bluuebunny/crossref_metadata_2025 huggingface dataset:
Crowdsourcing for Deployable Intelligent Systems
Authors: Lasecki Walter
Published: June 2022

My work aims to create a scaffold for deployable intelligent systems using crowdsourcing. Current approaches in artificial intelligence (AI) typically focus on solving a narrow subset of problems in a given space - for example: automatic speech recognition as part of a conversational assistant, machine vision as part of a question answering service for blind people, or planning as part of a home assistive robot. This approach is necessary to scope the solution, but often results in a large number of systems that are rarely deployed in real-world setting, but instead operate in toy domains, or in situations where other parts of the problem are assumed to be solved. The framework I have developed aims to use the crowd to help in two ways: (i) make it possible to use human intelligence to power parts of a system that automated approaches cannot or do not yet handle, and (ii) provide a means of enabling more effective deployable systems by people to provide reliable training data on-demand. This summary begins with a brief review of prior work, then outlines a number of different system that I have developed to demonstrate the capabilities of this framework, and concludes with future work to be completed as part of my thesis.
Sponsors
Authors: Hamilton Carol
Published: June 2022

List of sponsors of the AAAI-13 and IAAI-13 conferences.
Invited Talks
Authors: Hamilton Carol
Published: June 2022

List of invited talks (with author-supplied abstracts)  presented at AAAI-13, IAAI-13, and EAAI-13.
Online Group Feature Selection from Feature Streams
Authors: Li Haiguang , Wu Xindong , Li Zhao , Ding Wei
Published: June 2022

Standard feature selection algorithms deal with given candidate feature sets at the individual feature level. When features exhibit certain group structures, it is beneficial to conduct feature selection in a grouped manner. For high-dimensional features, it could be far more preferable to online generate and process features one at a time rather than wait for generating all features before learning begins. In this paper, we discuss a new and interesting problem of online group feature selection from feature streams at both the group and individual feature levels simultaneously from a feature stream. Extensive experiments on both real-world and synthetic datasets demonstrate the superiority of the proposed algorithm.
Subchloroplast Location Prediction via Homolog Knowledge Transfer and Feature Selection
Authors: Li Xiaomei , Wu Xindong , Wu Gongqing , Hu Xuegang
Published: June 2022

The accuracy of subchloroplast location prediction algorithms often depends on predictive and succinct features derived from proteins. Thus, to improve the prediction accuracy, this paper proposes a novel SubChloroplast location prediction method, called SCHOTS, which integrates the HOmolog knowledge Transfer and feature Selection methods. SCHOTS contains two stages. First, discriminating features are generated by WS-LCHI, a Weighted Gene Ontology (GO) transfer model based on bit-Score of proteins and Logarithmic transformation of CHI-square. Second, the more informative GO terms are selected from the features. Extensive studies conducted on three real datasets demonstrate that SCHOTS outperforms three off-the-shelf subchloroplast prediction methods.
The Role of Complex Network Dynamics in the Emergence of Multiagent Coalition
Authors: Hasan Mohammad , Raja Anita
Published: June 2022

Emergence of a single coalition among self-interested agents operating on large scale-free networks is a challenging task. Many existing approaches assume a given static network platform and do not use the network dynamics to facilitate the dynamics of agent interactions. In this paper, we present a decentralized game-theoretic approach to this single coalition emergence problem in which agent communications are limited only to their immediate neighbors. Our coalition emergence algorithm is based on the heuristic that agents benefit by forming coalitions with wealthy (higher payoff) and influential (higher accumulated coupling strength) neighbors. Simulation results show that the emergence phenomenon is significantly enhanced when the topological insights, such as increasing degree-heterogeneity and clustering, are embedded into the agent partner selection strategy.
Structure and Intractability of Optimal Multi-Robot Path Planning on Graphs
Authors: Yu Jingjin , LaValle Steven
Published: June 2022

In this paper, we study the structure and computational complexity of optimal multi-robot path planning problems on graphs. Our results encompass three formulations of the discrete multi-robot path planning problem, including a variant that allows synchronous rotations of robots along fully occupied, disjoint cycles on the graph. Allowing rotation of robots provides a more natural model for multi-robot path planning because robots can communicate.Our optimality objectives are to minimize the total arrival time, the makespan (last arrival time), and the total distance. On the structure side, we show that, in general, these objectives demonstrate a pairwise Pareto optimal structure and cannot be simultaneously optimized. On the computational complexity side, we extend previous work and show that, regardless of the underlying multi-robot path planning problem, these objectives are all intractable to compute. In particular, our NP-hardness proof for the time optimal versions, based on a minimal and direct reduction from the 3-satisfiability problem, shows that these problems remain NP-hard even when there are only two groups of robots (i.e. robots within each group are interchangeable).
Learning Collaborative Impedance-Based Robot Behaviors
Authors: Rozo Leonel , Calinon Sylvain , Caldwell Darwin , Jimenez Pablo , Torras Carme
Published: June 2022

