Reviewerly/exHarmony huggingface dataset:
Computing Plans with Control Flow and Procedures Using a Classical Planner

We propose a compilation that enhances a given classical planning task to compute plans that contain control flow and procedure calls. Control flow instructions and procedures allow us to generate compact and general solutions able to solve planning tasks for which multiple unit tests are defined. The paper analyzes the relation between classical planning and structured programming with unit tests and shows how to exploit this relation in a classical planning compilation. In experiments, we evaluate the empirical performance of the compilation using an off-the-shelf classical planner and show that we can compress classical planning solutions and that these compressed solutions can solve planning tasks with multiple tests.
Multi-Agent Path Finding for Self Interested Agents

Multi-agent pathfinding (MAPF) deals with planning paths for individual agents such that a global cost function (e.g., the sum of costs) is minimized while avoiding collisions between agents. Previous work proposed centralized or fully cooperative decentralized algorithms assuming that agents will follow paths assigned to them. When agents are {\em self-interested}, however, they are expected to follow a path only if they consider that path to be their most beneficial option. In this paper we propose the use of a taxation scheme to implicitly coordinate self-interested agents in MAPF. We propose several taxation schemes and compare them experimentally. We show that intelligent taxation schemes can result in a lower total cost than the non coordinated scheme even if we take into consideration both travel cost and the taxes paid by agents.
The Trial The Trail, Act 3 A Virtual Reality Drama Using Intelligent Agents

The Trail is an interactive drama running on an immersive VR system. Imagine Tarkovsky's Stalker, crossed with Alice Through the Looking Glass, crossed with Monty Python and the Holy Grail. Now imagine embarking on a guided journey through this warped yet familiar landscape. Your guides are two intelligent agents, Patofil and Filopat. We consider a virtual reality drama to be a scripted play in which the computational agents are actors who have copies of the script, and one human audience member has been drafted to be a participant, but doesn't have a copy of the script. The computational actors must improvise reactions to the human participant's actions, but keep the play moving along in as close agreement to the script as possible. The goal is to provide the human participant with a specific emotional experience.
The role of logic

We present role logic, a notation for describing properties of relational structures in shape analysis, databases, and knowledge bases. We construct role logic using the ideas of de Bruijn’s notation for lambda calculus, an encoding of first-order logic in lambda calculus, and a simple rule for implicit arguments of unary and binary predicates. The unrestricted version of role logic has the expressive power of first-order logic with transitive closure. Using a syntactic restriction on role logic formulas, we identify a natural fragment RL 2 of role logic. We show that the RL 2 fragment has the same expressive power as two-variable logic with counting C 2 , and is therefore decidable. We present a translation of an imperative language into the decidable fragment RL 2 , which allows compositional verification of programs that manipulate relational structures. In addition, we show how RL 2 encodes boolean shape anal
Sampling Hyrule: Multi-Technique Probabilistic Level Generation for Action Role Playing Games

Procedural Content Generation (PCG) using machine learning is a fast growing area of research. Action Role Playing Game (ARPG) levels represent an interesting challenge for PCG due to their multi-tiered structure and nonlinearity. Previous work has used Bayes Nets (BN) to learn properties of the topological structure of levels from The Legend of Zelda. In this paper we describe a method for sampling these learned distributions to generate valid, playable level topologies. We carry this deeper and learn a sampleable representation of the individual rooms using Principal Component Analysis. We combine the two techniques and present a multi-scale machine learned technique for procedurally generating ARPG levels from a corpus of levels from The Legend of Zelda.
Inferring Sentiment from Web Images with Joint Inference on Visual and Social Cues: A Regulated Matrix Factorization Approach

In this paper, we study the problem of understanding human sentiments from large scale collection of Internet images based on both image features and contextual social network information (such as friend comments and user description). Despite the great strides in analyzing user sentiment based on text information, the analysis of sentiment behind the image content has largely been ignored. Thus, we extend the significant advances in text-based sentiment prediction tasks to the higher-level challenge of predicting the underlying sentiments behind the images. We show that neither visual features nor the textual features are by themselves sufficient for accurate sentiment labeling. Thus, we provide a way of using both of them. We leverage the low-level visual features and mid-level attributes of an image, and formulate sentiment prediction problem as a non-negative matrix tri-factorization framework, which has the flexibility to incorporate multiple modalities of information and the capability to learn from heterogeneous features jointly. We develop an optimization algorithm for finding a local-optima solution under the proposed framework. With experiments on two large-scale datasets, we show that the proposed method improves significantly over existing state-of-the-art methods.
Qubit-channel metrology with very noisy initial states

