Miracle-dz/UniFAR-FTUs huggingface dataset:
Group play: determining factors on the gaming experience in multiplayer role-playing games

Role-playing games (RPGs) are a well-known game form, existing in a number of formats, including tabletop, live action, and various digital forms. Despite their popularity, empirical studies of these games are relatively rare. In particular there have been few examinations of the effects of the various formats used by RPGs on the gaming experience. This article presents the results of an empirical study, examining how multi-player tabletop RPGs are affected as they are ported to the digital medium. Issues examined include the use of disposition assessments to predict play experience, the effect of group dynamics, the influence of the fictional game characters and the comparative play experience between the two formats. The results indicate that group dynamics and the relationship between the players and their digital characters, are integral to the quality of the gaming experience in multiplayer RPGs, with the first being of greater importance to digital games and the latter to the tabletop version.
Lurking Behavior in Online Psychosocial Discussion Forums: Theoretical Perspectives and Implications for Practice

ABSTRACT Improving outcomes of telebehavioral psychoeducation requires rethinking program design when delivered wholly or partially for self-directed participation. Discussion forum participation often follows the “90-9-1 Rule” where 90% of participants lurk, rather than contribute content. A theoretical perspective on the behavior can help explain its adaptive functions, as well as the threats that this behavior poses to the lurker. Implications for practice require program redesign that actively links individual skills training and group-based discussion. The proposed linking design can synergize individual and group participation to support the development of mutual aid, as well as greater interaction with psychoeducation content and materials.
Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2D Face Pose Estimation and Heart Segmentation in 3D CT Images

Recently, a successful pose estimation algorithm, called Cascade Pose Regression (CPR), was proposed in the literature. Trained over Pose Index Feature, CPR is a regressor ensemble that is similar to Boosting. In this paper we show how CPR can be represented as a Neural Network. Specifically, we adopt a Graph Transformer Network (GTN) representation and accordingly train CPR with Back Propagation (BP) that permits globally tuning. In contrast, previous CPR literature only took a layer wise training without any post fine tuning. We empirically show that global training with BP outperforms layer-wise (pre-)training. Our CPR-GTN adopts a Multi Layer Percetron as the regressor, which utilized sparse connection to learn local image feature representation. We tested the proposed CPR-GTN on 2D face pose estimation problem as in previous CPR literature. Besides, we also investigated the possibility of extending CPR-GTN to 3D pose estimation by doing experiments using 3D Computed Tomography dataset for heart segmentation.
A 325 GHz frequency multiplier chain in a SiGe HBT technology

A single-chip 325-GHz ×18 frequency multiplier chain based on two cascaded active differential triplers and a balanced output doubler is presented. The multiplier operates over a 317 to 328 GHz bandwidth with a 0-dBm 18-GHz input signal. A peak output power of -8 dBm is obtained at 325 GHz. The multiplier chain is realized in an evaluation SiGe HBT technology with cut-off frequencies fT/fmax of 250 GHz / 380 GHz.
Protocols for Adaptive Wireless and Mobile Networking

The goal of the Monarch Project1 at Carnegie Mellon University is to develop networking protocols and protocol interfaces to allow truly seamless wireless and mobile host networking. The scope of our efforts includes protocol design, implementation, performance evaluation, and usage-based validation, spanning areas ranging roughly from portions of the ISO Data Link layer (layer 2) through the Presentation layer (layer 6). In this article, we give a status report of our current work in the Monarch Project, placing it in the context of broader efforts by the Internet mobile networking community. Our work will enable mobile hosts to communicate with each other and with stationary or wired hosts, transparently making the most efficient use of the best network connectivity available to the mobile host at any time. To this end, the networking protocols must support adaptive operation in a number of ways. For example, host mobility means that protocols must be able to adapt packet routing to reach each mobile host in its current location. In addition, different wireless networks, intended for example for local-area, metropolitan-area, and wide-area use, make different tradeoffs in factors such as bandwidth, latency, error rate, and usage cost, providing different levels of quality of network connection with each wireless networking product or service. Network protocols should be able to adapt to optimize use of the best available network connection for each mobile host at any time. Further, in order to allow higher-layer protocols and applications to adapt to these changes in network connection quality, network protocols should be able to provide information to higher layers when such changes take place. We are experimenting with our protocols in the context of the “Wireless Andrew” infrastructure,currently being built at Carnegie Mellon [10]. The Wireless Andrew infrastructure builds on the current wired network infrastructure on campus, consisting mostly of 10-megabit per second Ethernet equipment. For high-speed wireless access on campus, we are installing an AT&T WaveLAN network covering most of the campus buildings [32]. WaveLAN uses direct-sequence spread spectrum radio in the 900 MHz ISM band to provide a raw data rate of 2 megabits per second. For wireless access off campus or where otherwise out of range of the WaveLAN network, we are using Cellular Digital Packet Data (CDPD) [4]. The CDPD service uses idle voice channels on the existing AMPS cellular telephone network to transmit data packets at a raw data rate of 19.2 kilobits per second.
The Relation of Gender and Perception of Others for Support Erosion

