Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Skill Learning in a Large, Longitudinal MOBA Dataset

The exploration of the relationships between behavior and cognitive psychology of game players has gained impetus in recent years because such links provides an opportunity for improving user experiences and optimizing products in the games industry. At the same time, the volume and global scope of digital game telemetry data has opened up new experimental opportunities for studying human behavior at large scales. Prior research has demonstrated that a relation exists between learning rates and performance. Although many factors might contribute to this correlation at least one may be the presence of innate cognitive resources, as demonstrated in recent work relating IQ and performance in a Multi-player Online Battle Arena game. Here, we extend this work by examining the relationship between early learning rate and long term performance using a 400,000 player longitudinal dataset generated by new players of the widely-played MOBA League of Legends. We observed that the learning rate of new players in a competitive season explains a significant amount of variance in the performance at the end of the year. This analysis was then extended by training two multivariate classifiers (Logistic Regression, Random Forest) for predicting players who by the end of the season would be considered masters (top 0.05%), based on their performance in the first 10 matches of the same season. Both classifiers performed similarly (ROC AUC 0.888 for Logistic Regression, 0.878 for Random Forest), extending the time frame for skill prediction in games based on a relatively sparse sample of early data. We discuss the implications for these findings based on preexisting psychological studies of learning and intelligence, and close with challenges and direction for future research.
An Empirical Study of Multi-entity Changes in Real Bug Fixes

Prior studies showed that developers applied repeated bug fixes—similar or identical code changes—to multiple locations. According to the observation, researchers built tools to automatically generate candidate patches from the repeated bug-fixing patterns. However, all such research focuses on the recurring change patterns within single methods. We are curious whether there are also repeated bug fixes that change multiple program entities (e.g., classes, methods, and fields); and if so, how we can leverage such recurring change patterns to further help developers fix bugs. In this paper, we present a comprehensive empirical study on multi-entity bug fixes in terms of their frequency, composition, and semantic meanings. Specifically for each bug fix, we first used our approach InterPart to perform static inter-procedural analysis on partial programs (i.e., the old and new versions of changed Java files), and to extract change dependency graphs (CDGs)—graphs that connect multiple changed entities based on their syntactic dependencies. By extracting common subgraphs from the CDGs of different fixes, we identified the recurring change patterns. Our study on Aries, Cassandra, Derby, and Mahout shows that (1) 52-58% of bug fixes involved multi-entity changes; (2) 6 recurring change patterns commonly exist in all projects; and (3) 19-210 entity pairs were repetitively co-changed mainly because the pairs invoked the same methods, accessed the same fields, or contained similar content. These results helped us better understand the gap between the fixes generated by existing automatic program repair (APR) approaches and the real fixes. Our observations will shed light on the follow-up research of automatic program comprehension and modification.
RandomPad: Usability of Randomized Mobile Keypads for Defeating Inference Attacks

—The feasibility of malicious keystroke inference attacks on mobile device keypads has been demonstrated by multiple recent research efforts, but very little has been accomplished in the direction of protection against such attacks. One common assumption in these attacks is that the adversary has knowledge of the size and layout of the keypad employed by the target user, which is reasonable as keypad layouts and sizes are generally standard. Thus, an effective protection strategy against such keystroke inference attacks would be to randomly change the layout of the target keypad. However, before proposing unconventional changes to the widely used and highly familiar default keypads, a comprehensive usability evaluation is required. This paper accomplishes this goal by comprehensively studying the usability of randomized keypads that employ varying degrees of randomization in terms of key size, sequence and position. The privacy-usability trade-off of different randomized keypad strategies is then analyzed by empirically comparing their ease-of-usage and security assurance.
The Upper Campanian - lower Maastrichtian cephalopod fauna of Botellos, Nuevo León: a key to understand faunal turnover across the Campanian-Maastrichtian boundary in NE Mexico

A new cephalopod collection from the Campanian-Maastrichtian boundary interval of NE Mexico, consisting of 1076 individuals assigned to 29 species and 22 genera is presented. This collection is a mix of ammonoids, one coleoid and one nautilid, which originate from at least three ammonoid biozones: The upper Campanian Exiteloceras jenneyi and Nostoceras ( Nostoceras ) hyatti zones, and the lower Maastrichtian Pachydiscus ( Pachydiscus ) neubergicus Zone. The age of the collection is thus middle late Campanian to late early Maastrichtian, and it closes a stratigraphic gap between faunas described formerly from this region. The specimens are nuclei collected from the desert pavement. The abundance of specimens allows for a comparison to other Campanian–Maastrichtian ammonoid records from Mexico, North America and Europe.
Design and Development of a Low-Cost Open-Source Robotics Education Platform

