bluuebunny/crossref_metadata_2025 huggingface dataset:
Optical Properties of 1D ZnO/MoS\(_2\) Heterostructures Synthesized by Thermal Evaporation Method
Authors: Luu Thi Ha Thu , Do Quang Trung , Tran Manh Trung , Nguyen Tu , Nguyen Duy Hung , Pham Thanh Huy
Published: June 2022

MoS2 material attracts a great attention from researchers due to its graphene-like structure and the bandgap difference between its hexagonal monolayer and bulks. Recently, ZnO/MoS2 heterostructures have been received significant interest due to their distinguished properties. In this study, one-dimensional ZnO and ZnO/MoS2 heterostructures were successfully synthesized by a thermal co-evaporation method. Compare with ZnO, the band-to-band emission of ZnO/MoS2 heterostructures establishes a “blueshift” towards a shorter wavelength. It could be explained by the lattice strain in ZnO/MoS2 heterostructures due to the difference of primitive cell of ZnO and MoS2. Additionally, the quench in the visible region of the PL spectrum of ZnO/MoS2 heterostructures also explains the reduction of the defect in ZnO due to the presence of MoS2.
Determination of subsurface lineaments in the Hoang Sa islands using enhanced methods of gravity total horizontal gradient
Authors: Pham Thanh Luan , Oksum Erdinc , Kafadar Ozkan , Phan Trong Trinh , Nguyen Viet Dat , Vo Thanh Quynh , Le Thi Sang
Published: June 2022

The Hoang Sa islands, located in the northern part of the East Vietnam Sea, lack information on geological structural boundaries. The gravity data from the global marine gravity model were analyzed using the enhanced total horizontal gradient methods to delineate geological structures that appear as lineaments on the transformed gravity anomaly maps of the area. Before applying the techniques to gravity data of the Hoang Sa islands, their effectiveness was demonstrated by comparing them with the results from the total horizontal gradient method for a synthetic model. Applying the enhanced horizontal gradient methods shows that most of the lineaments identified in the Hoang Sa islands are trending in the WSW-ENE, NE-SW, E-W, WNW-ESE and NNW-SSE directions. These results provide a better understanding of the subsurface structural features of the islands.
Strategi Viralisasi Konten Dakwah di Kanal Youtube
Authors: Mazaya Vyki
Published: June 2022

Currently some da'wah activists are creating da'wah content channels on Youtube and other social media. This study aims to explore the strategy of viralization of the preaching content of Ustadz Abdul Somad, Gus Miftah and Gus Nur on the Youtube channel. Using a descriptive qualitative approach and through observation and documentation techniques, the results of this study reveal that the da'wah video content created by those three Islamic preachers that are considered viral have characteristics, first, the da'wah messages contain a conflict dimension that are likely to attract the curiosity of the audience. Second, technically the da'wah video content has an interesting title, offers a novelty theme, containing humor, answers religious issues clearly, and has quality da'wah material. Third, the da'i must be credible and have a da'wah strategy that is in accordance with the mad'u's needs.
O empresário brasileiro: um estudo comparativo
Authors: BIRCHAL SÉRGIO DE OLIVEIRA
Published: June 2022

Resumo Este trabalho examina o debate sobre o empreendedor brasileiro, com o objetivo de desafiar a visão amplamente aceita que as experiências paulista e carioca, no que diz respeito à formação da classe empresarial, pode ser estendida a outras partes do Brasil. O trabalho compara o contexto social e as fontes de capital dos empresários mineiro, paulista e carioca no século XIX. A análise revela que os empresários mineiros vieram da elite local constituída basicamente por brasileiros e que as fontes de capital (principalmente agricultura e comércio) utilizadas pelos empresários mineiros eram de natureza distinta, sendo o café e o comércio de importação e exportação menos relevantes.
Produto, capital e taxa de lucro - países industrializados e América Latina
Authors: KILSZTAJN SAMUEL
Published: June 2022

Resumo Este artigo compara a participação nos lucros, a taxa de produção de capital e a taxa de lucro de cinco países industrializados e cinco da América Latina. Usando dados das Estatísticas das Contas Nacionais da ONU e da Tabela Mundial da Penn (Marcos 5.6) em dólares americanos e PPP (paridade do poder de compra), o artigo também discute o nível de formação bruta de capital e a relação entre taxa de lucros, produtividade e salários.
Padrões de especialização, hiatos tecnológicos e crescimento com restrições de divisas
Authors: CANUTO OTAVIANO
Published: June 2022

Resumo Este artigo apresenta um modelo Norte-Sul sobre a interação entre comércio exterior e crescimento restrito de câmbio, modelo em que os padrões de especialização associados às estruturas de produção das economias desempenham um papel crucial. O crescimento econômico é condicionado pela dinâmica setorial da inovação e imitação, bem como pelas elasticidades-renda e elasticidade-preço.
DESENVOLVIMENTO DE APLICATIVO PARA APOIAR O RACIOCÍNIO DIAGNÓSTICO DO ENFERMEIRO NO CUIDADO AO PACIENTE CIRÚRGICO
Authors: Paschoal Jociani Gonçalves , Fioresi Mirian , Bringuente Maria Edla de Oliveira , Morais Sheila Coelho Ramalho Vasconcelos , Primo Cândida Caniçali , Furieri Lorena Barros
Published: June 2022

