Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Customer Reviews for Local and Chain Restaurants: Multilevel Approach to Google Reviews Data

Online reviews influence customer decisions and present publicly available data to investigate differences between customer evaluations for local and chain businesses. We conduct a text analysis on a sample of 80,728 online customer reviews of quick-service restaurants to examine how the impact of dining experience attributes on customer evaluation differs between the two restaurant types. Estimation of multilevel multinomial models reveals that customer reviews for local restaurants have less polarized sentiment than chain restaurants. This polarization is also evident for sentiment usage related to four dining experience attributes: food, service, ambience, and price. Although food offerings are essential to get high ratings for local restaurants, service quality has a relatively greater impact on customer satisfaction for chains. Although customer reviews favor local restaurants, they need powerful testimonials for differentiation due to high review valence among their local competitors.
Sea Surface Temperature Trends in the Coastal Zone of Southern England

ABSTRACT Kassem, H. ; Amos, C.L. , and Thompson, C.E. L. , 2023. Sea surface temperature trends in the coastal zone of southern England. Journal of Coastal Research, 39(1), 18–31. Charlotte (North Carolina), ISSN 0749-0208. Sea surface temperature (SST) trends along the south coast of England (northern English Channel) were examined based on data from systematic buoy measurements deployed by the National Network of Regional Coastal Monitoring Programmes of England (NNRCMP) since 2003. These data were supplemented with: (1) long-term, coastal SST measurements by the Centre for Environment, Fisheries and Aquaculture Science (CEFAS); (2) global data sets compiled by the Hadley Centre since 1900, and (3) satellite-derived observations from Moderate Resolution Imaging Spectroradiometer (MODIS) (Aqua) since 2002. These data sets were used to evaluate de-seasoned nearshore trends in SST along the south coast of England and examine links to regional ocean-atmosphere teleconnections. The analyses of long-term, CEFAS data support the proposal that prior to the mid-1980s there were no de-seasoned trends in SST and conditions from year to year were relatively stable. Subsequently, interannual fluctuations appear to have increased, associated with a period of warming between 1985 and 2003 (0.28 °C/decade). Post 2003, interannual fluctuations in SST monitored by the NNRCMP buoys continued, and the warming trend appears to be greater (0.42 °C/decade). This trend in SST is greatest in the nearshore and decreases with distance offshore. The warming in SST also varied greatly from month to month. The greatest warming took place from December to March, whilst the least heating (and sometimes cooling) occurred between September and November. Analysis of Hadley (HadSST1.1) and MODIS data sets substantiated these trends. The greatest warming (post 2003) was found west of Portland Bill (up to 0.76 °C/decade) and decreased towards the Strait of Dover. Despite this west-to-east trend, all 12 NNRCMP stations between Penzance and Folkestone showed remarkably similar results, suggesting regional and global sources of heat rather than local sources. This is corroborated through wavelet coherence analysis linking SST anomalies to regional/global ocean-atmosphere teleconnection indices at seasonal scales.
Assessing a Mathematical Inquiry Course: Do Students Gain an Appreciation for Mathematics?

Abstract This study evaluates changes in students' performance and appreciation for mathematics as the result of taking a required general education course designed for calculus-ready students. The purpose of the study is to determine whether the goals of this rigorous general education course are being met. Changes in students' performance are measured by comparing grades on a pre-course and a post-course test. Changes in students' appreciation of mathematics are measured by scores on a pre-course and post-course survey, including open-ended questions concerning beliefs about mathematics. The study reveals a significant increase in grades on the post-course test, indicating gains in students' performance. It also reveals a significant increase in scores concerning creativeness, indicating a greater appreciation for the creativity involved in doing mathematics. The study reveals a significant decrease in scores on questions concerning attitudes, indicating a decrease in students' feelings about mathematics as it relates to them personally. The open-ended responses show an increase in students' appreciation for proofs and for the role of mathematics in the world. This report concludes with a discussion of some of the perceived benefits and challenges of including a rigorous, non-calculus mathematics course in the general education requirements for calculus-ready students.
ecoCode: a SonarQube Plugin to Remove Energy Smells from Android Projects

To face the climate change, Android developers urge to become green software developers. But how to ensure carbon-efficient mobile apps at large? In this paper, we introduce ecoCode, a SonarQube plugin able to highlight code structures that are smelly from an energy perspective. It is based on a curated list of energy code smells likely to impact negatively the battery lifespan of Android-powered devices. The ecoCode plugin enables analysis of any native Android project written in Java in order to enforce green code. — Demo video on https://youtu.be/4XIYGyPEhXQ
ShoeTect: Detecting Body Posture, Ambulation Activity, Gait Abnormalities, and Terrain with Multisensory Smart Footwear