Previous research on social support and depressive symptoms has found a relation between decreases in social support and increases in depressive symptoms. However, it is not clear whether support leads to decreases in depressive symptoms (support alleviation) or depressive symptoms leads to a decrease in support provision (support erosion). The current study explored this causal relation by having pairs of same‐sex friends complete measures of support requests, receipt, provision, and other support variables within that friendship three times over a six‐week period. We also explored whether the relation depended on gender, possible mechanisms behind the relation, and conducted exploratory analysis on relationship quality. Results showed no evidence for support alleviation but evidence for support erosion occurring for females. Provider burden limitedly explains this finding. Overall relationship quality does not seem to be influenced by depression. Future research should address communal strength in conjunction with the depressive symptom and social support relation.
Victorious rebels and postwar politics

Abstract Why do victorious rebels sometimes form powerful postwar political parties and other times collapse into weak, factionalized organizations? This paper examines cases of rebel victories in civil wars in Africa and traces the links between war duration, the extent of external intervention, and whether or not the war was fought in a compact area with the nature of the postwar political parties. It argues that protracted wars in confined territory with little external assistance have different organizational legacies than quick wars fought over expansive territory with significant international involvement. Four cases – Uganda, Ethiopia, Democratic Republic of Congo, and Libya – are used to illustrate the argument.
Denoising a Point Cloud for Surface Reconstruction

Surface reconstruction from an unorganized point cloud is an important problem due to its widespread applications. White noise, possibly clustered outliers, and noisy perturbation may be generated when a point cloud is sampled from a surface. Most existing methods handle limited amount of noise. We develop a method to denoise a point cloud so that the users can run their surface reconstruction codes or perform other analyses afterwards. Our experiments demonstrate that our method is computationally efficient and it has significantly better noise handling ability than several existing surface reconstruction codes.
A Brief Introduction to Statistical Shape Analysis

This note aims at giving a brief introduction to the field of statistical shape analysis looked at from an image analysis point of view. Basic techniques such as Procrustes analysis, tangent space projection and Principal Component Analysis (PCA) are presented and subsequently demonstrated and discussed in depth through a biometric case study of hands. Prerequisites are limited to basic knowledge of linear algebra and statistics at the price of a somewhat less compact presentation. To encourage learning by exploration; images, annotations and data reports from the hand study are made available for download.
Step size adaptation in evolution strategies using reinforcement learning

We discuss the implementation of a learning algorithm for determining adaptation parameters in evolution strategies. As an initial test case, we consider the application of reinforcement learning for determining the relationship between success rates and the adaptation of step sizes in the (1+1)-evolution strategy. The results from the new adaptive scheme when applied to several test functions are compared with those obtained from the (1+1)-evolution strategy with a priori selected parameters. Our results indicate that assigning good reward measures seems to be crucial to the performance of the combined strategy.
Personalised Learning for Casual Games: The 'Language Trap' Online Language Learning Game

