virtual worlds present a challenge for intelligent mobile agents. They are required to generate maps of very large scale, dynamic and unstructured environments in a short amount of time. We investigate how to represent maps of ever growing virtual environments, how the agent can build, update and use these maps to navigate between points in the environment. We look at trails, the movement of other people and agents in the environment as a new information source. We can use trails to improve the generation of probabilistic roadmaps in these environments and enable the agent to segment space intelligently. Our future plans are to extend this to look at dynamic environments, where the agent will have to recognise change and update the map and how this will affect the map representation.
Effects of Representation on Solving Complex Spatial-Temporal Problems
Authors: Wetzel Baylor
Published: June 2022

We present a study of how humans represent space when solving Tower Defense puzzles, a complex spatial reasoning task requiring the subject to protect locations by arranging a set of defense towers at strategic positions. We have discovered that the representation humans use is significantly more complex than what is needed to describe the spatial situation. Strategy and spatial representations are tightly intertwined with spatial representations forgoing objective, atomically-defined spatial features for context-sensitive, goal-oriented spatial affordances. Spatial relationships exist not only between objects but between an object’s properties, second-order properties, joint spatial properties and temporal properties.
Time-Critical Influence Maximization in Social Networks with Time-Delayed Diffusion Process
Authors: Chen Wei , Lu Wei , Zhang Ning
Published: June 2022

Influence maximization is a problem of finding a small set of highly influential users in a social network such that the spread of influence under certain propagation models is maximized. In this paper, we consider time-critical influence maximization, in which one wants to maximize influence spread within a given deadline. Since timing is considered in the optimization, we also extend the Independent Cascade (IC) model to incorporate the time delay aspect of influence diffusion in social networks. We show that time-critical influence maximization under the time-delayed IC model maintains desired properties such as submodularity, which allows a greedy algorithm to achieve an approximation ratio of 1-1/e, to circumvent the NP-hardness of the problem. To overcome the inefficiency of the approximation algorithm, we design two heuristic algorithms: the first one is based on a dynamic programming procedure that computes exact influence in tree structures, while the second one converts the problem to one in the original IC model and then applies existing fast heuristics to it. Our simulation results demonstrate that our heuristics achieve the same level of influence spread as the greedy algorithm while running a few orders of magnitude faster, and they also outperform existing algorithms that disregard the deadline constraint and delays in diffusion.
Far Out: Predicting Long-Term Human Mobility
Authors: Sadilek Adam , Krumm John
Published: June 2022

Much work has been done on predicting where is one going to be in the immediate future, typically within the next hour. By contrast, we address the open problem of predicting human mobility far into the future, a scale of months and years. We propose an efficient nonparametric method that extracts significant and robust patterns in location data, learns their associations with contextual features (such as day of week), and subsequently leverages this information to predict the most likely location at any given time in the future. The entire process is formulated in a principled way as an eigendecomposition problem. Evaluation on a massive dataset with more than 32,000 days worth of GPS data across 703 diverse subjects shows that our model predicts the correct location with high accuracy, even years into the future. This result opens a number of interesting avenues for future research and applications.
A Well-Founded Semantics for Basic Logic Programs with Arbitrary Abstract Constraint Atoms
Authors: Wang Yisong , Lin Fangzhen , Zhang Mingyi , You Jia-Huai
Published: June 2022

Logic programs with abstract constraint atoms proposed by Marek and Truszczynski are very general logic programs.They are general enough to captureaggregate logic programs as well asrecently proposed description logic programs.In this paper, we propose a well-founded semantics for basic logic programs with arbitrary abstract constraint atoms, which are sets of rules whose heads have exactly one atom. Weshow that similar to the well-founded semanticsof normal logic programs, it has many desirable properties such as that it can becomputed in polynomial time, and is always correct with respect to theanswer set semantics. This paves the way for using our well-founded semanticsto simplify these logic programs. We also show how our semantics can be applied toaggregate logic programs and description logic programs, and compare itto the well-founded semantics already proposed for these logic programs.
Synthesizing Strategies for Epistemic Goals by Epistemic Model Checking: An Application to Pursuit Evasion Games
Authors: Huang Xiaowei , Van der Meyden Ron
Published: June 2022

