Reviewerly/exHarmony huggingface dataset:
An Argument for Large-Scale Breadth-First Search for Game Design and Content Generation via a Case Study of Fling!

Search is a recognized technique for procedural content generation and game design, and it has been used successfully as part of commercial and academic games. In this context, search has almost always referred to selective search, as opposed to larger brute-force searches. The argument against brute-force search is that the state spaces of the games are almost always too large to be amenable for brute-force search. We believe, however, that brute-force search should not be too quickly dismissed. State spaces with trillions or tens of trillions states can now be exhaustively searched with relative ease, and growth in parallelism and computational power is expected to continue to scale this trend. We believe that this, combined with appropriate abstraction, will allow exhaustive search to be applied to many problems once thought to be prohibitively large. We explore this argument in the context of a game called "Fling!," available for mobile devices, showing a system for interactively designing and analyzing puzzles.
Bimodality of Plasma Glucose Distributions in Whites: A Bootstrap Approach to Testing Mixture Models

The null distribution of the likelihood ratio test (LRT) of a onecomponent normal model versus two-component normal mixture model is unknown.In this paper, we take a bootstrap approach to the likelihood ratio test for testing bimodality of plasma glucose concentrations from Rancho Bernardo Diabetes Study.The small p-values from this approach support the hypothesis that a bimodal normal mixture model fits the data significantly better than a unimodal normal model.The size and power of the bootstrap based LRT are evaluated through simulations.The results suggest that a sample size of close to 500 would be necessary in order to attain a power of 90% for detecting the unbalanced mixtures with means and variances similar to those in the Rancho Bernardo data.Besides sample size, the power also depends on the two means and variances of the two components in the data.
Finding Schrödinger’s Gun

Interactive narratives are branching stories with events that change based on feedback from participants. One method of generating interactive narratives is a plan-based process called mediation. A sub-process within mediation called accommodation creates new story content when a participant deviates from the main storyline. We show that a model of character knowledge allows accommodation to find a novel class of branching stories previously inaccessible by the algorithm.
Plotter: Operationalizing the Master Book of All Plots

Pulp fiction author William Wallace Cook published Plotto: The Master Book of All Plots in 1928, which contains almost 2000 plot fragments and relatively formal instructions on how human authors could combine them to produce plots behind novels. In this paper we show one way that the methods in this book can be used to computationally generate plots from the fragments. We also show sample plots generated by our system called Plotter that uses this method. Finally we use them to discuss idiosyncrasies and limitations of the book.
Planning Single-Arm Manipulations with N-Arm Robots

Many robotic systems are comprised of two or more arms. Such systems range from dual-arm household manipulators to factory floors populated with a multitude of industrial robotic arms. While the use of multiple arms increases the productivity of the system and extends dramatically its workspace, it also introduces a number of challenges. One such challenge is planning the motion of the arm(s) required to relocate an object from one location to another. This problem is challenging because it requires reasoning over which arms and in which order should manipulate the object, finding a sequence of valid handoff locations between the consecutive arms and finally choosing the grasps that allow for successful handoffs. In this paper, we show how to exploit the characteristics of this problem in order to construct a planner that can solve it effectively. We analyze our approach experimentally on a number of simulated examples ranging from a 2-arm system operating at a table to a 3-arm system working at a bar and to a 4-arm system in a factory setting.
A LOUDSPEAKER ORCHESTRA FOR CONCERT HALL STUDIES

1-4 . These parameters, however, each provide only a single value computed from an impulse response. In concert halls, the impulse responses are often measured with an omnidirectional sound source and microphone. However, such a single source is far from a typical sound source - an orchestra. Therefore, a wide sound source would intuitively be preferable in studying the concert hall acoustics. This article describes a proposal for a loudspeaker setup that mimics a symphony orchestra. Several loudspeakers are installed on a concert hall stage as a loudspeaker orchestra. Individually recorded anechoic instrument tracks of orchestral works are then played through the loudspeakers as an ensemble in order to produce an impression of a symphony orchestra. The authors are not aware that a loudspeaker orchestra of this scale would have been used in acoustical music reproduction. Instead, large loudspeaker setups for electroacoustic music performances have been described 5-7 . In this article the loudspeaker setup is demonstrated in a single concert hall, although the system is particularly designed to be repeatable in other halls as well. This paper is organized as follows. First, the loudspeaker setup as well as the concert hall is described. Then, subjective opinions gathered with in-situ listening of the loudspeaker orchestra are presented. Finally, possible error sources are discussed and future enhancements of the system are proposed.
Minimum-weight codewords of the Hermitian codes are supported on complete intersections