The impact of robotics has grown beyond research laboratories and industrial facilities into home environments and primary and secondary school classrooms. Of particular interest to us are robots for education. In general, educational robotics kits are expensive and proprietary, or cheap and unreliable. This research seeks to bridge that gap by providing a hands on open-source robotics learning environment that is both inexpensive and reliable. In this paper, we review the applicability of such environments to support the synergistic learning of computational thinking (CT) and STEM, with an emphasis on Computer Science (CS) concepts and practices. The CT and Advanced Placement CS Principles frameworks (from the US) govern the design and implementation of our system. We discuss the hardware system of the robot and the accompanying software architecture that runs on Linux-based single board computers. We conclude with results from a small pilot study analyzing the usability and curricular effectiveness of the system.
The Importance of Transparency and Willingness to Share Personal Information

This study investigates the extent to which individuals are willing to share their sensitive personal information with companies. The study examines whether skepticism can influence willingness to share information. Additionally, it seeks to determine whether transparency can moderate the relationship between skepticism and willingness to share and whether 1) companies perceived motives, 2) individual’s prior privacy violations, 3) individuals’ propensity to take risks, and 4) individuals self-efficacy act as antecedents of skepticism. Partial Least Squares (PLS) regression is used to examine the relationships between all the factors. The findings indicate that skepticism does have a negative impact on willingness to share personal information and that transparency can reduce skepticism.
The Effect of High School Accounting on the Selection of College Major, Performance, Satisfaction, and Retention

The objective of this study is to examine the impact completion of a high school accounting course has on the selection of college major, academic performance in introductory accounting courses, satisfaction with one’s major, and retention. These relations are examined using data from a survey given to 208 students of various majors enrolled in the Paul College of Business and Economics at the University of New Hampshire. Multiple linear regression analyses were used to analyze the data. The results provide evidence of a significant positive relation between high school accounting and two variables: selection of college major, and performance in introduction to managerial accounting. The results also provide evidence consistent with a moderately significant relation between high school accounting and retention. The results do not show any significant relation between high school accounting and performance in introduction to financial accounting or major satisfaction. Overall, the results of this study contribute to a better understanding of the effect high school accounting can have on the development of future accounting professionals.
The winners and losers of tax reform: An assessment under financial integration

I quantify the macroeconomic and redistributive effects of the unilateral elimination of the capital income tax in a two-country, heterogeneous-agent incomplete markets model with progressive labor income taxes. Home , by implementing the reform, induces government responses where labor income is taxed in Home and mostly subsidized in Foreign . In addition, post-reform price dynamics reduce Home ’s wealth and suppress households’ ability to do consumption smoothing, with negative effects on the majority—particularly on the poor. In turn, Foreign accumulates wealth, and price movements work particularly in favor of the poor. As a result, a large majority in Home prefers the status quo whereas Foreign supports the reform unanimously. These findings are robust to alternative scenarios where (i) the borrowing constraints are relaxed, (ii) both countries jointly eliminate capital income taxes, (iii) foreign interest income is taxed, and (iv) Home capital income tax is reduced from 40% to 35%. © 2017 Elsevier B.V. All rights reserved.
Parent and Child Vagal Tone: Examining Parenting Behaviors as Moderators of the Association

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Symbolic Execution of Security Protocol Implementations: Handling Cryptographic Primitives

We show how to efficiently simulate cryptographic primitives during symbolic execution. This allows analysis of security protocol implementations, and revealed several flaws in implementations of WPA2’s 4-way handshake. Traditional symbolic execution engines cannot handle cryptographic primitives, because analyzing them results in complex symbolic expressions that cannot be handled by the SMT solver. We prevent this by simulating their behaviour under the Dolev-Yao model. This enables efficient symbolic execution of security protocols implementations, making it possible to detect common programming mistakes in them. We also show how to detect misuse of cryptographic primitives. That is, we can detect trivial timing side-channels, and we can identify decryption oracles where unauthenticated decrypted data influences the program’s behaviour. We apply our technique on three client-side implementations of WPA2’s 4-way handshake. This uncovered timing side-channels when verifying authentication tags, a denial-of-service attack, a stack-based buffer overflow, and also revealed a non-trivial decryption oracle. We confirmed all vulnerabilities in practice, and discuss them in detail.
A regression model for pooled data in a two-stage survey under informative sampling with application for detecting and estimating the presence of transgenic corn