Research in learning from demonstration has focused on transferring movements from humans to robots. However, a need is arising for robots that do not just replicate the task on their own, but that also interact with humans in a safe and natural way to accomplish tasks cooperatively. Robots with variable impedance capabilities opens the door to new challenging applications, where the learning algorithms must be extended by encapsulating force and vision information. In this paper we propose a framework to transfer impedance-based behaviors to a torque-controlled robot by kinesthetic teaching. The proposed model encodes the examples as a task-parameterized statistical dynamical system, where the robot impedance is shaped by estimating virtual stiffness matrices from the set of demonstrations. A collaborative assembly task is used as testbed. The results show that the model can be used to modify the robot impedance along task execution to facilitate the collaboration, by triggering stiff and compliant behaviors in an on-line manner to adapt to the user's actions.
Open-Loop Planning in Large-Scale Stochastic Domains
Authors: Weinstein Ari , Littman Michael
Published: June 2022

We focus on effective sample-based planning in the face of underactuation, high-dimensionality, drift, discrete system changes, and stochasticity. These are hallmark challenges for important problems, such as humanoid locomotion. In order to ensure broad applicability, we assume domain expertise is minimal and limited to a generative model. In order to make the method responsive, computational costs that scale linearly with the amount of samples taken from the generative model are required. We bring to bear a concrete method that satisfies all these requirements; it is a receding-horizon open-loop planner that employs cross-entropy optimization for policy construction. In simulation, we empirically demonstrate near-optimal decisions in a small domain and effective locomotion in several challenging humanoid control tasks.
GSMDPs for Multi-Robot Sequential Decision-Making
Authors: Messias João , Spaan Matthijs , Lima Pedro
Published: June 2022

Markov Decision Processes (MDPs) provide an extensive theoretical background for problems of decision-making under uncertainty. In order to maintain computational tractability, however, real-world problems are typically discretized in states and actions as well as in time. Assuming synchronous state transitions and actions at fixed rates may result in models which are not strictly Markovian, or where agents are forced to idle between actions, losing their ability to react to sudden changes in the environment. In this work, we explore the application of Generalized Semi-Markov Decision Processes (GSMDPs) to a realistic multi-robot scenario. A case study will be presented in the domain of cooperative robotics, where real-time reactivity must be preserved, and synchronous discrete-time approaches are therefore sub-optimal. This case study is tested on a team of real robots, and also in realistic simulation. By allowing asynchronous events to be modeled over continuous time, the GSMDP approach is shown to provide greater solution quality than its discrete-time counterparts, while still being approximately solvable by existing methods.
Multi-Target Detection and Recognition by UAVs Using Online POMDPs
Authors: Carvalho Chanel Caroline , Teichteil-Königsbuch Florent , Lesire Charles
Published: June 2022

This paper tackles high-level decision-making techniques for robotic missions, which involve both active sensing and symbolic goal reaching, under uncertain probabilistic environments and strong time constraints. Our case study is a POMDP model of an online multi-target detection and recognition mission by an autonomous UAV. The POMDP model of the multi-target detection and recognition problem is generated online from a list of areas of interest, which are automatically extracted at the beginning of the flight from a coarse-grained high altitude observation of the scene. The POMDP observation model relies on a statistical abstraction of an image processing algorithm's output used to detect targets. As the POMDP problem cannot be known and thus optimized before the beginning of the flight, our main contribution is an "optimize-while-execute" algorithmic framework: it drives a POMDP sub-planner to optimize and execute the POMDP policy in parallel under action duration constraints. We present new results from real outdoor flights and SAIL simulations, which highlight both the benefits of using POMDPs in multi-target detection and recognition missions, and of our "optimize-while-execute" paradigm.
A Cyclic Weighted Median Method for L1 Low-Rank Matrix Factorization with Missing Entries
Authors: Meng Deyu , Xu Zongben , Zhang Lei , Zhao Ji
Published: June 2022

A challenging problem in machine learning, information retrieval and computer vision research is how to recover a low-rank representation of the given data in the presence of outliers and missing entries. The L1-norm low-rank matrix factorization (LRMF) has been a popular approach to solving this problem. However, L1-norm LRMF is difficult to achieve due to its non-convexity and non-smoothness, and existing methods are often inefficient and fail to converge to a desired solution. In this paper we propose a novel cyclic weighted median (CWM) method, which is intrinsically a coordinate decent algorithm, for L1-norm LRMF. The CWM method minimizes the objective by solving a sequence of scalar minimization sub-problems, each of which is convex and can be easily solved by the weighted median filter. The extensive experimental results validate that the CWM method outperforms state-of-the-arts in terms of both accuracy and computational efficiency.
Reasoning about Saturated Conditional Independence Under Uncertainty: Axioms, Algorithms, and Levesque's Situations to the Rescue
Authors: Link Sebastian
Published: June 2022