Let H be the Hermitian curve defined over a finite field Fq2. In this paper we complete the geometrical characterization of the supports of the minimum-weight codewords of the algebraic-geometry codes over H, started in [26]: if d is the distance of the code, the supports are all the sets of d distinct Fq2-points on H complete intersection of two curves defined by polynomials with prescribed initial monomials w.r.t. DegRevLex. For most Hermitian codes, and especially for all those with distance d≥q2−q studied in [26], one of the two curves is always the Hermitian curve H itself, while if d<q the supports are complete intersection of two curves none of which can be H. Finally, for some special codes among those with intermediate distance between q and q2−q, both possibilities occur. We provide simple and explicit numerical criteria that allow to decide for each code what kind of supports its minimum-weight codewords have and to obtain a parametric description of the family (or the two families) of the supports.
Research Challenges in Combinatorial Search

I provide a personal view of some of the major research challenges in the area of combinatorial search. These include solving and playing games with chance, hidden information, and multiple players, optimally solving larger instances of well-known single-agent toy problems, applying search techniques to more realistic problem domains, analyzing the time complexity of heuristic search algorithms, and capitalizing on advances in computing hardware, such as very large external memories and multi-core processors.
Architecture-Based Behavioral Adaptation with Generated Alternatives and Relaxed Constraints

Software systems are increasingly required to autonomously adapt their architectural structures and/or behaviors to runtime environmental changes. However, existing architecture-based self-adaptation approaches mostly focus on structural adaptations within a predefined space of architectural alternatives (e.g., switching between two alternative services) while merely considering quality constraints (e.g., reliability and performance). In this paper, we propose a new architecture-based self-adaptation approach, which performs behavioral adaptations with automatically generated alternatives and supports relaxed functional constraints from the perspective of business value. Specifically, we propose a technique to automatically generate behavioral alternatives of a software system from the currently-employed architectural behavioral specification. We employ business value to comprehensively evaluate the behavioral alternatives while capturing the trade-offs among relaxed functional and quality constraints. We also introduce a genetic algorithm-based planning technique to efficiently search for the optimal (sometimes a near-optimal) behavioral alternative that can provide the best business value. The experimental study on an online order processing benchmark has shown promising results that the proposed approach can improve adaptation flexibility and business value with acceptable performance overhead.
A Critique of Statistical Machine Translation

Phrase-Based Statistical Machine Translation (PB-SMT) is clearly the leading paradigm in the field today. Nevertheless—and this may come as some surprise to the PB-SMT community—most translators and, somewhat more surprisingly perhaps, many experienced MT protagonists find the basic model extremely difficult to understand. The main aim of this paper, therefore, is to discuss why this might be the case. Our basic thesis is that proponents of PB-SMT do not seek to address any community other than their own, for they do not feel any need to do so. We demonstrate that this was not always the case; on the contrary, when statistical models of trans-lation were first presented, the language used to describe how such a model might work was very conciliatory, and inclusive. Over the next five years, things changed considerably; once SMT achieved dominance particularly over the rule-based paradigm, it had established a position where it did not need to bring along the rest of the MT community with it, and in our view, this has largely pertained to this day. Having discussed these issues, we discuss three additional issues: the role of automatic MT evaluation metrics when describing PB-SMT systems; the recent syntactic embellishments of PB-SMT, noting especially that most of these contributions have come from researchers who have prior experience in fields other than statistical models of translation; and the relationship between PB-SMT and other models of translation, suggesting that there are many gains to be had if the SMT community were to open up more to the other MT paradigms.
Discovering Response-Eliciting Factors in Social Question Answering : A Reddit Inspired Study

Questions form an integral part of our everyday communication, both offline and online. Getting responses to our questions from others is fundamental to satisfying our information need and in extending our knowledge boundaries. A question may be represented using various factors such as social, syntactic, semantic, etc. We hypothesize that these factors contribute with varying degrees towards getting responses from others for a given question. We perform a thorough empirical study to measure effects of these factors using a novel question and answer dataset from the website Reddit.com. We also use a sparse non-negative matrix factorization technique to automatically induce interpretable semantic factors from the question dataset. Such interpretable factor-based analysis overcomes limitations faced by prior related research. We also document various patterns on response prediction we observe during our analysis. For instance, we found that preference-probing questions are rarely answered by actors.
A Generalized Multidimensional Evaluation Framework for Player Goal Recognition