The features of video games that contribute to effective learning are drawing increasing attention in the world of technology enhanced learning. To date, game based learning has focussed on the learning benefits provided by the inherent motivation, rich visualizations, and low risk of failure, provided by contemporary educational games. Although these advantages create highly engaging and immersive learning environments, there remain additional techniques that can further aid the learning process. The integration of personalisation into educational games presents unique challenges, the most important being the preservation of the gaming experience. In consideration that a well designed educational game can seamlessly blend learning and gaming content, any adaptation of the learning content will ultimately affect the gaming experience. In effect all educational adaptations must be achieved in a manner that is non-invasive to the game play. In this paper we introduce the „Language Trap‟ German language learning game that provides learners with an online casual gaming environment that also benefits from a personalised learning experience. We demonstrate how a game playable online in a browser, using simple controls, and with low production costs, can effectively deliver this personalised learning experience. Through using the ALIGN (Adaptive Learning In Games through Non-invasion) system the game delivers adaptive dialogue difficulty, performance feedback, motivational support, and meta-cognitive hints, all within a highly interactive adventure role-playing game. The results of an authentic evaluation of the Language Trap game are presented, additionally we demonstrate how the ALIGN system can effectively adapt the learning experience within the game in manner that is non-invasive to the game play. A discussion on the benefits of adaptive educational games is presented, with particular reference to the benefits provided by the ALIGN system.
Application of spectral analysis techniques in the intercomparison of aerosol data: 1. An EOF approach to analyze the spatial‐temporal variability of aerosol optical depth using multiple remote sensing data sets

Many remote sensing techniques and passive sensors have been developed to measure global aerosol properties. While instantaneous comparisons between pixel‐level data often reveal quantitative differences, here we use Empirical Orthogonal Function (EOF) analysis, also known as Principal Component Analysis, to demonstrate that satellite‐derived aerosol optical depth (AOD) data sets exhibit essentially the same spatial and temporal variability and are thus suitable for large‐scale studies. Analysis results show that the first four EOF modes of AOD account for the bulk of the variance and agree well across the four data sets used in this study (i.e., Aqua MODIS, Terra MODIS, MISR, and SeaWiFS). Only SeaWiFS data over land have slightly different EOF patterns. Globally, the first two EOF modes show annual cycles and are mainly related to Sahara dust in the northern hemisphere and biomass burning in the southern hemisphere, respectively. After removing the mean seasonal cycle from the data, major aerosol sources, including biomass burning in South America and dust in West Africa, are revealed in the dominant modes due to the different interannual variability of aerosol emissions. The enhancement of biomass burning associated with El Niño over Indonesia and central South America is also captured with the EOF technique.
Color-based lip localization method

This paper is concerned with lip localization for visual speech recognition (VSR) system. We shall present an efficient method for localization human's lips/mouth in video images. This method is based on using the YCbCr approach to find at least any part of the lip as an initial step. Then we use all the available information about the segmented lip-pixels such as r, g, b, warped hue, etc. to segment the rest of the lip. The mean is calculated for each value, then for each pixel in ROI, Euclidian distance from the mean vector is calculated. Pixels with smaller distances are further clustered as lip pixels. Thus, the rest of the pixels in ROI will be clustered (to lip/non-lip pixel) depending on their distances from the mean vector of the initial segmented lip region. The method is evaluated on a new-recorded database of 780,000 frames; the experiments show that the method localizes the lips efficiently, with high level of accuracy (91.15%) that outperforms existing lip detection approaches.
Active Learning Agents with Artificial Neural Brains

Two case studies on active learning agents are described. In the first case, the agent is a robot arm whose task is to grasp a rolling ball. It learns from self-generated examples by initiating conversations with the external teacher. In the second case, the agent is an autonomous mobile robot that works in cooperation with other mobile agents. The mobile robots learn group behavior by actively moving, evaluating their moves, and adapting their internal models. In both cases, each agent has a neural network as its brain. Simulation results are provided that support the evidence that active learning agents not only look more intelligent but they also acquire domain knowledge or adapt to their environment more efficiently than conventional agents.
The Generalized CEM Algorithm

We propose a general approach for estimating the parameters of latent variable probability models to maximize conditional likelihood and discriminant criteria. Unlike joint likelihood, these objectives are better suited for classiication and regression. The approach utilizes and extends the previously introduced CEM framework (Conditional Expectation Maximization), which reformulates EM to handle the conditional likelihood case. We generalize the CEM algorithm to estimate any mixture of exponential family densities. This includes structured graphical models over exponential families , such as HMMs. The algorithm eeciently takes advantage of the factorization of the underlying graph. In addition, the new CEM bound is tighter and more rigorous than the original one. The nal result is a CEM algorithm that mirrors the EM algorithm where both estimate a variational lower bound on their respective incomplete objective functions, and both generate the same standard M-steps over complete likelihood for direct maximiza-tion. The equivalence of M-steps facilitates migration of current ML approaches to conditional criteria for improved classiication and regression results.
On the Role of Social Identity and Cohesion in Characterizing Online Social Communities