We consider an arbitrary qubit channel depending on a single parameter, which is to be estimated by a physical process. Using the quantum Fisher information per channel invocation to quantify the estimation accuracy, we consider various estimation protocols when the available initial states are mixed with very low purity $r$. We compare a protocol using a single channel invocation on one out of $n$ qubits prepared in a particular correlated input state to the optimal protocol using uncorrelated input states, with the same initial-state purity. We show that, to lowest order in initial-state purity, for a unital channel this correlated-state protocol enhances the estimation accuracy by a factor between $n\ensuremath{-}1$ and $n$, provided that $n{r}^{2}\ensuremath{\ll}1$. We also show that to lowest order in initial-state purity, a broad class of nonunital channels yields no gain regardless of the input state.
Indexing Arabic texts using association rule data mining

Purpose The purpose of this paper is to propose a new model to enhance auto-indexing Arabic texts. The model denotes extracting new relevant words by relating those chosen by previous classical methods to new words using data mining rules. Design/methodology/approach The proposed model uses an association rule algorithm for extracting frequent sets containing related items – to extract relationships between words in the texts to be indexed with words from texts that belong to the same category. The associations of words extracted are illustrated as sets of words that appear frequently together. Findings The proposed methodology shows significant enhancement in terms of accuracy, efficiency and reliability when compared to previous works. Research limitations/implications The stemming algorithm can be further enhanced. In the Arabic language, we have many grammatical rules. The more we integrate rules to the stemming algorithm, the better the stemming will be. Other enhancements can be done to the stop-list. This is by adding more words to it that should not be taken into consideration in the indexing mechanism. Also, numbers should be added to the list as well as using the thesaurus system because it links different phrases or words with the same meaning to each other, which improves the indexing mechanism. The authors also invite researchers to add more pre-requisite texts to have better results. Originality/value In this paper, the authors present a full text-based auto-indexing method for Arabic text documents. The auto-indexing method extracts new relevant words by using data mining rules, which has not been investigated before. The method uses an association rule mining algorithm for extracting frequent sets containing related items to extract relationships between words in the texts to be indexed with words from texts that belong to the same category. The benefits of the method are demonstrated using empirical work involving several Arabic texts.
Hybrid Modeling of Non-Rigid Scenes From RGBD Cameras

Recent advances in sensor technology have introduced low-cost RGB video plus depth sensors, such as the Kinect, which enable simultaneous acquisition of color and depth images at video rates. This paper introduces a framework for representation of general dynamic scenes from video plus depth acquisition. A hybrid representation is proposed which combines the advantages of prior surfel graph surface segmentation and modeling work with the higher resolution surface reconstruction capability of volumetric fusion techniques. The contributions are: 1) extension of a prior piecewise surfel graph modeling approach for improved accuracy and completeness; 2) combination of this surfel graph modeling with a truncated signed distance function surface fusion to generate dense geometry; and 3) proposal of means for validation of the reconstructed a 4D scene model against the input data and efficient storage of any unmodeled regions via residual depth maps. The approach allows arbitrary dynamic scenes to be efficiently represented with a temporally consistent structure and enhanced levels of detail and completeness where possible, but gracefully falls back to raw measurements where no structure can be inferred. The representation is shown to facilitate creative manipulation of real scene data which would previously require more complex capture set-ups or manual processing.
Ancient–Modern Chinese Translation with a New Large Training Dataset

Ancient Chinese brings the wisdom and spirit culture of the Chinese nation. Automatic translation from ancient Chinese to modern Chinese helps to inherit and carry forward the quintessence of the ancients. However, the lack of large-scale parallel corpus limits the study of machine translation in ancient–modern Chinese. In this article, we propose an ancient–modern Chinese clause alignment approach based on the characteristics of these two languages. This method combines both lexical-based information and statistical-based information, which achieves 94.2 F1-score on our manual annotation Test set. We use this method to create a new large-scale ancient–modern Chinese parallel corpus that contains 1.24M bilingual pairs. To our best knowledge, this is the first large high-quality ancient–modern Chinese dataset. Furthermore, we analyzed and compared the performance of the SMT and various NMT models on this dataset and provided a strong baseline for this task.
Single-image calibration method for multiple virtual binocular vision system