Abstract Group-testing regression methods are effective for estimating and classifying binary responses and can substantially reduce the number of required diagnostic tests. However, there is no appropriate methodology when the sampling process is complex and informative. In these cases, researchers often ignore stratification and weights that can severely bias the estimates of the population parameters. In this paper, we develop group-testing regression models for analysing two-stage surveys with unequal selection probabilities and informative sampling. Weights are incorporated into the likelihood function using the pseudo-likelihood approach. A simulation study demonstrates that the proposed model reduces the bias in estimation considerably compared to other methods that ignore the weights. Finally, we apply the model for estimating the presence of transgenic corn in Mexico and we give the SAS code used for the analysis.
Going from Image to Video Saliency: Augmenting Image Salience with Dynamic Attentional Push

We present a novel method to incorporate the recent advent in static saliency models to predict the saliency in videos. Our model augments the static saliency models with the Attentional Push effect of the photographer and the scene actors in a shared attention setting. We demonstrate that not only it is imperative to use static Attentional Push cues, noticeable performance improvement is achievable by learning the time-varying nature of Attentional Push. We propose a multi-stream Convolutional Long Short-Term Memory network (ConvLSTM) structure which augments state-of-the-art in static saliency models with dynamic Attentional Push. Our network contains four pathways, a saliency pathway and three Attentional Push pathways. The multi-pathway structure is followed by an augmenting convnet that learns to combine the complementary and time-varying outputs of the ConvLSTMs by minimizing the relative entropy between the augmented saliency and viewers fixation patterns on videos. We evaluate our model by comparing the performance of several augmented static saliency models with state-of-the-art in spatiotemporal saliency on three largest dynamic eye tracking datasets, HOLLYWOOD2, UCF-Sport and DIEM. Experimental results illustrates that solid performance gain is achievable using the proposed methodology.
Planar Shape Detection at Structural Scales

Interpreting 3D data such as point clouds or surface meshes depends heavily on the scale of observation. Yet, existing algorithms for shape detection rely on trial-and-error parameter tunings to output configurations representative of a structural scale. We present a framework to automatically extract a set of representations that capture the shape and structure of man-made objects at different key Abstraction levels. A shape-collapsing process first generates a fine-to-coarse sequence of shape representations by exploiting local planarity. This sequence is then analyzed to identify significant geometric variations between successive representations through a supervised energy minimization. Our framework is flexible enough to learn how to detect both existing structural formalisms such as the CityGML Levels Of Details, and expert-specified levels of Abstraction. Experiments on different input data and classes of man-made objects, as well as comparisons with existing shape detection methods, illustrate the strengths of our approach in terms of efficiency and flexibility.
Near-infrared spectroscopy used to predict soybean seed germination and vigour

Abstract Rapid, non-destructive methods for measuring seed germination and vigour are valuable. Standard germination and seed vigour were determined using 81 soybean seed lots. From these data, seed lots were separated into high and low germinating seed lots as well as high, medium and low vigour seed lots. Near-infrared spectra (950–1650 nm) were collected for training and validation samples for each seed category and used to create partial least squares (PLS) prediction models. For both germination and vigour, qualitative models provided better discrimination of high and low performing seed lots compared with quantitative models. The qualitative germination prediction models correctly identified low and high germination seed lots with an accuracy between 85.7 and 89.7%. For seed vigour, qualitative predictions for the 3-category (low, medium and high vigour) models could not adequately separate high and medium vigour seeds. However, the 2-category (low, medium plus high vigour) prediction models could correctly identify low vigour seed lots between 80 and 100% and the medium plus high vigour seed lots between 96.3 and 96.6%. To our knowledge, the current study is the first to provide near-infrared spectroscopy (NIRS)-based predictive models using agronomically meaningful cut-offs for standard germination and vigour on a commercial scale using over 80 seed lots.
Deep Adversarial Metric Learning

Learning an effective distance metric between image pairs plays an important role in visual analysis, where the training procedure largely relies on hard negative samples. However, hard negatives in the training set usually account for the tiny minority, which may fail to fully describe the distribution of negative samples close to the margin. In this paper, we propose a deep adversarial metric learning (DAML) framework to generate synthetic hard negatives from the observed negative samples, which is widely applicable to supervised deep metric learning methods. Different from existing metric learning approaches which simply ignore numerous easy negatives, the proposed DAML exploits them to generate potential hard negatives adversarial to the learned metric as complements. We simultaneously train the hard negative generator and feature embedding in an adversarial manner, so that more precise distance metrics can be learned with adequate and targeted synthetic hard negatives. Extensive experimental results on three benchmark datasets including CUB-200-2011, Cars196 and Stanford Online Products show that DAML effectively boosts the performance of existing deep metric learning approaches through adversarial learning.
Lattice model for the surface states of a topological insulator with applications to magnetic and exciton instabilities

