Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Service Quality on Consumer Satisfaction in Restaurants

: Purpose: The aim of this paper is to examine the effect of service quality on the customer satisfaction in restaurants in Pune, India as well as to determine that which dimensions of service quality makes the major contribution to overall customer satisfaction in restaurants. Method: This is a quantitative research study, where the data was collected by using random sampling method. Self-administered 300 questionnaires were distributed across 30 restaurants consisting of 10 questionnaires per restaurant and out of these 256 questionnaires were valid and suitable for further analysis. Service Quality was measured by using the seven SERVQUAL dimensions of Tangibles, Reliability, Responsiveness, Assurance, Empathy, Food Quality, Menu and Customer Satisfaction which are identified through literature. Based on the dimensions of Service Quality selected, 7 hypotheses were proposed, all of which are supported. Statistical Analysis: Descriptive Statistics were used for Frequency Distribution, Mean, Standard Deviation; Cronbach Alpha for Reliability Test, Variance Inflation Factor (VIF) for Multicollinearity, Karl Pearson Correlation Coefficient, Two-Tailed –T-Test, F-Test, ANOVA, and Multiple Regression Analysis. And all the data was processed through IBM SPSS 23. Findings: The findings suggested that more the quality of service, more the customer satisfaction and there is a significant and positive relationship exists between service quality and customer satisfaction in restaurants. The study implies that service quality plays an important role as a driver for attaining higher customer satisfaction level in restaurants. This study also revealed that the independent variables of service quality having the most influence on the dependent variable customer satisfaction in the restaurants is Tangible (22.4 %) followed by Food Quality (19.2%), Menu (18.2%), Reliability (16.9%), Assurance (14.4%), Responsiveness (11.7%) and the least influential is Empathy with (11.1%). Index Terms:
Supplementary Material for Ev-NeRF : Event Based Neural Radiance Field 1

We implement Ev-NeRF using PyTorch using the NeRF formulation. For positional encoding, we use 10 frequencies for x and 4 for d. The weight λ of Lthres in Equation 8 is set large to 1000 to avoid thresholds from continuing to decrease. For all experiments, we use the Adam optimizer [3] with a learning rate of 5 × 10−4. Ev-NeRF takes about an hour in RTX 3090 GPU to train per scene and 1.5 seconds to render a single image. Similar to NeRF, the neural network takes the 3D coordinate and the ray direction as input and outputs the volume density and emitted radiance. Sinusoidal positional encoding, γ (·), is applied to input variables. Figure 1 shows the detailed architecture of our network. The architecture mostly follows that of NeRF [4], however it predicts the emitted luminance value instead of color values. Following NeRF [4], we add zero-mean, unit-variance Gaussian random noise to the density σ for slightly improved performance.
Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning

Abstract. Machine learning (ML) models are becoming a meaningful tool for modeling air pollutant concentrations. ML models are capable of learning and modeling complex nonlinear interactions between variables, and they require less computational effort than chemical transport models (CTMs). In this study, we used gradient-boosted tree (GBT) and multi-layer perceptron (MLP; neural network) algorithms to model near-surface nitrogen dioxide (NO2) and ozone (O3) concentrations over Germany at 0.1∘ spatial resolution and daily intervals. We trained the ML models using TROPOspheric Monitoring Instrument (TROPOMI) satellite column measurements combined with information on emission sources, air pollutant precursors, and meteorology as feature variables. We found that the trained GBT model for NO2 and O3 explained a major portion of the observed concentrations (R2=0.68–0.88 and RMSE=4.77–8.67 µg m−3; R2=0.74–0.92 and RMSE=8.53–13.2 µg m−3, respectively). The trained MLP model performed worse than the trained GBT model for both NO2 and O3 (R2=0.46–0.82 and R2=0.42–0.9, respectively). Our NO2 GBT model outperforms the CAMS model, a data-assimilated CTM but slightly underperforms for O3. However, our NO2 and O3 ML models require less computational effort than CTM. Therefore, we can analyze people's exposure to near-surface NO2 and O3 with significantly less effort. During the study period (30 April 2018 and 1 July 2021), it was found that around 36 % of people lived in locations where the World Health Organization (WHO) NO2 limit was exceeded for more than 25 % of the days during the study period, while 90 % of the population resided in areas where the WHO O3 limit was surpassed for over 25 % of the study days. Although metropolitan areas had high NO2 concentrations, rural areas, particularly in southern Germany, had high O3 concentrations. Furthermore, our ML models can be used to evaluate the effectiveness of mitigation policies. Near-surface NO2 and O3 concentration changes during the 2020 COVID-19 lockdown period over Germany were indeed reproduced by the GBT model, with meteorology-normalized near-surface NO2 having significantly decreased (by 23±5.3 %) and meteorology-normalized near-surface O3 having slightly increased (by 1±4.6 %) over 10 major German metropolitan areas when compared to 2019. Finally, our O3 GBT model is highly transferable to neighboring countries and locations where no measurements are available (R2=0.87–0.94), whereas our NO2 GBT model is moderately transferable (R2=0.32–0.64).
Securing the Future of 5G Smart Dust: Optimizing Cryptographic Algorithms for Ultra-Low SWaP Energy-Harvesting Devices

) While 5G energy harvesting makes 5G smart dust possible, stretching computation across power cycles affects cryptographic algorithms. This effect may lead to new security issues that make the system vulnerable to adversary attacks. Therefore, security measures are needed to protect data at rest and in transit across the network. In this paper, we identify the security requirements of existing 5G networks and the best-of-breed cryptographic algorithms for ultra-low SWaP devices in an energy harvesting context. To do this, we quantify the performance vs. energy tradespace, investigate the device features that impact the tradespace the most, and assess the security impact when the attacker has access to intermediate results. Our open-source energy-harvesting-tolerant versions of the cryptographic algorithms provide algorithm and device recommendations and ultra-low SWaP energy-harvesting-device-optimized versions of the cryptographic algorithms.
ARMore: Pushing Love Back Into Binaries

Static rewriting enables late-state code changes (e.g., to add mitigations, to remove unnecessary code, or to instrument for code coverage) at low overhead in security-critical environments. Most research on static rewriting has so far focused on the x86 architecture. However, the prevalence and proliferation of ARM-based devices along with a large amount of personal data (e.g., health and sensor data) that they process calls for e ffi cient introspection and analysis capabilities on the ARM platform. Addressing the unique challenges on aarch64, we introduce ARMore, the first e ffi cient, robust, and heuristic-free static binary rewriter for arbitrary aarch64 binaries that produces reassembleable assembly. The key improvements introduced by ARMore make the recovery of indirect control flow an option rather than a necessity. Instead of crashing, the cost of an uncovered target only causes the small overhead of an additional branch. ARMore can rewrite binaries from di ff erent languages and compilers (even arbitrary hand-written assembly),bothon PIC andnon-PIC code,withorwithoutsym-bols, including exception handling for C ++ and Go binaries, and also including binaries with mixed data and text. ARMore is sound as it does not rely on any assumptions about the input binary. ARMore is also e ffi cient: it does not employ any expensive dynamic translation techniques, incurring negligible overhead ( < 1% in our evaluated benchmarks). Our AFL ++ coverage instrumentation pass enables fuzzing of closed-source aarch64 binaries at three times the speed compared to the state-of-the-art (AFL-QEMU), and we found 58 unique crashes in closed-source software. ARMore is the only static rewriter whose rewritten binaries correctly pass all SQLite3 and core-utils test cases and autopkgtest of 97.5% Debian packages.
BlindPerm: Efficient MEV Mitigation with an Encrypted Mempool and Permutation