RESUMO Objetivo: desenvolver um aplicativo móvel para apoiar o raciocínio diagnóstico do enfermeiro no cuidado ao paciente cirúrgico Método: trata-se de uma pesquisa aplicada com produção tecnológica realizada num hospital da região metropolitana do Espírito Santo no período de setembro de 2018 a dezembro de 2019. O estudo foi dividido em quatro etapas: elaboração de conteúdo teórico através da realização de revisão integrativa de literatura dos principais sinais e sintomas observados em pacientes no pós-operatório de cirurgias gastrointestinais, torácicas, de cabeça e pescoço, e mapeamento cruzado entre os sinais e sintomas encontrados na revisão de literatura e os diagnósticos de enfermagem da NANDA Internacional; desenvolvimento de 10 estudos de caso que simulam situações clínico-cirúrgicas elaborados de acordo com os diagnósticos de enfermagem mapeados conforme referencial de Lunney; avaliação dos estudos de caso por especialistas utilizando índice de validade de conteúdo; e construção do aplicativo em parceria com especialista em design gráfico pelo método do Design Centrado no Usuário. Resultados: o aplicativo “CuidarTech® Cirúrgico”, apresenta quatro opções de navegação: 1) “Processo de Enfermagem” 0 exibe conteúdos teóricos; 2) “Diagnósticos e ações de enfermagem” - com diagnósticos e intervenções de enfermagem para assistência a pacientes em período pós-operatório de cirurgias gastrointestinais, torácicas, de cabeça e pescoço; 3) “Estudos de caso” - traz 10 estudos validados para treinamento do raciocínio clínico; e 4) “Créditos” - equipe executora. Conclusão: o aplicativo é uma tecnologia inédita e constitui um suporte interativo na assistência e ensino do processo de enfermagem e do raciocínio clínico do enfermeiro.
SERIES CONSUMPTION AMONG YOUNG STUDENTS: NARRATIVES AND CLUES OF INDIVIDUATION
Authors: Pinheiro Leandro R.
Published: June 2022

ABSTRACT We seek to discuss the relationship between individuation processes and cultural consumption of series by young students from different levels of education in public institutions in the peripheries of Porto Alegre, Brazil. Contributions by Danilo Martuccelli and Martín-Barbero are the main references for this study, which was developed based on the application of questionnaires, discussion groups and interviews. Given the relationship between the increase in the consumption of series and the expansion of schooling, we identified that preferences comprise melodramatic narrative elements and reflective-identity configurations, inflecting social individuations towards a certain contextual inconsistency and the figuration of singularization.
TRINTA ANOS DE UM TEXTO-FETICHE: MARIANO FERNÁNDEZ ENGUITA E O TEMA DA PROLETARIZAÇÃO DO TRABALHO
Authors: Alves Wanderson Ferreira
Published: June 2022

RESUMO O objetivo deste artigo é analisar a concepção de proletarização do trabalho mobilizada pelo sociólogo Mariano Fernández Enguita, autor que, em certo momento, encontrou importante acolhida entre os pesquisadores brasileiros. O presente texto se apoia na sociologia do trabalho, particularmente remontando às questões teóricas e epistemológicas que integram o debate sobre a qualificação do trabalho. O texto delineia os aspectos básicos da tese da proletarização, busca identificar seus fundamentos e o quadro teórico que lhe dá sustentação, encontrando suas raízes profundas no romantismo econômico do século XIX. Conclui apresentando uma síntese do que foi tratado e apontando a importância de uma compreensão mais abrangente da qualificação do trabalho, notadamente aquela aberta pela perspectiva de Pierre Naville.
Manutenção predial com ênfase em sistemas hidrossanitários: revisão sistemática da literatura
Authors: Bottega Gabriela Schneider de Sousa , Roscoff Nathália , Pilz Silvio Edmundo , Dalcanton Francieli , Costella Marcelo Fabiano
Published: June 2022

