bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis of polycalciumphenylsiloxane and composites based on the skeleton of a sea urchin with the resulting polymer
Authors: Shapkin Nikolai Р. , Khalchenko Irina G. , Kim Kvan H. , Papynov Evgeniy K. , Fedorets Alexander N. , Maslova Natalia V. , Balanov Michael I.
Published: June 2022

In this work, we obtained polycalciumphenylsiloxane (PCPS) by the interaction of calcium bis (acetylacetonate) with polyphenylsiloxane. The first method consisted in boiling the starting reagents in toluene for several hours; the second was as follows: the mixture of the starting reagents was preliminarily treated mechanically in a ball mill, followed by boiling in toluene for several hours. Two fractions, soluble and insoluble, were isolated in both syntheses. They were investigated using IR, NMR spectroscopy, thermogravimetric analysis, and gel permeation chromatography. It was shown that the insoluble fraction is a mixture of calcium acetylacetonate and polyphenylsiloxane with a small calcium ion content. The soluble fraction is polycalciumphenylsiloxane. The yield of the soluble fraction is higher in the second synthesis method. The polymers obtained in the first and second synthesis methods are similar in composition and structure, which was confirmed by physicochemical methods. Next, the skeleton of the sea urchin Strongylocentrotus intermedius was treated with a soluble fraction in toluene. In this case, a composite was obtained, which was treated with 2–3% hydrochloric acid and then calcined at a temperature of 600 °C. At each stage, the composition of the composites was investigated using elemental analysis and IR spectroscopy. The morphology was investigated using scanning electron microscopy.
A agenda de médio prazo no Brasil e o futuro da Petrobrás
Authors: RODRIGUES ADRIANO PIRES , GIAMBIAGI FABIO
Published: June 2022

Resumo Este artigo discute a possível privatização da Petrobrás. Argumenta-se que, apesar das razões econômicas que recomendam sua venda, provavelmente os pré-requisitos para ela não existirão no curto prazo. O objetivo do artigo é contribuir para a discussão desta proposta, através da organização dos argumentos relacionados à privatização - o “modelo YPF”. A alternativa é o “modelo PDVSA”, abrindo o setor de petróleo, mantendo a Petrobrás uma empresa estatal. Por fim, são sugeridas algumas questões para reflexão e apresentados alguns dados associados ao valor real da empresa.
Reflections on death and dying
Authors: Koenig Anne Marise , Teixeira Luciana de Almeida Silva
Published: June 2022

Abstract This theoretical essay aims to propose reflections on death and dying through the prism of possible antagonisms, illness and human occupations involved in this process. The antagonisms are put up for discussion obeying the sense of mutual opposition between life and death, birth and death, as well as between their prediction and their negation. Illness is discussed from the perspective of Laplantine's dynamic and ontological models, opening up reflections on the meaning of illness for the patient, who is often expropriated from their dying process. It also addresses how death is currently pushed behind the scenes of social life. Finally, the occupations of death based on the “principles of good death” are approached from the perspective of occupational therapy, discussing funeral preparations and rites developed by the deceased, their social circle and by health professionals, religious people, funeral agents and cemetery workers. We understand that death is a social process.
The Tekkotsu "Crew": Teaching Robot Programming at a Higher Level
Authors: Touretzky David , Tira-Thompson Ethan
Published: June 2022

The Tekkotsu "crew" is a collection of interacting software components designed to relieve a programmer of much of the burden of specifying low-level robot behaviors. Using this abstract approach to robot programming we can teach beginning roboticists to develop interesting robot applications with relatively little effort.
Maximum Entropy Context Models for Ranking Biographical Answers to Open-Domain Definition Questions
Authors: Figueroa Alejandro , Atkinson John
Published: June 2022

In the context of question-answering systems, there are several strategies for scoring candidate answers to definition queries including centroid vectors, bi-term and context language models. These techniques use only positive examples (i.e., descriptions) when building their models. In this work, a maximum entropy based extension is proposed for context language models so as to account for regularities across non-descriptions mined from web-snippets. Experiments show that this extension outperforms other strategies increasing the precision of the top five ranked answers by more than 5%. Results suggest that web-snippets are a cost-efficient source of non-descriptions, and that some relationships extracted from dependency trees are effective to mine for candidate answer sentences.
Creative Introspection and Knowledge Acquisition
Authors: Veale Tony , Li Guofu
Published: June 2022