Our feet are not just used to walk upright, feet can also reveal important context information on our physical constitution and context. In this research, we present a proof-of-concept multisensory approach to uncover the hidden information our feet hold. We instrumented a shoe with an accelerometer, a gyroscope, FSRs, a microphone, and a humidity sensor, while we drove a conventional machine learning approach. Our results show that we are capable to extract information on body posture (e.g., standing, sitting, kneeling), ambulation activity (e.g., walking, jumping, climbing stairs), gait abnormalities (e.g., overpronation, supination), and terrain (e.g., lawn, gravel, sand). These insights may show a new direction for the future of smart shoes.
Scale Temperament as Applied to Piano Tuning

A number of pianos were tested immediately after tuning to determine whether the actual tuning follows that of the “equal temperament” as defined in physics, within the limits of the ability of the tuner to set the strings, or whether there is a systematic deviation from the equally tempered scale and what the nature of such a systematic deviation is. The method for testing the tuning was by means of the chromatic stroboscope as described in a previous paper. The stroboscope used was an improved model of the type described in the last paper. Tests were made with several tuners and where possible with several pianos of each tuner. This gave an opportunity to determine whether different tuners have individual “styles” of tuning or whether there are marked general trends of a definite character. The results are plotted on graphs showing the deviations of each tone as read from the corresponding tone of the equally tempered scale. Comparisons of charts are made and averages are taken to show trends. In additi.
Candela‐class high‐brightness InGaN/AlGaN double‐heterostructure blue‐light‐emitting diodes

Candela‐class high‐brightness InGaN/AlGaN double‐heterostructure (DH) blue‐light‐emitting diodes(LEDs) with the luminous intensity over 1 cd were fabricated. As an active layer, a Zn‐doped InGaN layer was used for the DH LEDs. The typical output power was 1500 μW and the external quantum efficiency was as high as 2.7% at a forward current of 20 mA at room temperature. The peak wavelength and the full width at half‐maximum of the electroluminescence were 450 and 70 nm, respectively. This value of luminous intensity was the highest ever reported for blue LEDs.
Online Personalisation of Deep Mobile Activity Recognisers

Activity recognition is an increasingly important feature in mobile consumer devices that enables the design of context-aware applications and the customisation of user experiences. Recent deep learning-based recognisers demonstrate promising performances for hand activity recognition –e. g. , washing hands– or human activity recognition –e. g. , cycling– when exploiting accelerometer data from smartwatches or Magnetic Angular Rate and Gravity (MARG) data from body-worn sensors. However, the robustness and the generalisation capabilities of deep learning models can be an issue, since the consumers’ locomotion data is typically not available at training time. In this paper, we propose for the first time a pipeline of pre-trained deep feature extractors and online learning-capable shallow classifiers –such as passive-aggressive classifiers, random forests, and support vector classifiers– for the personalisation of mobile activity recognition systems. Our suggested solution could enable efficient on-device online learning for personalisation, thereby contributing to users’ privacy preservation. We report a series of experiments exploring the potential and the limitations of this approach to increase user-specific performance given only a limited number of training samples. Our results are encouraging and show (i) that a single additional data point per activity class (in a 25-class problem) from a new user can reduce recognition errors relatively by up to 20 %, and (ii) that with an increasing number of training samples our personalised models outperform comparable approaches in the literature, achieving accuracies of up to 91.4 %.
Sensitivity of flow dynamics and orographic precipitation to changing ambient conditions in idealised model simulations

Idealised numerical simulations using the non-hydrostatic weather prediction model of the Consortium for Small-scale Modeling (COSMO) in a three-dimensional (3D) configuration were conducted to investigate the relationship between ambient conditions, flow characteristics, and orographic precipitation patterns. By changing the model input parameters of wind speed, static stability, temperature, and relative humidity, different flow effects from conditionally unstable flow to upstream deceleration are considered. It is shown that latent heat release significantly delays the onset of flow stagnation, which can be understood using the moist stability concept. However, due to the vertical variations of saturated and unsaturated layers, it is not possible to apply this concept for determining gravity waves. Both the drying ratio, expressing the conversion of the upstream moisture flux into precipitation, and the location of the precipitation maxima can be described to a certain extent by the saturated nondimensional mountain height, M m = N m H/U, where H is the mountain height, N m is the saturated stability, and U is the undisturbed wind speed. In the flow around regime, the precipitation maxima are associated with the extended gravity wave and are located downstream of the mountain crest. With increasing direct mountain overflow (decreasing M m ), the precipitation maxima are shifted to a location upstream of the crest. The transition from purely stratiform precipitation to embedded convection occurs abruptly when M m becomes imaginary, indicating conditional instability.
Feedforward Control Strategies for Hot Rolling in a Reversing Plate Mill

