Miracle-dz/UniFAR-FTUs huggingface dataset:
GameRTS: A Regression Testing Framework for Video Games

Continuous game quality assurance is of great importance to satisfy the increasing demands of users. To respond to game issues reported by users timely, game com-panies often create and maintain a large number of releases, updates, and tweaks in a short time. Regression testing is an essential technique adopted to detect regression issues during the evolution of the game software. However, due to the special characteristics of game software (e.g., frequent updates and long-running tests), traditional regression testing techniques are not directly applicable. To bridge this gap, in this paper, we perform an early exploratory study to investigate the challenges in regression testing of video games. We first performed empirical studies to better understand the game development process, bugs introduced during game evolution, and the context sensitivity. Based on the results of the study, we proposed the first regression test selection (RTS) technique for game software, which is a compromise between safety and practicality. In particular, we model the test suite of game software as a State Transition Graph (STG) and then perform the RTS on the STG. We establish the dependencies between the states/actions of STG and game files, including game art resources, game design files, and source code, and perform change impact analysis to identify the states/actions (in the STG) that potentially execute such changes. We implemented our framework in a tool, named GameRTS, and evaluated its usefulness on 10 tasks of a large-scale commercial game, including a total of 1,429 commits over three versions. The experimental results demonstrate the usefulness and effectiveness of GameRTS in game RTS. For most tasks, GameRTS only selected one trace from STG, which can significantly reduce the testing time. Furthermore, GameRTS detects all the regression bugs from the test evaluation suites. Compared with the file-level RTS, GameRTS selected fewer states/actions/traces (i.e., 13.77%, 23.97%, 6.85%). In addition, GameRTS identified 2 new critical regression bugs in the game.
Electromagnetic Induction Sensing of Soil Dielectric Permittivity and Electrical Conductivity from 10 MHz to 30 MHz

Electromagnetic induction modality for soil sensing applications can result in higher sensitivity to electrical conductivity and dielectric permittivity in the frequency range of up to 30 MHz. In this paper, we extend the analysis of a high-frequency electromagnetic induction (HFEMI) sensor, made with shielded printed circuit board coils, to the entire high-frequency range. We analyzed the HFEMI sensor experimentally in the laboratory and the field and verified the results using finite element analysis (FEM). In the laboratory part of the study, we measured the sensor response above a container filled with deionized water whose conductivity and permittivity were controlled by adding salt and sucrose. In the field experiment, the sensor response was measured above the soil irrigated with deionized and saline water. The results agree with the FEM analysis, and they indicate the possibility of the simultaneous determination of the electrical conductivity and dielectric permittivity of the soil.
PTPDroid: Detecting Violated User Privacy Disclosures to Third-Parties of Android Apps

Android apps frequently access personal information to provide customized services. Since such information is sensitive in general, regulators require Android app vendors to publish privacy policies that describe what information is collected and why it is collected. Existing work mainly focuses on the types of the collected data but seldom considers the entities that collect user privacy, which could falsely classify problematic declarations about user privacy collected by third-parties into clear disclosures. To address this problem, we propose PTPDroid, a flow-to-policy consistency checking approach and an automated tool, to comprehensively uncover from the privacy policy the violated disclosures to third-parties. Our experiments on real-world apps demonstrate the effectiveness and superiority of PTPDroid, and our empirical study on 1,000 popular real-world apps reveals that violated user privacy disclosures to third-parties are prevalent in practice.
A real-time traffic alert system based on image recognition: A case of study in Spain