A surface of a strong topological insulator (STI) is characterized by an odd number of linearly dispersing gapless electronic surface states. It is well known that such a surface cannot be described by an effective two-dimensional lattice model (without breaking the time-reversal symmetry), which often hampers theoretical efforts to quantitatively understand some of the properties of such surfaces, including the effect of strong disorder, interactions, and various symmetry-breaking instabilities. Here we formulate a lattice model that can be used to describe a pair of STI surfaces and has an odd number of Dirac fermion states with wave functions localized on each surface. The Hamiltonian consists of two planar tight-binding models with spin-orbit coupling, representing the two surfaces, weakly coupled by terms that remove the extra Dirac points from the low-energy spectrum. We illustrate the utility of this model by studying the magnetic and exciton instabilities of the STI surface state driven by short-range repulsive interactions and show that this leads to results that are consistent with calculations based on the continuum model as well as three-dimensional lattice models. We expect the model introduced in this work to be widely applicable to studies of surface phenomena in STIs.
Developers Deserve Security Warnings, Too: On the Effect of Integrated Security Advice on Cryptographic API Misuse

Cryptographic API misuse is responsible for a large number of software vulnerabilities. In many cases developers are overburdened by the complex set of programming choices and their security implications. Past studies have identi-ﬁed signiﬁcant challenges when using cryptographic APIs that lack a certain set of usability features (e. g. easy-to-use documentation or meaningful warning and error messages) leading to an especially high likelihood of writing functionally correct but insecure code. To support software developers in writing more secure code, this work investigates a novel approach aimed at these hard-to-use cryptographic APIs. In a controlled online exper-iment with 53 participants, we study the eﬀectiveness of API-integrated security advice which informs about an API misuse and places secure programming hints as guidance close to the developer. This allows us to address insecure cryptographic choices including encryption algorithms, key sizes, modes of operation and hashing algorithms with helpful documentation in the guise of warnings. Whenever possible, the security advice proposes code changes to ﬁx the responsible security issues. We ﬁnd that our approach sig-niﬁcantly improves code security. 73% of the participants who received the security advice ﬁxed their insecure code. We evaluate the opportunities and challenges of adopting API-integrated security advice and illustrate the potential to reduce the negative implications of cryptographic API misuse and help developers write more secure code.
A revised moment error expression for the AIRGA algorithm

Abstract The fully adaptive rational global Arnoldi method (AIRGA) for the modelorder reduction of second-order multi-input multi-output systems with proportional damping is revisited. The method automatically generates a reduced system approximating the transfer function. It is based on a moment-matching approach. The expansion points are determined iteratively. The reduced order and the number of moments matched per expansion point are determined adaptively using a heuristic based on an error estimation. A revised moment error expression is presented as well as some related findings.
Seasonal and Interannual Variability in Coastal Circulations in the Northern South China Sea

Seasonal cycle and interannual variability in coastal circulations in the northern South China Sea (NSCS) are investigated using satellite altimeter data from March 1993 to September 2016. Altimeter-derived velocity anomalies are in good agreement with acoustic Doppler current profilers (ADCP) observations at an adjacent location. Along-shelf volume transport anomalies in the NSCS indicate northeastward transports from mid-spring to summer and southwestward transports from mid-autumn to winter, which are consistent with previous studies in this region. According to convergence and divergence in the target control volumes, cross-shelf volume transports are estimated as the differences between two neighboring along-shelf volume transport anomalies, with the assumption that long-term mean along-shelf volume transports at each cross-sections are identical. The results show onshore transports in mid-autumn and offshore transports in early summer. The comparison between altimeter-derived and ADCP-estimated cross-shelf volume transports is encouraging, especially when the region has relatively low mesoscale activities and a low freshwater input. Reconstructed cross-shelf volume transports through multiple linear regression reveal that seasonal harmonics is the primary force in driving cross-shelf volume transports in the NSCS, while wind and El Nino have secondary effects on controlling cross-shelf volume transports in different regions. The present study helps to quantify the long-term coastal circulations, especially cross-shelf volume transports, based on altimeter data, which has important implications on the dynamics in coastal regions where observational data is limited.