This contribution is devoted to a new feedforward control concept for the thickness control in a hot rolling mill. The core of this concept is an accurate forward slip model for the calculation of the entry thickness profile. This forward slip is derived by means of the fundamental equations of hydrodynamics for a viscoplastic material in the roll gap. The proposed control strategy advantageously uses the additional information of the rolled plate in form of this entry thickness profile. Simulated and measured data demonstrate the feasibility of the forward slip model and the control concept. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Towards Understanding the Runtime Performance of Rust

Rust is a young systems programming language, but it has gained tremendous popularity thanks to its assurance of memory safety. However, the performance of Rust has been less systematically understood, although many people are claiming that Rust is comparable to C/C++ regarding efficiency. In this paper, we aim to understand the performance of Rust, using C as the baseline. First, we collect a set of micro benchmarks where each program is implemented with both Rust and C. To ensure fairness, we manually validate that the Rust version and the C version implement the identical functionality using the same algorithm. Our measurement based on the micro benchmarks shows that Rust is in general slower than C, but the extent of the slowdown varies across different programs. On average, Rust brings a 1.77x “performance overhead” compared to C. Second, we dissect the root causes of the overhead and unveil that it is primarily incurred by run-time checks inserted by the compiler and restrictions enforced by the language design. With the run-time checks disabled and the restrictions loosened, Rust presents a performance indistinguishable from C.
Reentrancy Vulnerability Detection and Localization: A Deep Learning Based Two-phase Approach

Smart contracts have been widely and rapidly used to automate financial and business transactions together with blockchains, helping people make agreements while minimizing trusts. With millions of smart contracts deployed on blockchain, various bugs and vulnerabilities in smart contracts have emerged. Following the rapid development of deep learning, many recent studies have used deep learning for vulnerability detection to conduct security checks before deploying smart contracts. These approaches show effective results on detecting whether a smart contract is vulnerable or not whereas their results on locating suspicious statements responsible for the detected vulnerability are still unsatisfactory. To address this problem, we propose a deep learning based two-phase smart contract debugger for reentrancy vulnerability, one of the most severe vulnerabilities, named as ReVulDL: Reentrancy Vulnerability Detection and Localization. ReVulDL integrates the vulnerability detection and localization into a unified debugging pipeline. For the detection phase, given a smart contract, ReVulDL uses a graph-based pre-training model to learn the complex relationships in propagation chains for detecting whether the smart contract contains a reentrancy vulnerability. For the localization phase, if a reentrancy vulnerability is detected, ReVulDL utilizes interpretable machine learning to locate the suspicious statements in smart contract to provide interpretations of the detected vulnerability. Our large-scale empirical study on 47,398 smart contracts shows that ReVulDL achieves promising results in detecting reentrancy vulnerabilities (e.g., outperforming 16 state-of-the-art vulnerability detection approaches) and locating vulnerable statements (e.g., 70.38% of the vulnerable statements are ranked within Top-10).
Enriching Compiler Testing with Real Program from Bug Report

Researchers have proposed various approaches to generate test programs. The state-of-the-art approaches can be roughly divided into random-based and mutation-based approaches: random-based approaches generate random programs and mutation-based approaches mutate programs to generate more test programs. Both lines of approaches mainly generate random code, but it is more beneficial to use real programs, since it is easier to learn the impacts of compiler bugs and it becomes reasonable to use both valid and invalid code. However, most real programs from code repositories are ineffective to trigger compiler bugs, partially because they are compiled before they are submitted. In this experience paper, we apply two techniques such as differential testing and code snippet extraction to the specific research domain of compiler testing. Based on our observations on the practice of testing compilers, we identify bug reports of compilers as a new source for compiler testing. To illustrate the benefits of the new source, we implement a tool, called LeRe, that extracts test programs from bug reports and uses differential testing to detect compiler bugs with extracted programs. After we enriched the test programs, we have found 156 unique bugs in the latest versions of gcc and clang. Among them, 103 bugs are confirmed as valid, and 9 bugs are already fixed. Our found bugs contain 59 accept-invalid bugs and 33 reject-valid bugs. In these bugs, compilers wrongly accept invalid programs or reject valid programs. The new source enables us detecting accept-invalid and reject-valid bugs that were usually missed by the prior approaches. The prior approaches seldom report the two types of bugs. Besides our found bugs, we also present our analysis on our invalid bug reports. The results are useful for programmers, when they are switching from one compiler to another, and can provide insights, when researchers apply differential testing to detect bugs in more types of software.
Analysis and Design of a MuSiC-Based Angle of Arrival Positioning System