The implication problem of probabilistic conditional independencies is investigated in the presence of missing data. Here, graph separation axioms fail to hold for saturated conditional independencies, unlike the known idealized case with no missing data. Several axiomatic, algorithmic, and logical characterizations of the implication problem for saturated conditional independencies are established. In particular, equivalences are shown to the implication problem of a propositional fragment under Levesque's situations, and that of Lien's class of multivalued database dependencies under null values.
Uncorrelated Lasso
Authors: Chen Si-Bao , Ding Chris , Luo Bin , Xie Ying
Published: June 2022

Lasso-type variable selection has increasingly expanded its machine learning applications. In this paper, uncorrelated Lasso is proposed for variable selection, where variable de-correlation is considered simultaneously with variable selection, so that selected variables are uncorrelated as much as possible. An effective iterative algorithm, with the proof of convergence, is presented to solve the sparse optimization problem. Experiments on benchmark data sets show that the proposed method has better classification performance than many state-of-the-art variable selection methods.
Time-Dependent Trajectory Regression on Road Networks via Multi-Task Learning
Authors: Zheng Jiangchuan , Ni Lionel M.
Published: June 2022

Road travel costs are important knowledge hidden in large-scale GPS trajectory data sets, the discovery of which can benefit many applications such as intelligent route planning and automatic driving navigation. While there are previous studies which tackled this task by modeling it as a regression problem with spatial smoothness taken into account, they unreasonably assumed that the latent cost of each road remains unchanged over time. Other works on route planning and recommendation that have considered temporal factors simply assumed that the temporal dynamics be known in advance as a parametric function over time, which is not faithful to reality. To overcome these limitations, in this paper, we propose an extension to a previous static trajectory regression framework by learning the temporal dynamics of road travel costs in an innovative non-parametric manner which can effectively overcome the temporal sparsity problem. In particular, we unify multiple different trajectory regression problems in a multi-task framework by introducing a novel cross-task regularization which encourages temporal smoothness on the change of road travel costs. We then propose an efficient block coordinate descent method to solve the resulting problem by exploiting its separable structures and prove its convergence to global optimum. Experiments conducted on both synthetic and real data sets demonstrate the effectiveness of our method and its improved accuracy on travel time prediction.
Improving the Performance of Consistency Algorithms by Localizing and Bolstering Propagation in a Tree Decomposition
Authors: Karakashian Shant , Woodward Robert , Choueiry Berthe
Published: June 2022

The tractability of a Constraint Satisfaction Problem (CSP)is guaranteed by a direct relationship between its consistencylevel and a structural parameter of its constraint network suchas the treewidth. This result is not widely exploited in practicebecause enforcing higher-level consistencies can be costlyand can change the structure of the constraint network andincrease its width. Recently, R(*,m)C was proposed as a relational consistency property that does not modify the structureof the graph and, thus, does not affect its width. In this paper,we explore two main strategies, based on a tree decomposition of the CSP, for improving the performance of enforcingR(*,m)C and getting closer to the above tractability condition. Those strategies are: a) localizing the application ofthe consistency algorithm to the clusters of the tree decomposition, and b) bolstering constraint propagation betweenclusters by adding redundant constraints at their separators,for which we propose three new schemes. We characterizethe resulting consistency properties by comparing them, theoretically and empirically, to the original R(*,m)C and thepopular GAC and maxRPWC, and establish the benefits ofour approach for solving difficult problems.
Filtering With Logic Programs and Its Application to General Game Playing
Authors: Thielscher Michael
Published: June 2022

Motivated by the problem of building a basic reasoner for general game playing with imperfect information, we address the problem of filtering with logic programs, whereby an agent updates its incomplete knowledge of a program by observations. We develop a filtering method by adapting an existing backward-chaining and abduction method for so-called open logic programs. Experimental results show that this provides a basic effective and efficient "legal" player for general imperfect-information games.
Joint Extraction and Labeling via Graph Propagation for Dictionary Construction
Authors: Kim Doo Soon , Verma Kunal , Yeh Peter
Published: June 2022

In this paper, we present an approach that jointly infers the boundaries of tokens and their labels to construct dictionaries for Information Extraction. Our approach for joint-inference is based on graph propagation, and extends it in two novel ways. First, we extend the graph representation to capture ambiguities that occur during the token extraction phase. Second, we modify the labeling phase (i.e., label propagation) to utilize this new representation, allowing evidence from labeling to be used for token extraction. Our evaluation shows these extensions (and hence our approach) significantly improve the performance of the outcome dictionaries over pipeline-based approaches by preventing aggressive commitment. Our evaluation also shows that our extensions over a base graph-propagation framework improve the precision without hurting the recall.
A Concave Conjugate Approach for Nonconvex Penalized Regression with the MCP Penalty
Authors: Zhang Shubao , Qian Hui , Chen Wei , Zhang Zhihua
Published: June 2022

