Reviewerly/exHarmony huggingface dataset:
The General Mediation Engine

The General Mediation Engine is an architecture for producing game experiences with world mechanics drawn from a planning domain and problem. The system models possible player actions, state updates, and system responses in a graph representation derived from planner input. Paired with an interface that displays state and action information to the player, GME structures gameplay as an on-line expansion of its graph representation.
A Stratified Learning Approach for Predicting the Popularity of Twitter Idioms

Twitter Idioms are one of the important types of hashtags that spread in Twitter. In this paper, we propose a classifier that can stratify the Idioms from the other kind of hashtags with 86.93% accuracy and high precision and recall rate. We then learn regression models on the stratified samples (Idioms and non-Idioms) separately to predict the popularity of the Idioms. This stratification not only itself allows us to make more accurate predictions but also makes it possible to include Idiom-specific features to separately improve the accuracy for the Idioms. Experimental results show that such stratification during the training phase followed by inclusion of Idiom-specific features leads to an overall improvement of 11.13% and 19.56% in correlation coefficient over the baseline method after the 7th and the 11th month respectively.
Evaluating Resistance to False-Name Manipulations in Elections

In many mechanisms (especially online mechanisms), a strategic agent can inﬂuence the outcome by creating multiple false identities. We consider voting settings where the mechanism designer cannot completely prevent false-name manipulation, but may use false-name-limiting methods such as CAPTCHAs to inﬂuence the amount and characteristics of such manipulation. Such a designer would prefer, ﬁrst, a high probability of obtaining the “correct” outcome, and second, a statistical method for evaluating the correctness of the outcome. In this paper, we focus on settings with two alternatives. We model voters as independently drawing a number of identities from a distribution that may be inﬂuenced by the choice of the false-name-limiting method. We give a criterion for the evaluation and comparison of these distributions. Then, given the results of an election in which false-name manipulation may have occurred, we propose and justify a statistical test for evaluating the outcome.
On Case Base Formation in Real-Time Heuristic Search

Real-time heuristic search algorithms obey a constant limit on planning time per move. Agents using these algorithms can execute each move as it is computed, suggesting a strong potential for application to real-time video-game AI. Recently, a breakthrough in real-time heuristic search performance was achieved through the use of case-based reasoning. In this framework, the agent optimally solves a set of problems and stores their solutions in a case base. Then, given any new problem, it seeks a similar case in the case base and uses its solution as an aid to solve the problem at hand. A number of ad hoc approaches to the case base formation problem have been proposed and empirically shown to perform well. In this paper, we investigate a theoretically driven approach to solving the problem. We mathematically relate properties of a case base to the suboptimality of the solutions it produces and subsequently develop an algorithm that addresses these properties directly. An empirical evaluation shows our new algorithm outperforms the existing state of the art on contemporary video-game pathfinding benchmarks.
Approximate multiple kernel learning with least-angle regression

Kernel methods provide a principled way for general data representations. Multiple kernel learning and kernel approximation are often treated as separate tasks, with considerable savings in time and memory expected if the two are performed simultaneously. Our proposed Mklaren algorithm selectively approximates multiple kernel matrices in regression. It uses Incomplete Cholesky Decomposition and Least-angle regression (LAR) to select basis functions, achieving linear complexity both in the number of data points and kernels. Since it approximates kernel matrices rather than functions, it allows to combine an arbitrary set of kernels. Compared to single kernel-based approximations, it selectively approximates different kernels in different regions of the input spaces. The LAR criterion provides a robust selection of inducing points in noisy settings, and an accurate modelling of regression functions in continuous and discrete input spaces. Among general kernel matrix decompositions, Mklaren achieves minimal approximation rank required for performance comparable to using the exact kernel matrix, at a cost lower than 1% of required operations. Finally, we demonstrate the scalability and interpretability in settings with millions of data points and thousands of kernels.
A Mouse-Trajectory Based Model for Predicting Query-URL Relevance