The management of road traffic incidents is a problem faced by governments in many countries. Normally, road operators have the infrastructure in place to monitor such incidents, albeit in a reactive manner. In Spain, there are traffic cameras on major roads to check for possible incidents, however, incident notification is slow and not automated. As an alternative, this paper proposes a system for automatic real-time traffic alerts. Thus, 1,500 camera images from the Dirección General de Tráfico (DGT) deployed on the main Spanish roads are analyzed in real time every 4 minutes. These images are not preprocessed, they have different qualities and are also affected by weather conditions such as fog, rain, sun reflections, etc. The system uses several Deep Learning classification models trained on a well-known dataset of traffic images including flowing traffic, dense traffic, accidents and fires. These models are used to classify the DGT images in real time, with satisfactory initial results, detecting both flowing traffic and dense traffic.
A Miniaturized Multiband Hexagonal Antenna with High Isolation for UWB MIMO Applications

A modest, 32×18 mm2, 2-element UWB-MIMO microstrip-fed antenna has been proposed for Radiating Electromagnetic Energy for Future UWB Technology. The proposed structure is hexagonal in shape with small steps slits on the radiating patch. By putting two-directional hexagonal structures orthogonally, the mutual interaction between elements is decreased. To strengthen isolation and enhance bandwidth, 7,I-shaped vents are introduced to the patch. The frequency range covered by UWB bandwidth is 3–11 GHz. The suggested structure employs slits onto the radiating patch to achieve band notches at various bands. With discrete notched bands at 4.8-5.3 GHz for WLAN and 6.97-10.72 GHz consisting of C-Band and X-Band in the UWB range of 3–11 GHz with improved resonance at -22dB for C band and -29dB for X band is attained in this proposed antenna. Due to its low coefficient of correlation, the results demonstrate that the suggested structure is a preferred methodology for UWB applications across the exiting notched band with less than 0.5 and low mutual coupling with less than -10 dB. Using multiple antennas for transmission and reception, UWB-MIMO is Better adapted for radar, tracking, monitoring systems of communications equipment, and infrastructure applications.
Hedonic Price Analysis of Used Tractors

in demand in our agricultural tractor division,” Todd said . “However, one issue we face is continued Abstract This case follows Nate Shepard, a fictionalized data analyst for John Deere, as he is tasked with finding a suitable method for predicting used tractor prices. Nate uses hedonic price theory to specify and estimate a regression equation that can be used to evaluate marginal values of specific tractor attributes and predict out-of-sample tractor prices. Beyond price prediction, Nate must also consider the inflationary environment the used tractor market has been experiencing of late in his regression specification as well as compare the John Deere brand to rival manufacturers. The case allows readers to go along with Nate in the journey as he completes the process of data collection and cleaning, initial model specification based on relevant literature and theory, model estimation, evaluation of the model for misspecification issues, model revision and re-estimation, and model interpretation and use. The case provides an excellent example of empirical regression analysis in an agribusiness setting and gives readers an opportunity to familiarize themselves with hedonic price theory using a data set of actual used tractor auction results from 2020–2022.
A new global oceanic multi-model net primary productivity data product

Abstract. Net primary production of the oceans contributes approximately half of the total global net primary production, and long-term observational records are required to assess any climate-driven changes. The Ocean Colour Climate Change Initiative (OC-CCI) has proven to be robust whilst also being one of the longest records of ocean colour. However, to date, only one primary production algorithm has been applied to this data product, with other algorithms typically applied to single-sensor missions. The data product presented here addresses this issue by applying five algorithms to the OC-CCI data product, which allows the user to interrogate the range of distributions across multiple models and to identify consensus or outliers for their specific region of interest. Outputs are compared to single-sensor data missions, highlighting good overall global agreement, with some small regional discrepancies. Inter-model assessments address the source of these discrepancies, highlighting the choice of the mixed-layer data product as a vital component for accurate primary production estimates. The datasets are published in the Zenodo repository at https://doi.org/10.5281/zenodo.7849935, https://doi.org/10.5281/zenodo.7858590, https://doi.org/10.5281/zenodo.7860491 and https://doi.org/10.5281/zenodo.7861158 (Ryan-Keogh et al., 2023a, b, c, d).
Agglomeration Over the Long Run: Evidence from County Seat Wars∗

