bluuebunny/crossref_metadata_2025 huggingface dataset:
Eliminating the Weakest Link: Making Manipulation Intractable?
Authors: Davies Jessica , Narodytska Nina , Walsh Toby
Published: June 2022

Successive elimination of candidates is often a route to making manipulation intractable to compute. We prove that eliminating candidates does not necessarily increase the computational complexity of manipulation. However, for many voting rules used in practice, the computational complexity increases. For example, it is already known that it is NP-hard to compute how a single voter can manipulate the result of single transferable voting (the elimination version of plurality voting). We show here that it is NP-hard to compute how a single voter can manipulate the result of the elimination version of veto voting, of the closely related Coombs’ rule, and of the elimination versions of a general class of scoring rules.
Stability Via Convexity and LP Duality in OCF Games
Authors: Zick Yair , Markakis Evangelos , Elkind Edith
Published: June 2022

The core is a central solution concept in cooperative game theory, and therefore it is important to know under what conditions the core of a game is guaranteed to be non-empty. Two notions that prove to be very useful in this context are Linear Programming (LP) duality and convexity. In this work, we apply these tools to identify games with overlapping coalitions (OCF games) that admit stable outcomes. We focus on three notions of the core defined in (Chalkiadakis et al. 2010) for such games, namely, the conservative core, the refined core and the optimistic core. First, we show that the conservative core of an OCF game is non-empty if and only if the core of a related classic coalitional game is non-empty. This enables us to improve the result of (Chalkiadakis et al. 2010) by giving a strictly weaker sufficient condition for the non-emptiness of the conservative core. We then use LP duality to characterize OCF games with non-empty refined core; as a corollary, we show that the refined core of a game is non-empty as long as the superadditive cover of its characteristic function is convex. Finally, we identify a large class of OCF games that can be shown to have a non-empty optimistic core using an LP based argument.
Sample Bounded Distributed Reinforcement Learning for Decentralized POMDPs
Authors: Banerjee Bikramjit , Lyle Jeremy , Kraemer Landon , Yellamraju Rajesh
Published: June 2022

Decentralized partially observable Markov decision processes (Dec-POMDPs) offer a powerful modeling technique for realistic multi-agent coordination problems under uncertainty. Prevalent solution techniques are centralized and assume prior knowledge of the model. We propose a distributed reinforcement learning approach, where agents take turns to learn best responses to each other’s policies. This promotes decentralization of the policy computation problem, and relaxes reliance on the full knowledge of the problem parameters. We derive the relation between the sample complexity of best response learning and error tolerance. Our key contribution is to show that sample complexity could grow exponentially with the problem horizon. We show empirically that even if the sample requirement is set lower than what theory demands, our learning approach can produce (near) optimal policies in some benchmark Dec-POMDP problems.
The Deployment-to-Saturation Ratio in Security Games
Authors: Jain Manish , Leyton-Brown Kevin , Tambe Milind
Published: June 2022

Stackelberg security games form the backbone of systems like ARMOR, IRIS and PROTECT, which are in regular use by the Los Angeles International Police, US Federal Air Marshal Service and the US Coast Guard respectively. An understanding of the runtime required by algorithms that power such systems is critical to furthering the application of game theory to other real-world domains. This paper identifies the concept of the deployment-to-saturation ratio in random Stackelberg security games, and shows that problem instances for which this ratio is 0.5 are computationally harder than instances with other deployment-to-saturation ratios for a wide range of different equilibrium computation methods, including (i) previously published different MIP algorithms, and (ii) different underlying solvers and solution mechanisms. This finding has at least two important implications. First, it is important for new algorithms to be evaluated on the hardest problem instances. We show that this has often not been done in the past, and introduce a publicly available benchmark suite to facilitate such comparisons. Second, we provide evidence that this computationally hard region is also one where optimization would be of most benefit to security agencies, and thus requires significant attention from researchers in this area. Furthermore, we use the concept of phase transitions to better understand this computationally hard region. We define a decision problem related to security games, and show that the probability that this problem has a solution exhibits a phase transition as the deployment-to-saturation ratio crosses 0.5. We also demonstrate that this phase transition is invariant to changes both in the domain and the domain representation, and that the phase transition point corresponds to the computationally hardest instances.
Semi-Supervised Kernel Matching for Domain Adaptation
Authors: Xiao Min , Guo Yuhong
Published: June 2022

In this paper, we propose a semi-supervised kernel matching method to address domain adaptation problems where the source distribution substantially differs from the target distribution. Specifically, we learn a prediction function on the labeled source data while mapping the target data points to similar source data points by matching the target kernel matrix to a submatrix of the source kernel matrix based on a Hilbert Schmidt Independence Criterion. We formulate this simultaneous learning and mapping process as a non-convex integer optimization problem and present a local minimization procedure for its relaxed continuous form. Our empirical results show the proposed kernel matching method significantly outperforms alternative methods on the task of across domain sentiment classification.
Online Kernel Selection: Algorithms and Evaluations
Authors: Yang Tianbao , Mahdavi Mehrdad , Jin Rong , Yi Jinfeng , Hoi Steven
Published: June 2022