The minimax concave plus penalty (MCP) has been demonstrated to be effective in nonconvex penalization for feature selection. In this paper we propose a novel construction approach for MCP. In particular, we show that MCP can be derived from a concave conjugate of the Euclidean distance function. This construction approach in turn leads us to an augmented Lagrange multiplier method for solving the penalized regression problem with MCP. In our method each tuning parameter corresponds to a feature, and these tuning parameters can be automatically updated. We also develop a d.c. (difference of convex functions) programming approach for the penalized regression problem. We find that the augmented Lagrange multiplier method degenerates into the d.c. programming method under specific conditions. Experimental analysis is conducted on a set of simulated data. The result is encouraging.
Progression of Decomposed Situation Calculus Theories
Authors: Ponomaryov Denis , Soutchanski Mikhail
Published: June 2022

In many tasks related to reasoning about consequences of a logical theory, it is desirable to decompose the theory into a number of components with weakly-related or independent signatures. This facilitates reasoning when signature of a query formula belongs to only one of the components. However, an initial theory may be subject to change due to execution of actions affecting features mentioned in the theory. Having once computed a decomposition of a theory, one would like to know whether a decomposition has to be computed again for the theory obtained from taking into account the changes resulting from execution of an action. In the paper, we address this problem in the scope of the situation calculus, where change of an initial theory is related to the well-studied notion of progression. Progression provides a form of forward reasoning; it relies on forgetting values of those features which are subject to change and computing new values for them. We prove new results about properties of decomposition components under forgetting and show when a decomposition can be preserved in progression of an initial theory.
Interdependent Multi-Issue Negotiation for Energy Exchange in Remote Communities
Authors: Alam Muddasser , Rogers Alex , Ramchurn Sarvapali
Published: June 2022

We present a novel negotiation protocol to facilitate energy exchange between off-grid homes that are equipped with renewable energy generation and electricity storage. Our protocol imposes restrictions over negotiation such that it reduces the complex interdependent multi-issue negotiation to one where agents have a strategy profile in subgame perfect Nash equilibrium. We show that our negotiation protocol is tractable, concurrent, scalable and leads to Pareto-optimal outcomes in a decentralised manner. We empirically evaluate our protocol and show that, in this instance, a society of agents can (i) improve the overall utilities by 14% and (ii) reduce their overall use of the batteries by 37%.
Symmetry-Aware Marginal Density Estimation
Authors: Niepert Mathias
Published: June 2022

The Rao-Blackwell theorem is utilized to analyze and improve the scalability of inference in large probabilistic models that exhibit symmetries. A novel marginal density estimator is introduced and shown both analytically and empirically to outperform standard estimators by several orders of magnitude. The developed theory and algorithms apply to a broad class of probabilistic models including statistical relational models considered not susceptible to lifted probabilistic inference.
Train Outstable Scheduling as Constraint Satisfaction
Authors: Chun Andy
Published: June 2022

This paper outlines the design of a scheduling algorithm that allocates outstabling locations to railway trains. From time to time railway trains may need to be outstabled to temporary locations, such as stations, sidings, depots, etc., until they are needed for regular operations. This is common for urban rail transit, and especially so for those that do not operate 24 hours. During non-traffic hours (NTH), trains are outstabled to various locations along the rail network so that when operations start again next day, the trains will be nearby their originating station or conveniently located so that they can be put into service whenever needed. However, this is complicated by the fact that engineering works, such as rail testing, installation, regular maintenance, etc. are done during the NTH. Therefore, passenger trains must be outstabled in such a way that they do not interfere with night-time engineering works or the movements of associated engineering trains. Since the engineering works scheduling is done separate to outstabling, this is a mixed-system problem. This paper shows how we modeled this as a constraint-satisfaction problem (CSP) and implemented into an “Outstabling System” (OSS) for the Hong Kong Mass Transit Railway (MTR) using a two-stage search algorithm
Learning about Representational Modality: Design and Programming Projects for Knowledge-Based AI
Authors: Goel Ashok , Kunda Maithilee , Joyner David , Vattam Swaroop
Published: June 2022

Many AI courses include design and programming projects that provide students with opportunities for experiential learning. Design and programming projects in courses on knowledge-based AI typically explore topics in knowledge, memory, reasoning, and learning. Traditional AI curricula, however, seldom highlight issues of modality of representations, often focusing solely on propositional representations. In this paper, we report on an investigation into learning about representational modality through a series of projects based around geometric analogy problems similar to the Raven’s Progressive Matrices test of intelligence. We conducted this experiment over three years, from Fall 2010 through Fall 2012, in a class on knowledge-based AI. We used the methodology of action research in which the teacher is also the researcher. We discovered that students found these projects motivating, engaging, and challenging, in several cases investing significant time and posting their work online. From our perspective, the projects accomplished the goal of learning about representational modality in addition to knowledge representation and reasoning.
Indirect Reciprocity and Costly Assessment in Multiagent Systems
Authors: Santos Fernando , Pacheco Jorge , Santos Francisco
Published: June 2022