We study the long-run patterns of urban growth using a regression discontinuity design and a unique setting of close elections that determined “county seats” (capitals) in the frontier United States. An election victory signaled a community’s prominence in the mobile frontier period and winners rapidly attracted new migrants, thereby selecting one of many spatial equilibria. Election victors went through structural change that remained persistent – they are causally 3.2 times larger and have 8-18% higher income. We show that the benefits of density are relatively insensitive to size and even very small towns benefit. However, the timing of the historical shock matters substantially. Shocks early in history matter most and we see few effects around 40 years into a county’s history, implying that population distributions became increasingly fixed over time. Using generalized random forests, we also show the benefits to locations were not locally zero-sum and the “ideal” choice for county seat could increase long-run county income and population. In contrast to other important capital city shifts, county governments have a limited role in employment and infrastructure, suggesting these effects are driven by broad-based, historical migration. ∗We thank Ryan Schwartz, Fatima Najeeb, Ben Kremer, Roque Bescos, Jake Burgess, and Risa Wagner for their excellent research assistance. Thank you also to Treb Allen, Dave Donaldson, James Fenske, Hoyt Bleakley, and other seminar participants at Dartmouth College the University of Warwick for their advice and comments. †University of Maryland (AREC), corybsmith@gmail.com ‡Author order was randomly determined via coin toss. §University of Warwick, Amrita.Kulka@warwick.ac. uk
Genetic variability in sesame ( Sesamum indicum L.) for yield and yield related traits

Availability of genetic variability among a certain crop population and knowledge of the genetic parameters of yield and yield-related traits are the key preconditions to enhance seed yield. Therefore, sixty-four sesame genotypes consisting of fifty-nine accessions and five varieties were assessed in order to evaluate sesame genotypes for yield and yield-related traits and estimate the genetic parameters. An 8 x 8 simple lattice design was used to evaluate the experimental materials. Data were collected for the twenty traits. Analysis of variance revealed that sesame genotypes were significantly different (P<0.05) except for internode length and seed shattering-related traits. ASARC-ACC-SG-013 was the highest-yielding accession, while accession GK-012 (2) gave the highest oil content (60.09%), and the mean thousands seeds weight ranged from 2.00 g to 2.75 g, indicating the existence of elite sesame genotypes that can be considered to maximize yield, seed size, and oil content. The number of primary branches plant -1 , the number of capsules on the main stem plant -1 , and total capsules plant -1 all showed high coefficients of variation, demonstrating that the genotypes under study had sufficient variability for these traits. High heritability and genetic advance were obtained for traits such as plant height to the first branch (60.70%; 21.90%), capsule length (81.10%; 24.00%), primary branches plant -1 (63.10%; 36.30%), number of capsules on the main stem plant -1 (74.40%; 45.80%), and total capsules plant -1 (64.90%; 51.40%). Thus, the finding suggests a trustworthy estimate of the genetic advancement that may be anticipated through phenotypic selection for these traits.
A hybrid method for electromagnetic scattering from target above composite rough surface of ground and near sea in adjacent region

ABSTRACT This paper proposes a hybrid method for computing electromagnetic scattering from target above composite rough surface of ground and near sea in adjacent region. First, a kind of composite rough surface for simulating ground and near sea in adjacent region is modeled by utilizing the Monte Carlo method, in which the ground region is modeled by adopting Gauss spectral function; the near sea region is modeled by adopting JONSWAP spectral function combined with shallow coefficient; diverse weighted inverse tangent functions are introduced to structure different boundaries and connect regions. Second, aiming at fast and accurately computing composite scattering from target above this kind of composite rough surface, a hybrid method with several acceleration strategies is proposed by combining adaptive iterative physical optics (AIPO) with method of equivalent currents (MEC), in which MEC meliorates edge diffraction. Finally, some examples verify the validation of the proposed hybrid method showing that: this hybrid method is an effective tool for analyzing the composite scattering characteristics of target above practical environment during radar detection and SAR imaging, and we also give the composite scattering from target above composite rough surface with different incident angle, shape of boundary and statistic parameters. Moreover, composite scattering from different targets above this kind of composite rough surface model is studied in order to provide some useful conclusions.
Modelling non-stationary flood frequency in England and Wales using physical covariates