Kernel methods have been successfully applied to many machine learning problems. Nevertheless, since the performance of kernel methods depends heavily on the type of kernels being used, identifying good kernels among a set of given kernels is important to the success of kernel methods. A straightforward approach to address this problem is cross-validation by training a separate classifier for each kernel and choosing the best kernel classifier out of them. Another approach is Multiple Kernel Learning (MKL), which aims to learn a single kernel classifier from an optimal combination of multiple kernels. However, both approaches suffer from a high computational cost in computing the full kernel matrices and in training, especially when the number of kernels or the number of training examples is very large. In this paper, we tackle this problem by proposing an efficient online kernel selection algorithm. It incrementally learns a weight for each kernel classifier. The weight for each kernel classifier can help us to select a good kernel among a set of given kernels. The proposed approach is efficient in that (i) it is an online approach and therefore avoids computing all the full kernel matrices before training; (ii) it only updates a single kernel classifier each time by a sampling technique and therefore saves time on updating kernel classifiers with poor performance; (iii) it has a theoretically guaranteed performance compared to the best kernel predictor. Empirical studies on image classification tasks demonstrate the effectiveness of the proposed approach for selecting a good kernel among a set of kernels.
Margin-Based Feature Selection in Incomplete Data
Authors: Lou Qiang , Obradovic Zoran
Published: June 2022

This study considers the problem of feature selection in incomplete data. The intuitive approach is to first impute the missing values, and then apply a standard feature selection method to select relevant features. In this study, we show how to perform feature selection directly, without imputing missing values. We define the objective function of the uncertainty margin-based feature selection method to maximize each instance’s uncertainty margin in its own relevant subspace. In optimization, we take into account the uncertainty of each instance due to the missing values. The experimental results on synthetic and 6 benchmark data sets with few missing values (less than 25%) provide evidence that our method can select the same accurate features as the alternative methods which apply an imputation method first. However, when there is a large fraction of missing values (more than 25%) in data, our feature selection method outperforms the alternatives, which impute missing values first.
Topic Correlation Analysis for Cross-Domain Text Classification
Authors: Li Lianghao , Jin Xiaoming , Long Mingsheng
Published: June 2022

Cross-domain text classification aims to automatically train a precise text classifier for a target domain by using labeled text data from a related source domain. To this end, the distribution gap between different domains has to be reduced. In previous works, a certain number of shared latent features (e.g., latent topics, principal components, etc.) are extracted to represent documents from different domains, and thus reduce the distribution gap. However, only relying the shared latent features as the domain bridge may limit the amount of knowledge transferred. This limitation is more serious when the distribution gap is so large that only a small number of latent features can be shared between domains. In this paper, we propose a novel approach named Topic Correlation Analysis (TCA), which extracts both the shared and the domain-specific latent features to facilitate effective knowledge transfer. In TCA, all word features are first grouped into the shared and the domain-specific topics using a joint mixture model. Then the correlations between the two kinds of topics are inferred and used to induce a mapping between the domain-specific topics from different domains. Finally, both the shared and the mapped domain-specific topics are utilized to span a new shared feature space where the supervised knowledge can be effectively transferred. The experimental results on two real-world data sets justify the superiority of the proposed method over the stat-of-the-art baselines.
Adaptive Step-Size for Online Temporal Difference Learning
Authors: Dabney William , Barto Andrew
Published: June 2022

The step-size, often denoted as α, is a key parameter for most incremental learning algorithms. Its importance is especially pronounced when performing online temporal difference (TD) learning with function approximation. Several methods have been developed to adapt the step-size online. These range from straightforward back-off strategies to adaptive algorithms based on gradient descent. We derive an adaptive upper bound on the step-size parameter to guarantee that online TD learning with linear function approximation will not diverge. We then empirically evaluate algorithms using this upper bound as a heuristic for adapting the step-size parameter online. We compare performance with related work including HL(λ) and Autostep. Our results show that this adaptive upper bound heuristic out-performs all existing methods without requiring any meta-parameters. This effectively eliminates the need to tune the learning rate of temporal difference learning with linear function approximation.
Markov Network Structure Learning: A Randomized Feature Generation Approach
Authors: Van Haaren Jan , Davis Jesse
Published: June 2022

The structure of a Markov network is typically learned in one of two ways. The first approach is to treat this task as a global search problem. However, these algorithms are slow as they require running the expensive operation of weight (i.e., parameter) learning many times. The second approach involves learning a set of local models and then combining them into a global model. However, it can be computationally expensive to learn the local models for datasets that contain a large number of variables and/or examples. This paper pursues a third approach that views Markov network structure learning as a feature generation problem. The algorithm combines a data-driven, specific-to-general search strategy with randomization to quickly generate a large set of candidate features that all have support in the data. It uses weight learning, with L1 regularization, to select a subset of generated features to include in the model. On a large empirical study, we find that our algorithm is equivalently accurate to other state-of-the-art methods while exhibiting a much faster run time.
Sparse Principal Component Analysis with Constraints
Authors: Grbovic Mihajlo , Dance Christopher , Vucetic Slobodan
Published: June 2022