Maximal extractable value (MEV) in the context of blockchains and cryptocurrencies refers to the highest potential profit that an actor, particularly a miner or validator, can achieve through their ability to include, exclude, or re-order transactions within the blocks. MEV has become a topic of concern within the Web3 community as it impacts the fairness and security of the cryptocurrency ecosystem. In this work, we propose and explore techniques that utilize randomized permutations to shuffle the order of transactions of a committed block before they are executed. We also show that existing MEV mitigation approaches using an encrypted mempool can be readily extended by permutation-based techniques, thus providing multi-layer protection. With a focus on BFT consensus, we then propose BlindPerm , a framework enhancing an encrypted mempool with permutation at essentially no additional overheads and present various optimizations. Finally, we demonstrate how to extend our mitigation technique to support PoW longest-chain consensus protocols.
Semiparametric, parametric, and possibly sparse models for multivariate long-range dependence

Several available formulations, parametric models and sparsity settings for multivariate long-range dependence (MLRD) are discussed. Furthermore, a new parametric identifiable model for a general formulation of MLRD is introduced in any dimension, and another sparsity setting is identified of potential interest in MLRD modeling. Estimation approaches for MLRD are also reviewed, including some recent progress and open questions about estimation in higher dimensions and sparse settings.
NAP: Neural 3D Articulation Prior

We propose Neural 3D Articulation Prior (NAP), the first 3D deep generative model to synthesize 3D articulated object models. Despite the extensive research on generating 3D objects, compositions, or scenes, there remains a lack of focus on capturing the distribution of articulated objects, a common object category for human and robot interaction. To generate articulated objects, we first design a novel articulation tree/graph parameterization and then apply a diffusion-denoising probabilistic model over this representation where articulated objects can be generated via denoising from random complete graphs. In order to capture both the geometry and the motion structure whose distribution will affect each other, we design a graph-attention denoising network for learning the reverse diffusion process. We propose a novel distance that adapts widely used 3D generation metrics to our novel task to evaluate generation quality, and experiments demonstrate our high performance in articulated object generation. We also demonstrate several conditioned generation applications, including Part2Motion, PartNet-Imagination, Motion2Part, and GAPart2Object.
THE PROSPECT OF ANTIRACISM RACIAL RESENTMENT AND RESISTANCE TO CHANGE

Racial reckoning in response to racial injustice has compelled individuals, organizations, and institutions to acknowledge and adopt policies that actively challenge racial injustice. A central tenet of this era of reckoning is that it is no longer acceptable to ignore racist behaviors and expressions. To the extent that active opposition to racial prejudice is an effective strategy for individuals to pursue, we examine individual inclinations to act on matters of racial prejudice. We argue that in spite of best intentions, the motivation to act against racism, what we call “antiracism action orientation,” can be disrupted by system-justifying beliefs that raise questions about deservingness, legit-imize the status quo, and therefore defend inaction. Survey data from the 2020 Congressional Election Study show that antiracism action orientation is strongest among African Americans, and those with more positive affect toward racial-ethnic minorities, and supporters of change. Among Whites, racial resentment dominates the motivations for antiracism to the point that typical political allies like Democrats, liberals, and those who acknowledge White privilege reduce their anti-racism action orientation to lower levels than Republicans, conserva-tives, and deniers of White privilege. We conclude that most Americans, but especially Whites, have a high bar for change, making racism an ongoing American dilemma because of both racial attitudes and the costs of change.
Genetic diversity and population structure of Capitulum mitella (Linnaeus, 1767) in Fujian (China) revealed by mtDNA COI sequences [version 2; peer review: 1 approved, 1 approved with reservations]