Non-stationary methods of flood frequency analysis are widespread in research but rarely implemented by practitioners. One reason may be that research papers on non-stationary statistical models tend to focus on model fitting rather than extracting the sort of results needed by designers and decision makers. It can be difficult to extract useful results from non-stationary models that include stochastic covariates for which the value in any future year is unknown. We explore the motivation for including such covariates, whether on their own or in addition to a covariate based on time. We set out a method for expressing the results of non-stationary models as an integrated flow estimate, which removes the dependence on the covariates. This can be defined either for a particular year or over a longer period of time. The methods are illustrated by application to a set of 375 river gauges across England and Wales. We find annual rainfall to be a useful covariate at many gauges, sometimes in conjunction with a time-based covariate. For estimating flood frequency in future conditions, we advocate exploring hybrid approaches that combine the best attributes of non-stationary statistical models and simulation models that can represent changes in climate and river catchments.
Strong Forward Induction in Monotonic Multi-Sender Signaling Games

5 We introduce a new solution concept called strong forward induction which is implied by strategic stability in generic finite multi-sender signaling games (Proposition 1) and can be easily extended to and applied in arbitrary extensive form games with perfect recall. We apply this notion to infinite monotonic signaling games and show that a unique pure strong forward induction equilib- 10 rium exists and its outcome is necessarily non-distorted (Theorem 1). Finally, we show that in this class of games the non-distorted equilibrium outcomes are limits of stable outcomes of finite games (Proposition 2)
A Compact CPW-Fed Triple Band Monopole Antenna with Simple Structure for WLAN/WiMAX Applications

In this paper, a new compact coplanar waveguide (CPW)-fed triple band monopole antenna with dual circular rings and two L-shaped parasitic strips on the ground plane is presented. The fabricated antenna with new simple structure has a compact size of 26 mm × 34 mm. The proposed antenna has impedance bandwidths of 2.39-2.69 GHz, 3.21- 4.12GHz and 5.08 -5.86GHz with a reﬂection coefficient of less than -10dB. The measured performances are compared with the simulated results and show good agreement. The proposed triple band monopole antenna also has good omnidirectional radiation patterns and available gain. The simple structure, ease of implementation, compact size and good performances make the proposed antenna an excellent candidate for WLAN and WiMAX applications.
Optimal reinsurance problems with extrapolative claim expectation

The insurer is subject to extrapolative bias and updates his perceived claim rate along with claim realizations: he believes that claims will continue to increase (decrease) in the near future if they have been increasing (decreasing) recently. To model this belief, we define an extrapolative expectation, which is a weighted average of past claims and the initial estimation of expectation of claims, to forecast the expectation of future claims. Moreover, this extrapolative expectation changes the premiums as claims change. The optimal reinsurance strategies for maximizing the expected exponential utility and mean‐variance utility of terminal wealth are derived in closed form. We find that extrapolative bias decreases the reinsurance demand. Moreover, the mean‐variance optimizer's risk retention level is greater than that of the exponential utility optimizer, although they are the same in the standard case without extrapolative bias.
Housing and Savings Behavior across Family Types

Does marital status affect households’ investment choices? Is accounting for distinct family types necessary for the correct evaluation of policies that aim at stimulating housing demand? To answer these questions, I develop a life-cycle model of housing and financial portfolio choice with dynamic and heterogeneous family types. I find that divorce risk encourages precautionary savings of couples in the form of liquid assets and reduces their demand for illiquid housing. Expected marriage, low income levels and larger exposure to income fluctuations prevent singles from becoming homeowners. Abstracting from distinct family types amplifies the attractiveness of housing and, as a result, overstates the effectiveness of housing policies such as lowering property taxes and reducing transaction costs by a factor greater than two. This mis-specification is largest for young households who are most likely to be single and whose marital transition risk is highest. In contrast, regulations that facilitate stock market participation help to foster wealth accumulation, because they encourage investment in high return assets that are cheaper to liquidate in the event of a (marital or labor income) shock.
FedHQL: Federated Heterogeneous Q-Learning