The sparse principal component analysis is a variant of the classical principal component analysis, which finds linear combinations of a small number of features that maximize variance across data. In this paper we propose a methodology for adding two general types of feature grouping constraints into the original sparse PCA optimization procedure.We derive convex relaxations of the considered constraints, ensuring the convexity of the resulting optimization problem. Empirical evaluation on three real-world problems, one in process monitoring sensor networks and two in social networks, serves to illustrate the usefulness of the proposed methodology.
Investigating Contingency Awareness Using Atari 2600 Games
Authors: Bellemare Marc , Veness Joel , Bowling Michael
Published: June 2022

Contingency awareness is the recognition that some aspects of a future observation are under an agent's control while others are solely determined by the environment. This paper explores the idea of contingency awareness in reinforcement learning using the platform of Atari 2600 games. We introduce a technique for accurately identifying contingent regions and describe how to exploit this knowledge to generate improved features for value function approximation. We evaluate the performance of our techniques empirically, using 46 unseen, diverse, and challenging games for the Atari 2600 console. Our results suggest that contingency awareness is a generally useful concept for model-free reinforcement learning agents.
Visual Saliency Map from Tensor Analysis
Authors: Li Bing , Xiong Weihua , Hu Weiming
Published: June 2022

Modeling visual saliency map of an image provides important information for image semantic understanding in many applications. Most existing computational visual saliency models follow a bottom-up framework that generates independent saliency map in each selected visual feature space and combines them in a proper way. Two big challenges to be addressed explicitly in these methods are (1) which features should be extracted for all pixels of the input image and (2) how to dynamically determine importance of the saliency map generated in each feature space. In order to address these problems, we present a novel saliency map computational model based on tensor decomposition and reconstruction. Tensor representation and analysis not only explicitly represent image's color values but also imply two important relationships inherent to color image. One is reflecting spatial correlations between pixels and the other one is representing interplay between color channels. Therefore, saliency map generator based on the proposed model can adaptively find the most suitable features and their combinational coefficients for each pixel. Experiments on a synthetic image set and a real image set show that our method is superior or comparable to other prevailing saliency map models.
Construction of New Medicines via Game Proof Search
Authors: Heifets Abraham , Jurisica Igor
Published: June 2022

The production of any new medicine requires solutions to many planning problems. The most fundamental of these is determining the sequence of chemical reactions necessary to physically create the drug. Surprisingly, these organic syntheses can be modeled as branching paths in a discrete, fully-observable state space, making the construction of new medicines an application of heuristic search. We describe a model of organic chemistry that is amenable to traditional AI techniques from game tree search, regression, and automatic assembly sequencing. We demonstrate the applicability of AND/OR graph search by developing the first chemistry solver to use proof-number search. Finally, we construct a benchmark suite of organic synthesis problems collected from undergraduate organic chemistry exams, and we analyze our solvers performance both on this suite and in recreating the synthetic plan for a multibillion dollar drug.
Opinion Target Extraction Using a Shallow Semantic Parsing Framework
Authors: Li Shoushan , Wang Rongyang , Zhou Guodong
Published: June 2022

In this paper, we present a simplified shallow semantic parsing approach to extracting opinion targets. This is done by formulating opinion target extraction (OTE) as a shallow semantic parsing problem with the opinion expression as the predicate and the corresponding targets as its arguments. In principle, our parsing approach to OTE differs from the state-of-the-art sequence labeling one in two aspects. First, we model OTE from parse tree level, where abundant structured syntactic information is available for use, instead of word sequence level, where only lexical information is available. Second, we focus on determining whether a constituent, rather than a word, is an opinion target or not, via a simplified shallow semantic parsing framework. Evaluation on two datasets shows that structured syntactic information plays a critical role in capturing the domination relationship between an opinion expression and its targets. It also shows that our parsing approach much outperforms the state-of-the-art sequence labeling one.
Modeling Textual Cohesion for Event Extraction
Authors: Huang Ruihong , Riloff Ellen
Published: June 2022

Event extraction systems typically locate the role fillers for an event by analyzing sentences in isolation and identifying each role filler independently of the others. We argue that more accurate event extraction requires a view of the larger context to decide whether an entity is related to a relevant event. We propose a bottom-up approach to event extraction that initially identifies candidate role fillers independently and then uses that information as well as discourse properties to model textual cohesion. The novel component of the architecture is a sequentially structured sentence classifier that identifies event-related story contexts. The sentence classifier uses lexical associations and discourse relations across sentences, as well as domain-specific distributions of candidate role fillers within and across sentences. This approach yields state-of-the-art performance on the MUC-4 data set, achieving substantially higher precision than previous systems.
Width and Complexity of Belief Tracking in Non-Deterministic Conformant and Contingent Planning
Authors: Bonet Blai , Geffner Hector
Published: June 2022