RESUMO Com a crescente demanda de edificações, torna-se cada vez mais necessário que as atividades de conservação e manutenção dos sistemas prediais sejam especificadas e cumpridas adequadamente. Esta pesquisa é uma revisão sistemática da literatura sobre a importância, as consequências e o desempenho quanto a manutenção nos Sistemas Prediais Hidráulicos e Sanitários (SPHS). As buscas pelos artigos foram realizadas no Portal de Periódicos CAPES abrangendo o período de 2007 a 2017. Foram encontrados 252 artigos, mas, com base no tema, apenas 26 foram selecionados. O conteúdo dos artigos foi discutido em cinco questões de pesquisa, abordando as manifestações patológicas nos SPHS, a manutenção predial e as ferramentas de avaliação de desempenho e suscetibilidade a riscos desses sistemas. Observou-se que as manifestações patológicas nos SPHS são frequentes, principalmente vazamentos, os quais geram custos financeiros, emocionais e de tempo para os usuários. Além disso, muitas dessas manifestações patológicas estão relacionadas à ausência ou falha na manutenção. Porém, a manutenção é muito influenciada por fatores como tipo de material, custos e expectativa dos usuários, sendo necessários programas eficientes de manutenção e equipes treinadas a fim de garantir o bom desempenho da edificação e seus sistemas. Ademais, algumas ferramentas têm sido desenvolvidas a fim de avaliar o desempenho dos sistemas prediais e a suscetibilidade a riscos, auxiliando na tomada de decisões de manutenções e concepção de projetos.
A Centralized Multi-Agent Negotiation Approach to Collaborative Air Traffic Resource Management Planning
Authors: Jarvis Peter , Wolfe Shawn , Enomoto Francis , Nado Robert , Sierhuis Maarten
Published: June 2022

Demand and capacity imbalances in the US national airspace are resolved using traffic management initiatives designed, in current operations, with little collaboration with the airspace users. NASA and its partners have developed a new collaborative concept of operations that requires the users and airspace service provider to work together to choose initiatives that better satisfy the business needs of the users while also ensuring safety to the same standard as today. In this paper, we describe an approach to implementing this concept through a software negotiation framework underpinned by technology developed in the artificial intelligence community. We describe our exploration of peer-to-peer negotiation and how the number of conversation threads and the time sensitivity of offer acceptance led us to a centralized approach. The centralized approach uses hill climbing to evaluate airport slot allocations from a user perspective and a linear programming solver to seek solutions compatible across the user community. Our experiments with full sized problems identify the potential operational benefits as well as limitations, and where future research needs to be focused.
Sketch Worksheets: A Sketch-Based Educational Software System
Authors: Yin Panrong , Forbus Kenneth , Usher Jeffrey , Sageman Brad , Jee Benjamin
Published: June 2022

Intelligent tutoring systems and learning environments can provide important benefits for education, but few have been developed for heavily spatial domains. One bottleneck has been the lack of rich models of visual and conceptual processing in sketch understanding, so that what students draw can be interpreted in a human-like way. This paper describes Sketch Worksheets, a form of sketch-based educational software that mimics aspects of pencil and paper worksheets commonly found in classrooms, but provides on-the-spot feedback and support for richer off-line assessments. The basic architecture of sketch worksheets is described, including an authoring environment that allows non-developers to create them and a coach that uses analogy to compare student and instructor sketches as a means to provide feedback. A pilot experiment where sketch worksheets were used successfully in a college geoscience class in Fall 2009 is summarized to show the potential of the idea.
Designing the Finch: Creating a Robot Aligned to Computer Science Concepts
Authors: Lauwers Tom , Nourbakhsh Illah
Published: June 2022

We present a new robot platform, the Finch, that was designed to align with the learning goals and concepts taught in introductory computer science courses. The Finch was developed in the context of the CSbots program, the goal of which is to improve retention and learning in computer science courses through the use of robots and other physically embodied hardware. This paper concentrates on design constraints that were determined in earlier CSbots studies and how those constraints were instantiated by the Finch. We also present some preliminary results from pilot studies in which Finch robots were used in CS1 and CS2 classes
Teaching Artificial Intelligence and Robotics Via Games
Authors: Wong Daniel , Zink Ryan , Koenig Sven
Published: June 2022

The Department of Computer Science at the University of Southern California recently created two new degree programs, namely a Bachelor's Program in Computer Science (Games) and a Master's Program in Computer Science (Game Development). In this paper, we discuss two projects that use games as motivator. First, the Computer Games in the Classroom Project develops stand-alone projects on standard artificial intelligence topics that use video-game technology to motivate the students but do not require the students to use game engines. Second, the Pinball Project develops the necessary hardware and software to enable students to learn concepts from robotics by developing games on actual pinball machines.
Hybrid Tractable Classes of Binary Quantified Constraint Satisfaction Problems
Authors: Gao Jian , Yin Minghao , Zhou Junping
Published: June 2022

In this paper, we investigate the hybrid tractability of binary Quantified Constraint Satisfaction Problems (QCSPs). First, a basic tractable class of binary QCSPs is identified by using the broken-triangle property. In this class, the variable ordering for the broken-triangle property must be same as that in the prefix of the QCSP. Second, we break this restriction to allow that existentially quantified variables can be shifted within or out of their blocks, and thus identify some novel tractable classes by introducing the broken-angle property. Finally, we identify a more generalized tractable class, i.e., the min-of-max extendable class for QCSPs.
Generating Explanations for Complex Biomedical Queries
Authors: Öztok Umut , Erdem Esra
Published: June 2022

We present a computational method to generate explanations to answers of complex queries over biomedical ontologies and databases, using the high-level representation and efficient automated reasoners of Answer Set Programming. We show the applicability of our approach with some queries related to drug discovery over PHARMGKB, DRUGBANK, BIOGRID, CTD and SIDER.
Controlling Selection Bias in Causal Inference
Authors: Bareinboim Elias , Pearl Judea
Published: June 2022