Social norms can help solving cooperation dilemmas, constituting a key ingredient in systems of indirect reciprocity (IR). Under IR, agents are associated with different reputations, whose attribution depends on socially adopted norms that judge behaviors as good or bad. While the pros and cons of having a certain public image depend on how agents learn to discriminate between reputations, the mechanisms incentivizing agents to report the outcome of their interactions remain unclear, especially when reporting involves a cost (costly reputation building). Here we develop a new model---inspired in evolutionary game theory---and show that two social norms can sustain high levels of cooperation, even if reputation building is costly. For that, agents must be able to anticipate the reporting intentions of their opponents. Cooperation depends sensitively on both the cost of reporting and the accuracy level of reporting anticipation.
Negative-Aware Influence Maximization on Social Networks
Authors: Chen Yipeng , Li Hongyan , Qu Qiang
Published: June 2022

How to minimize the impact of negative users within the maximal set of influenced users? The Influenced Maximization (IM) is important for various applications. However, few studies consider the negative impact of some of the influenced users.We propose a negative-aware influence maximization problem by considering users' negative impact. A novel algorithm is proposed to solve the problem. Experiments on real-world datasets show the proposed algorithm can achieve 70% improvement on average in expected influence compared with rivals.
Automated Question Answering System for Community-Based Questions
Authors: Pithyaachariyakul Chanin , Kulkarni Anagha
Published: June 2022

We present our attempt at developing an efficient Question Answering system for both factoid and non-factoid questions from any domain. Empirical evaluation of our system using multiple datasets demonstrates that our system outperforms the best system from the TREC LiveQA tracks, while keeping the response time to under less than half a minute.
Path-Based Attention Neural Model for Fine-Grained Entity Typing
Authors: Zhang Denghui , Li Manling , Cai Pengshan , Jia Yantao , Wang Yuanzhuo
Published: June 2022

Fine-grained entity typing aims to assign entity mentions in the free text with types arranged in a hierarchical structure. It suffers from the label noise in training data generated by distant supervision. Although recent studies use many features to prune wrong label ahead of training, they suffer from error propagation and bring much complexity. In this paper, we propose an end-to-end typing model, called the path-based attention neural model (PAN), to learn a noise-robust performance by leveraging the hierarchical structure of types. Experiments on two data sets demonstrate its effectiveness.
The island/non-island distinction in long-distance extraction: Evidence from L2 acceptability
Authors: Kim Boyoung , Goodall Grant
Published: June 2022

Experimental studies regularly find that extraction out of an embedded clause (“long-distance extraction”) results in a substantial degradation in acceptability but that the degradation is much greater when the embedded clause is an island structure. We explore these two facts by means of a series of acceptability experiments with L1 and L2 speakers of English. We find that the L2 speakers show greater degradation than L1 speakers for extraction out of non-islands, even though the two groups behave very similarly for extraction out of islands. Moreover, the L2 degradation with non-islands becomes smaller and more L1-like as exposure to the language increases. These initially surprising findings make sense if we assume that speakers must actively construct environments in which extraction out of embedded clauses is possible and that learning how to do this takes time. Evidence for this view comes from cross-linguistic variation in long-distance extraction, long-distance extraction in child English, and lexical restrictions on long-distance extraction. At a broader level, our results suggest that long-distance extraction does not come “for free” once speakers have acquired embedded clauses and extraction.
The utility of frequent laboratory monitoring for patients on tumor necrosis factor-alpha inhibitors in dermatology
Authors: Houpe Jessica , Fan Emily , Siddiqui Fariha , Seger Edward , Rajpara Anand
Published: June 2022

Introduction. Tumor necrosis factor-alpha inhibitors (TNF-ai) are becoming increasingly common to use among patients with skin disease. To safely take these medications, it is recommended to monitor laboratory values routinely; however, the utility of this practice and the risk-benefit of frequent laboratory monitoring has not been fully explored in patients with skin disease. The purpose of this study was to evaluate the necessity of routine laboratory monitoring in patients taking a TNF-ai with a dermatological disease. 
Methods. Retrospective chart review evaluated laboratory abnormalities (complete blood counts and liver function tests) in adult patients who took a TNF-ai for a dermatologic disease at The University of Kansas Hospital.
Results. There were 27 patients included for a total of 45 entries. The most common skin disease was hidradenitis suppurative (23/45) and infliximab (22/45) was most the commonly used medication. Of the 45 entries, there were only 7 patients that developed abnormal monitoring laboratory values related to initiation of TNF-ai. These abnormalities were transient and most frequently occurred after 12 months with (2/45) resulting in no discontinuation or dose reduction of TNF-ai. One patient discontinued medication due to anemia that did not improve after medication withdrawal.
Conclusions. Laboratory abnormalities due to TNF-ai were infrequent and when they did occur were transient and mild. The study is limited by the small sample size of patients, and larger prospective studies are needed to fully evaluate these findings. However, dermatologists may be able to safely employ less frequent laboratory monitoring for patients on TNF-ai.
(Des)Colonialidade Linguística e Interculturalidade nas Duas Principais Rotas da Mobilidade Estudantil Brasileira
Authors: Borges Rovênia Amorim
Published: June 2022