It has been shown recently that the complexity of belief tracking in deterministic conformant and contingent planning is exponential in a width parameter that is often bounded and small. In this work, we introduce a new width notion that applies to non-deterministic conformant and contingent problems as well. We also develop a belief tracking algorithm for non-deterministic problems that is exponential in the problem width, analyze the width of non-deterministic benchmarks, compare the new notion to the previous one over deterministic problems, and present experimental results.
Stochastic Safest and Shortest Path Problems
Authors: Teichteil-Königsbuch Florent
Published: June 2022

Optimal solutions to Stochastic Shortest Path Problems (SSPs) usually require that there exists at least one policy that reaches the goal with probability 1 from the initial state. This condition is very strong and prevents from solving many interesting problems, for instance where all possible policies reach some dead-end states with a positive probability. We introduce a more general and richer dual optimization criterion, which minimizes the average (undiscounted) cost of only paths leading to the goal among all policies that maximize the probability to reach the goal. We present policy update equations in the form of dynamic programming for this new dual criterion, which are different from the standard Bellman equations, but produce the same solution if there exists one policy leading to the goal with probability 1 from the initial state. We demonstrate that our equations converge in infinite horizon without any condition on the structure of the problem or on its policies, which actually extends the class of SSPs that can be solved. We experimentally show that our dual criterion provides well-founded solutions to SSPs that can not be solved by the standard criterion, and that using a discount factor with the latter certainly provides solution policies but which are not optimal considering our well-founded criterion.
Catch Me If You Can: Pursuit and Capture in Polygonal Environments with Obstacles
Authors: Klein Kyle , Suri Subhash
Published: June 2022

We resolve a several-years old open question in visibility-based pursuit evasion: how many pursuers are needed to capture an evader in an arbitrary polygonal environment with obstacles? The evader is assumed to be adversarial, moves with the same maximum speed as pursuers, and is "sensed'' by a pursuer only when it lies inline-of-sight of that pursuer. The players move in discrete time steps, and the capture occurs when a pursuer reaches the position of the evader on its move. Our main result is that O(√h + log n) pursuers can always win the game with a deterministic search strategy in any polygon with n vertices and h obstacles (holes). In order to achieve this bound, however, we argue that the environment must satisfy a minimum feature size property, which essentially requires the minimum distance between any two vertices to be of the same order as the speed of the players. Without the minimum feature size assumption, we show that Ω < ( √(n/log n)) pursuers are needed in the worst-case even for simply-connected (hole-free) polygons of n vertices!  This reveals an unexpected subtlety that seems to have been overlookedin previous work claiming that O(log n) pursuers can always win insimply-connected n-gons.  Our lower bound also shows that capturing an evader is inherently more difficult than just "seeing" it because O(log n) pursuers are provably sufficient for line-of-sight detection even against an arbitrarily fast evaderin simple n-gons.
Parsing Outdoor Scenes from Streamed 3D Laser Data Using Online Clustering and Incremental Belief Updates
Authors: Triebel Rudolph , Paul Rohan , Rus Daniela , Newman Paul
Published: June 2022

In this paper, we address the problem of continually parsing a stream of 3D point cloud data acquired from a laser sensor mounted on a road vehicle. We leverage an online star clustering algorithm coupled with an incremental belief update in an evolving undirected graphical model. The fusion of these techniques allows the robot to parse streamed data and to continually improve its understanding of the world. The core competency produced is an ability to infer object classes from similarities based on appearance and shape features, and to concurrently combine that with a spatial smoothing algorithm incorporating geometric consistency. This formulation of feature-space star clustering modulating the potentials of a spatial graphical model is entirely novel. In our method, the two sources of information: feature similarity and geometrical consistency are fed continu- ally into the system, improving the belief over the class distributions as new data arrives. The algorithm obviates the need for hand-labeled training data and makes no apriori assumptions on the number or characteristics of object categories. Rather, they are learnt incrementally over time from streamed input data. In experiments per- formed on real 3D laser data from an outdoor scene, we show that our approach is capable of obtaining an ever- improving unsupervised scene categorization.
Coordinated Multi-Robot Exploration Under Communication Constraints Using Decentralized Markov Decision Processes
Authors: Matignon Laetitia , Jeanpierre Laurent , Mouaddib Abdel-Illah
Published: June 2022

Recent works on multi-agent sequential decision making using decentralized partially observable Markov decision processes have been concerned with interaction-oriented resolution techniques and provide promising results. These techniques take advantage of local interactions and coordination. In this paper, we propose an approach based on an interaction-oriented resolution of decentralized decision makers. To this end, distributed value functions (DVF) have been used by decoupling the multi-agent problem into a set of individual agent problems. However existing DVF techniques assume permanent and free communication between the agents. In this paper, we extend the DVF methodology to address full local observability, limited share of information and communication breaks. We apply our new DVF in a real-world application consisting of multi-robot exploration where each robot computes locally a strategy that minimizes the interactions between the robots and maximizes the space coverage of the team even under communication constraints. Our technique has been implemented and evaluated in simulation and in real-world scenarios during a robotic challenge for the exploration and mapping of an unknown environment. Experimental results from real-world scenarios and from the challenge are given where our system was vice-champion.
Mirror Perspective-Taking with a Humanoid Robot
Authors: Hart Justin , Scassellati Brian
Published: June 2022

