Reviewerly/exHarmony huggingface dataset:
Maximum a Posteriori Estimation by Search in Probabilistic Programs

We introduce an approximate search algorithm for fast maximum a posteriori probability estimation in probabilistic programs, which we call Bayesian ascent Monte Carlo (BaMC). Probabilistic programs represent probabilistic models with varying number of mutually dependent finite, countable, and continuous random variables. BaMC is an anytime MAP search algorithm applicable to any combination of random variables and dependencies. We compare BaMC to other MAP estimation algorithms and show that BaMC is faster and more robust on a range of probabilistic models.
Swedish Archaeology in Perspective and the Possibility of Reflexivity

In exploring the possibilities of reflexivity in the Cultural Resource Management archaeology in Sweden, a comparison is made between the excavation at Çatalhöyük in Turkey and an excavation at Burlöv in Malmö, Sweden. The former, a large-scale research project that aims at implementing post-processual ideas, differs in many ways from the latter, which is part of a large-scale rescue project, Öresundsförbindelsen. There are also similarities, and in different ways reflexivity seems to be achieved in both cases.
The Common European Framework of Reference for Languages: a European framework for foreign language speech development?

Author/s&#x0D; Bettina BeinhoffAnglia Ruskin University Cambridge, United Kingdom&#x0D; &#x0D; ABSTRACT&#x0D; This paper explores the representation of speech development and particularly perceptive skills in the CEFR level descriptions. The speech-related CEFR sections and related level descriptions are vague and none of the assumptions made therein (such as the supposed linear progression between levels) have been sufficiently tested yet. This paper presents an exploratory study on speech perception in language learners at different levels of proficiency and from different first language (L1) backgrounds (Spanish and German). The study is based on transcriptions in response to short narratives, and investigates what kind of influence listeners’ levels of proficiency in the second language (in this case English) and their L1 backgrounds have on how intelligibility is perceived. The results suggest that proficiency levels and L1 background do indeed influence intelligibility (though not always as anticipated) and partially confirm the idea of a linear progression as proposed in the CEFR.
Natural Noun Phrase Variation for Interactive Characters

Interactive characters that cohabit a shared space with human partners need to generate and interpret references to elements of the virtual world. Natural language allows for a wide range of phrasings for referring to any particular object, and this variation is thought to reflect not only spatial but also cognitive and linguistic factors. Our study attempts to account for the variability in referring forms found in a set of dialogs of two human partners performing a treasure-hunt task in a virtual world. A decision tree classifier was built that predicts the form of 51% of the referring expressions, compared to a baseline of 39% achieved by a heuristic classifier. The classification algorithm can be used by conversational characters to generate referring expressions of the appropriate form.
FACTORS AFFECTING THE ONLINE ECONOMY ON A REGIONAL BASIS

The online economy takes a huge role in today's globalized world.Internet as a general purpose technology changes how companies operate, but it also changes the markets.The online economy has a huge effect on people's everyday life, private and public companies as well.The goal of this paper is to analyse the global online economy by countries of origin answering the question which countries dominate the internet.After deep literature survey on the subject the Alexa Top 500 database is used to analyse it by the most visited websites in the world.The analysis shows the online economy is dominated by the USA and China.Correlation analysis confirms that GDP correlates with websites originating from that country.This means highly developed countries have better online economy with more websites, dominating the online economy.
Preliminary Semantic Analysis of Political Blogs

In this paper, we present a series of semantic analyses of words in political blogs in the setting of categorization of two opposite political orientations: liberal vs. conservative. We classify nouns, verbs, adjectives and adverbs into semantic categories by using the General Inquirer dictionary. Then distributions of these categories and correlations among them are examined both within and between blogs of the two opposite political leanings. Results show that although words of certain categories tend to appear together while others do not within blogs of a political leaning, the semantic category distribution of words used by left-wing bloggers is very similar to those by right-wing bloggers, suggesting single words alone do not account for major difference between these two major categories of blogs. Lastly, by examining preliminary results of association rule mining of nouns, verbs, adjectives and adverbs in sentences, we posit that the similarity and/or difference between blogs of opposite political orientations can be detected by extracting opinion expressions around collocation of nouns and verbs (together with modifiers).
Design and Simulation of Dual Axis Solar Tracker for Optimum Solar Energy Absorption