The paper identifies a special case in which the complex problem of synthesis from specifications in temporal-epistemic logic can be reduced to the simpler problem of model checking such specifications. An application is given of strategy synthesis in pursuit-evasion games, where one or more pursuers with incomplete information aim to discover theexistence of an evader. Experimental results are provided to evaluate the feasibility of the approach.
A First-Order Interpreter for Knowledge-Based Golog with Sensing based on Exact Progression and Limited Reasoning
Authors: Fan Yi , Cai Minghui , Li Naiqi , Liu Yongmei
Published: June 2022

While founded on the situation calculus, current implementations of Golog are mainly based on the closed-world assumption or its dynamic versions or the domain closure assumption. Also, they are almost exclusively based on regression. In this paper, we propose a first-order interpreter for knowledge-based Golog with sensing based on exact progression and limited reasoning. We assume infinitely many unique names and handle first-order disjunctive information in the form of the so-called proper+ KBs. Our implementation is based on the progression and limited reasoning algorithms for proper+ KBs proposed by Liu, Lakemeyer and Levesque. To improve efficiency, we implement the two algorithms by grounding via a trick based on the unique name assumption. The interpreter is online but the programmer can use two operators to specify offline execution for parts of programs. The search operator returns a conditional plan, while the planning operator is used when local closed-world information is available and calls a modern planner to generate a sequence of actions.
Optimal Auctions for Spiteful Bidders
Authors: Tang Pingzhong , Sandholm Tuomas
Published: June 2022

Designing revenue-optimal auctions for various settings is perhaps the most important, yet sometimes most elusive, problem in mechanism design. Spiteful bidders have been intensely studied recently, especially because spite occurs in many applications in multiagent system and electronic commerce. We derive the optimal auction for such bidders (as well as bidders that are altruistic). It is a generalization of Myerson’s (1981) auction. It chooses an allocation that maximizes agents’ virtual valuations, but for a generalized definition of virtual valuation. The payment rule is less intuitive. For one, it takes each bidder’s own report into consideration when determining his payment. Moreover, bidders pay even if the seller keeps the item; a similar phenomenon has been shown in other settings with neg- ative externalities (Jehiel, Moldovanu, and Stacchetti 1996; Deng and Pekec 2011). On the other hand, a novel aspect of our auction is that it sometimes subsidizes losers when the item is sold to some other bidder. We also derive a revenue equivalence theorem for this setting. Using it, we generate a short proof of (a slight generalization of) the previously known result that, in two-bidder settings with independently uniformly drawn valuations, second-price auctions yield greater expected revenue than first-price auctions. Finally, we present a template for comparing the expected revenues of any two auction mechanisms that have the same allocation rule (for the valuations distributions at hand).
Housing Markets with Indifferences: A Tale of Two Mechanisms
Authors: Aziz Haris , De Keijzer Bart
Published: June 2022

The (Shapley-Scarf) housing market is a well-studied and fundamental model of an exchange economy. Each agent owns a single house and the goal is to reallocate the houses to the agents in a mutually beneﬁcial and stable manner. Recently, Alcalde-Unzu and Molis (2011) and Jaramillo and Manjunath (2011) independently examined housing markets in which agents can express indiﬀerences among houses. They proposed two important families of mechanisms, known as TTAS and TCR respectively. We formulate a family of mechanisms which not only includes TTAS and TCR but also satisﬁes many desirable properties of both families. As a corollary, we show that TCR is strict core selecting (if the strict core is non-empty). Finally, we settle an open question regarding the computational complexity of the TTAS mechanism. Our study also raises a number of interesting research questions.
Decision Support for Agent Populations in Uncertain and Congested Environments
Authors: Varakantham Pradeep , Cheng Shih-Fen , Gordon Geoff , Ahmed Asrar
Published: June 2022