The ability to use a mirror as an instrument for spatial reasoning enables an agent to make meaningful inferences about the positions of objects in space based on the appearance of their reflections in mirrors.  The model presented in this paper enables a robot to infer the perspective from which objects reflected in a mirror appear to be observed, allowing the robot to use this perspective as a virtual camera.  Prior work by our group presented an architecture through which a robot learns the spatial relationship between its body and visual sense, mimicking an early form of self-knowledge in which infants learn about their bodies and senses through their interactions with each other.  In this work, this self-knowledge is utilized in order to determine the mirror's perspective.  Witnessing the position of its end-effector in a mirror in several distinct poses, the robot determines a perspective that is consistent with these observations.  The system is evaluated by measuring how well the robot's predictions of its end-effector's position in 3D, relative to the robot's egocentric coordinate system, and in 2D, as projected onto it's cameras, match measurements of a marker tracked by its stereo vision system.  Reconstructions of the 3D position end-effector, as computed from the perspective of the mirror, are found to agree with the forward kinematic model within a mean of 31.55mm.  When observed directly by the robot's cameras, reconstructions agree within 5.12mm.  Predictions of the 2D position of the end-effector in the visual field agree with visual measurements within a mean of 18.47 pixels, when observed in the mirror, or 5.66 pixels, when observed directly by the robot's cameras.
Exact Lifted Inference with Distinct Soft Evidence on Every Object
Authors: Bui Hung , Huynh Tuyen , De Salvo Braz Rodrigo
Published: June 2022

The presence of non-symmetric evidence has been a barrier for the application of lifted inference since the evidence destroys the symmetry of the first-order probabilistic model. In the extreme case, if distinct soft evidence is obtained about each individual object in the domain then, often, all current exact lifted inference methods reduce to traditional inference at the ground level. However, it is of interest to ask whether the symmetry of the model itself before evidence is obtained can be exploited. We present new results showing that this is, in fact, possible. In particular, we show that both exact maximum a posteriori (MAP) and marginal inference can be lifted for the case of distinct soft evidence on a unary Markov Logic predicate. Our methods result in efficient procedures for MAP and marginal inference for a class of graphical models previously thought to be intractable.
Conditioning in First-Order Knowledge Compilation and Lifted Probabilistic Inference
Authors: Van den Broeck Guy , Davis Jesse
Published: June 2022

Knowledge compilation is a powerful technique for compactly representing and efficiently reasoning about logical knowledge bases. It has been successfully applied to numerous problems in artificial intelligence, such as probabilistic inference and conformant planning. Conditioning, which updates a knowledge base with observed truth values for some propositions, is one of the fundamental operations employed for reasoning. In the propositional setting, conditioning can be efficiently applied in all cases. Recently, people have explored compilation for first-order knowledge bases. The majority of this work has centered around using first-order d-DNNF circuits as the target compilation language. However, conditioning has not been studied in this setting. This paper explores how to condition a first-order d-DNNF circuit. We show that it is possible to efficiently condition these circuits on unary relations. However, we prove that conditioning on higher arity relations is #P-hard. We study the implications of these findings on the application of performing lifted inference for first-order probabilistic models.This leads to a better understanding of which types of queries lifted inference can address.
Strategic Advice Provision in Repeated Human-Agent Interactions (Abstract)
Authors: Azaria Amos , Rabinovich Zinovi , Kraus Sarit , Goldman Claudia , Gal Ya'akov
Published: June 2022

This paper addresses the problem of automated advice provision in settings that involve repeated interactions between people and computer agents. This problem arises in many real world applications such as route selection systems and office assistants. To succeed in such settings agents must reason about how their actions in the present influence people's future actions. The paper describes several possible models of human behavior that were inspired by behavioral economic theories of people's play in repeated interactions. These models were incorporated into several agent designs to repeatedly generate offers to people playing the game. These agents were evaluated in extensive empirical investigations including hundreds of subjects that interacted with computers in different choice selections processes. The results revealed that an agent that combined a hyperbolic discounting model of human behavior with a social utility function was able to outperform alternative agent designs. We show that this approach was able to generalize to new people as well as choice selection processes that were not used for training. Our results demonstrate that combining computational approaches with behavioral economics models of people in repeated interactions facilitates the design of advice provision strategies for a large class of real-world settings.
Improving Convergence of CMA-ES Through Structure-Driven Discrete Recombination
Authors: Brys Tim , Nowé Ann
Published: June 2022