Solar energy is becoming one of the most expanding renewable energy it is getting more prominence.Sun trackers can substantially improve the electricity production of a photovoltaic (PV) system.This paper proposes a novel design of a dual-axis solar tracking PV system that utilizes the feedback control theory along with a four-quadrant light-dependent resistor (LDR) sensor and simple electronic circuits to provide robust system performance.Our project will include the design and construction of a microcontroller-based solar panel tracking system.In hardware development we utilize LDR's as sensors and two servo motors to direct the position of the solar panel.Dual-axis solar tracking allows more energy to be produced because the solar array is able to remain aligned perpendicular to the sun.In this paper to make better analysis Dual axis solar tracker is implemented for standalone system and simulation analysis is performed on the grid-connected PV station.To perform the experimental analysis the data is collected from Meteorology of Asmara and NASA.Comparative results have been presented between static and automatic solar dual-axis stand-alone systems.Moreover, comparison will be made among the output power results of our system with the conventional (Static) solar system.Simulation results have presented for the grid integrated system which shows better performance results.
Bayesian Computation of the Intrinsic Structure of Factor Analytic Models

The study of factor analytic models often has to address two important issues: (a) the determination of the "optimum" number of factors and (b) the derivation of a unique simple structure whose interpretation is easy and straightforward.The classical approach deals with these two tasks separately, and sometimes resorts to ad-hoc methods.This paper proposes a Bayesian approach to these two important issues, and adapts ideas from stochastic geometry and Bayesian finite mixture modelling to construct an ergodic Markov chain having the posterior distribution of the complete collection of parameters (including the number of factors) as its equilibrium distribution.The proposed method uses an Automatic Relevance Determination (ARD) prior as the device of achieving the desired simple structure.A Gibbs sampler updating scheme is then combined with the simulation of a continuous-time birth-and-death point process to produce a sampling scheme that efficiently explores the posterior distribution of interest.The MCMC sample path obtained from the simulated posterior then provides a flexible ingredient for most of the inferential tasks of interest.Illustrations on both artificial and real tasks are provided, while major difficulties and challenges are discussed, along with ideas for future improvements.
Building Test Beds for AI with the Q3 Mode Base

We extend the open source Quake 3 Arena code base released by Id Software to include modification friendly code segments, art asset blueprints, external agent control and perception interfaces, game interaction logging, and a tool set that allows the creation of test beds for artificial agents. Our current modification work in the Urban Combat Testbed using the Q3 Mod Base is currently being used to study transfer learning in human and agent players and as an AI teaching tool.
TripEneer: User-Based Travel Plan Recommendation Application

Current travel recommendation systems are helpful in addressing a traveler's information needs to certain extent, however, most of them fail to factor in the user in their recommendations. TripEneer proposes travel recommendations to a traveler by keeping the user preferences and constraints as first class citizens. We present an intuitive UI for helping users plan their travel trips quickly and easily. In the current demo we present various global and user-specific ranking models used for recommending travel destinations. Our preliminary evaluation showed that the users found the personalized recommendations, based on the user model, most useful.
Using Backwards Generated Goals for Heuristic Planning

Forward State Planning with Reachability Heuristics is arguably the most successful approach to Automated Planning up to date. In addition to an estimation of the distance to the goal, relaxed plans obtained with such heuristics provide the search with useful information such as helpful actions and look-ahead states. However, this information is extracted only from the beginning of the relaxed plan. In this paper, we propose using information extracted from the last actions in the relaxed plan to generate intermediate goals backwards. This allows us to use information from previous computations of the heuristic and reduce the depth of the search tree.
Feature Sentiment Diversification of User Generated Reviews: The FREuD Approach

Online discussions, user reviews and comments onthe Social Web are valuable sources of informationabout products, services, or shared contents. The rapidlygrowing popularity and activity of Web communitiesraises novel questions of appropriate aggregation anddiversification of such social contents. In many cases,users are interested in gaining an extensive overviewover pros and cons of a particular track of contributions.We address the problem of social content diversificationby combining latent semantic analysis with featurecentricsentiment analysis. Our FREuD approach providesa representative overview of sub-topics and aspectsof discussions, characteristic user sentiments underdifferent aspects, and reasons expressed by differentopponents. In experiments with real world productreviews we compare FREuD to the typical implementationof ranking reviews by the usefulness rating providedby users as well as a naive sentiment diversificationalgorithms based on star ratings. To this end we hadhuman users provide a fine-grained gold standard aboutthe coverage of features and sentiments in reviews forseveral products in three categories. We observed thatFREuD clearly outperforms the baseline algorithms ingenerating a sentiment-diversified set of user reviewsfor a given product.
Planning Is the Game: Action Planning as a Design Tool and Game Mechanism