This research is motivated by large scale problems in urban transportation and labor mobility where there is congestion for resources and uncertainty in movement. In such domains, even though the individual agents do not have an identity of their own and do not explicitly interact with other agents, they effect other agents. While there has been much research in handling such implicit effects, it has primarily assumed de- terministic movements of agents. We address the issue of decision support for individual agents that are identical and have involuntary movements in dynamic environments. For instance, in a taxi fleet serving a city, when a taxi is hired by a customer, its movements are uncontrolled and depend on (a) the customers requirement; and (b) the location of other taxis in the fleet. Towards addressing decision support in such problems, we make two key contributions: (a) A framework to represent the decision problem for selfish individuals in a dynamic population, where there is transitional uncertainty (involuntary movements); and (b) Two techniques (Fictitious Play for Symmetric Agent Populations, FP-SAP and Soft- max based Flow Update, SMFU) that converge to equilibrium solutions. We show that our techniques (apart from providing equilibrium strategies) outperform “driver” strategies with re- spect to overall availability of taxis and the revenue obtained by the taxi drivers. We demonstrate this on a real world data set with 8,000 taxis and 83 zones (representing the entire area of Singapore).
A Complexity-of-Strategic-Behavior Comparison between Schulze's Rule and Ranked Pairs
Authors: Parkes David , Xia Lirong
Published: June 2022

Schulze's rule and ranked pairs are two Condorcet methods that both satisfy many natural axiomatic properties. Schulze's rule is used in the elections of many organizations, including the Wikimedia Foundation, the Pirate Party of Sweden and Germany, the Debian project, and the Gento Project. Both rules are immune to control by cloning alternatives, but little is otherwise known about their strategic robustness, including resistance to manipulation by one or more voters, control by adding or deleting alternatives, adding or deleting votes, and bribery. Considering computational barriers, we show that these types of strategic behavior are NP-hard for ranked pairs (both constructive, in making an alternative a winner, and destructive, in precluding an alternative from being a winner). Schulze's rule, in comparison, remains vulnerable at least to constructive manipulation by a single voter and destructive manipulation by a coalition. As the first such polynomial-time rule known to resist all such manipulations, and considering also the broad axiomatic support, ranked pairs seems worthwhile to consider for practical applications.
Competing with Humans at Fantasy Football: Team Formation in Large Partially-Observable Domains
Authors: Matthews Tim , Ramchurn Sarvapali , Chalkiadakis Georgios
Published: June 2022

We present the first real-world benchmark for sequentially-optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker's beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers' performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players.
A Robust Bayesian Truth Serum for Small Populations
Authors: Witkowski Jens , Parkes David
Published: June 2022

Peer prediction mechanisms allow the truthful elicitation of private signals (e.g., experiences, or opinions) in regard to a true world state when this ground truth is unobservable. The original peer prediction method is incentive compatible for any number of agents n >= 2, but relies on a common prior, shared by all agents and the mechanism. The Bayesian Truth Serum (BTS) relaxes this assumption. While BTS still assumes that agents share a common prior, this prior need not be known to the mechanism. However, BTS is only incentive compatible for a large enough number of agents, and the particular number of agents required is uncertain because it depends on this private prior. In this paper, we present a robust BTS for the elicitation of binary information which is incentive compatible for every n >= 3, taking advantage of a particularity of the quadratic scoring rule. The robust BTS is the first peer prediction mechanism to provide strict incentive compatibility for every n >= 3 without relying on knowledge of the common prior. Moreover, and in contrast to the original BTS, our mechanism is numerically robust and ex post individually rational.
Bayes-Adaptive Interactive POMDPs
Authors: Ng Brenda , Boakye Kofi , Meyers Carol , Wang Andrew
Published: June 2022

We introduce the Bayes-Adaptive Interactive Partially Observable Markov Decision Process (BA-IPOMDP), the first multiagent decision model that explicitly incorporates model learning. As in I-POMDPs, the BA-IPOMDP agent maintains beliefs over interactive states, which include the physical states as well as the other agents’ models. The BA-IPOMDP assumes that the state transition and observation probabilities are unknown, and augments the interactive states to include these parameters. Beliefs are maintained over this augmented interactive state space. This (necessary) state expansion exacerbates the curse of dimensionality, especially since each I-POMDP belief update is already a recursive procedure (because an agent invokes belief updates from other agents’ perspectives as part of its own belief update, in order to anticipate other agents’ actions). We extend the interactive particle filter to perform approximate belief update on BA-IPOMDPs. We present our findings on the multiagent Tiger problem.
Classification of Sparse Time Series via Supervised Matrix Factorization
Authors: Grabocka Josif , Nanopoulos Alexandros , Schmidt-Thieme Lars
Published: June 2022