Introspection is a question-led process in which one builds on what one already knows to explore what is possible and plausible. In creative introspection, whether in art or in science, framing the right question is as important as finding the right answer. Presupposition-laden questions are themselves a source of knowledge, and in this paper we show how widely-held beliefs about the world can be dynamically acquired by harvesting such questions from the Web. We show how metaphorical reasoning can be modeled as an introspective process, one that builds on questions harvested from the Web to pose further speculative questions and queries. Metaphor is much more than a knowledge-hungry rhetorical device: it is a conceptual lever that allows a system to extend its model of the world.
Continual Planning with Sensing for Web Service Composition
Authors: Kaldeli Eirini , Lazovik Alexander , Aiello Marco
Published: June 2022

Web Service (WS) domains constitute an application field where automated planning can significantly contribute towards achieving customisable and adaptable compositions. Following the vision of using domain-independent planning and declarative complex goals to generate compositions based on atomic service descriptions, we apply a planning framework based on Constraint Satisfaction techniques to a domain consisting of WSs with diverse functionalities. One of the key requirements of such domains is the ability to address the incomplete knowledge problem, as well as recovering from failures that may occur during execution. We propose an algorithm for interleaving planning, monitoring and execution, where continual planning via altering the CSP is performed, under the light of the feedback acquired at runtime. The system is evaluated against a number of scenarios including real WSs, demonstrating the leverage of situations that can be effectively tackled with respect to previous approaches.
Fast Query Recommendation by Search
Authors: Jiang Qixia , Sun Maosong
Published: June 2022

Query recommendation can not only effectively facilitate users to obtain their desired information but alsoincrease ads’ click-through rates. This paper presentsa general and highly efficient method for query recommendation. Given query sessions, we automatically generate many similar and dissimilar query-pairs as the prior knowledge. Then we learn a transformation from the prior knowledge to move similar queries closer such that similar queries tend to have similar hash values.This is formulated as minimizing the empirical error on the prior knowledge while maximizing the gap between the data and some partition hyperplanes randomly generated in advance. In the recommendation stage, we search queries that have similar hash values to the given query, rank the found queries and return the top K queries as the recommendation result. All the experimental results demonstrate that our method achieves encouraging results in terms of efficiency and recommendation performance.
Integrating Community Question and Answer Archives
Authors: Wei Wei , Cong Gao , Li Xiaoli , Ng See-Kiong , Li Guohui
Published: June 2022

Question and answer pairs in Community Question Answering (CQA) services are organized into hierarchical structures or taxonomies to facilitate users to find the answers for their questions conveniently. We observe that different CQA services have their own knowledge focus and used different taxonomies to organize their question and answer pairs in their archives. As there are no simple semantic mappings between the taxonomies of the CQA services, the integration of CQA services is a challenging task. The existing approaches on integrating taxonomies ignore the hierarchical structures of the source taxonomy. In this paper, we propose a novel approach that is capable of incorporating the parent-child and sibling information in the hierarchical structures of the source taxonomy for accurate taxonomy integration. Our experimental results with real world CQA data demonstrate that the proposed method significantly outperforms state-of-the-art methods.
Generating True Relevance Labels in Chinese Search Engine Using Clickthrough Data
Authors: Song Hengjie , Miao Chunyan , Shen Zhiqi
Published: June 2022

In current search engines, ranking functions are learned from a large number of labeled  pairs in which the labels are assigned by human judges, describing how well the URLs match the different queries. However in commercial search engines, collecting high quality labels is time-consuming and labor-intensive. To tackle this issue, this paper studies how to produce the true relevance labels for   pairs using clickthrough data. By analyzing the correlations between query frequency, true relevance labels and users’ behaviors, we demonstrate that the users who search the queries with similar frequency have similar search intents and behavioral characteristics. Based on such properties, we propose an efficient discriminative parameter estimation in a multiple instance learning algorithm (MIL) to automatically produce true relevance labels for   pairs. Furthermore, we test our approach using a set of real world data extracted from a Chinese commercial search engine. Experimental results not only validate the effectiveness of the proposed approach, but also indicate that our approach is more likely to agree with the aggregation of the multiple judgments when strong disagreements exist in the panel of judges. In the event that the panel of judges is consensus, our approach provides more accurate automatic label results. In contrast with other models, our approach effectively improves the correlation between automatic labels and manual labels.
Learning to Suggest Questions in Online Forums
Authors: Zhou Tom , Lin Chin-Yew , King Irwin , Lyu Michael R. , Song Young-In , Cao Yunbo
Published: June 2022

Online forums contain interactive and semantically related discussions on various questions. Extracted question-answer archive is invaluable knowledge, which can be used to improve Question Answering services. In this paper, we address the problem of Question Suggestion, which targets at suggesting questions that are semantically related to a queried question. Existing bag-of-words approaches suffer from the shortcoming that they could not bridge the lexical chasm between semantically related questions. Therefore, we present a new framework to suggest questions, and propose the Topicenhanced Translation-based Language Model (TopicTRLM) which fuses both the lexical and latent semantic knowledge. Extensive experiments have been conducted with a large real world data set. Experimental results indicate our approach is very effective and outperforms other popular methods in several metrics.
Temporal Dynamics of User Interests in Tagging Systems
Authors: Yin Dawei , Hong Liangjie , Xue Zhenzhen , Davison Brian
Published: June 2022