Recent development in game AI has seen action planning and its derivates being adapted for controlling agents in classical types of games, such as FPSs or RPGs. Complementary, one can seek new types of gameplay elements inspired by planning. We propose and formally define a new game "genre" called anticipation games and demonstrate that planning can be used as their key concept both at design time and run time. In an anticipation game, a human player observes a computer controlled agent or agents, tries to predict their actions and indirectly helps them to achieve their goal. The paper describes an example prototype of an anticipation game we developed. The player helps a burglar steal an artifact from a museum guarded by guard agents. The burglar has incomplete knowledge of the environment and his plan will contain pitfalls. The player has to identify these pitfalls by observing burglar's behavior and change the environment so that the burglar replans and avoids the pitfalls. The game prototype is evaluated in a small-scale human-subject study, which suggests that the anticipation game concept is promising.
Automated error analysis for multiword expressions: Using BLEU-type scores for automatic discovery of potential translation errors

We describe the results of a research project aimed at automatic detection of MT errors using state-of-the-art MT evaluation metrics, such as BLEU. Currently, these automated metrics give only a general indication of translation quality at the corpus level and cannot be used directly for identifying gaps in the coverage of MT systems. Our methodology uses automatic detection of frequent multiword expressions (MWEs) in sentence-aligned parallel corpora and computes an automated evaluation score for concordances generated for such MWEs which indicates whether a particular expression is systematically mistranslated in the corpus. The method can be applied both to source and target MWEs to indicate, respectively, whether MT can successfully deal with source expressions, or whether certain frequent target expressions can be successfully generated. The results can be useful for systematically checking the coverage of MT systems in order to speed up the development cycle of rule-based MT. This approach can also enhance current techniques for finding translation equivalents by distributional similarity and for automatically identifying features of MT-tractable language.
Touch Screens for Touchy Issues: Analysis of Accessing Sensitive Information from Mobile Devices

Smartphones, and other similar devices, are ideal for private activities: They are carried on the body and can be physically secured from prying eyes. They work anywhere. And they are not typically shared among family members or classmates, making them perfect for searching and exploring sensitive information - both via web search or through social information seeking. This paper validates these intuitions empirically, and builds on these observations to investigate the expression of sensitive information needs, such as inquiring about unplanned pregnancies, among users of mobile devices. We examine a large set of queries from the United States (4.2 million), submitted to Yahoo! Answers, and compare these to the 6.7 million queries submitted to Yahoo! search. Focusing on community question answering allows much richer analysis of the way information needs are expressed, setting our work apart form previous studies. For the first time, we empirically show that people prefer to express sensitive needs on mobile devices, as this manifests at both the lexical level and the semantic level, using a pre-defined taxonomy of topics. Further, we find that preference for mobile devices for sensitive topics holds true even when controlling for age and gender, which is facilitated by the large sample of users in our study (1.5 million). In particular, we show that young users, especially females, are more likely to inquire on sensitive issues, and are more likely to do this from locations distant from their common place of web access. To our knowledge, this is the largest study to date of mobile social information seeking, and is unique in terms of demographic diversity, the granularity of information needs examined, and the analysis of the location-dependence of sensitive information needs.
Fuzzy Affective Player Models: A Physiology-Based Hierarchical Clustering Method

Current approaches to game design improvements rely on time-consuming gameplay testing processes, which rely on highly subjective feedback from a target audience. In this paper, we propose a generalizable approach for building predictive models of players’ emotional reactions across different games and game genres, as well as other forms of digital stimuli. Our input agnostic approach relies on the following steps: (a) collecting players' physiologically-inferred emotional states during actual gameplay sessions, (b) extrapolating the causal relations between changes in players' emotional states and recorded game events, and (c) building hierarchical cluster models of players' emotional reactions that can later be used to infer individual player models via fuzzy cluster membership vectors. We expect this work to benefit game designers by accelerating the affective play-testing process through the offline simulation of players' reactions to game design adaptations, as well as to contribute towards individually-tailored affective gaming.
ScriptEase II: Platform Independent Story Creation Using High-Level Patterns

As the video game industry grows, both developers and creative authors seek new ways to simplify the process of controlling story content using scripts. This paper describes a story model and its software implementation, ScriptEase II, designed to solve this game design bottleneck. ScriptEase II is the second generation of the ScriptEase system, whose goal was to enable game authors with no programming ability to generate scripting code from high-level game patterns. ScriptEase II differs from the original in two important ways. First, ScriptEase II uses game-dependent translators to generate scripts for any game engine. Second, ScriptEase II uses a drag-and-drop interface that simplifies the story component creation menus that grew cumbersome in the original ScriptEase. The feasibility of code generation has been validated using three different game engines and the advantages of the simple drag-and-drop interface have been validated by a user study.
When Players Quit (Playing Scrabble)