Data sparsity is an emerging real-world problem observed in a various domains ranging from sensor networks to medical diagnosis. Consecutively, numerous machine learning methods were modeled to treat missing values. Nevertheless, sparsity, defined as missing segments, has not been thoroughly investigated in the context of time series classification. We propose a novel principle for classifying time series, which in contrast to existing approaches, avoids reconstructing the missing segments in time series and operates solely on the observed ones. Based on the proposed principle, we develop a method that prevents adding noise that incurs during the reconstruction of the original time series. Ourmethod adapts supervised matrix factorization by projecting time series in a latent space through stochasticlearning. Furthermore the projected data is built in a supervised fashion via a logistic regression. Abundant experiments on a large collection of 37 data sets demonstrate the superiority of our method, which in the majority of cases outperforms a set of baselines that do not follow our proposed principle.
Colorization by Matrix Completion
Authors: Wang Shusen , Zhang Zhihua
Published: June 2022

Given a monochrome image and some manually labeled pixels, the colorization problem is a computer-assisted process of adding color to the monochrome image. This paper proposes a novel approach to the colorization problem by formulating it as a matrix completion problem. In particular, taking a monochrome image and parts of the color pixels (labels) as inputs, we develop a robust colorization model and resort to an augmented Lagrange multiplier algorithm for solving the model. Our approach is based on the fact that a matrix can be represented as a low-rank matrix plus a sparse matrix. Our approach is effective because it is able to handle the potential noises in the monochrome image and outliers in the labels. To improve the performance of our method, we further incorporate a so-called local-color-consistency idea into our method. Empirical results on real data sets are encouraging.
Weighted Clustering
Authors: Ackerman Margareta , Ben-David Shai , Brânzei Simina , Loker David
Published: June 2022

We investigate a natural generalization of the classical clustering problem, considering clustering tasks in which different instances may have different weights. We conduct the first extensive theoretical analysis on the influence of weighted data on standard clustering algorithms in both the partitional and hierarchical settings, characterizing the conditions under which algorithms react to weights. Extending a recent framework for clustering algorithm selection, we propose intuitive properties that would allow users to choose between clustering algorithms in the weighted setting and classify algorithms accordingly.
Hierarchical Modeling with Tensor Inputs
Authors: Zhu Yada , He Jingrui , Lawrence Rick
Published: June 2022

In many real applications, the input data are naturally expressed as tensors, such as virtual metrology in semiconductor manufacturing, face recognition and gait recognition in computer vision, etc. In this paper, we propose a general optimization framework for dealing with tensor inputs. Most existing methods for supervised tensor learning use only rank-one weight tensors in the linear model and cannot readily incorporate domain knowledge. In our framework, we obtain the weight tensor in a hierarchical way — we first approximate it by a low-rank tensor, and then estimate the low-rank approximation using the prior knowledge from various sources, e.g., different domain experts. This is motivated by wafer quality prediction in semiconductor manufacturing. Furthermore, we propose an effective algorithm named H-MOTE for solving this framework, which is guaranteed to converge. The time complexity of H-MOTE is linear with respect to the number of examples as well as the size of the weight tensor. Experimental results show the superiority of H-MOTE over state-of-the-art techniques on both synthetic and real data sets.
Rule Ensemble Learning Using Hierarchical Kernels in Structured Output Spaces
Authors: Nair Naveen , Saha Amrita , Ramakrishnan Ganesh , Krishnaswamy Shonali
Published: June 2022

The goal in Rule Ensemble Learning (REL) is simultaneous discovery of a small set of simple rules and their optimal weights that lead to good generalization. Rules are assumed to be conjunctions of basic propositions concerning the values taken by the input features. It has been shown that rule ensembles for classification can be learnt optimally and efficiently using hierarchical kernel learning approaches that explore the exponentially large space of conjunctions by exploiting its hierarchical structure. The regularizer employed penalizes large features and thereby selects a small set of short features. In this paper, we generalize the rule ensemble learning using hierarchical kernels (RELHKL) framework to multi class structured output spaces. We build on the StructSVM model for sequence prediction problems and employ a ρ-norm hierarchical regularizer for observation features and a conventional 2-norm regularizer for state transition features. The exponentially large feature space is searched using an active set algorithm and the exponentially large set of constraints are handled using a cutting plane algorithm. The approach can be easily extended to other structured output problems. We perform experiments on activity recognition datasets which are prone to noise, sparseness and skewness. We demonstrate that our approach outperforms other approaches.
TD-DeltaPi: A Model-Free Algorithm for Efficient Exploration
Authors: Da Silva Bruno , Barto Andrew
Published: June 2022