Collaborative tagging systems are now deployed extensivelyto help users share and organize resources.Tag prediction and recommendation systems generallymodel user behavior as research has shown that accuracycan be significantly improved by modeling users’preferences. However, these preferences are usuallytreated as constant over time, neglecting the temporalfactor within users’ interests. On the other hand, littleis known about how this factor may influence predictionin social bookmarking systems. In this paper, weinvestigate the temporal dynamics of user interests intagging systems and propose a user-tag-specific temporalinterests model for tracking users’ interests overtime. Additionally, we analyze the phenomenon of topicswitches in social bookmarking systems, showing that atemporal interests model can benefit from the integrationof topic switch detection and that temporal characteristicsof social tagging systems are different fromtraditional concept drift problems. We conduct experimentson three public datasets, demonstrating the importanceof personalization and user-tag specializationin tagging systems. Experimental results show that ourmethod can outperform state-of-the-art tag predictionalgorithms. We also incorporate our model within existingcontent-based methods yielding significant improvementsin performance.
The Stock Sonar — Sentiment Analysis of Stocks Based on a Hybrid Approach
Authors: Feldman Ronen , Rosenfeld Benjamin , Bar-Haim Roy , Fresko Moshe
Published: June 2022

The Stock Sonar (TSS) is a stock sentiment analysis application based on a novel hybrid approach. While previous work focused on document level sentiment classification, or extracted only generic sentiment at the phrase level, TSS integrates sentiment dictionaries, phrase-level compositional patterns, and predicate-level semantic events. TSS generates precise in-text sentiment tagging as well as sentiment-oriented event summaries for a given stock, which are also aggregated into sentiment scores. Hence, TSS allows investors to get the essence of thousands of articles every day and may help them to make timely, informed trading decisions. The extracted sentiment is also shown to improve the accu- racy of an existing document-level sentiment classifier.
Statistical Anomaly Detection for Train Fleets
Authors: Holst Anders , Bohlin Markus , Ekman Jan , Sellin Ola , Lindström Björn , Larsen Stefan
Published: June 2022

We have developed a method for statistical anomaly detection which has been deployed in a tool for condition monitoring of train fleets. The tool is currently used by several railway operators over the world to inspect and visualize the occurrence of “event messages” generated on the trains. The anomaly detection component helps the operators to quickly find significant deviations from normal behavior and to detect early indications for possible problems. The savings in maintenance costs comes mainly from avoiding costly breakdowns, and have been estimated to several million Euros per year for the tool. In the long run, it is expected that maintenance costs can be reduced with between 5 and 10% by using the tool.
Cost-Sensitive Risk Stratification in the Diagnosis of Heart Disease
Authors: Uguroglu Selen , Carbonell Jaime , Doyle Mark , Biederman Robert
Published: June 2022

We investigate machine learning methods for diagnostic screening of heart disease. Coronary heart disease is the leading cause of death in the US, causing more deaths than all types of cancers combined. Early diagnosis of heart disease in women is harder than it is in men and typically requires the administration of several clinical tests on the patient. Most risk stratification methods aggregate the results of such tests, including the risky, invasive procedures that cannot be administered on all patients. In this paper, our goal is to identify patients who are under high-risk of having heart disease and related adverse events, using a minimal number of diagnostic tests, especially less invasive ones. The low frequency of patients with severe heart disease in the dataset is challenging for most conventional machine learning methods. To overcome this problem, we develop and apply a cost-sensitive k nearest neighbor algorithm. Our contributions are two fold: First, we compare the predictive value of several diagnostic procedures for heart disease, including electrocardiography, angiography, radionuclide perfusion and conclude that in womens heart disease, certain combinations of noninvasive techniques are more predictive than some of the widely used invasive procedures. Then, we evaluate held out data and achieve an AUROC over 0.70, signifying valuable clinical utility, using only the least costly and least invasive tests.
Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar
Authors: Sheldon Daniel , Farnsworth Andrew , Irvine Jed , Van Doren Benjamin , Webb Kevin , Dietterich Thomas , Kelling Steve
Published: June 2022

Archived data from the WSR-88D network of weather radars in the US hold detailed information about the continent-scale migratory movements of birds over the last 20 years. However, significant technical challenges must be overcome to understand this information and harness its potential for science and conservation. We present an approximate Bayesian inference algorithm to reconstruct the velocity fields of birds migrating in the vicinity of a radar station. This is part of a larger project to quantify bird migration at large scales using weather radar data.
Model Predictive Control with Uncertainty in Human Driven Systems
Authors: Styler Alexander , Nourbakhsh Illah
Published: June 2022