Evolutionary Strategies (ES) are a class of continuous optimization algorithms that have proven to perform very well on hard optimization problems. Whereas in earlier literature, both intermediate and discrete recombination operators were used, we now see that most ES, e.g. CMA-ES, use only intermediate recombination. While CMA-ES is considered state-of-the-art in continuous optimization, we believe that reintroducing discrete recombination can improve the algorithms' ability to escape local optima. Specifically, we look at using information on the problem's structure to create building blocks for recombination.
An Investigation of Sensitivity on Bagging Predictors: An Empirical Approach
Authors: Liang Guohua
Published: June 2022

As growing numbers of real world applications involve imbalanced class distribution or unequal costs for mis- classification errors in different classes, learning from imbalanced class distribution is considered to be one of the most challenging issues in data mining research. This study empirically investigates the sensitivity of bagging predictors with respect to 12 algorithms and 9 levels of class distribution on 14 imbalanced data-sets by using statistical and graphical methods to address the important issue of understanding the effect of vary- ing levels of class distribution on bagging predictors. The experimental results demonstrate that bagging NB and MLP are insensitive to various levels of imbalanced class distribution.
Threats and Trade-Offs in Resource Critical Crowdsourcing Tasks Over Networks
Authors: Nath Swaprava , Dayama Pankaj , Garg Dinesh , Narahari Y. , Zou James
Published: June 2022

In recent times, crowdsourcing over social networks has emerged as an active tool for complex task execution. In this paper, we address the problem faced by a planner to incentivize agents in the network to execute a task and also help in recruiting other agents for this purpose. We study this mechanism design problem under two natural resource optimization settings: (1) cost critical tasks, where the planner's goal is to minimize the total cost, and (2) time critical tasks, where the goal is to minimize the total time elapsed before the task is executed. We define a set of fairness properties that should be ideally satisfied by a crowdsourcing mechanism. We prove that no mechanism can satisfy all these properties simultaneously. We relax some of these properties and define their approximate counterparts. Under appropriate approximate fairness criteria, we obtain a non-trivial family of payment mechanisms. Moreover, we provide precise characterizations of cost critical and time critical mechanisms.
Real-Time Collaborative Planning with the Crowd
Authors: Lasecki Walter , Bigham Jeffrey , Allen James , Ferguson George
Published: June 2022

Planning is vital to a wide range of domains, including robotics, military strategy, logistics, itinerary generation and more, that both humans and computers find difficult. Collaborative planning holds the promise of greatly improving performance on these tasks by leveraging the strengths of both humans and automated planners. However, this requires formalizing the problem domain and input, which must be done by hand, a priori, restricting its use in general real-world domains. We propose using a real-time crowd of workers to simultaneously solve the planning problem, formalize the domain, and train an automated system. As plans are developed, the system is able to learn the domain, and contribute larger segments of work.
Temporally Expressive Planning Based on Answer Set Programming with Constraints
Authors: Bao Forrest , Zhang Yuanlin
Published: June 2022

Recently, a new language AC(C) was proposed to integrate answer set programming (ASP) and constraint logic programming (CLP). In this paper, we show that temporally expressive planning problems in PDDL2.1 can be translated into AC(C) and solved using AC(C) solvers. Compared with existing approaches, the new approach puts less restrictions on the planning problems and is easy to extend with new features like PDDL axioms. It can also leverage the inference engine for AC(C) which has the potential to exploit the best reasoning mechanisms developed in the ASP, SAT and CP communities.
A Testbed for Learning by Demonstration from Natural Language and RGB-Depth Video
Authors: Song Young Chol , Kautz Henry
Published: June 2022

We are developing a testbed for learning by demonstration combining spoken language and sensor data in a natural real-world environment. Microsoft Kinect RGB-Depth cameras allow us to infer high-level visual features, such as the relative position of objects in space, with greater precision and less training than required by traditional systems. Speech is recognized and parsed using a “deep” parsing system, so that language features are available at the word, syntactic, and semantic levels. We collected an initial data set of 10 episodes of 7 individuals demonstrating how to “make tea”, and created a “gold standard” hand annotation of the actions performed in each. Finally, we are constructing “baseline” HMM-based activity recognition models using the visual and language features, in order to be ready to evaluate the performance of our future work on deeper and more structured models.
Symmetry Breaking Constraints: Recent Results
Authors: Walsh Toby
Published: June 2022

Symmetry is an important problem in many combinatorial problems. One way of dealing with symmetry is to add constraints that eliminate symmetric solutions. We survey recent results in this area, focusing especially on two common and useful cases: symmetry breaking constraints for row and column symmetry, and symmetry breaking constraints for eliminating value symmetry.
Planning as an Iterative Process
Authors: Smith David
Published: June 2022

Activity planning for missions such as the Mars Exploration Rover mission presents many technical challenges, including oversubscription, consideration of time, concurrency, resources, preferences, and uncertainty. These challenges have all been addressed by the research community to varying degrees, but significant technical hurdles still remain. In addition, the integration of these capabilities into a single planning engine remains largely unaddressed. However, I argue that there is a deeper set of issues that needs to be considered -- namely the integration of planning into an iterative process that begins before the goals, objectives, and preferences are fully defined. This introduces a number of technical challenges for planning, including the ability to more naturally specify and utilize constraints on the planning process, the ability to generate multiple qualitatively different plans, and the ability to provide deep explanation of plans.
Radiation-shielding properties of titanium dioxide-added composites
Authors: Choudhary Manju D , Akkurt Iskender , Almisned Ghada , Tekin Huseyin Ozan
Published: June 2022