Two prevailing theories for explaining social group or community structure are cohesion and identity. The social cohesion approach posits that social groups arise out of an aggregation of individuals that have mutual interpersonal attraction as they share common characteristics. These characteristics can range from common interests to kinship ties and from social values to ethnic backgrounds. In contrast, the social identity approach posits that an individual is likely to join a group based on an intrinsic self-evaluation at a cognitive or perceptual level. In other words group members typically share an awareness of a common category membership. In this work we seek to understand the role of these two contrasting theories in explaining the behavior and stability of social communities in Twitter. A specific focal point of our work is to understand the role of these theories in disparate contexts ranging from disaster response to socio-political activism. We extract social identity and social cohesion features-of-interest for large scale datasets of five real-world events and examine the effectiveness of such features in capturing behavioral characteristics and the stability of groups. We also propose a novel measure of social group sustainability based on the divergence in group discussion. Our main findings are: 1) Sharing of social identities (especially physical location) among group members has a positive impact on group sustainability, 2) Structural cohesion (represented by high group density and low average shortest path length) is a strong indicator of group sustainability, and 3) Event characteristics play a role in shaping group sustainability, as social groups in transient events behave differently from groups in events that last longer.
Identifying the intended addressee in mixed human-human and human-computer interaction from non-verbal features

Against the background of developments in the area of speech-based and multimodal interfaces, we present research on determining the addressee of an utterance in the context of mixed human-human and multimodal human-computer interaction. Working with data that are taken from realistic scenarios, we explore several features with respect to their relevance to the question who is the addressee of an utterance: eye gaze both of speaker and listener, dialogue history and utterance length. With respect to eye gaze, we inspect the detailed timing of shifts in eye gaze between different communication partners (human or computer). We show that these features result in an improved classification of utterances in terms of addressee-hood relative to a simple classification algorithm that assumes that "the addressee is where the eye is", and compare our results to alternative approaches.
Bellevue Urban Wildlife Habitat Literature Review

The City of Bellevue seeks to address the presence, function and value of habitat within its limits. Wildlife use of urban habitat is a relatively new field of study, and presently no standard means of evaluating either wildlife use or the value of urban habitat is widely available. It is generally accepted and well documented that urbanization has a profound impact on wildlife and habitat. A comprehensive review of the scientific literature pertaining to the subject of urban habitat reveals a number of trends, collated and described in the following document. Particular attention is paid to the City of Bellevue's species of local importance, so designated because they are in danger of extirpation; they have special recreation, commercial, tribal or other value; and their continued existence in the City is dependent on protections that are not adequately addressed by other jurisdictions. Results of this literature review are used in support of a standardized means of assessing urban habitat value, the accompanying Functional Assessment Model. As the United States grows in population and cities, suburbs, and rural areas support greater densities, natural wild areas become fewer and urban natural areas become increasingly valuable to both wildlife and humans. Recent scientific research has responded in kind, and a growing knowledge base confirms what is best captured in the summary: " All urban areas have the potential to contribute to conservation of wildlife diversity " (Marzluff and Rodewald 2008). In the Pacific Northwest, native habitat exists to varying extents within even the most densely developed areas. This document summarizes the best available science addressing the impacts of ongoing human development in urbanizing areas and the interactions among humans, wildlife, and urban natural environs. Urbanization, the increase in human settlement density and associated intensification of land use, has a profound and lasting effect on the natural environment and wildlife habitat (McKinney 2002, Blair 2004, Marzluff 2005, Munns 2006), is a major cause of native species local extinctions (Czech et al. 2000), and is likely to become the primary cause of extinctions in the coming century (Marzluff et al. 2001a). Cities are typically located along rivers, on coastlines, or near large bodies of water. The associated floodplains and riparian systems make up a relatively small percentage of land cover in the western United States, yet they provide habitat for rich wildlife communities (Knopf et al. 1988), which in turn provide a source for urban habitat …
Integrating user performance time models in the design of tangible UIs

One of the aspects that are important for judging an application is the time an experienced user needs to complete a task. This can be assessed by a Keystroke-Level Model (KLM) of that task. In this paper we show a method that allows designers to prototype hardware applications entirely in software and still be able to draw conclusions about the time to completion of given tasks on the envisioned hardware implementation. We provide versatile, easily extensible tools and examples that give developers quick access to KLM data for their prototypes and applications.