Human driven systems present a unique optimization challenge for robot control. Generally, operators of these systems behave rationally given environmental factors and desired goals. However, information available to subsystem controllers is often incomplete, and the operator becomes more difficult to model without this input information. In this work we present a data-driven, nonparametric model to capture both expectation and uncertainty of the upcoming duty for a subsystem controller. This model is a modified k-nearest neighbor regressor used to generate weighted samples from a distribution of upcoming duty, which are then exploited to generate an optimal control. We test the model on a simulated heterogeneous energy pack manager in an Electric Vehicle operated by a human driver. For this domain, upcoming load on the energy pack strongly affects the optimal use and charging strategy of the pack. Given incomplete information, there is a natural uncertainty in upcoming duty due to traffic, destination, signage, and other factors. We test against a dataset of real driving data gathered from volunteers, and compare the results other models and the optimal upper bound.
Large Landscape Conservation — Synthetic and Real-World Datasets
Authors: Dilkina Bistra , Lai Katherine , Le Bras Ronan , Xue Yexiang , Gomes Carla , Sabharwal Ashish , Suter Jordan , McKelvey Kevin , Schwartz Michael , Montgomery Claire
Published: June 2022

Biodiversity underpins ecosystem goods and services and hence protecting it is key to achieving sustainability. However, the persistence of many species is threatened by habitat loss and fragmentation due to human land use and climate change. Conservation efforts are implemented under very limited economic resources, and therefore designing scalable, cost-efficient and systematic approaches for conservation planning is an important and challenging computational task. In particular, preserving landscape connectivity between good habitat has become a key conservation priority in recent years. We give an overview of landscape connectivity conservation and some of the underlying graph-theoretic optimization problems. We present a synthetic generator capable of creating families of randomized structured problems, capturing the essential features of real-world instances but allowing for a thorough typical-case performance evaluation of different solution methods. We also present two large-scale real-world datasets, including economic data on land cost, and species data for grizzly bears, wolverines and lynx.
Artificial Conversational Companions
Authors: Danilava Sviatlana
Published: June 2022

This document describes the problem statement, the methodological framework, the current state of the work and the expected contribution of my doctoral dissertation. The main focus of my dissertation is long-term interaction with an Artificial Conversational Companion in the context of conversation training for second language acquisition. I use a data-driven approach and conversation analysis methods to build computational models for long-term interaction as a meaningful activity. I work on the concept of interaction profiles for human-agent interaction. The resulting models will be integrated in an AIML-based chatbot that helps to practice conversation in a foreign language.
Understanding Descriptions of Visual Scenes Using Graph Grammars
Authors: Bauer Daniel
Published: June 2022

Automatic generation of 3D scenes from descriptions has applications in communication, education, and entertainment, but requires deep understanding of the input text. I propose thesis work on language understanding using graph-based meaning representations that can be decomposed into primitive spatial relations. The techniques used for analyzing text and transforming it into a scene representation are based on context-free graph grammars. The thesis develops methods for semantic parsing with graphs, acquisition of graph grammars, and satisfaction of spatial and world-knowledge constraints during parsing.
Multi-Strategy Learning of Robotic Behaviours via Qualitative Reasoning
Authors: Wiley Timothy
Published: June 2022

When given a task, an autonomous agent must plan a series of actions to perform in order to complete the goal. In robotics, planners face additional challenges as the domain is typically large (even infinite) continuous, noisy, and non- deterministic. Typically stochastic planning has been used to solve robotic control tasks. Such planners have been very successful in their various domains. The downside to such approaches is that the models and planners are highly specialised to a single control task. To change the control task, requires developing an entirely new planner. The research in my thesis focuses on the problem of specialisation in continuous, noisy and non-deterministic robotic domains by developing a more generic planner. It builds on previous research in the area, specifically using the technique of Multi-Strategy Learning. Qualitative Modelling and Qualitative Reasoning is used to provide the generality, from which specific, Quantitative controllers can be quickly learnt. The resulting system is applied to a real world robotic platform for rough terrain navigation.
Efficient Algorithms for Strong Local Consistencies in Constraint Satisfaction Problems
Authors: Paparrizou Anastasia
Published: June 2022