The virtual binocular vision system (VBVS) based on single camera and mirrors has been paid increasing attention, with the advantages of high flexibility and compactness. However, in its calibration process, the conventional methods require several images and obtain different intrinsic parameters, which should be identical between different virtual cameras. To simplify the calibration process and obtain identical intrinsic parameters, a single-image calibration method for the VBVS based on one camera and mirrors is proposed. Through the single image including multiple sub-images of the calibration planes, the intrinsic parameters and distortion coefficients can be obtained. Then, the global calibration of multiple virtual binocular structures can be performed efficiently. To test the performance of the proposed approach, a VBVS composed of four pairs of virtual binocular structures was designed and the calibration experiments were performed using an artificial three-dimensional calibration target. Moreover, an accuracy comparison with the conventional method was carried out. The results exhibited the feasibility of this method and proved its accuracy.
Data flow analysis from capability lists, with application to RBAC

For the analysis of access control networks, where capability lists, access control matrices, or RBAC permissions are available, it can be very useful to be able to determine which subjects can be able to know, or which objects can be able to store, data originating from objects in the network. This information can be used in order to answer questions of secrecy, integrity and privacy, related to the data flow analysis problem. On the basis of a logical method, we present a graphical formalism capable to represent such networks and for which the data flow problems can be defined. We present algorithms to calculate answers to data flow questions. Complexity analysis and simulations show that these questions can be practically answered for networks of sizes up to several tens of thousands of subjects and objects, which is the size of many real-life organizations. We also show that the results obtained can be used in the process of role engineering in Role based access control, for determining secrecy levels, as well as for eliminating or combining roles or objects. Finally, a method is demonstrated to go from capability lists to Label-based access control systems.
Examining the hierarchical nature of scene representations in memory.

How are scene representations stored in memory? Researchers have often posited that scene representations have a hierarchical structure with background elements providing a scaffold for more detailed foreground elements. To further investigate scene representation and the role of background and foreground information, we introduced a new stimulus set: chimera scenes, which have the central block of objects belonging to one scene category (foreground), and the surrounding structure belonging to another (background). We used a contextual cueing paradigm and emphasized the relative importance of each by having the target placed on either the background or foreground. In a transfer block, we found that though changes to the background were highly detrimental to search performance for background targets, search performance was only slightly affected by changes to either the foreground or the background for foreground targets. These results indicate that rather than a fixed hierarchy, the structure of scene representations are more aptly captured by a parallel model that stores information flexibly. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
The Monte Carlo wave-function method: A robust adaptive algorithm and a study in convergence

We present a stepwise adaptive-timestep version of the Quantum Jump (Monte Carlo wave-function) algorithm. Our method has proved to remain robust even for problems where the integrating implementation of the Quantum Jump method is numerically problematic. The only specific parameter of our algorithm is the single a priory parameter of the Quantum Jump method, the maximal allowed total jump probability per timestep. We study the convergence of ensembles of trajectories to the solution of the full master equation as a function of this parameter. This study is expected to pertain to any possible implementation of the Quantum Jump method.
Hybridizing kernel‐based fuzzy c‐means with hierarchical selective neural network ensemble model for business failure prediction

Abstract More and more ensemble models are used to forecast business failure. It is generally known that the performance of an ensemble relies heavily on the diversity between each base classifier. To achieve diversity, this study uses kernel‐based fuzzy c‐means (KFCM) to organize firm samples and designs a hierarchical selective ensemble model for business failure prediction (BFP). First, three KFCM methods—Gaussian KFCM (GFCM), polynomial KFCM (PFCM), and Hyper‐tangent KFCM (HFCM)—are employed to partition the financial data set into three data sets. A neural network (NN) is then adopted as a basis classifier for BFP, and three sets, which are derived from three KFCM methods, are used to build three classifier pools. Next, classifiers are fused by the two‐layer hierarchical selective ensemble method. In the first layer, classifiers are ranked based on their prediction accuracy. The stepwise forward selection method is employed to selectively integrate classifiers according to their accuracy. In the second layer, three selective ensembles in the first layer are integrated again to acquire the final verdict. This study employs financial data from Chinese listed companies to conduct empirical research, and makes a comparative analysis with other ensemble models and all its component models. It is the conclusion that the two‐layer hierarchical selective ensemble is good at forecasting business failure.
Measurement-Based Adaptation Protocol with Quantum Reinforcement Learning in a Rigetti Quantum Computer

We present an experimental realisation of a measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti cloud quantum computer. The experiment in this few-qubit superconducting chip faithfully reproduces the theoretical proposal, setting the first steps towards a semiautonomous quantum agent. This experiment paves the way towards quantum reinforcement learning with superconducting circuits.
Integration of ROS and RT tasks using message pipe mechanism on Xenomai for telepresence robot