Selection bias, caused by preferential exclusion of units (or samples) from the data, is a major obstacle to valid causal inferences, for it cannot be removed or even detected by randomized experiments. This paper highlights several graphical and algebraic methods capable of mitigating and sometimes eliminating this bias. These nonparametric methods generalize and improve previously reported results, and identify the type of knowledge that need to be available for reasoning in the presence of selection bias
Discovering Latent Strategies
Authors: Xu Xiaoxi
Published: June 2022

Strategy mining is a new area of research about discovering strategies in decision-making. In this paper, we formulate the strategy-mining problem as a clustering problem, called the latent-strategy problem. In a latent-strategy problem, a corpus of data instances is given, each of which is represented by a set of features and a decision label. The inherent dependency of the decision label on the features is governed by a latent strategy. The objective is to find clusters, each of which contains data instances governed by the same strategy. Existing clustering algorithms are inappropriate to cluster dependency because they either assume feature independency (e.g., K-means) or only consider the co-occurrence of features without explicitly modeling the special dependency of the decision label on other features (e.g., Latent Dirichlet Allocation (LDA)). In this paper, we present a baseline unsupervised learning algorithm for dependency clustering. Our model-based clustering algorithm iterates between an assignment step and a minimization step to learn a mixture of decision tree models that represent latent strategies. Similar to the Expectation Maximization algorithm, our algorithm is grounded in the statistical learning theory. Different from other clustering algorithms, our algorithm is irrelevant-feature resistant and its learned clusters (modeled by decision trees) are strongly interpretable and predictive. We systematically evaluate our algorithm using a common law dataset comprised of actual cases. Experimental results show that our algorithm significantly outperforms K-means and LDA on clustering dependency.
Extending the Applications of Recent Real-Time Heuristic Search
Authors: Huntley Daniel , Bulitko Vadim
Published: June 2022

Real-time heuristic search algorithms that precompute search space-specific databases have demonstrated exceptional performance in video-game pathfinding. We discuss the first steps towards extending these algorithms to other search spaces that also benefit from the real-time property. We present our initial progress in characterizing the performance of current algorithms based on the features of a search space, and discuss future directions of this research.
Using Conditional Random Fields to Exploit Token Structure and Labels for Accurate Semantic Annotation
Authors: Goel Aman , Knoblock Craig , Lerman Kristina
Published: June 2022

Automatic semantic annotation of structured data enables unsupervised integration of data from heterogeneous sources but is difficult to perform accurately due to the presence of many numeric fields and proper-noun fields that do not allow reference-based approaches and the absence of natural language text that prevents the use of language-based approaches. In addition, several of these semantic types have multiple heterogeneous representations, while sharing syntactic structure with other types. In this work, we propose a new approach to use conditional random fields (CRFs) to perform semantic annotation of structured data that takes advantage of the structure and labels of the tokens for higher accuracy of field labeling, while still allowing the use of exact inference techniques. We compare our approach with a linear-CRF based model that only labels fields and also with a regular-expression based approach.
Active Dual Collaborative Filtering with Both Item and Attribute Feedback
Authors: He Luheng , Liu Nathan , Yang Qiang
Published: June 2022

The new user problem (aka user cold start) is very common in online recommender systems. Active collaborative ﬁltering (active CF) tries to solve this problem by intelligently soliciting user feedback in order to build an initial user proﬁle with minimal costs. Existing methods only query the user for feedback on items, while users can have preferences over items as well as certain item attributes. In this paper, we extend active CF via user feedback on both items and attributes. For example, when making movie recommendations, the system can ask users for not only their favorite movies, but also attributes such as genres, actors, etc. We design a uniﬁed active CF framework for incorporating both item and attribute feedback based on the random walk model. We test the active CF algorithm on real-world movie recommendation data sets to demonstrate that appropriately querying for both item and feature feedback can signiﬁcantly reduce the overall user effort measured in terms of number of queries. We show that we can achieve much better recommendation quality as compared to traditional active CF methods that support only item feedback.
Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure
Authors: Vyas Nisarg , Farringdon Jonathan , Andre David , Stivoric John
Published: June 2022

In this paper we provide insight into the BodyMedia FIT® armband system— a wearable multi-sensor technology that achieves the goals of continuous physiological monitoring (especially energy expenditure estimation) and weight management using machine learning and data modeling methods. This system has been commercially available since 2001 and more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges that arise in applying machine learning techniques to the health care domain and present various solutions utilized in the armband system. We demonstrate how machine learning and multi-sensor data fusion techniques are critical to the system’s success.
Learning a Skill-Teaching Curriculum with Dynamic Bayes Nets
Authors: Green Derek , Walsh Thomas , Cohen Paul , Chang Yu-Han
Published: June 2022