The existing complete methods for solving Constraint Satisfaction Problems (CSPs) are usually based on a combination of exhaustive search and constraint propagation techniques for the reduction of the search space. Such propagation techniques are the local consistency algorithms. Arc Consistency (AC) and Generalized Arc Consistency (GAC) are the most widely studied local consistencies that are predominantly used in constraint solvers. However, many stronger local consistencies than (G)AC have been proposed, even recently, but have been rather overlooked due to their prohibitive cost. This research proposes efficient algorithms for strong consistencies for both binary and non-binary constraints that can be easily adopted by standard CP solvers. Experimental results have so far demonstrated that the proposed algorithms are quite competitive and often more efficient than state-of-the-art methods, being orders of magnitude faster on various problem classes.
Planning with Multi-Valued Landmarks
Authors: Zhang Lei , Wang Chong-Jun , Wu Jun , Liu Meilin , Xie Jun-Yuan
Published: June 2022

Landmark heuristics are perhaps the most accurate current known admissible heuristics for optimal planning. A disjunctive action landmark can be seen a form of at-least-one constraint on the actions it contains. In many domains, some critical propositions have to be established for a number of times.Propositional landmarks are too weak to express this kind of constraints.In this paper, we propose to generalize landmarks to multi-valued landmarks to represent the more general cardinality constraints. We present a class of local multi-valued landmarks that can be efficiently extracted from propositional landmarks.By encoding multi-valued landmarks into CNF formulas, we can also use SAT solvers to systematically extract multi-valued landmarks.Experiment evaluations show that multi-valued landmark based heuristics are more close to $h^*$ andcompete favorably with the state-of-the-art of admissible landmark heuristics on benchmark domains.
WordNet Based Multi-Way Concept Hierarchy Construction from Text Corpus
Authors: Tu Ding , Chen Ling , Chen Gencai
Published: June 2022

In this paper, we propose an approach to build a multi-way concept hierarchy from a text corpus, which is based on WordNet and multi-way hierarchical clustering. In addition, a new evaluation metric is presented, and our approach is compared with 4 kinds of existing methods on the Amazon Customer Review data set.
Graphical Model-Based Learning in High Dimensional Feature Spaces
Authors: Song Zhao , Zhu Yuke
Published: June 2022

Digital media tend to combine text and images to express richer information, especially on image hosting and online shopping websites. This trend presents a challenge in understanding the contents from different forms of information. Features representing visual information are usually sparse in high dimensional space, which makes the learning process intractable. In order to understand text and its related visual information, we present a new graphical model-based approach to discover more meaningful information in rich media. We extend the standard Latent Dirichlet Allocation (LDA) framework to learn in high dimensional feature spaces.
Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior
Authors: Kim Been , Chacha Caleb , Shah Julie
Published: June 2022

We aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time-critical domains, such as military field operations and disaster response. Deployment plans for these operations are frequently negotiated on-the-fly by teams of human planners. A human operator then translates the agreed upon plan into machine instructions for the robots. We present an algorithm that reduces this translation burden by inferring the final plan from a processed form of the human team's planning conversation. Our approach combines probabilistic generative modeling with logical plan validation used to compute a highly structured prior over possible plans. This hybrid approach enables us to overcome the challenge of performing inference over the large solution space with only a small amount of noisy data from the team planning session. We validate the algorithm through human subject experimentation and show we are able to infer a human team's final plan with 83% accuracy on average. We also describe a robot demonstration in which two people plan and execute a first-response collaborative task with a PR2 robot. To the best of our knowledge, this is the first work that integrates a logical planning technique within a generative model to perform plan inference.
A Topic-Based Coherence Model for Statistical Machine Translation
Authors: Xiong Deyi , Zhang Min
Published: June 2022

Coherence that ties sentences of a text into a meaningfully connected structure is of great importance to text generation and translation. In this paper, we propose a topic-based coherence model to produce coherence for document translation, in terms of the continuity of sentence topics in a text. We automatically extract a coherence chain for each source text to be translated. Based on the extracted source coherence chain, we adopt a maximum entropy classifier to predict the target coherence chain that defines a linear topic structure for the target document. The proposed topic-based coherence model then uses the predicted target coherence chain to help decoder select coherent word/phrase translations. Our experiments show that incorporating the topic-based coherence model into machine translation achieves substantial improvement over both the baseline and previous methods that integrate document topics rather than coherence chains into machine translation.
Multiagent Learning with a Noisy Global Reward Signal
Authors: Proper Scott , Tumer Kagan
Published: June 2022