Embora frequentar universidades em Portugal e nos Estados Unidos seja ainda um privilégio para quem vem de famílias brasileiras com elevado capital econômico, políticas para o fomento da internacionalização têm levado, na última década, a uma intensificação e diversificação desses fluxos de mobilidade estudantil. Guiando-nos pelos estudos descoloniais, apresentamos, neste artigo, uma análise da interseccionalidade de raça e domínio de língua inglesa de estudantes de nacionalidade brasileira em Portugal e nos Estados Unidos. Os resultados referem duas investigações empíricas realizadas entre 2013 e 2020, e apontam que estudantes negras/os, participantes do mesmo programa de mobilidade com bolsas de estudo nestes dois países, apresentaram menor proficiência em inglês em comparação com bolsistas brancas/os. Em contrapartida, estudantes de uma elite econômica branca não indicaram a insuficiência no domínio de inglês como fator de decisão pela escolha de Portugal. A nosso ver, essas assimetrias devem ser percebidas e problematizadas a partir da colonialidade no ensino de inglês no Brasil que, no espaço de educação internacional, tem limitado escolhas e (re)produzido desigualdades. Todavia, nos tempos pandêmicos que apressam a transição para a mobilidade virtual, a maior diversidade étnico-racial e amplitude socioeconômica da mobilidade estudantil do Brasil para universidades portuguesas suscita outras e mais aprofundadas reflexões sobre as interações interculturais (presenciais) que resultam desses deslocamentos. As experiências de estudar em Portugal têm sido marcadas por alguns desencontros linguísticos, a exemplo dos imaginários de um subalterno português brasileiro e de um superior português de Portugal. Os constrangimentos que resultam dessas (in)comunicações interculturais entre estudantes do Brasil e de Portugal podem ser explicados, pelo menos em parte, pela reverberação, na contemporaneidade, da colonialidade do ensino da língua portuguesa nos dois países. Vamos argumentar que essas tensões, presentes em espaços acadêmicos da antiga metrópole do Brasil, potencializam o que temos designado de “despertar descolonial”.
Produção de Conhecimento, Reparação Histórica e Construção de Futuros Alternativos. Entrevista Com Miguel de Barros
Authors: Cabecinhas Rosa , Barros Miguel de
Published: June 2022

A pandemia da COVID-19 exacerbou de modo dramático desigualdades sociais pré-existentes e tornou mais urgente a consciencialização da necessidade de transformação social. Os movimentos sociais para a descolonização do conhecimento e dos sistemas de governança ganharam novo ímpeto assim como as demandas de reparação histórica e de justiça climática, sanitária e alimentar. A reparação histórica tem sido por vezes equacionada apenas em termos de restituição de bens materiais ou de compensação financeira, mas trata-se de uma tarefa bem mais complexa que passa necessariamente por tornar a produção de conhecimento um processo mais envolvente e participativo, dentro e fora da academia, colocando em diálogo diversos saberes com vista à construção de futuros mais justos e inclusivos...
A Decolonial Perspective on Online Media Discourses in the Context of Violence Against People With Disabilities in South Africa
Authors: Dalvit Lorenzo
Published: June 2022

As one of the most violent and unequal societies globally, South Africa is still profoundly shaped by a legacy of segregation and oppression. While race, gender and socio-economic status receive much attention, (dis)ability is an important yet often neglected dimension of inequality. In this article, I adopt a decolonial perspective in discussing online media articles about violence against people with disabilities. By focusing on stories related to issues that received extensive media coverage (e.g. mental health, police brutality and gender-based violence), I problematise the Eurocentric human-rights discourse informing public and scholarly discussions. I also explore the link between current understandings of (dis)ability and the legacy of a violent colonial and apartheid past. As a result of the intersectional nature of (dis)ability, many of the stories involve multiple layers of inequality and different forms of oppression. An explicit focus on extreme forms of institutional and physical violence, while restricting the scope of enquiry, brings the brutality of western modernity and its effects on the people affected into sharp focus. Legal recurse appears to lead to incomplete reparation at best while its failures perpetuate a cycle of marginalisation and oppression. Rather than problematising these structural failures as a result of western modernity and neoliberalism, the media inadvertently obfuscates such links by performing its normative, that is, by identifying and exposing individual culprits or by blaming contextual factors.
Para Não Esquecer: Memória, Poder e Arquivo Malê em Narrativa Amadiana
Authors: Ferreira Tatiane Almeida
Published: June 2022