For the learning-to-ranking algorithms used in commercial search engines, a conventional way to generate the training examples is to employ professional annotators to label the relevance of query-url pairs. Since label quality depends on the expertise of annotators to a large extent, this process is time-consuming and labor-intensive. Automatically generating labels from click-through data has been well studied to have comparable or better performance than human judges. Click-through data present users’ action and imply their satisfaction on search results, but exclude the interactions between users and search results beyond the page-view level (e.g., eye and mouse movements). This paper proposes a novel approach to comprehensively consider the information underlying mouse trajectory and click-through data so as to describe user behaviors more objectively and achieve a better understanding of the user experience. By integrating multi-sources data, the proposed approach reveals that the relevance labels of query-url pairs are related to positions of urls and users’ behavioral features. Based on their correlations, query-url pairs can be labeled more accurately and search results are more satisfactory to users. The experiments that are conducted on the most popular Chinese commercial search engine (Baidu) validated the rationality of our research motivation and proved that the proposed approach outperformed the state-of-the-art methods.
Modelling Character Knowledge in Plan-Based Interactive Narrative to Extend Accomodative Mediation

In plan-based interactive narrative systems, user actions must be integrated with an ideal story structure in order to strike a reasonable balance between user agency and story quality. A process called mediation addresses this issue by selecting appropriate responses to possible detrimental user actions in a given interactive narrative plan. One way the algorithm mediates exceptional user activity is accommodation, the process of replanning future events to incorporate an unwanted action and accomplish the story's authorial goals. However, this approach is limited by an underlying assumption that the story world exists as an object outside the user's subjective experience. In this paper, we incorporate a model of user knowledge into plan-based mediation in order to widen the space of plans the algorithm may search during accommodation by allowing the process to replan past events that occur outside a user's imperfect knowledge of the story world.
Study of Multi-Classification of Advanced Daily Life Activities on SHIMMER Sensor Dataset

Today the field of wireless sensors have the dominance in almost every person’s daily life. Therefore researchers are exasperating to make these sensors more dynamic, accurate and high performance computational devices as well as small in size, and also in the application area of these small sensors. The wearable sensors are the one type which are used to acquire a person’s behavioral characteristics. The applications of wearable sensors are healthcare, entertainment, fitness, security and military etc. Human activity recognition (HAR) is the one example, where data received from wearable sensors are further processed to identify the activities executed by the individuals. The HAR system can be used in fall detection, fall prevention and also in posture recognition. The recognition of activities is further divided into two categories, the un-supervised learning and the supervised learning. In this paper we first discussed some existing wearable sensors based HAR systems, then briefly described some classifiers (supervised learning) and then the methodology of how we applied the multiple classification techniques using a benchmark data set of the shimmer sensors placed on human body, to recognize the human activity. Our results shows that the methods are exceptionally accurate and efficient in comparison with other classification methods. We also compare the results and analyzed the accuracy of different classifiers.
Comparing Player Skill, Game Variants, and Learning Rates Using Survival Analysis

Game designers can use computer-aided game design methods to quantitatively compare player skill levels, different game variants, and learning rates, for the purpose of modeling how players will likely experience a game. We use Monte-Carlo simulation, hazard functions, and survival analysis to show how difficulty will quantitatively change throughout a game level as we vary skill, game parameters, and learning rates. We give a mathematical overview of survival analysis, present empirical data analyses of our player models for each game variant, and provide theoretical probability distributions for each game. This analysis shows the quantitative reasons why balancing a game for a wide range of player skill can be difficult; our player modeling provides tools for tuning this game balance. We also analyze the score distribution of over 175 million play sessions of a popular online Flappy Bird variant to demonstrate how learning effects can impact scores, implying that learning is crucial aspect of player modeling.
Integrating Mobile Display Energy Saving into Cloud-Based Video Streaming via Rate-Distortion-Display Energy Profiling

Mobile displays have been recognized as the major contributors to the energy consumption of contemporary mobile video services. Existing display energy reduction (DER) algorithms focus on local video/image processing by utilizing the computation resources on the mobile devices. As such, a per-device DER strategy is highly inefficient from the systematic perspective since the same computation is repeated among millions of individual mobile devices. In this new era of cloud-based video streaming, a natural question to ask is can ubiquitous cloud resources be exploited to overcome these drawbacks. It is based on this motivation that we design a paradigm-shifting strategy to integrate the display energy saving engine into cloud-based video streaming in order to simultaneously benefit massive mobile devices with a one-time video processing in the cloud. By taking full advantage of the computational and storage resources in the cloud, a family of Rate-Distortion-Display Energy (R-D-DE) profiles can be created for a given video source and for different types of mobile devices. The preliminary experimental results with OLED displays prove that we can achieve the desired DER performance through jointly managing the display energy and user experience by implementing the proposed R-D-DE integrated video encoding engine in the cloud.
Educational Game “Mengenal Indonesia” as a Medium to Introduce Indonesia to the Kids