Scaling multiagent reinforcement learning to domains with many agents is a complex problem. In particular, multiagent credit assignment becomes a key issue as the system size increases. Some multiagent systems suffer from a global reward signal that is very noisy or difficult to analyze. This makes deriving a learnable local reward signal very difficult. Difference rewards (a particular instance of reward shaping) have been used to alleviate this concern, but they remain difficult to compute in many domains. In this paper we present an approach to modeling the global reward using function approximation that allows the quick computation of local rewards. We demonstrate how this model can result in significant improvements in behavior for three congestion problems: a multiagent ``bar problem'', a complex simulation of the United States airspace, and a generic air traffic domain. We show how the model of the global reward may be either learned on- or off-line using either linear functions or neural networks. For the bar problem, we show an increase in reward of nearly 200% over learning using the global reward directly. For the air traffic problem, we show a decrease in costs of 25% over learning using the global reward directly.
m-Transportability: Transportability of a Causal Effect from Multiple Environments
Authors: Lee Sanghack , Honavar Vasant
Published: June 2022

We study m-transportability, a generalization of transportability, which offers a license to use causal information elicited from experiments and observations in m>=1 source environments to estimate a causal effect in a given targetenvironment. We provide a novel characterization of m-transportability that directly exploits the completeness of do-calculus to obtain the necessary and sufficient conditions for m-transportability. We provide an algorithm for deciding m-transportability that determines whether a causal relation is m-transportable; and if it is, produces a transport formula, that is, a recipe for estimating the desired causal effect by combining experimental information from m source environments with observational information from the target environment.
Supervised Nonnegative Tensor Factorization with Maximum-Margin Constraint
Authors: Wu Fei , Tan Xu , Yang Yi , Tao Dacheng , Tang Siliang , Zhuang Yueting
Published: June 2022

Non-negative tensor factorization (NTF) has attracted great attention in the machine learning community. In this paper, we extend traditional non-negative tensor factorization into a supervised discriminative decomposition, referred as Supervised Non-negative Tensor Factorization with Maximum-Margin Constraint(SNTFM2). SNTFM2 formulates the optimal discriminative factorization of non-negative tensorial data as a coupled least-squares optimization problem via a maximum-margin method. As a result, SNTFM2 not only faithfully approximates the tensorial data by additive combinations of the basis, but also obtains a strong generalization power to discriminative analysis (in particularfor classification in this paper). The experimental results show the superiority of our proposed model over state-of-the-art techniques on both toy and real world data sets.
Social Rankings in Human-Computer Committees
Authors: Bitan Moshe , Gal Ya’akov , Kraus Sarit , Dokow Elad , Azaria Amos
Published: June 2022

Despite committees and elections being widespread in thereal-world, the design of agents for operating in humancomputer committees has received far less attention than thetheoretical analysis of voting strategies. We address this gapby providing an agent design that outperforms other voters ingroups comprising both people and computer agents. In oursetting participants vote by simultaneously submitting a ranking over a set of candidates and the election system uses a social welfare rule to select a ranking that minimizes disagreements with participants’ votes. We ran an extensive studyin which hundreds of people participated in repeated votingrounds with other people as well as computer agents that differed in how they employ strategic reasoning in their votingbehavior. Our results show that over time, people learn todeviate from truthful voting strategies, and use heuristics toguide their play, such as repeating their vote from the previous round. We show that a computer agent using a bestresponse voting strategy was able to outperform people in thegame. Our study has implication for agent designers, highlighting the types of strategies that enable agents to succeedin committees comprising both human and computer participants. This is the ﬁrst work to study the role of computeragents in voting settings involving both human and agent participants.
Bundling Attacks in Judgment Aggregation
Authors: Alon Noga , Falik Dvir , Meir Reshef , Tennenholtz Moshe
Published: June 2022

We consider judgment aggregation over multiple independent issues, where the chairperson has her own opinion, and can try to bias the outcome by bundling several issues together. Since for each bundle judges must give a uniform answer on all issues, different partitions of the issues may result in an outcome that significantly differs from the "true," issue-wise, decision. We prove that the bundling problem faced by the chairperson, i.e. trying to bias the outcome towards her own opinion, is computationally difficult in the worst case. Then we study the probability that an effective bundling attack exists as the disparity between the opinions of the judges and the chair varies. We show that if every judge initially agrees with the chair on every issue with probability of at least 1/2, then there is almost always a bundling attack (i.e. a partition) where the opinion of the chair on all issues is approved. Moreover, such a partition can be found efficiently. In contrast, when the probability is lower than 1/2 then the chair cannot force her opinion using bundling even on a single issue.
Integrating Digital Pens in Breast Imaging for Instant Knowledge Acquisition
Authors: Sonntag Daniel , Weber Markus , Hammon Matthias , Cavallaro Alexander
Published: June 2022