This study introduces the problem setting of Federated Reinforcement Learning with Heterogeneous And bLack-box agEnts (FedRL-HALE), in which multiple RL agents with varying policy parameter-izations, training configurations, and exploration strategies work together to optimize their policies through the proposed Federated Heterogeneous Q-Learning (FedHQL) algorithm. Empirical results demonstrate the effectiveness of FedHQL in improving system performance and increasing the sample efficiency of individual agents with high confidence.
An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0

Abstract. Many methods exist to model snow densification in order to calculate the depth of a single snow layer or the depth of the total snow cover from its mass. Most of these densification models need to be tightly integrated with an accumulation and melt model and need many forcing variables at high temporal resolution. However, when trying to model snow depth (HS) on climatological timescales, which is often needed for winter tourism-related applications, these preconditions can cause barriers. Often, for these types of applications, empirical snow models are used. These can estimate snow accumulation and snowmelt based on daily precipitation and temperature data only. To convert the resultant snow water equivalent (SWE) time series into snow depth, we developed the empirical model SWE2HS. SWE2HS is a multilayer densification model which uses daily snow water equivalent as sole input. A constant new snow density is assumed and densification is calculated via exponential settling functions. The maximum snow density of a single layer changes over time due to overburden and SWE losses. SWE2HS has been calibrated on a data set derived from a network of manual snow stations in Switzerland. It has been validated against independent data derived from automatic weather stations (AWSs) in the European Alps (Austria, France, Germany, Switzerland) and against withheld data from the Swiss manual observer station data set which was not used for calibration. The model fits the calibration data with root mean squared error (RMSE) of 8.4 cm, coefficient of determination (R2) of 0.97, and bias of −0.3 cm; it is able to achieve RMSE of 20.5 cm, R2 of 0.92, and bias of 2.5 cm on the validation data set from automatic weather stations and RMSE of 7.9 cm, R2 of 0.97, and bias of −0.3 cm on the validation data set from manual stations. The temporal evolution of the bulk density can be reproduced reasonably well on all three data sets. Due to its simplicity, the model can be used as post-processing tool for output of any other snow model that provides daily snow water equivalent output. Owing to its empirical nature, SWE2HS should only be used in regions with a similar snow climatology as the European Alps or has to be recalibrated for other snow climatological conditions. The SWE2HS model is available as a Python package which can be easily installed and used.
ATLAS Levels Up: Early Searches for Diboson Resonances in Semi-Hadronic Decay Channels at √ s = 13 TeV Center of Mass Energy

ATLAS Levels Up: Early Searches for Diboson Resonances in Semi-Hadronic Decay Channels at √ s = 13 TeV Center of Mass Energy Steve Alkire Searches are made for narrow diboson resonances, ZZ and ZW , in the final states ``qq and ννqq at ATLAS, with (13.2±0.4) fb−1data collected from pp-collisions with center of mass energy √ s=13 TeV during 2015 and 2016, the first two years of Run 2 of the Large Hadron Collider. The hadronic decay products of the vector boson, V → qq, are reconstructed as jets in the electromagnetic and hadronic calorimeters. When the vector boson is sufficiently boosted the decay products are reconstructed as a single anti-kt R = 1.0 jet. Otherwise two anti-kt R = 0.4 jets are matched to identify the vector boson. The Z boson in its leptonic decay is identified by either reconstructing two electrons in the calorimeters, or opposite sign muons in the muon spectrometer. Limits on the production cross section for 3 benchmark signals, a heavy scalar, a spin-1 heavy vector triplet, and a spin-2 graviton are set in the mass range 300 GeV to 5000 GeV.