In radiation protection physics, development of alternative shielding materials to lead and lead-based materials is very popular nowadays, and thus, the present work aims to study the radiation-shielding parameters of a newly developed titanium dioxide (TiO2)-added hydroxyapatite composite material. The gamma-ray-shielding parameters were given in terms of the linear attenuation coefficient, half-value length, effective atomic number (Zeff) and effective electron density (Neff) for four different composites. The variations of these parameters with the titanium dioxide rate were also investigated.
Increasing the undrained shear strength of soft ground improved by vacuum consolidation
Authors: Hayashi Hirochika , Hashimoto Hijiri
Published: June 2022

In this paper, the use of vacuum consolidation to improve soft ground and increase its undrained shear strength (Su) is discussed. Since peat and organic clay have very low Su values, the construction of embankments and structures on these soils often cause ground failure. For the trial embankment conducted in this research project, soft ground composed of peat, organic clay and clay was improved by using vacuum consolidation, resulting in a significant increase in Su values, and an embankment 10.7 m high could be constructed on the improved ground without causing ground failure. It was demonstrated that the increased Su was the result of synergy of the relatively high normalised strength (Su/σ′v, where σ′v is the effective overburden stress) of peat and organic clay, and the unique loading mechanism of vacuum consolidation. It was also demonstrated that in the case of a relatively narrow embankment (e.g. road embankment and river levee), the effect of isotropic consolidation also contributes to increase the stability of the vacuum consolidated peaty ground. It was also found that if the (Su/σ′v)NC of the original ground and the overconsolidation ratio are known, the (Su/σ′v)OC of the given ground overconsolidated by vacuum consolidation can be predicted.
Brivaracetam for dyskinetic cerebral palsy
Authors: Papaliagkas Vasileios , Notas Konstantinos , Chatzikyriakou Evangelia , Moschou Maria , Bostanjopoulou Sevasti , Hadjigeorgiou Georgios , Kimiskidis Vasilios K.
Published: June 2022

Content available: Video
Integración económica de Colombia: logros y desafíos ante el mundo globalizado
Authors: Martínez Zabaleta Mercedes
Published: June 2022

Esta lectura crítica realizada antes de la pandemia analiza algunos logros y desafíos de Colombia en su participación en los procesos de integración económica (PIE). El propósito fue determinar los logros que ha obtenido Colombia de los acuerdos de integración más importantes en los que tiene participación, así como conocer cuáles son los retos en el futuro inmediato que le representa los mercados globalizados cada vez más exigentes y llenos de incertidumbre por las políticas que asumen otros países. La metodología utilizada es documental. Entre las fuentes de información, se destacan el Ministerio de Comercio Industria y Turismo y la Cancillería de la República, además de aportes de artículos científicos, tesis doctorales y de grado. Entre los principales resultados puede mencionarse que Colombia ha obtenido importantes beneficios de los diferentes procesos de integración, destacando el incremento de las exportaciones y los capitales invertidos en el país. Pero también mostraron que la principal vulnerabilidad es la falta de competencia de las empresas colombianas. Como conclusión, se establece que Colombia ocupa un lugar importante dentro de todo el comercio de la región, que ha crecido su economía a partir de Alianza del Pacífico, la Comunidad Andina de Naciones (CAN), el tratado de libre comercio (TLC) con Estados Unidos y China.
Reflexiones sobre la importancia interpretativa de los índices de salud pública con la COVID-19
Authors: Boada Antonio , Jaramillo Francisco
Published: June 2022

Continuamente se publican estadísticas acerca de las afectaciones de la COVID-19, pero casi en el total de los casos se revisan cifras absolutas y se desconoce la relatividad de dichas cifras respecto a parámetros tan simples como el tamaño poblacional, eso equivale a pensar que es igual que haya cincuenta decesos por la enfermedad en un país tan poblado como China a que el mismo número de decesos se dé en un país poco poblado como Panamá. Para el desarrollo de este texto se registraron datos a agosto 11 de 2020. Inicialmente se revisaron las cifras y se hizo una comparación para todos los países del mundo, luego para América Latina y el Caribe y, finalmente, se realizó esa revisión en los distintos departamentos de Colombia. Valga señalar que no se trata tampoco de proponer la eliminación de las cifras absolutas, ya que su relevancia es obvia; la invitación es a presentar ambos tipos de cifras simultáneamente.
Reflexiones, aprender sin barreras de tiempo o espacio: educación bajo el Principio de Ubicuidad
Authors: Cardona Montoya Giovanny , Mazo Cuervo Diego Mauricio
Published: June 2022