Educational game is a game especially for the kid, that contains educational materials designed in an interactive game that aims to improve intelligence as well as providing knowledge to the users. The opportunity to make android-based education game is open wide, especially the game that exploit local content. In this research, we will develop the education game that has the main goal to introduce Indonesia to the learners. Games that will be built is android-based game that explore the knowledge or specific fact about certain region or provinces in Indonesia, so it will called as “Mengenal Indonesia”. This game is expected can act as a learning medium to introduce about Indonesia to the student, in interesting way. The game also be equipped with quiz about specific fact on the region that selected by the player. The game is built through ADDIE model as an approach. ADDIE (Analysis, Design, Development, Implementation, and Evaluation) is an approach that common use in design a learning content. The tools that used to build the game is FlashDevelop and using ActionScript 3.0 as programming language. After the game built, we conduct the survey to test the respond of the user. The result show that most of the user feel “Mengenal Indonesia” can help them improving knowledge about Indonesia. Therefore, it is necessary to build the game that has the content the knowledge about Indonesia. This paper will discuss the approach, design and making of educational game that contains information about Indonesia area. This content is selected because currently there is no educational game with a similar content available in the market and this content can be act as additional resource on the Social Sciences subject at elementary school level. The game, we call as “Mengenal Indonesia”, previous name is “know your country”, focusing to introduce some unique aspect about specific region in Indonesia. The source of knowledge we take from the book “RIPUT” (Ringkasan Pengetahuan Umum), that contains common knowledge about Indonesia, such as the longest river, the highest mountain, the fact about certain place in Indonesia, and many more. The game was built using ADDIE approach, that consist 5 steps: analysis, design, deployment, implementation, and evaluation. The game was built using FlashDevelop and ActionScript 3.0 as language script.
Stability of nonlinear systems using optimal fuzzy controllers and its simulation by Java programming

In this paper, at first, the single input rule modules (SIRMs) dynamically connected fuzzy inference model is used to stabilize a double inverted pendulum system. Then, a multiobjective particle swarm optimization (MOPSO) is implemented to optimize the fuzzy controller parameters in order to decrease the distance error of the cart and summation of the angle errors of the pendulums, simultaneously. The feasibility and efficiency of the proposed Pareto front is assessed in comparison with results reported in literature and obtained from other algorithms. Finally, the Java programming with applets is utilized to simulate the stability of the nonlinear system and explain the internet-based control.
Impact Of Spatial Visualization Topic Order On Student Performance And Attitudes

Spatial visualization skills are critical to many scientific and technical careers.At Michigan Tech, we identify first year engineering students who may not have fully developed their spatial skills and invite them to take an optional 1-credit course to help develop these skills.This class meets for 1.5 hours once a week for 14 weeks.The topics covered in this class include: isometric drawing, orthographic projections, flat patterns, 3-D rotations, object reflections and planes of symmetry, cutting planes, surfaces and solids of revolution, and combining solids.Topics are listed in the order that was thought to develop 3-D spatial skills and in the order they have been traditionally covered in this class.In the past we have noticed that 1) some students have a difficult time jumping right into isometric sketching and 2) that by the end of the semester students seem bored, especially when coving the topics of surfaces and solids of revolution and combining solids.To determine if attitudes and learning at the beginning and end of the course could be improved, the order of topics covered was altered in one of the three sections of the spatial visualization course during the Fall 2007 semester.At the same time, we wanted to ensure that changing the order in which topics were covered did not negatively impact improvements in spatial skills that we have been able to achieve through this course.Therefore, students in all sections were pre-and post-tested to determine whether changing the order of topics had a negative impact on spatial skill development.Attitudes of students taking the course with the traditional order of topics were compared to attitudes of students taking the course with the modified order through weekly evaluation forms.The evaluation form asked students to rate things such as their perception of the difficulty of the topic, how much time they spent on the material outside of class, and their enjoyment of the material covered.It was found that the new order of course material did not have an impact on the development of spatial skills or student perception of the difficulty of the material.However, students did show a slight improvement in their overall enjoyment of the material.This improvement was statistically significant for the module on combining solids which was moved to the beginning of the semester.The improvement in enjoyment was also significant for some of the more difficult modules, inclined and curved surfaces and rotations about a single axis.
Teaching Data Mining in the Era of Big Data

Bucknell University, where he teaches introductory courses in programming, as well as advanced courses
Improving a Planner’s Performance through Online Heuristic Configuration of Domain Models