We propose an intelligent tutoring system that constructs a curriculum of hints and problems in order to teach a student skills with a rich dependency structure. We provide a template for building a multi-layered Dynamic Bayes Net to model this problem and describe how to learn the parameters of the model from data. Planning with the DBN then produces a teaching policy for the given domain. We test this end-to-end curriculum design system in two human-subject studies in the areas of finite field arithmetic and artificial language and show this method performs on par with hand-tuned expert policies.
Abductive Inference for Combat: Using SCARE-S2 to Find High-Value Targets in Afghanistan
Authors: Shakarian Paulo , Nagel Margo , Schuetzle Brittany , Subrahmanian V. S.
Published: June 2022

Recently, geospatial abduction was introduced by the authors in (Shakarian, Subrahmanian, and Sapino 2010) as a way to infer unobserved geographic phenomena from a set of known observations and constraints between the two. In this paper, we introduce the SCARE- S2 software tool which applies geospatial abduction to the environment of Afghanistan. Unlike previous work, where we looked for small weapon caches supporting local attacks, here we look for insurgent high-value targets (HVT’s), supporting insurgent operations in two provinces. These HVT’s include the locations of insurgent leaders and major supply depots. Applying this method of inference to Afghanistan introduces several practical issues not addressed in previous work. Namely, we are conducting inference in a much larger area (24, 940 sq km as compared to 675 sq km in previous work), on more varied terrain, and must consider the influence of many local tribes. We address all of these problems and evaluate our software on 6 months of real-world counter-insurgency data. We show that we are able to abduce regions of a relatively small area (on average, under 100 sq km and each containing, on aver- age, 4.8 villages) that are more dense with HVT’s (35× more than the overall area considered).
Modeling Player Retention in Madden NFL 11
Authors: Weber Ben , John Michael , Mateas Michael , Jhala Arnav
Published: June 2022

Video games are increasingly producing huge datasets available for analysis resulting from players engaging in interactive environments. These datasets enable investigation of individual player behavior at a massive scale, which can lead to reduced production costs and improved player retention. We present an approach for modeling player retention in Madden NFL 11, a commercial football game. Our approach encodes gameplay patterns of specific players as feature vectors and models player retention as a regression problem. By building an accurate model of player retention, we are able to identify which gameplay elements are most influential in maintaining active players. The outcome of our tool is recommendations which will be used to influence the design of future titles in the Madden NFL series.
Accelerating the Discovery of Data Quality Rules: A Case Study
Authors: Yeh Peter , Puri Colin , Wagman Mark , Easo Ajay
Published: June 2022

Poor quality data is a growing and costly problem that af- fects many enterprises across all aspects of their business ranging from operational efficiency to revenue protection. In this paper, we present an application – Data Quality Rules Accelerator (DQRA) – that accelerates Data Quality (DQ) efforts (e.g. data profiling and cleansing) by automatically discovering DQ rules for detecting inconsistencies in data. We then present two evaluations. The first evaluation compares DQRA to existing solutions; and shows that DQRA either outperformed or achieved performance comparable with these solutions on metrics such as precision, recall, and runtime. The second evaluation is a case study where DQRA was piloted at a large utilities company to improve data quality as part of a legacy migration effort. DQRA was able to discover rules that detected data inconsistencies directly impacting revenue and operational efficiency. Moreover, DQRA was able to significantly reduce the amount of effort required to develop these rules compared to the state of the practice. Finally, we describe ongoing efforts to deploy DQRA.
A Real-Time Decision Support System for High Cost Oil-Well Drilling Operations
Authors: Gundersen Odd , Sørmo Frode , Aamod Agnar , Pål Skalle Pål Skalle
Published: June 2022

In this paper we present DrillEdge - a commercial and award winning software system that monitors oil-well drilling operations in order to reduce non-productive time (NPT). DrillEdge utilizes case-based reasoning with temporal representations on streaming real-time data, pattern matching and agent systems to predict problems and give advice on how to mitigate the problems. The methods utilized, the architecture, the GUI and development cost in addition to two case studies are documented.
Mechanix: A Sketch-Based Tutoring System for Statics Courses
Authors: Valentine Stephanie , Vides Francisco , Lucchese George , Turner David , Kim Hong-hoe , Li Wenzhe , Linsey Julie , Hammond Tracy
Published: June 2022

Introductory engineering courses within large universities often have annual enrollments which can reach up to a thousand students. It is very challenging to achieve differentiated instruction in classrooms with class sizes and student diversity of such great magnitude. Professors can only assess whether students have mastered a concept by using multiple choice questions, while detailed homework assignments, such as planar truss diagrams, are rarely assigned because professors and teaching assistants would be too overburdened with grading to return assignments with valuable feedback in a timely manner. In this paper, we introduce Mechanix, a sketch-based deployed tutoring system for engineering students enrolled in statics courses. Our system not only allows students to enter planar truss and free body diagrams into the system just as they would with pencil and paper, but our system checks the student’s work against a hand-drawn answer entered by the instructor, and then returns immediate and detailed feedback to the student. Students are allowed to correct any errors in their work and resubmit until the entire content is correct and thus all of the objectives are learned. Since Mechanix facilitates the grading and feedback processes, instructors are now able to assign free response questions, increasing teacher’s knowledge of student comprehension. Furthermore, the iterative correction process allows students to learn during a test, rather than simply displaying memorized information.
Using a Critic to Promote Less Popular Candidates in a People-to-People Recommender System
Authors: Krzywicki A. , Wobcke W. , Cai X. , Bain M. , Mahidadia A. , Compton P. , Kim Y.
Published: June 2022