Recent years have seen a growing interest in player modeling, which supports the creation of player-adaptive digital games. A central problem of player modeling is goal recognition, which aims to recognize players’ intentions from observable gameplay behaviors. Player goal recognition offers the promise of enabling games to dynamically adjust challenge levels, perform procedural content generation, and create believable NPC interactions. A growing body of work is investigating a wide range of machine learning-based goal recognition models. In this paper, we introduce GOALIE, a multidimensional framework for evaluating player goal recognition models. The framework integrates multiple metrics for player goal recognition models, including two novel metrics, n-early convergence rate and standardized convergence point. We demonstrate the application of the GOALIE framework with the evaluation of several player goal recognition models, including Markov logic network-based, deep feedforward neural network-based, and long short-term memory network-based goal recognizers on two different educational games. The results suggest that GOALIE effectively captures goal recognition behaviors that are key to next-generation player modeling.
Weighted Lateral Learning in Real-Time Heuristic Search

Real-time heuristic search models an autonomous agent solving a search task. The agent operates in a real-time setting by interleaving local planning, learning and move execution. In this paper we propose a simple parametric algorithm that combines weighting with learning from multiple neighbors. Doing so breaks heuristic admissibility but allows the agent to escape heuristic depressions more quickly. We prove completeness of the algorithm and empirically compare it to several competitors more than twenty years apart. In a large-scale evaluation the new algorithm found better solutions than the recent algorithms, despite not learning additional information that they do. Finally, we study robustness of the algorithms to noise in the heuristic function — a desirable property in a physical implementation of real-time heuristic search. The new algorithm outperforms its contemporaries.
Enabling And Sustaining Educational Innovation

This paper reviews the responses to questions regarding innovation in engineering education posed to a nine-person panel assembled for The Carnegie Foundation for the Advancement of Teaching's pre-conference workshop at the Frontiers in Education (FIE) 2000 in Kansas City.Questions were focused on the inspiration, enablers, and challenges for innovation.In addition, issues related to innovation institutionalization (or sustainability) and to evidence gathered by the panelists to assess and evaluate the institutionalizing process
Creating a Hyper-Agent for Solving Angry Birds Levels

Over the past few years the Angry Birds AI competition has been held in an attempt to develop intelligent agents that can successfully and efficiently solve levels for the video game Angry Birds. Many different agents and strategies have been developed to solve the complex and challenging physical reasoning problems associated with such a game. However, the performance of these various agents is non-transitive and varies significantly across different levels. No single agent dominates all situations presented, indicating that different procedures are better at solving certain levels than others. We therefore propose the construction of a hyper-agent that selects from a portfolio of sub-agents whichever it believes is best at solving any given level. This hyper-agent utilises key features that can be observed about a level to rank the available candidate algorithms based on their expected score.The proposed method exhibits a significant increase in performance over the individual sub-agents, and demonstrates the potential of using such an approach to solve other physics-based games or problems.
Private Information Retrieval From Transversal Designs

Private information retrieval (PIR) protocols allow a user to retrieve entries of a database without revealing the index of the desired item. Information-theoretical privacy can be achieved by the use of several servers and specific retrieval algorithms. Most known PIR protocols focus on decreasing the number of bits exchanged between the client and the server(s) during the retrieval process. On another side, Fazeli et al. introduced so-called PIR codes in order to reduce the storage overhead on the servers. However, few works address the issue of the computation complexity of the servers. In this paper, we show that a specific encoding of the database yields PIR protocols with reasonable communication complexity, low storage overhead, and optimal computational complexity for the servers. This encoding is based on incidence matrices of transversal designs, from which a natural and efficient recovering algorithm is derived. We also present several instances for our construction, which make use of finite geometries and orthogonal arrays. We finally give a generalization of our main construction in order to resist collusions of servers.
Calibrated steganalysis of mp3stego in multi-encoder scenario

Experimenting with different mp3 encoders shows subtle differences in their outputs indicating they have been implemented differently. If this characteristic is not addressed properly, it could degrade performance of mp3 steganalysis. Additionally, calibration is a powerful technique which has not found its true potential in mp3 steganalysis. This paper tries to fill these gaps. First, we propose a new set of calibrated features based on quantization step. To that end, we show that quantization steps of mp3 bit stream and steganography-induced noise are respectively band-limited and wide-band signals. We use this observation and apply a low pass filter on quantization steps of mp3 bit stream and then use the result for calibrating of features. Then, we present our analysis on different encoders and show that their statistical properties are quite dissimilar for all fields of mp3 bitstream. We use this observation and propose an encoder classifier that have near-perfect accuracy. This accuracy is achieved after testing a wide range of feature selection methods and finally using genetic algorithm due to its superior performance. Our simulations show that the proposed method achieved accuracy of 95.8% and 95.2% at 3.12% of maximum embedding rate in the single and multi-encoder scenarios, respectively.
An Empirical Evaluation of a Generative Method for the Expression of Personality Traits through Action Choice