Neste artigo, se problematiza as relações entre o poder, a memória e o arquivo que circundam a revolta dos malês, acontecimento histórico narrado em Bahia de Todos os Santos: Guia de Ruas e Mistérios, do escritor Jorge Amado (1977), buscando compartilhar reflexões, tensionamentos e intenções que o contato com os estudos pós-estruturalistas e decoloniais podem provocar ante a história, significando um movimento de insubmissão capaz de potencializar uma crítica ao pensamento oficial e às narrativas eleitas. A resistência e a busca malê para resguardar sua identidade é uma potência expressa no livro amadiano, que denuncia a violência física, linguística, religiosa, social e histórica vivenciada pela desumanização dos corpos negros e o apagamento da história nacional dessas figuras populares que tiveram uma participação em lutas em prol da liberdade e foram silenciadas nas narrativas oficias da nação. O trabalho desenvolvido tem por intuito ainda compreender como o romancista brasileiro aborda essas estruturas forjadas nas relações de poder e de controle da história e da memória utilizadas como mecanismos para apagar identidades das minorias étnicas em solos brasileiros. Para o desenvolvimento deste estudo, foram utilizadas as concepções teóricas de Derrida (1995/2001), Deleuze (1969/2009), Foucault (1969/2008), Mignolo (2003), Grosfoguel (1996), Quijano (2005) e Reis (1986).
Субъективное восприятие образовательного неравенства школьников
Authors: Керша Юлия , Звягинцев Роман
Published: June 2022

В аналитическом докладе представлены результаты изучения нового для российской исследовательской повестки феномена — субъективного восприятия образовательного неравенства. В качестве базы данных использовались результаты Мониторинга экономики образования 2020 г., анкета домохозяйств. Авторы описывают и интерпретируют результаты с использованием традиционной рамки образовательного неравенства и рамки восприятия экономического неравенства, не столь часто применяемой в исследованиях образования. Субъективное восприятие системы образования значимо различается у людей с разным уровнем доходов и образования. Дискуссия строится вокруг вопроса о том, какие выводы могут быть сделаны для образовательной политики и, что более важно, для проблематики информирования населения, способов преодоления сложившейся ситуации. Результаты исследования могут быть полезны для государственных служащих в сфере образования.
Участие школьников в дополнительном образовании
Authors: Косарецкий Сергей , Гошин Михаил , Иванов Иван
Published: June 2022

В информационном бюллетене представлены результаты исследования масштаба и структуры участия российских школьников в дополнительном образовании, равенства возможностей различных групп детей, стратегий семей. Эмпирической базой для исследования являются результаты проведенного в рамках «Мониторинга экономики образования» в 2020 г. опроса родителей, дети которых посещают общеобразовательные организации. Анализируются вовлеченность современных школьников в занятия дополнительным образованием в школе и вне школы, ее особенности в зависимости от возраста, типа образовательной организации, места жительства, социально-экономических характеристик семьи и других факторов. Описываются структура вовлеченности школьников в различные направления и формы дополнительного образования (включая репетиторство), распределение времени, затрачиваемого ребенком на различные виды учебных занятий, в том числе соотношение платных и бесплатных занятий, изменение доступности бесплатных занятий в последние годы. Рассматриваются барьеры, препятствующие вовлечению школьников в дополнительное образование в школе и вне школы. Анализируются эффекты занятий дополнительным образованием, их взаимосвязь с успешностью ребенка и планами продолжения образования. Формулируются рекомендации для государственной политики по обеспечению доступности дополнительного образования, равенства в использовании его возможностей для развития и профессионального самоопределения.
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Authors: A H
Published: June 2022

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[RETRACTED] Condor CBD Gummies Reviews: Best CBD Gummies Work, Side Effects & Scam v1
Authors: not provided sampollack
Published: June 2022

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Whole Mount In Situ Hybridization in Zebrafish v1
Authors: not provided penguinos
Published: June 2022

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Authors: Nootropic Brain Gummies Myco
Published: June 2022

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[RETRACTED] ViaKeto Apple Gummies Australia Reviews:- Keto BHB Gummies, Work, Results & Price v1
Authors: Gummies ViaKeto
Published: June 2022

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Sosyal Bilgiler Öğretmenlerinin Sınıf İçi Sorunlarının Çözümünde Eğitim İçerikli Filmlere İlişkin Görüşleri
Authors: ALTAY Nalan
Published: June 2022