Future radiology practices assume that the radiology reports should be uniform, comprehensive, and easily managed. This means that reports must be “readable” to humans and machines alike. In order to improve reporting practices in breast imaging, we allow the radiologist to write structured reports with a special pen on paper with an invisible dot pattern. In this way, we provide a knowledge acquisition system for printed mammography patient forms for the combined work with printed and digital documents. In this domain, printed documents cannot be easily replaced by computer systems because they contain free-form sketches and textual annotations, and the acceptance of traditional PC reporting tools is rather low among the doctors. This is due to the fact that current electronic reporting systems significantly add to the amount of time it takes to complete the reports. We describe our real-time digital paper application and focus on the use case study of our deployed application. We think that our results motivate the design and implementation of intuitive pen-based user interfaces for the medical reporting process and similar knowledge work domains. Our system imposes only minimal overhead on traditional form-filling processes and provides for a direct, ontology-based structuring of the user input for semantic search and retrieval applications, as well as other applied artificial intelligence scenarios which involve manual form-based data acquisition.
Clustering Hand-Drawn Sketches via Analogical Generalization
Authors: Chang Maria , Forbus Kenneth
Published: June 2022

One of the major challenges to building intelligent educational software is determining what kinds of feedback to give learners. Useful feedback makes use of models of domain-specific knowledge, especially models that are commonly held by potential students. To empirically determine what these models are, student data can be clustered to reveal common misconceptions or common problem-solving strategies. This paper describes how analogical retrieval and generalization can be used to cluster automatically analyzed hand-drawn sketches incorporating both spatial and conceptual information. We use this approach to cluster a corpus of hand-drawn student sketches to discover common answers. Common answer clusters can be used for the design of targeted feedback and for assessment.
Detecting the Moment of Snap in Real-World Football Videos
Authors: Mahasseni Behrooz , Chen Sheng , Fern Alan , Sinisa Sinisa
Published: June 2022

In recent years, there has been a great increase in the use of web services for the storage, annotation, and sharing of sports video by athletic teams. Most of these web services, however, do not provide enhanced functional- ities to their users that would enable, e.g., faster access to certain video moments, or reduce manual labor in video annotation. One such web service specializes in American football videos, supporting over 13,000 high school and college teams. Its users often need to fast- forward the video to certain moments of snap when the corresponding plays of the football game start. To our knowledge, this paper describes the first effort toward automating this enhanced functionality. Under a very tight running-time budget, our approach reliably detects the start of a play in an arbitrary football video with minimal assumptions about the scene, viewpoint, video resolution and shot quality. We face many challenges that are rarely addressed by a typical computer vision system, such as, e.g., a wide range of camera viewing angles and distances, and poor resolution and lighting conditions. Extensive empirical evaluation shows that our approach is very close to being usable in a real- world setting.
Scalable Models for Patterns of Life
Authors: Folsom-Kovarik J. T. , Schatz Sae , Jones Randolph , Bartlett Kathleen , Wray Robert
Published: June 2022

Patterns of life (POL) are emergent properties of com- plex social systems such as neighborhoods or even cities. Accurately modeling POL is not only an academic pursuit; complex training and analysis efforts rely upon these models. Computational POL models will benefit academic researchers by giving them new tools to generate and validate theories about complex sociocultural patterns. POL presents an interesting computational modeling challenge for research in many AI fields because of its complex, multi-level interaction. POL modeling differs from standard agent-based modeling in that not only individual behaviors, but also their collective emergent patterns, are targets of study. At the same time, the behavior of any single individual is potentially important.
Model AI Assignments 2013
Authors: Neller Todd , Lanctot Marc , Subramanian Devika , August Stephanie
Published: June 2022

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of three AI assignments from the 2013 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.
Training Autoencoders in Sparse Domain
Authors: Bhattacharya Biswarup , Ghosh Arna , Basu Roy Chowdhury Somnath
Published: June 2022

Autoencoders (AE) are essential in learning representation of large data (like images) for dimensionality reduction. Images are converted to sparse domain using transforms like Fast Fourier Transform (FFT) or Discrete Cosine Transform (DCT) where information that requires encoding is minimal. By optimally selecting the feature-rich frequencies, we are able to learn the latent vectors more robustly. We successfully show enhanced performance of autoencoders in sparse domain for images.
Comparing Reward Shaping, Visual Hints, and Curriculum Learning
Authors: Pocius Rey , Isele David , Roberts Mark , Aha David
Published: June 2022