A promising approach integrating non-real-time (NRT) robot operating system (ROS) packages and RT tasks is proposed to enhance the development of RT robot control applications. Since ROS alone does not provide RT properties essential for achieving precise control period in manipulating multiple devices and complicated software, Xenomai, an RT extension of Linux is adapted. However, using NRT ROS packages inside RT tasks triggers mode switching that causes inability to satisfy critical temporal constraints. To address this issue, a message pipe mechanism termed cross-domain datagram protocol (XDDP) is applied. In comparison to traditional inter-task mechanisms, XDDP provides a communication interface between RT and NRT tasks. This greatly improves robot application development utilising ROS tools and packages with RT tasks on the Xenomai domain ensuring priority-based scheduling and deterministic response in a multitasking environment. Feasibility of the proposed method was validated for practical use by realisation on the open embedded controller for a telepresence robot. Experiments were conducted to actuate the mobile base of the robot using ROS navigation packages. The results indicate that the robot accomplishes its objectives while satisfying RT constraints.
Aligning Security Practice with Policy: Guiding and Nudging towards Better Behavior

Despite an abundance of policies being directed towards them, users often struggle to follow good cybersecurity practice.Recognizing that such behaviors do not come naturally, a logical approach is to ensure that users are guided and supported in knowing what to do and how to do it.Unfortunately, such support is often lacking.The paper uses the example of password authentication as a specific context in which cybersecurity behavior is frequently criticized, but where users are often left to manage without sufficient support (as evidenced by examining the lack of related guidance and enforcement of good practice on leading websites).The discussion then proceeds to look at the effect of actively supporting the user, drawing upon the results from two experimental studies (one looking at the practical impact of guidance and feedback upon users' password choices, and the other examining the effect of gamifying the password selection experience).The results collectively show that such efforts can have tangible positive effects upon user behaviors.While the specific findings are focused upon passwords, similar principles could also be applied to other aspects of user-facing security.
Informatics for cultural heritage instruction: an ontological framework

Purpose The purpose of this paper is to suggest a framework that creates a common language to enhance the connection between the domains of cultural heritage (CH) artifacts and instruction. Design/methodology/approach The CH and instruction domains are logically deconstructed into dimensions of functions, semiotics, CH, teaching/instructional materials, agents and outcomes. The elements within those dimensions can be concatenated to create natural-English sentences that describe aspects of the problem domain. Findings The framework is valid using traditional social sciences content, semantic, practical and systemic validity constructs. Research limitations/implications The framework can be used to map current research literature to discover areas of heavy, light and no research. Originality/value The framework provides a new way for CH and education stakeholders to describe and visualize the problem domain, which could allow for significant enhancements of each. Better understanding the problem domain would serve to enhance instruction informed from collections and vice versa. The educational process would have more depth due to better access to primary sources. Increased use of collections would reveal more ways through which they could be used in instruction. The framework can help visualize the past and present of the domain, and envisage its future.
Multi-hypothesis contextual modeling for semantic segmentation

Semantic segmentation (i.e. image parsing) aims to annotate each image pixel with its corresponding semantic class label. Spatially consistent labeling of the image requires an accurate description and modeling of the local contextual information. Segmentation result is typically improved by Markov Random Field (MRF) optimization on the initial labels. However this improvement is limited by the accuracy of initial result and how the contextual neighborhood is defined. In this paper, we develop generalized and flexible contextual models for segmentation neighborhoods in order to improve parsing accuracy. Instead of using a fixed segmentation and neighborhood definition, we explore various contextual models for fusion of complementary information available in alternative segmentations of the same image. In other words, we propose a novel MRF framework that describes and optimizes the contextual dependencies between multiple segmentations. Simulation results on two common datasets demonstrate significant improvement in parsing accuracy over the baseline approaches.
On the Potential for Open-Endedness in Neural Networks

Natural evolution gives the impression of leading to an open-ended process of increasing diversity and complexity. If our goal is to produce such open-endedness artificially, this suggests an approach driven by evolutionary metaphor. On the other hand, techniques from machine learning and artificial intelligence are often considered too narrow to provide the sort of exploratory dynamics associated with evolution. In this article, we hope to bridge that gap by reviewing common barriers to open-endedness in the evolution-inspired approach and how they are dealt with in the evolutionary case—collapse of diversity, saturation of complexity, and failure to form new kinds of individuality. We then show how these problems map onto similar ones in the machine learning approach, and discuss how the same insights and solutions that alleviated those barriers in evolutionary approaches can be ported over. At the same time, the form these issues take in the machine learning formulation suggests new ways to analyze and resolve barriers to open-endedness. Ultimately, we hope to inspire researchers to be able to interchangeably use evolutionary and gradient-descent-based machine learning methods to approach the design and creation of open-ended systems.