Este ensayo es el resultado de una reflexión continua que los autores vienen haciendo desde tiempos anteriores a la pandemia de COVID-19, la cual se inspira en las experiencias que ha tenido el CEIPA con su educación virtual y con la articulación de las mediaciones y recursos didácticos de la virtualidad para fortalecer la educación presencial. El ensayo se centra en el principio de ubicuidad como eje de los cambios venideros de la educación en CEIPA y, muy seguramente, en el mundo. Los autores son integrantes del grupo de investigación Orygen de la Institución Universitaria CEIPA.
Análisis prospectivos de estrategias de sostenibilidad en el mercado de bebidas (Postobón)
Authors: Mejía Rodríguez Humberto Yecid , Ruiz Bolívar Daniela Carolina , Tobón Villalobo Loraine Paola , Chartuni Garrido Gloria Marina , Barrios Arteaga Karla Michelle
Published: June 2022

El mercado de bebidas se caracteriza por estar en constante transformación debido a las tendencias de consumo y a la situación de contexto que ponen en un riesgo medio o alto todas las estrategias de mercado que lleven a una compañía a consolidarse como la primera opción de compra del consumidor final, por esto se propone un estudio de información de mercado de una empresa de bebidas (Postobón S. A.) que se inicia con el análisis del entorno, lo cual permite tener una visión clara y real de lo que para la compañía representarán a futuro todos los componentes económicos, sociales, legales, etc.… que determinarán aquellas variables que representan una ventaja o desventaja y le permiten hacer una relación en la cual puede determinar qué influencia tiene una sobre la otra, qué tanto puede inferir actualmente y en el futuro. Luego se valida con expertos del ámbito de la compañía, estos aportes enriquecerán la viabilidad de las acciones a realizar en los distintos escenarios futuros para la empresa, lo que permitirá la toma de decisiones de sostenibilidad en el tiempo, con grandes logros para la compañía. Finalmente se describen dichos escenarios y se plantean recomendaciones para la empresa.
Dos palabras: productividad y competitividad
Authors: Gómez Gómez Iván Orestes
Published: June 2022

En la revisión de la literatura, las palabras productividad y competitividad tienen múltiples definiciones y conceptos cuyo enfoque está determinado por la perspectiva del autor que se siga. Este artículo quiere dilucidar unas definiciones más universales y, de paso, contestar a la pregunta central: ¿están conectadas la productividad y la competitividad? Para dar respuesta a esta pregunta abordaremos la evolución teórica de los diferentes conceptos que dan sustento tanto a las definiciones como a sus aplicaciones.
Indole-3 asetik asit’in Pimpla turionellae L., 1758 (Hymenoptera: Ichneumonidae) ve Galleria mellonella L., 1758 (Lepidoptera: Pyralidae)’nın konukçu-parazitoit sisteminde fenoloksidaz ve hemolitik aktivitelerine beslenme yoluyla etkileri
Authors: BECERİKLİ AKSAN Gülşah , UÇKAN Fevzi , ER Aylin
Published: June 2022

Gıda ihtiyacının sürekliliği, modern tarımın büyük ölçüde kimyasal girdilere bağımlı hale gelmesine neden olmuştur. Bitki büyüme düzenleyicileri, kemosterilant aktiviteye sahip doğal olarak oluşan veya sentetik bitki kaynaklı kimyasallardır ve zararlı böcekleri baskılama potansiyeline sahiptir. Zararlılarla birlikte, agroekolojik sistemlerdeki parazitoitler gibi hedef olmayan organizmaların, konukçuları aracılığıyla dolaylı temastan veya konukçu ve bitkilerle tritrofik etkileşim yoluyla doğrudan temastan etkilenmesi muhtemeldir. Bu çalışma bitki büyüme düzenleyicisi indol-3-asetik asidin (IAA) depolanmış ürün zararlısı Galleria mellonella L., 1758 (Lepidoptera: Pyralidae) ve parazitoit Pimpla turionellae L., 1758 (Hymenoptera: Ichneumonidae)’nın hemolitik aktivitesi ve fenoloksidaz aktivitesi üzerindeki besinsel etkilerini göstermektedir. Galleria mellonella ve P. turionellae hemolenfinin hemolitik aktivitelerinde istatistiksel olarak önemli artışlar 500, 5000 ve 10000 ppm IAA dozları tatbik edilen gruplarda gözlenmiştir. Galleria mellonella hemolenfinin fenoloksidaz aktivitesi, kontrole kıyasla tüm IAA uygulanan dozlarda önemli ölçüde azalırken, P. turionellae hemolenf fenoloksidaz aktivitesindeki azalmalar 500 ve 10000 ppm arasında anlamlı bulundu. IAA, hem konukçu hem de parazitoit bağışıklığı ile etkileşime girdiğinden, konukçu-parazitoit popülasyonu yüksek olan tarım alanlarında dikkatle kullanılmalıdır.
Behavioral Testing - Open Field and Dyskinesia Scoring v1
Authors: Nelson Alexandra
Published: June 2022

This protocol describes behavioral testing to assess motor deficits in mice. It includes open field locomotor testing and scoring of levodopa-induced dyskinesia.