In this research article, a concept for a secondary RAdio Direction And Ranging (RADAR) angle of arrival based system with cooperative targets transmitting at 2.4 GHz and using Multiple Signal Classification (MuSiC) to determine the angles of incidence is investigated. In addition to introducing common algorithms and presenting thorough derivations, the system is first examined through simulations. To prove the concept, hardware, firmware, and software are developed. For MuSiC, we propose three novel methods to obtain the correct incident angle from the spectrum, especially in strong multipath environments. These methods work either for a single spectrum or for a combination recorded at multiple times. Together with the estimated angles of incidence, our methods determine measures on the respective likelihoods. Based on this, we additionally propose two algorithms for computing the final position. Our system is characterized in both a simple 20 m × 15 m outdoor and a 17 m × 13 m multipath indoor environment, where we achieve a mean angular error of 3∘ and a mean positioning error of 0.67 m for the former using only four base stations with four antennas each. Our novel approach shows position accuracy improvements of 15% outdoors and 25% indoors compared to classical MuSiC estimation.
Fastbot2: Reusable Automated Model-based GUI Testing for Android Enhanced by Reinforcement Learning

We introduce a reusable automated model-based GUI testing technique for Android apps to accelerate the testing cycle. Our key insight is that the knowledge of event-activity transitions from the previous testing runs, i.e., executing which events can reach which activities, is valuable for guiding the follow-up testing runs to quickly cover major app functionalities. To this end, we propose (1) a probabilistic model to memorize and leverage this knowledge during testing, and (2) design a model-based guided testing strategy (enhanced by a reinforcement learning algorithm). We implemented our technique as an automated testing tool named Fastbot2. The evaluation on two popular industrial apps (with billions of user installations), Douyin and Toutiao, shows that Fastbot2 outperforms the state-of-the-art testing tools (Monkey, Ape and Stoat) in both activity coverage and fault detection in the context of continuous testing. To date, Fastbot2 has been deployed in the CI pipeline at ByteDance for nearly two years, and 50.8% of the developer-fixed crash bugs were reported by Fastbot2, which significantly improves app quality. Fastbot2 has been made publicly available to benefit the community at: https://github.com/bytedance/Fastbot_Android.
PRCBERT: Prompt Learning for Requirement Classification using BERT-based Pretrained Language Models

Software requirement classification is a longstanding and important problem in requirement engineering. Previous studies have applied various machine learning techniques for this problem, including Support Vector Machine (SVM) and decision trees. With the recent popularity of NLP technique, the state-of-the-art approach NoRBERT utilizes the pre-trained language model BERT and achieves a satisfactory performance. However, the dataset PROMISE used by the existing approaches for this problem consists of only hundreds of requirements that are outdated according to today’s technology and market trends. Besides, the NLP technique applied in these approaches might be obsolete. In this paper, we propose an approach of prompt learning for requirement classification using BERT-based pretrained language models (PRCBERT), which applies flexible prompt templates to achieve accurate requirements classification. Experiments conducted on two existing small-size requirement datasets (PROMISE and NFR-Review) and our collected large-scale requirement dataset NFR-SO prove that PRCBERT exhibits moderately better classification performance than NoRBERT and MLM-BERT (BERT with the standard prompt template). On the de-labeled NFR-Review and NFR-SO datasets, Trans_PRCBERT (the version of PRCBERT which is fine-tuned on PROMISE) is able to have a satisfactory zero-shot performance with 53.27% and 72.96% F1-score when enabling a self-learning strategy.
Diffusive scattering of energetic electrons by intense whistler-mode waves in an inhomogeneous plasma

Electron resonant interactions with electromagnetic whistler-mode waves play an important role in electron flux dynamics in various space plasma systems: planetary radiation belts, bow shocks, solar wind and magnetic reconnection regions. Two key wave characteristics determining the regime of wave–particle interactions are the wave intensity and the wave coherency. The classical quasi-linear diffusion approach describes well electron diffusion by incoherent and low-amplitude waves, whereas the nonlinear resonant models describe electron phase bunching and trapping by highly coherent intense waves. This study is devoted to the investigation of the regime of electron resonant interactions with incoherent but intense waves. Although this regime is characterized by electron diffusion, we show that diffusion rates scale linearly with the wave amplitude, $D\propto B_w$, in contrast to the quasi-linear diffusion scaling $D_{QL}\propto B_w^2$. Using observed wave amplitude distributions, we demonstrate that the quasi-linear diffusion model significantly overestimates electron scattering by incoherent, but intense whistler-mode waves. We discuss the results obtained in the context of simulations of long-term electron flux dynamics in space plasma systems.