Miracle-dz/UniFAR-FTUs huggingface dataset:
A Difficulty-Aware Framework for Churn Prediction and Intervention in Games

User's leaving from the system without further return, called user churn, is a severe negative signal in online games. Therefore, churn prediction and intervention are of great value for improving players' experiences and system performance. However, the problem has not been well-studied in the game scenario. Especially, some crucial factors, such as game difficulty, have not been considered for large-scale churn analysis. In this paper, a novel Difficulty-Aware Framework (DAF) for churn prediction and intervention is proposed. Firstly, a Difficulty Flow for each user is proposed, which is utilized to derive users' Personalized Perceived Difficulty during the game process. Then, a survival analysis modelD-Cox-Time is designed to model the Dynamic Influence of Perceived Difficulty on player churn intention. Finally, thePersonalized Perceived Difficulty ~(PPD) andDynamic Difficulty Influence ~(DDI) are incorporated to churn prediction and intervention. The proposed DAF framework has been specified in a real-world puzzle game as an example for churn prediction and intervention. Extensive offline experiments show significant improvements in churn prediction by introducing difficulty-related features. Besides, we conduct an online intervention system to adjust difficulty dynamically in the online game. A/B test results verify that the proposed intervention system enhances user retention and engagement significantly. To the best of our knowledge, it is the first framework in games that illustrates an in-depth understanding and leveraging dynamic and personalized perceived difficulty during game playing, which is easy to be integrated with various churn prediction and intervention models.
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization

Vertical federated learning (VFL) is an effective paradigm of training the emerging cross-organizational (e.g., different corporations, companies and organizations) collaborative learning with privacy preserving. Stochastic gradient descent (SGD) methods are the popular choices for training VFL models because of the low per-iteration computation. However, existing SGD-based VFL algorithms are communication-expensive due to a large number of communication rounds. Meanwhile, most existing VFL algorithms use synchronous computation which seriously hamper the computation resource utilization in real-world applications. To address the challenges of communication and computation resource utilization, we propose an asynchronous stochastic quasi-Newton (AsySQN) framework for VFL, under which three algorithms, i.e. AsySQN-SGD, -SVRG and -SAGA, are proposed. The proposed AsySQN-type algorithms making descent steps scaled by approximate (without calculating the inverse Hessian matrix explicitly) Hessian information convergence much faster than SGD-based methods in practice and thus can dramatically reduce the number of communication rounds. Moreover, the adopted asynchronous computation can make better use of the computation resource. We theoretically prove the convergence rates of our proposed algorithms for strongly convex problems. Extensive numerical experiments on real-word datasets demonstrate the lower communication costs and better computation resource utilization of our algorithms compared with state-of-the-art VFL algorithms.
ProtoPShare: Prototypical Parts Sharing for Similarity Discovery in Interpretable Image Classification

In this work, we introduce an extension to ProtoPNet called ProtoPShare which shares prototypical parts between classes. To obtain prototype sharing we prune prototypical parts using a novel data-dependent similarity. Our approach substantially reduces the number of prototypes needed to preserve baseline accuracy and finds prototypical similarities between classes. We show the effectiveness of ProtoPShare on the CUB-200-2011 and the Stanford Cars datasets and confirm the semantic consistency of its prototypical parts in user-study.
PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network

Recommender systems are powerful tools for information filtering with the ever-growing amount of online data. Despite its success and wide adoption in various web applications and personalized products, many existing recommender systems still suffer from multiple drawbacks such as large amount of unobserved feedback, poor model convergence, etc. These drawbacks of existing work are mainly due to the following two reasons: first, the widely used negative sampling strategy, which treats the unlabeled entries as negative samples, is invalid in real-world settings; second, all training samples are retrieved from the discrete observations, and the underlying true distribution of the users and items is not learned. In this paper, we address these issues by developing a novel framework named PURE, which trains an unbiased positive-unlabeled discriminator to distinguish the true relevant user-item pairs against the ones that are non-relevant, and a generator that learns the underlying user-item continuous distribution. For a comprehensive comparison, we considered 14 popular baselines from 5 different categories of recommendation approaches. Extensive experiments on two public real-world data sets demonstrate that PURE achieves the best performance in terms of 8 ranking based evaluation metrics.
MTrajRec: Map-Constrained Trajectory Recovery via Seq2Seq Multi-task Learning