This paper shows how to improve the recommendations of an interaction-based collaborative filtering (IBCF) recommender used in online dating. Previous work has shown that IBCF works well in this domain, although it tends to rank popular candidates highly, which leads to these users receiving a large number of contacts. We address this problem by using a Decision Tree model as a “critic” to re-rank the candidates generated by IBCF, effectively promoting less popular candidates. This method was first evaluated on historical data from a large online dating site and then trialled live on the same site by providing recommendations to a large number of users throughout a 9 week period. The live trial confirmed the consistency of the analysis on historical data and the ability of the method to generate suitable candidates over an extended period. Our recommendations gave higher success rates than those for a control group made with a baseline recommender.
QuickPup: A Heuristic Backtracking Algorithm for the Partner Units Configuration Problem
Authors: Teppan Erich , Friedrich Gerhard , Falkner Andreas
Published: June 2022

The Partner Units Problem (PUP) constitutes a challenging real-world configuration problem with diverse application domains such as railway safety, security monitoring, electrical engineering, or distributed systems. Although using the latest problem-solving methods including Constraint Programming, SAT Solving, Integer Programming, and Answer Set Programming, current methods fail to generate solutions for midsized real-world problems in acceptable time. This paper presents the QuickPup algorithm based on backtrack search combined with smart variable orderings and restarts. QuickPup outperforms the available methods by orders of magnitude and thus makes it possible to automatically solve problems which couldn’t be solved without human expertise before. Furthermore, the runtimes of QuickPup are typically below one second for real-world problem instances.
PAC Optimal Planning for Invasive Species Management: Improved Exploration for Reinforcement Learning from Simulator-Defined MDPs
Authors: Dietterich Thomas , Alkaee Taleghan Majid , Crowley Mark
Published: June 2022

Often the most practical way to define a Markov Decision Process (MDP) is as a simulator that, given a state and an action, produces a resulting state and immediate reward sampled from the corresponding distributions. Simulators in natural resource management can be very expensive to execute, so that the time required to solve such MDPs is dominated by the number of calls to the simulator. This paper presents an algorithm, DDV, that combines improved confidence intervals on the Q values (as in interval estimation) with a novel upper bound on the discounted state occupancy probabilities to intelligently choose state-action pairs to explore. We prove that this algorithm terminates with a policy whose value is within epsilon of the optimal policy (with probability 1-delta) after making only polynomially-many calls to the simulator. Experiments on one benchmark MDP and on an MDP for invasive species management show very large reductions in the number of simulator calls required.
Concurrent Inference Graphs
Authors: Schlegel Daniel
Published: June 2022

Since their popularity began to rise in the mid-2000s there has been significant growth in the number of multi-core and multi-processor computers available. Knowledge representation systems using logical inference have been slow to embrace this new technology. We present the concept of inference graphs, a natural deduction inference system which scales well on multi-core and multi-processor machines. Inference graphs enhance propositional graphs by treating propositional nodes as tasks which can be scheduled to operate upon messages sent between nodes via the arcs that already exist as part of the propositional graph representation. The use of scheduling heuristics within a prioritized message passing architecture allows inference graphs to perform very well in forward, backward, bi-directional, and focused reasoning. Tests demonstrate the usefulness of our scheduling heuristics, and show significant speedup in both best case and worst case inference scenarios as the number of processors increases.
EAAI-13 Preface
Authors: Brown Laura , Kauchak David
Published: June 2022

The Symposium on Educational Advances in Artificial Intelligence (EAAI-13) seeks to advance the AAAI goal of improving the teaching and training of AI practitioners. The symposium provides a venue for both researchers and educators to discuss pedagogical issues and share resources related to teaching AI and using AI in education across a variety of curricular levels (K-12 through postgraduate training).
Advice Provision in Multiple Prospect Selection Problems
Authors: Azaria Amos , Kraus Sarit
Published: June 2022

When humans face a broad spectrum of topics, where each topic consists of several options, they usually make a decision on each topic separately. Usually, a person will perform better by making a global decision, however, taking all consequences into account is extremely difficult. We present a novel computational method for advice-generation in an environment where people need to decide among multiple selection problems. This method is based on the prospect theory and uses machine learning techniques. We graphically present this advice to the users and compare it with an advice which encourages the users to always select the option with a higher expected outcome. We show that our method outperforms the expected outcome approach in terms of user happiness and satisfaction.
Locate the Hate: Detecting Tweets against Blacks
Authors: Kwok Irene , Wang Yuzhou
Published: June 2022