Common approaches to learn complex tasks in reinforcement learning include reward shaping, environmental hints, or a curriculum. Yet few studies examine how they compare to each other, when one might prefer one approach, or how they may complement each other. As a first step in this direction, we compare reward shaping, hints, and curricula for a Deep RL agent in the game of Minecraft. We seek to answer whether reward shaping, visual hints, or the curricula have the most impact on performance, which we measure as the time to reach the target, the distance from the target, the cumulative reward, or the number of actions taken. Our analyses show that performance is most impacted by the curriculum used and visual hints; shaping had less impact. For similar navigation tasks, the results suggest that designing an effective curriculum and providing appropriate hints most improve the performance. Common approaches to learn complex tasks in reinforcement learning include reward shaping, environmental hints, or a curriculum, yet few studies examine how they compare to each other. We compare these approaches for a Deep RL agent in the game of Minecraft and show performance is most impacted by the curriculum used and visual hints; shaping had less impact. For similar navigation tasks, this suggests that designing an effective curriculum with hints most improve the performance.
Desescolarización Planetaria. La educación ecuatoriana en tiempos de pandemia.
Authors:   Universidad de Guayaquil, Reza Suárez Lilian Betty , Rodríguez Astudillo Edith Simona ,   Universidad de Guayaquil, Gómez Sigcha Jaqueline María ,   Universidad de Guayaquil, Calderón Reza Pamela Lilian
Published: June 2022

Lo normal de este apartado es dar a conocer el plan y los puntos de vista utilizados durante la redacción académica, previniendo cualquier cuestionamiento u error, adelantándose a las críticas, pero el presente texto es una crónica ecuatoriana, con relación a lo ocurrido en el planeta por la afectación de la pandemia COVID 19, respecto de la cual, en cuanto a salud ni los científicos sabían en un inicio cómo enfrentarla; y, en educación pues mucho menos, por lo antes mencionado este libro se fue construyendo conforme se iban dando los hechos, con actualizaciones semestrales.
STRATEGIC PLANNING AS A BASIS FOR SUSTAINABLE TOURISM DEVELOPMENT AND THE PRESERVATION OF CULTURAL HERITAGE IN THE REGIONS OF THE RUSSIAN FEDERATION
Authors:   Lomonosov Moscow State University, Kruzhalin Viktor I. , Menshikova Tatyana N. , Kruzhalin Kirill V. ,   Lomonosov Moscow State University
Published: June 2022

Sustainable development of tourism in the regions of the Russian Federation is a priority task of the state. The development of the tourism sector is of great socio-economic importance and involves efforts aimed at the tourist and recreational development of territories and increasing the share of the non-resource sector in the economy. For the development of domestic and inbound tourism, Russia has all the necessary resources and unique opportunities to meet the vital needs of the country's population in tourism and recreation. Sustainable tourism development is directly related to strategic planning, involving the development and systematic implementation of concepts, strategies, programs, roadmaps, and master plans for the development of territories. Most documents of strategic planning of the tourism sector include a cluster approach, which contributes to the sustainable development of tourism and is an effective tool for integrated management of territories and increasing the regions’ competitiveness. The management of territories aimed at the sustainable development of tourism should be systematically linked to the existing set of programs for the regions’ socio-economic development. The article presents an analysis of the documents of strategic planning of the tourism sector at the federal and regional levels, which revealed the achievements and shortcomings of their implementation. Particular attention is paid to the analysis of the federal target program ‘Development of domestic and inbound tourism in the Russian Federation (2011–2018)’ and its effectiveness, the regions’ activities in the development and implementation of a system of strategic planning documents, and the effectiveness of management of the sustainable tourism development.
THE LEVEL OF URBANIZATION OF A TERRITORY: THE CASE OF SWEDEN
Authors:   St. Petersburg State University of Economics, Balabeikina Olga A. , Kuznetsov Leonid M. ,   St. Petersburg State University of Economics, Poputneva Maria I. ,   St. Petersburg State University of Economics
Published: June 2022

Against a background of intensively growing urban population as well as expanding urban network and complicating agglomeration forms, the development of a system of objective indicators that would reflect the level of urbanization of a particular territory remains an unresolved problem. The same applies to the system of corresponding calculations. The paper presents a modified version of the authors’ methodology for determining a territory’s urbanization level through the example of Sweden, taking into consideration the individual national and regional characteristics of the country. The use of a scoring system based on indexing the specially selected indicators made it possible to identify the numerical limits of values necessary for grouping Swedish municipalities into super-urbanized, highly urbanized, and medium-urbanized. A comprehensive methodology proposed by the Russian economic geographer Ernest Faibusovich in 2018 was used as a methodological basis. However, it required significant changes to its components in relation to Sweden, which was due to the differences in the criteria applied in Russia and Sweden to categorize different types of settlements according to their population, due to the size of the territory of the country and its individual municipalities. We also introduced a new indicator for indexing – the share of urban areas in the total area of the region. So far, there have not been any studies presented in scientific literature that would make an attempt to determine the level of a territory’s urbanization based on the example of a foreign state. This work can serve as an impetus for further development of this direction in geo-urban studies, and the methodology used can be taken as an analog base when studying states where the principles of territorial planning are close to the Swedish ones.