With the increasing adoption of GPS modules, there are a wide range of urban applications based on trajectory data analysis, such as vehicle navigation, travel time estimation, and driver behavior analysis. The effectiveness of urban applications relies greatly on the high sampling rates of trajectories precisely matched to the map. However, a large number of trajectories are collected under a low sampling rate in real-world practice, due to certain communication loss and energy constraints. To enhance the trajectory data and support the urban applications more effectively, many trajectory recovery methods are proposed to infer the trajectories in free space. In addition, the recovered trajectory still needs to be mapped to the road network, before it can be used in the applications. However, the two-stage pipeline, which first infers high-sampling-rate trajectories and then performs the map matching, is inaccurate and inefficient. In this paper, we propose a Map-constrained Trajectory Recovery framework, MTrajRec, to recover the fine-grained points in trajectories and map match them on the road network in an end-to-end manner. MTrajRec implements a multi-task sequence-to-sequence learning architecture to predict road segment and moving ratio simultaneously. Constraint mask, attention mechanism, and attribute module are proposed to overcome the limits of coarse grid representation and improve the performance. Extensive experiments based on large-scale real-world trajectory data confirm the effectiveness and efficiency of our approach.
A Study of Factors Affecting Foreign Language Learning Motivation in Chinese Higher Education

With the further development of economic globalization, China has built cooperative relationship with other countries around the world. Therefore, foreign language talents in China play an essential role in cultural and trade exchanges. Training language talents to effectively adapt to foreign exchange policy also becomes important to China. Researching motivation change in learning enables the government to scientifically improve teaching quality. It is one of the most efficient and effective methods to cultivate high-quality talents. This study aims to find out the critical factors influencing students' motivational change. Based on Keller's ARCS model of motivation and Dörnyei's Three-level motivational theory, we divided influencing factors into 14 elements. After factor analysis, it showed group-specific motivational components, course-specific components and teacher-specific components are the most critical factors affecting motivation level in language learning. In 2018, National Standard for Teaching Quality of Foreign Language and Literature was published, to adjust course setting and strengthen teachers team building to improve teaching quality. However, even after two years of the above modification, the author found out there are still problems in higher education which badly influence student's learning motivation. This study helps improving teaching quality, promoting foreign language learning and cultivating language talents.
MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems

Graph neural networks (GNNs) have recently emerged as state-of-the-art collaborative filtering (CF) solution. A fundamental challenge of CF is to distill negative signals from the implicit feedback, but negative sampling in GNN-based CF has been largely unexplored. In this work, we propose to study negative sampling by leveraging both the user-item graph structure and GNNs' aggregation process. We present the MixGCF method---a general negative sampling plugin that can be directly used to train GNN-based recommender systems. In MixGCF, rather than sampling raw negatives from data, we design the hop mixing technique to synthesize hard negatives. Specifically, the idea of hop mixing is to generate the synthetic negative by aggregating embeddings from different layers of raw negatives' neighborhoods. The layer and neighborhood selection process are optimized by a theoretically-backed hard selection strategy. Extensive experiments demonstrate that by using MixGCF, state-of-the-art GNN-based recommendation models can be consistently and significantly improved, e.g., 26% for NGCF and 22% for LightGCN in terms of NDCG@20.
Quantifying Assimilate-Contrast Effects in Online Rating Systems: Modeling, Analysis and Application

Online rating system serves as an indispensable building block for many web applications such as Amazon, TripAdvior and Yelp. It enables production quality estimation via aggregate ratings (a.k.a. wisdom of the crowd) as well as product recommendation via inferring user preference from ratings, etc. Previous studies showed that due to assimilate-contrast effects, historical ratings can significantly distort user's ratings, leading to low accuracy of product quality estimation and recommendation. To understand assimilate-contrast effects, an "accurate'' model is still missing as previous models do not capture important factors like rating recency, selection bias, etc. Furthermore, an analytical framework to characterize product estimation accuracy under assimilate-contrast effects is also missing. This paper aims to fill in this gap. We propose a mathematical model to quantify the aforementioned important factors on assimilate-contrast effects. Our model attains a good balance between model complexity and model accuracy, such that it is neat enough for us to develop an analytical framework to study assimilate-contrast effects. Based on our model, we derive sufficient conditions, under which the product estimation and collective opinion converges to the "ground-truth''. These conditions reveal important insights on how the aforementioned factors influence the convergence and guide the online rating system operator to design appropriate rating aggregation rules and rating displaying strategies. To demonstrate the versatility of our model, we apply to rating prediction tasks and product recommendation tasks. Experiment results on four public datasets show that our model can improve the rating prediction and and recommendation accuracy over previous models significantly.
HGK-GNN: Heterogeneous Graph Kernel based Graph Neural Networks