Although the social medium Twitter grants users freedom of speech, its instantaneous nature and retweeting features also amplify hate speech. Because Twitter has a sizeable black constituency, racist tweets against blacks are especially detrimental in the Twitter community, though this effect may not be obvious against a backdrop of half a billion tweets a day.1 We apply a supervised machine learning approach, employing inexpensively acquired labeled data from diverse Twitter accounts to learn a binary classifier for the labels “racist” and “nonracist.” The classifier has a 76% average accuracy on individual tweets, suggesting that with further improvements, our work can contribute data on the sources of anti-black hate speech.
Robot Motion Planning with Dynamics as Hybrid Search
Authors: Plaku Erion
Published: June 2022

This paper presents a framework for motion planning with dynamics as hybrid search over the continuous space of feasible motions and the discrete space of a low-dimensional workspace decomposition. Each step of the hybrid search consists of expanding a frontier of regions in the discrete space using cost heuristics as guide followed by sampling-based motion planning to expand a tree of feasible motions in the continuous space to reach the frontier. The approach is geared towards robots with many degrees-of-freedom (DOFs), nonlinear dynamics, and nonholonomic constraints, which make it difficult to follow discrete-search paths to the goal, and hence require a tight coupling of motion planning and discrete search. Comparisons to related work show significant computational speedups.
A Simple, but NP-Hard, Motion Planning Problem
Authors: Erickson Lawrence , LaValle Steven
Published: June 2022

Determining the existence of a collision-free path between two points is one of the most fundamental questions in robotics.  However, in situations where crossing an obstacle is costly but not impossible, it may be more appropriate to ask for the path that crosses the fewest obstacles.  This may arise in both autonomous outdoor navigation (where the obstacles are rough but not completely impassable terrain) or indoor navigation (where the obstacles are doors that can be opened if necessary).  This problem, the minimum constraint removal problem, is at least as hard as the underlying path existence problem.  In this paper, we demonstrate that the minimum constraint removal problem is NP-hard for navigation in the plane even when the obstacles are all convex polygons, a case where the path existence problem is very easy.
Towards Cohesive Anomaly Mining
Authors: Xiong Yun , Zhu Yangyong , Yu Philip , Pei Jian
Published: June 2022

In some applications, such as bioinformatics, social network analysis, and computational criminology, it is desirable to find compact clusters formed by a (very) small portion of objects in a large data set. Since such clusters are comprised of a small number of objects, they are extraordinary and anomalous with respect to the entire data set. This specific type of clustering task cannot be solved well by the conventional clustering methods since generally those methods try to assign most of the data objects into clusters. In this paper, we model this novel and application-inspired task as the problem of mining cohesive anomalies. We propose a general framework and a principled approach to tackle the problem. The experimental results on both synthetic and real data sets verify the effectiveness and efficiency of our approach.
Improving WalkSAT for Random k-Satisfiability Problem with k > 3
Authors: Cai Shaowei , Su Kaile , Luo Chuan
Published: June 2022

Stochastic local search (SLS) algorithms are well known for their ability to efficiently find models of random instances of the Boolean satisfiablity (SAT) problem. One of the most famous SLS algorithms for SAT is WalkSAT, which is an initial algorithm that has wide influence among modern SLS algorithms. Recently, there has been increasing interest in WalkSAT, due to the discovery of its great power on large random 3-SAT instances. However, the performance of WalkSAT on random $k$-SAT instances with $k>3$ lags far behind. Indeed, there have been few works in improving SLS algorithms for such instances. This work takes a large step towards this direction. We propose a novel concept namely $multilevel$ $make$. Based on this concept, we design a scoring function called $linear$ $make$, which is utilized to break ties in WalkSAT, leading to a new algorithm called WalkSAT$lm$. Our experimental results on random 5-SAT and 7-SAT instances show that WalkSAT$lm$ improves WalkSAT by orders of magnitudes. Moreover, WalkSAT$lm$ significantly outperforms state-of-the-art SLS solvers on random 5-SAT instances, while competes well on random 7-SAT ones. Additionally, WalkSAT$lm$ performs very well on random instances from SAT Challenge 2012, indicating its robustness.
Rank Aggregation via Low-Rank and Structured-Sparse Decomposition
Authors: Pan Yan , Lai Hanjiang , Liu Cong , Tang Yong , Yan Shuicheng
Published: June 2022

Rank aggregation, which combines multiple individual rank lists toobtain a better one, is a fundamental technique in various applications such as meta-search and recommendation systems. Most existing rank aggregation methods blindly combine multiple rank lists with possibly considerable noises, which often degrades their performances. In this paper, we propose a new model for robust rank aggregation (RRA) via matrix learning, which recovers a latent rank list from the possibly incomplete and noisy input rank lists. In our model, we construct a pairwise comparison matrix to encode the order information in each input rank list. Based on our observations, each comparison matrix can be naturally decomposed into a shared low-rank matrix, combined with a deviation error matrix which is the sum of a column-sparse matrix and a row-sparse one. The latent rank list can be easily extracted from the learned low-rank matrix. The optimization formulation of RRA has an element-wise multiplication operator to handle missing values, a symmetric constraint on the noise structure, and a factorization trick to restrict the maximum rank of the low-rank matrix. To solve this challenging optimization problem, we propose a novel procedure based on the Augmented Lagrangian Multiplier scheme. We conduct extensive experiments on meta-search and collaborative filtering benchmark datasets. The results show that the proposed RRA has superior performance gain over several state-of-the-art algorithms for rank aggregation.
An Extended GHKM Algorithm for Inducing Lambda-SCFG
Authors: Li Peng , Liu Yang , Sun Maosong
Published: June 2022