Bu çalışma eğitim içerikli filmlerin sosyal bilgiler öğretmenlerinin sınıf içi uygulamalarına etkisini ortaya koymak için yapılmıştır. Çalışmada nitel araştırma yöntemi kullanılmış; durum çalışması şeklinde desenlenmiştir. Araştırmada katılımcı grup sosyo-ekonomik farklılıklardan kaynaklı sınıf içi sorunların çözümünde eğitim içerikli filmlerden en az birini izleyen sosyal bilgiler öğretmenleridir. Bu amaçla İzmir ilindeki ortaokullarda görev yapan sosyal bilgiler öğretmenlerinden veri toplanmıştır. Veri toplama sürecinde araştırmaya katılan dokuz öğretmenin tamamının “Özgürlük Yazarları” filmini izlediği ortaya çıkmıştır. Bu nedenle yarı yapılandırılmış görüşme formu bu filme göre düzenlenerek veri toplanmıştır. Toplanan veriler içerik analizi yöntemiyle analiz edilmiştir. İzlenen filmin, öğrencilerin gelişimini destekleyen bir yaklaşım geliştirme ve öğrenciyle iletişim kurma boyutunda öğretmenlik mesleğine katkı sağladığı görülmüştür. Sınıf içi sorunların çözümünde film, değer ve beceri öğretimi alanında öğretmenleri motive etmiştir. Ayrıca filmi izleyen öğretmenler öğrencilerin dezavantajlarının göz önüne alınarak eğitim sürecinin yapılandırılma konusunda da filmden etkilenmiştir. Filmin akılda kalıcı sahnelerinin içeriği ise öğrenciyi kazanma ve öğretmen niteliklerine ilişkin sahnelerdir.
Investigación Basada en las Artes: «Soy yo»
Authors: Cortés Cortés Maitane
Published: June 2022

Vivimos en una sociedad jerárquica llena de trampas culturales que señala y categoriza desde las primeras bases educativas. Algo tan simple como los términos de “niño” o “niña” generan una confusión que resulta a veces irreversible para la construcción de identidad. Una inequidad injusta desde la que la palabra “diversidad” queda como una falsa ilusión del programa educativo.

La principal necesidad que lleva a realizar este trabajo de campo está fundamentada en el desconocimiento social que existe actualmente hacia el colectivo de personas transexuales. Esa ignorancia como fruto del desconocimiento, nos hace asentar unas premisas erróneas, impregnadas de actos de rechazo y discriminación que dejan enormes secuelas a su paso. Afortunadamente el avance tecnológico y los medios sanitarios dan pasos hacia el logro de su adaptación, pero lo cierto es que, sin la conciencia de educación inclusiva en la sociedad, cualquier colectivo fuera de lo «normalmente establecido» es a priori, una persona desviada. El objetivo por tanto es explorar la transexualidad desde un punto de vista interno para empatizar y conocer la realidad de este colectivo de manera profunda, y comprobar si verdaderamente se cumple ese rechazo social que hipotéticamente es lo que nos ha llevado a realizar la investigación.

Desde este marco de estudio etnográfico de investigación basada en artes, se integra un estudio de caso sustentado en la experiencia de vida de una mujer transexual, utilizando varios métodos artísticos: por un lado, la escritura creativa (ahondando en la narrativa de la historia de vida de la protagonista) y por otro, las artes plásticas, profundizando en la expresión de emociones. Es por esto por lo que podemos dar cuenta de que es una investigación basada en artes, puesto que están presentes los métodos artísticos anteriormente mencionados como herramientas de extracción de datos, ofreciéndonos las bases pertinentes para contrastar la premisa previa de la que partimos.

Además, desde este artículo se expresa la justificación, unos breves apuntes sobre la fundamentación, objetivos, y como se ha llevado a cabo todo el proceso metodológico desde diferentes formatos visuales y audiovisuales. Pues desde ellos puede apreciarse a la perfección todo el desarrollo de la investigación realizada.
hilo en la Mitología, la Literatura y el Arte clásicos y su significado reivindicativo en el Arte contemporáneo
Authors: Bellido-Márquez María del Carmen
Published: June 2022

Este trabajo analiza el uso del hilo en la Mitología clásica con valores y significaciones en relación con la mujer y la representación de éste en la Literatura y el Arte tradicionales, estudiando después la utilidad del hilo en el Arte contemporáneo como material no representado, sino presentado con sentido reivindicativo. Con todo ello, el estudio trata del hilo como vehículo de significado mitológico, literario y artístico y lo compara con su nuevo uso y aplicación artísticos, convertido en un elemento de denuncia y valor social.
Why Learn Artistic Thought?
Authors: Kettel Joachim
Published: June 2022

My presentation collage looks at image productions that were individually (but also collectively) developed by my students within the concept of art education during their studies for a degree in Art Education. This is combined with the search for the semantic content of the terms "thought", "aesthetic thought" and "artistic thought". Where these aspects are concerned, I am interested in the question as to how artistic art education differs from other concepts. On the one hand, other concepts eliminate the mediation of art because there is apparently nothing to learn and therefore nothing to teach about art (see Ehmer et al.) but on the other hand, they legitimise the existence of art lessons in schools by reducing them to display procedures focusing on image analysis and/or the pragmatic examination of images (Bering, Niehoff et al.) or favour the everyday aesthetic experiences of children and young people without reaching more in-depth and independent transformational processes as a result. The ultimate question that needs to be asked is what are the educational potentials and methods of art education that focuses on training artistic thought?
“If you don't want to think, you're out!” (himself) (Joseph Beuys, 1977, documenta 6 in Kassel, Germany) (Image: Beuys card)