While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of applications, recent studies exposed important shortcomings in their ability to capture heterogeneous structures and attributes of an underlying graph. Furthermore, though many Heterogeneous GNN (HGNN) variants have been proposed and have achieved state-of-the-art results, there are limited theoretical understandings of their properties. To this end, we introduce graph kernel to HGNNs and develop a Heterogeneous Graph Kernel-based Graph Neural Networks (HGK-GNN). Specifically, we incorporate the Mahalanobis distance (MD) to build a Heterogeneous Graph Kernel (HGK), and incorporating it into deep neural architectures, thus leveraging a heterogeneous GNN with a heterogeneous aggregation scheme. Also, we mathematically bridge HGK-GNN to metapath-based HGNNs, which are the most popular and effective variants of HGNNs. We theoretically analyze HGK-GNN with the indispensable Encoder and Aggregator component in metapath-based HGNNs, through which we provide a theoretical perspective to understand the most popular HGNNs. To the best of our knowledge, we are the first to introduce HGK into the field of HGNNs, and mark a first step in the direction of theoretically understanding and analyzing HGNNs. Correspondingly, both graph and node classification experiments are leveraged to evaluate HGK-GNN, where HGK-GNN outperforms a wide range of baselines on six real-world datasets, endorsing the analysis.
Can Elite Australian Football Player’s Game Performance Be Predicted?

Abstract In elite Australian football (AF) many studies have investigated individual player performance using a variety of outcomes (e.g. team selection, game running, game rating etc.), however, none have attempted to predict a player’s performance using combinations of pre-game factors. Therefore, our aim was to investigate the ability of commonly reported individual player and team characteristics to predict individual Australian Football League (AFL) player performance, as measured through the official AFL player rating (AFLPR) (Champion Data). A total of 158 variables were derived for players (n = 64) from one AFL team using data collected during the 2014-2019 AFL seasons. Various machine learning models were trained (cross-validation) on the 2014-2018 seasons, with the 2019 season used as an independent test set. Model performance, assessed using root mean square error (RMSE), varied (4.69-5.03 test set RMSE) but was generally poor when compared to a singular variable prediction (AFLPR pre-game rating: 4.72 test set RMSE). Variation in model performance (range RMSE: 0.14 excusing worst model) was low, indicating different approaches produced similar results, however, glmnet models were marginally superior (4.69 RMSE test set). This research highlights the limited utility of currently collected pre-game variables to predict week-to-week game performance more accurately than simple singular variable baseline models.
Prognostic value of H‐index in patients surgically treated for squamous cell carcinoma of the larynx

Recently, a novel host‐related index, the Host‐index (H‐index), including both inflammatory and nutritional markers, has been described and observed to stratify prognosis in patients with squamous cell carcinoma (SCC) of the oral cavity more accurately than other host‐related indexes This study aimed to investigate the prognostic performance of the H‐index using pretreatment blood tests in patients receiving up‐front surgery for SCC of the larynx.
Developing research knowledge and capability in undergraduate nurses: evaluation of targeted placements

Background There are few opportunities for undergraduate nurses to undertake experiential learning about research design and conduct. The project aim was to implement and evaluate a placement to support the development of research knowledge and skills. Methods Establishment of a four-week placement, with allocation to a registered quality improvement/service evaluation project. Evaluation was obtained through questionnaires; student experience (pre, post and one year), supervisor experience and organisational impact (presentations, conference and grant submissions). Results 24 students (five cohorts) were allocated to 17 projects (2017–2019). 100% of students enjoyed the placement, gained research knowledge and insight into clinical care. At one year (n = 15), 88% of students would consider undertaking a service evaluation and 65% (n = 11) would consider further post-graduate study. All supervisors (n = 20) reported the initiative valuable for student development. All project results were shared with relevant local teams to enable service planning and results from five projects been presented at eight national and international conferences. Three projects have contributed directly to further research grant submissions. Conclusion The programme supports the development of undergraduate nurses to be research ready and facilitates organisations to address high-priority safety and quality topics. Further resources are required to be able to increase placement capacity.
Lockeans against labor mixing