Semantic parsing, which aims at mapping a natural language (NL) sentence into its formal meaning representation (e.g., logical form), has received increasing attention in recent years. While synchronous context-free grammar (SCFG) augmented with lambda calculus (lambda-SCFG) provides an effective mechanism for semantic parsing, how to learn such lambda-SCFG rules still remains a challenge because of the difficulty in determining the correspondence between NL sentences and logical forms. To alleviate this structural divergence problem, we extend the GHKM algorithm, which is a state-of-the-art algorithm for learning synchronous grammars in statistical machine translation, to induce lambda-SCFG from pairs of NL sentences and logical forms. By treating logical forms as trees, we reformulate the theory behind GHKM that gives formal semantics to the alignment between NL words and logical form tokens. Experiments on the GEOQUERY dataset show that our semantic parser achieves an F-measure of 90.2%, the best result published to date.
Lazy Gaussian Process Committee for Real-Time Online Regression
Authors: Xiao Han , Eckert Claudia
Published: June 2022

A significant problem of Gaussian process (GP) is its unfavorable scaling with a large amount of data. To overcome this issue, we present a novel GP approximation scheme for online regression. Our model is based on a combination of multiple GPs with random hyperparameters. The model is trained by incrementally allocating new examples to a selected subset of GPs. The selection is carried out efficiently by optimizing a submodular function. Experiments on real-world data sets showed that our method outperforms existing online GP regression methods in both accuracy and efficiency. The applicability of the proposed method is demonstrated by the mouse-trajectory prediction in an Internet banking scenario.
Model-Lite Case-Based Planning
Authors: Zhuo Hankz Hankui , Nguyen Tuan , Kambhampati Subbarao
Published: June 2022

There is increasing awareness in the planning community that depending on complete models impedes the applicability of planning technology in many real world domains where the burden of specifying complete domain models is too high. In this paper, we consider a novel solution for this challenge that combines generative planning on incomplete domain models with a library of plan cases that are known to be correct. While this was arguably the original motivation for case-based planning, most existing case-based planners assume (and depend on) from-scratch planners that work on complete domain models. In contrast, our approach views the plan generated with respect to the incomplete model as a ``skeletal plan'' and augments it with directed mining of plan fragments from library cases. We will present the details of our approach and present an empirical evaluation of our method in comparison to a state-of-the-art case-based planner that depends on complete domain models.
RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models
Authors: Noessner Jan , Niepert Mathias , Stuckenschmidt Heiner
Published: June 2022

RockIt is a maximum a-posteriori (MAP) query engine for statistical relational models. MAP inference in graphical models is an optimization problem which can be compiled to integer linear programs (ILPs).We describe several advances in translating MAP queries to ILP instances and present the novel meta-algorithm cutting plane aggregation (CPA). CPA exploits local context-specific symmetries and bundles up sets of linear constraints. The resulting counting constraints lead to more compact ILPs and make the symmetry of the ground model more explicit to state-of-the-art ILP solvers. Moreover, RockIt parallelizes most parts of the MAP inference pipeline taking advantage of ubiquitous shared-memory multi-core architectures. We report on extensive experiments with Markov logic network (MLN) benchmarks showing that RockIt outperforms the state-of-the-art systems Alchemy, Markov TheBeast, and Tuffy both in terms of efficiency and quality of results.
Reciprocal Hash Tables for Nearest Neighbor Search
Authors: Liu Xianglong , He Junfeng , Lang Bo
Published: June 2022

Recent years have witnessed the success of hashingtechniques in approximate nearest neighbor search. Inpractice, multiple hash tables are usually employed toretrieve more desired results from all hit buckets ofeach table. However, there are rare works studying theunified approach to constructing multiple informativehash tables except the widely used random way. In thispaper, we regard the table construction as a selectionproblem over a set of candidate hash functions. Withthe graph representation of the function set, we proposean efficient solution that sequentially applies normal-ized dominant set to finding the most informative andindependent hash functions for each table. To furtherreduce the redundancy between tables, we explore thereciprocal hash tables in a boosting manner, where thehash function graph is updated with high weights em-phasized on the misclassified neighbor pairs of previoushash tables. The construction method is general andcompatible with different types of hashing algorithmsusing different feature spaces and/or parameter settings.Extensive experiments on two large-scale benchmarksdemonstrate that the proposed method outperforms bothnaive construction method and state-of-the-art hashingalgorithms, with up to 65.93% accuracy gains.