The presence of interesting and compelling characters is an essential component of effective narrative. Well-developed characters have features that enable them to significantly enhance the believability and quality of a story. In this paper, we describe the results of an experiment to evaluate a planning-based narrative generation system that focuses on the generation of stories that express character. The system is designed to automatically produce narratives that show character personality traits through the choices characters make when selecting the means by which they achieve their goals. Results from our study support the hypothesis that an audience presented with stories generated by Mask will attribute personality traits to the story characters that have significant correlation with the computational model of personality used to drive the characters' choices.
A novel automated absolute intensity difference based technique for optimal MR brain image thresholding

The traditional Otsu technique for thresholding based on 2D histogram does not give accurate results. A bi-level thresholding case partitions the 2D histogram into four quadrants. This converts the global probability distribution to a probability distribution of two regions only resulting in partial loss of information. The brain regions (white matter (WM), gray matter (GM), cerebrospinal fluid (CSF)) have closely defined gray values, which require the intensity difference information for thresholding. So we propose a novel automated absolute intensity difference based (AIDB) technique for optimal MR brain image thresholding using adaptive coral reef optimization (ACRO). The basic idea is to extract the intensity difference information of the brain image from the 2D histogram matrix. A first-hand objective function, depending on the classical between class variance concepts, is investigated, which is maximized by ACRO. The key achievements of our technique are (i) improved thresholding results, (ii) obtaining more homogenous regions of the image and (iii) obtaining more precise shape of the edges. The proposed technique is tested with one hundred slices of the axial T2 – weighted MR brain images of Harvard medical dataset. The Otsu method for multilevel thresholding using the 2D histogram is considered for a comparison. Several performance measures are considered for the evaluation. It is observed that our technique outperforms the other standard methods.
Quantum state tomography with time-continuous measurements: reconstruction with resource limitations

We propose and analyze quantum state estimation (tomography) using continuous quantum measurements with resource limitations, allowing the global state of many qubits to be constructed from only measuring a few. We give a proof-of-principle investigation demonstrating successful tomographic reconstruction of an arbitrary initial quantum state for three different situations: single qubit, remote qubit, and two interacting qubits. The tomographic reconstruction utilizes only a continuous weak probe of a single qubit observable, a fixed coupling Hamiltonian, together with single-qubit controls. In the single-qubit case, a combination of the continuous measurement of an observable and a Rabi oscillation is sufficient to find all three unknown qubit state components. For two interacting qubits, where only one observable of the first qubit is measured, the control Hamiltonian can be implemented to transfer all quantum information to the measured observable, via the qubit–qubit interaction and Rabi oscillation controls applied locally on each qubit. We discuss different sets of controls by analyzing the unitary dynamics and the Fisher information matrix of the estimation in the limit of weak measurement, and simulate tomographic results numerically. As a result, we obtained reconstructed state fidelities in excess of 0.98 with a few thousand measurement runs.
Identifying author heritage using surname data: An application for Russian surnames

This research article puts forward a method to identify the national heritage of authors based on the morphology of their surnames. Most studies in the field use variants of dictionary-based surname methods to identify ethnic communities, an approach that suffers from methodological limitations. Using the public file of ORCID (Open Researcher and Contributor ID) identifiers in 2015, we developed a surname-based identification method and applied it to infer Russian heritage from suffix-based morphological regularities. The method was developed conceptually and tested in an undersampled control set. Identification based on surname morphology was then complemented by using first-name data to eliminate false-positive results. The method achieved 98% precision and 94% recall rates-superior to most other methods that use name data. The procedure can be adapted to identify the heritage of a variety of national groups with morphologically regular naming traditions. We elaborate on how the method can be employed to overcome long-standing limitations of using name data in bibliometric datasets. This identification method can contribute to advancing research in scientific mobility and migration, patenting by certain groups, publishing and collaboration, transnational and scientific diaspora links, and the effects of diversity on the innovative performance of organizations, regions, and countries.