The separation of planner logic from domain knowledge supports the use of reformulation and configuration techniques, such as macro-actions and entanglements, which transform the model representation in order to improve a planner’s performance. One drawback of such an approach is that it may require a potentially expensive training phase. In this paper, we introduce heuristic approaches for the online configuration of planning domain models. The proposed heuristics consider different aspects of PDDL-encoded operators for reordering such operators in the domain model, relying on the assumption that the way in which operators are encoded carries useful information about their expected use.
Reduced-Complexity Deep Neural Networks Design Using Multi-Level Compression

Deep Neural Network has achieved great success in many fields. However, many DNN models are both deep and large thereby causing high storage and energy consumption during the training and inference phases. This paper proposes multi-level compression framework. By utilizing cross-layer parameter-reducing techniques ranging from structure compression to weight compression to representation compression, the proposed compression strategy can enable order-of-magnitude reduction in network size for both training and inference with negligible accuracy loss, thereby leading to very high-efficiency and high-accuracy DNN models. Experiments show that the proposed strategy can achieve around 1.8K compression ratio in terms of dense matrices and around 30x for the overall model.
Tuning Belief Revision for Coordination with Inconsistent Teammates

Coordination with an unknown human teammate is a notable challenge for cooperative agents. Behavior of human players in games with cooperating AI agents is often sub-optimal and inconsistent leading to choreographed and limited cooperative scenarios in games. This paper considers the difficulty of cooperating with a teammate whose goal and corresponding behavior change periodically. Previous work uses Bayesian models for updating beliefs about cooperating agents based on observations. We describe belief models for on-line planning, discuss tuning in the presence of noisy observations, and demonstrate empirically its effectiveness in coordinating with inconsistent agents in a simple domain. Further work in this area promises to lead to techniques for more interesting cooperative AI in games.
Automatic Real-Time Music Generation for Games

Music composition can be a challenge for many small- to medium-sized game companies, largely due to the expense and difficulty in creating original music for each level of a game. To address this, we developed a tool that automatically generates original music, by training a music generator on pieces whose style the game designer wishes to imitate. The generator then creates original music in that style in real-time, and switches between styles when signaled by the game. This software has been refined to produce music that is coherent and imitates a composer’s larger music structure.
Optimizing Players’ Expected Enjoyment in Interactive Stories

In interactive storytelling systems and other story-based computer games, a drama manager is a background agent that aims to bring about an enjoyable and coherent experience for the players. In this paper, we present a personalized drama manager that increases a player's expected enjoyment without removing player agency. Our personalized drama manager models a player's preference using data-driven techniques, predicts the probability the player transitioning to different story experiences, selects an objective experience that can maximize the player's expected enjoyment, and guides the player to the selected story experience. Human study results show that our drama manager can significantly increase players' enjoyment ratings in an interactive storytelling testbed, compared to drama managers in previous research.
Using Expressive Language Generation to Increase Authorial Leverage

It is widely agreed that the interaction possibilities in interactive narrative are limited by the current approach to dialog creation, which typically relies on teams of scriptwriters. This paper reports on experiments testing whether authorial leverage can be increased by methods for natural language generation of dialog in a story world called Heart of Shadows. Our experiments show that (1) expert writers spend less time authoring dialogue variations by editing automatically generated content than creating it from scratch and (2) expert writers are more critical of automatically generated dialogue than amateur writers.
An Expressive Dilemma Generation Model for Players and Artificial Agents

Dilemma scenarios and knowledge of social values support engaging and believable narrative gameplay. Previous work in dilemma generation has explored scenarios involving utility payoffs between two to three people. However, these models primarily require character relationships as preconditions, and do not extend to more complex choices that relate only to causes and values. This paper builds upon past work to create an expressive model of dilemma categorization for player and non-player characters.
Per-Map Algorithm Selection in Real-Time Heuristic Search

Real-time heuristic search is suitable for time-sensitive pathfinding and planning tasks when an AI-controlled non-playable character must interleave its planning and plan execution. Since its inception in the early 90s, numerous real-time heuristic search algorithms have been proposed. Many of the algorithms also have control parameters leaving a practitioner with a bewildering array of choices. Recent work treated the task of algorithm and parameter selection as a search problem in itself. Such automatically found algorithms outperformed previously known manually designed algorithms on the standard video-game pathfinding benchmarks. In this paper we follow up by selecting an algorithm and parameters automatically per map. Our sampling-based approach is efficient on the standard video-game pathfinding benchmarks. We also apply the approach to per-problem algorithm selection and while it is effective there as well, it is not practical. We offer suggestions on making it so.