What features contribute to player enjoyment and player retentionhas been a popular research topic in video games research;however, the question of what causes players to quit agame has received little attention by comparison. In this paper,we examine 5 quantitative features of the game Scrabblesquein order to determine what behaviors are predictors ofa player prematurely ending a game session. We identified afeature transformation that notably improves prediction accuracy.We used a naive Bayes model to determine that there areseveral transformed feature sequences that are accurate predictorsof players terminating game sessions before the endof the game.We also identify several trends that exist in thesesequences to give a more general idea as to what behaviorsare characteristic early indicators of players quitting.
Using Expectations to Drive Cognitive Behavior

Generating future states of the world is an essential component of high-level cognitive tasks such as planning. We explore the notion that such future-state generation is more widespread and forms an integral part of cognition. We call these generated states expectations, and propose that cognitive systems constantly generate expectations, match them to observed behavior and react when a difference exists between the two. We describe an ACT-R model that performs expectation-driven cognition on two tasks – pedestrian tracking and behavior classification. The model generates expectations of pedestrian movements to track them. The model also uses differences in expectations to identify distinctive features that differentiate these tracks. During learning, the model learns the association between these features and the various behaviors. During testing, it classifies pedestrian tracks by recalling the behavior associated with the features of each track. We tested the model on both single and multiple behavior datasets and compared the results against a k-NN classifier. The k-NN classifier outperformed the model in correct classifications, but the model had fewer incorrect classifications in the multiple behavior case, and both systems had about equal incorrect classifications in the single behavior case.
TEAM-IT : Location-Based Gaming in Real and Virtual Environments

Location-based games are an emerging paradigm fortraining, simulation, entertainment, health and many other domains. In this paper, we consider the role of artificialagents in such games. We also examine how human teams perform when given the same game, playedin both a real environment with mobile devices and alsoin a virtual environment that replicates the real environment.We perform the first direct comparison of real andvirtual instantiations of the same location-based game.We show the similarities and differences in game playand then investigate how adding an advice-giving agentchanges the experience.
A Multi-Domain Evaluation of Scaling in a General Episodic Memory

Episodic memory endows agents with numerous general cognitive capabilities, such as action modeling and virtual sensing. However, for long-lived agents, there are numerous unexplored computational challenges in supporting useful episodic-memory functions while maintaining real-time reactivity. In this paper, we review the implementation of episodic memory in Soar and present an expansive evaluation of that system. We demonstrate useful applications of episodic memory across a variety of domains, including games, mobile robotics, planning, and linguistics. In these domains, we characterize properties of environments, tasks, and episodic cues that affect performance, and evaluate the ability of Soar’s episodic memory to support hours to days of real-time operation.
Bringing Research And New Technology Into The Undergraduate Curriculum: A Course In Computational Fluid Dynamics

As technology advances in the industries which graduating engineers wish to enter, technology in the undergraduate curriculum must also advance.A course in computational fluid dynamics was recently developed which meets the challenge of bringing advanced topics to undergraduate students.This paper addresses techniques used to enable undergraduates to enter the work force with the ability to solve and physically understand fluid dynamics problems requiring commercially available computational fluid dynamics codes and related software.Student projects involving grid generation, the solution to two-dimensional and three-dimensional problems, and the solution to multi-dimensional species flow problems are presented.Additionally, final term projects obtained from the students' cooperative employers are discussed.
THE USE OF ADOBE ANIMATE AS A NEW TECHNOLOGY IN TEACHING ACADEMIC ESSAY WRITING IN DARMA PERSADA UNIVERSITY

Abstract&#x0D; The development of information and technology has affected many aspects of life including education. Writing has an important role in the language academic process. Technology also gives a positive effect toward the students’ motivation in learning English, especially in essay writing. Nowadays, a computer technology application is developing writing materials by using adobe animate. The purpose of this research is to show that the role of technology today is very important in learning process specifically in teaching academic writing, so that it can help teacher and students to make lots variations in teaching. In the past, teaching writing only used conventional ways like books but nowadays teachers and students can use multimedia technology like education interactive software, smartphone, and computer which can make studying more effective and interactive. In this qualitative research the writers will analyse and demonstrate the new technology as a solution to the old problem of teaching academic essay writing.