The idea that labor mixing confers property in unowned resources is, for many, the very heart of the Lockean system of property. In this essay I shall argue that this common view is mistaken. Lockean theorists should reject labor mixing as the preferred method of first appropriation, and should adopt a different account of first appropriation instead. This is because labor mixing does not serve the central justification for the institution of property embraced by Lockeans. Thus, my argument is internal to the Lockean system; I rely only on premises that (many) Lockean theorists embrace. Though Lockeans should forsake labor mixing, that does not mean they should give up on property rights and the idea of first appropriation. In the paper’s final section, I sketch an account of first appropriation that Lockeans should embrace.
Emotional and Behavioral Assessment of Youths With Visual Impairments Utilizing the BASC-2

Introduction: Few studies have examined the psychological and mental health effects of visual impairments (i.e., blindness and low vision) on youths. Our objectives were to describe the behavioral and emotional profile of students with visual impairments, compare the BASC-2 composite scores for youths with visual impairments with the normative data to determine if significant differences exist, and compare the BASC-2 scores of students categorized as having low vision with students categorized as being totally blind. Methods: A total of 63 students with visual impairments entering a state school for the deaf and blind between 2012 and 2017 had a parent complete the BASC-2 Parent Rating Scale as part of the intake procedure. A retrospective study was conducted to determine patterns of emotional and behavioral strengths and weaknesses in students with low vision and blindness. A Multivariate Analysis of Covariance (MANCOVA) and Wilcoxon Rank Sums test were performed to test for differences between children who are blind and children with low vision. Results: Students with visual impairments showed higher levels of clinical symptoms such as anxiety and withdrawal. Statistically significant higher deficits were detected for the students with visual impairments on the Behavioral Symptoms Index and Internalizing Problems composites and significant lower differences for the Adaptive Skills and Externalizing Problems composites compared to the BASC-2 normative sample. When compared with youths with low vision, students who are blind had statistically significant scores (i.e., deficits) for adaptive skills and resiliency. Discussion: Our results suggest that a substantial proportion of students with visual impairments may experience more emotional and behavioral difficulties than the average youth while lacking the adaptive skills to manage their conduct adequately. Implications for practitioners: Professionals working with youths with visual impairments face a number of daunting challenges, since the conditions under the visually impaired umbrella are heterogeneous, and variation exists within a statistically low-incidence disability category. Therefore, those who work with youths with visual impairments have to apply various and unique methods to understand, train, teach, rehabilitate, or counsel these youths.
Experimental study of a virtual allowables approach for the design of composite aircraft structures

Statistically derived allowables are representative for the behavior of a composite material system in a given structural context where data variability needs to be contemplated. In particular, they define the strength of the material as characterized by various coupon tests according to ASTM standard procedure subjected to the actual manufacturing process and layup stacking sequences. The current paper presents a detailed comparison between virtual allowables predictions obtained using MSC Digimat and the experimentally determined values of a certified material backed up by a 3x2x3 (batch/panel/specimen) stochastic approach. Tests generate the required data for building material model ab initio of carbon fibers reinforced epoxide resin in form of 2x2 twill woven fabric used for qualifying an innovative composite wing for the Next Generation Tilt Rotor (NGCTR). Numerical models of in-plane tension, compression and shear test methods have been assembled along with an associated material model allowing numerical predictions to be validated with the coupon level experimental results as final purpose. Standard statistical model has been adopted in order to include material and manufacturing process variabilities to define B-basis allowables. The calibrated statistical FEM method performed for different layup configurations have been used to fully characterize the mechanical behavior of the analyzed CFRP material and to predict performances for thicker laminates.
Development of Functional Connectome Gradients during Childhood and Adolescence

Connectome mapping studies have documented a principal primary-to-transmodal gradient in the adult brain network, capturing a functional spectrum which ranges from perception and action to abstract cognition. However, how this gradient pattern develops and whether its development is linked to cognitive growth, topological reorganization, and gene expression profiles remain largely unknown. Using longitudinal resting-state functional magnetic resonance imaging data from 305 children (ages 6-14), we describe substantial changes in the primary-to-transmodal gradient between childhood and adolescence, including emergence as the principal gradient, expansion of global topography, and focal tuning in primary and default-mode regions. These gradient changes are mediated by developmental changes in network integration and segregation, and are associated with abstract processing functions such as working memory and expression levels of calcium ion regulated exocytosis, synaptic transmission, and axon and synapse part related genes. Our findings have implications for understanding connectome maturation principles in normal development and developmental disorders. Teaser Our study reported the maturation of the core connectome gradient and its association with cognitions and genes expression.