We study the problem of finding efficient exploration policies for the case in which an agent is momentarily not concerned with exploiting, and instead tries to compute a policy for later use. We first formally define the Optimal Exploration Problem as one of sequential sampling and show that its solutions correspond to paths of minimum expected length in the space of policies. We derive a model-free, local linear approximation to such solutions and use it to construct efficient exploration policies. We compare our model-free approach to other exploration techniques, including one with the best known PAC bounds, and show that ours is both based on a well-defined optimization problem and empirically efficient.
Multi-Label Learning by Exploiting Label Correlations Locally
Authors: Huang Sheng-Jun , Zhou Zhi-Hua
Published: June 2022

It is well known that exploiting label correlations is important for multi-label learning. Existing approaches typically exploit label correlations globally, by assuming that the label correlations are shared by all the instances. In real-world tasks, however, different instances may share different label correlations, and few correlations are globally applicable. In this paper, we propose the ML-LOC approach which allows label correlations to be exploited locally. To encode the local influence of label correlations, we derive a LOC code to enhance the feature representation of each instance. The global discrimination fitting and local correlation sensitivity are incorporated into a unified framework, and an alternating solution is developed for the optimization. Experimental results on a number of image, text and gene data sets validate the effectiveness of our approach.
Pairwise Exemplar Clustering
Authors: Yang Yingzhen , Chu Xinqi , Liang Feng , Huang Thomas
Published: June 2022

Exemplar-based clustering methods have been extensively shown to be effective in many clustering problems. They adaptively determine the number of clusters and hold the appealing advantage of not requiring the estimation of latent parameters, which is otherwise difficult in case of complicated parametric model and high dimensionality of the data. However, modeling arbitrary underlying distribution of the data is still difficult for existing exemplar-based clustering methods. We present Pairwise Exemplar Clustering (PEC) to alleviate this problem by modeling the underlying cluster distributions more accurately with non-parametric kernel density estimation. Interpreting the clusters as classes from a supervised learning perspective, we search for an optimal partition of the data that balances two quantities: 1 the misclassification rate of the data partition for separating the clusters; 2 the sum of within-cluster dissimilarities for controlling the cluster size. The broadly used kernel form of cut turns out to be a special case of our formulation. Moreover, we optimize the corresponding objective function by a new efficient algorithm for message computation in a pairwise MRF. Experimental results on synthetic and real data demonstrate the effectiveness of our method.
Learning SVM Classifiers with Indefinite Kernels
Authors: Gu Suicheng , Guo Yuhong
Published: June 2022

Recently, training support vector machines with indefinite kernels has attracted great attention in the machine learning community. In this paper, we tackle this problem by formulating a joint optimization model over SVM classifications and kernel principal component analysis. We first reformulate the kernel principal component analysis as a general kernel transformation framework, and then incorporate it into the SVM classification to formulate a joint optimization model. The proposed model has the advantage of making consistent kernel transformations over training and test samples. It can be used for both binary classification and multi-class classification problems. Our experimental results on both synthetic data sets and real world data sets show the proposed model can significantly outperform related approaches.
Compressed Least-Squares Regression on Sparse Spaces
Authors: Milani Fard Mahdi , Grinberg Yuri , Pineau Joelle , Precup Doina
Published: June 2022

Recent advances in the area of compressed sensing suggest that it is possible to reconstruct high-dimensional sparse signals from a small number of random projections. Domains in which the sparsity assumption is applicable also offer many interesting large-scale machine learning prediction tasks. It is therefore important to study the effect of random projections as a dimensionality reduction method under such sparsity assumptions. In this paper we develop the bias-variance analysis of a least-squares regression estimator in compressed spaces when random projections are applied on sparse input signals. Leveraging the sparsity assumption, we are able to work with arbitrary non i.i.d. sampling strategies and derive a worst-case bound on the entire space. Empirical results on synthetic and real-world datasets shows how the choice of the projection size affects the performance of regression on compressed spaces, and highlights a range of problems where the method is useful.