Background: Capitulum mitella is a widely distributed and ecologically important stalked barnacle that settles extensively on rocky shores. This species contributes to the structural complexity of intertidal habitats and plays a critical role in the marine ecosystem. This study aimed to reveal the genetic diversity and population structure of C. mitella by analyzing the mitochondrial cytochrome oxidase I (COI) gene. Methods: A 683bp fragment of the COI gene was sequenced from 390 individuals sampled from six localities in Fujian, China. Results: A total of 84 distinct haplotypes were identified through the analysis of 82 polymorphic sites, resulting in an average haplotype diversity (h) of 0.660 and nucleotide diversity (π) of 0.00182. Analysis of molecular variance (AMOVA) and pairwise F ST statistics showed no significant population structure. Neutrality tests and mismatch distributions provided evidence of recent population expansion for the species. Conclusions: We suggest that the species' high dispersal ability, and ocean currents coupled with limited physical barriers in the region, contribute to its current phylogeographic structure. These findings enhance our comprehension of the genetic diversity and population structure of C. mitella , providing valuable insights for future conservation efforts.
System design for 3D wound imaging using low-cost mobile devices

The state-of-the art method of wound assessment is a manual, imprecise and time-consuming procedure. Per- formed by clinicians, it has limited reproducibility and accuracy, large time consumption and high costs. Novel technologies such as laser scanning microscopy, multi-photon microscopy, optical coherence tomography and hyper-spectral imaging, as well as devices relying on the structured light sensors, make accurate wound assessment possible. However, such methods have limitations due to high costs and may lack portability and availability. In this paper, we present a low-cost wound assessment system and architecture for fast and accurate cutaneous wound assessment using inexpensive consumer smartphone devices. Computer vision techniques are applied either on the device or the server to reconstruct wounds in 3D as dense models, which are generated from images taken with a built-in single camera of a smartphone device. The system architecture includes imaging (smartphone), processing (smartphone or PACS) and storage (PACS) devices. It supports tracking over time by alignment of 3D models, color correction using a reference color card placed into the scene and automatic segmentation of wound regions. Using our system, we are able to detect and document quantitative characteristics of chronic wounds, including size, depth, volume, rate of healing, as well as qualitative characteristics as color, presence of necrosis and type of involved tissue.
Ecological Modelling

Network particle tracking (NPT), building on the foundation of network environ analysis (NEA), is a new development in the deﬁnition of coherence relations within and between connected systems. This paper evaluates three ecosystem models in a comparison of throughﬂow-and storage-based NEA and NPT. Compartments in models with high indirect effects and Finn cycling showed low correlation of NEA storage and throughﬂow with particle repeat visits and numbers of particles in compartments at steady state. Conversely, the correlation between NEA and NPT results was high with two models having lower indirecteffectsandFinncycling.AnalysisofecologicalorientorsassociatedwithNEAshowedNPTtofully support conventional NEA results when the common conditions of donor control and steady state are satisﬁed. Particle trajectories are recorded in the new concept of a particle “passport”. Ability to track and record particle in-system histories enables views of multiple scales and opens the possibility of making pathway-dependent modeling decisions. NPT may also enable modeling of time, allowing integration of Newtonian, organismal and stochastic modeling perspectives in a single comprehensive analysis.
Development of low-cost air quality stations for next-generation monitoring networks: calibration and validation of NO2 and O3 sensors

Abstract. A pre-deployment calibration and a field validation of two low-cost (LC) stations equipped with O3 and NO2 metal oxide sensors were addressed. Pre-deployment calibration was performed after developing and implementing a comprehensive calibration framework including several supervised learning models, such as univariate linear and non-linear algorithms, and multiple linear and non-linear algorithms. Univariate linear models included linear and robust regression, while univariate non-linear models included a support vector machine, random forest, and gradient boosting. Multiple models consisted of both parametric and non-parametric algorithms. Internal temperature, relative humidity, and gaseous interference compounds proved to be the most suitable predictors for multiple models, as they helped effectively mitigate the impact of environmental conditions and pollutant cross-sensitivity on sensor accuracy. A feature analysis, implementing dominance analysis, feature permutations, and the SHapley Additive exPlanations method, was also performed to provide further insight into the role played by each individual predictor and its impact on sensor performances. This study demonstrated that while multiple random forest (MRF) returned a higher accuracy than multiple linear regression (MLR), it did not accurately represent physical models beyond the pre-deployment calibration dataset, so a linear approach may overall be a more suitable solution. Furthermore, as well as being less computationally demanding and generally more suitable for non-experts, parametric models such as MLR have a defined equation that also includes a few parameters, which allows easy adjustments for possible changes over time. Thus, drift correction or periodic automatable recalibration operations can be easily scheduled, which is particularly relevant for NO2 and O3 metal oxide sensors. As demonstrated in this study, they performed well with the same linear model form but required unique parameter values due to intersensor variability.
Detection with Diffusion-weighted MR Imaging Neonatal Hypoxic-ischemic Encephalopathy:

BACKGROUND AND PURPOSE: Although diffusion-weighted imaging has been shown to be highly sensitive in detecting acute cerebral infarction in adults, its use in detecting neonatal hypoxic-ischemic encephalopathy (HIE) has not been fully assessed. We examined the ability of this technique to detect cerebral changes of acute neonatal HIE in different brain locations. METHODS: Fifteen MR examinations were performed in 14 neonates with HIE (median age, 6.5 days; range, 2–11 days). Imaging comprised conventional T1-weighted, proton density– weighted, and T2-weighted sequences and echo-planar diffusion-weighted sequences. The location, extent, and image timing of ischemic damage on conventional and diffusion-weighted sequences and apparent diffusion coefﬁcient (ADC) maps were compared. RESULTS: Although conventional sequences showed cerebral changes consistent with ischemia on all examinations, diffusion-weighted imaging showed signal hyperintensity associated with decreased ADC values in only seven subjects (47%). All subjects with isolated cortical infarction on conventional sequences had corresponding hyperintensity on diffusion-weighted images and decreased ADC values, as compared with 14% of subjects with deep gray matter/ perirolandic cortical damage. The timing of imaging did not signiﬁcantly alter diffusion-weighted imaging ﬁndings. CONCLUSION: Diffusion-weighted imaging, performed with the technical parameters in this study, may have a lower correlation with clinical evidence of HIE than does conventional MR imaging. The sensitivity of diffusion-weighted imaging in detecting neonatal HIE appears to be affected by the pattern of ischemic damage, with a lower sensitivity if the deep gray matter is affected as compared with isolated cerebral cortex involvement.
Climatology, sources, and transport characteristics of observed water vapor extrema in the lower stratosphere

Abstract. Stratospheric water vapor (H2O) is a substantial component of the global radiation budget and therefore important to variability in the climate system. Efforts to understand the distribution, transport, and sources of stratospheric water vapor have increased in recent years, with many studies utilizing long-term satellite observations. Previous work to examine stratospheric H2O extrema has typically focused on the stratospheric overworld (pressures ≤ 100 hPa) to ensure the observations used are truly stratospheric. However, this leads to the broad exclusion of the lowermost stratosphere, which can extend over depths of more than 5 km below the 100 hPa level in the midlatitudes and polar regions and has been shown to be the largest contributing layer to the stratospheric H2O feedback. Moreover, focusing on the overworld only can lead to a large underestimation of stratospheric H2O extrema occurrence. Therefore, we expand on previous work by examining 16 years of Microwave Limb Sounder (MLS) observations of water vapor extrema (≥ 8 ppmv) in both the stratospheric overworld and the lowermost stratosphere to create a new lower-stratosphere climatology. The resulting frequency of H2O extrema increases by more than 300 % globally compared to extrema frequencies within stratospheric overworld observations only, though the percentage increase varies substantially by region and season. Additional context is provided for this climatology through a backward isentropic trajectory analysis to identify potential sources of the extrema. We show that, in general, tropopause-overshooting convection presents itself as a likely source of H2O extrema in much of the world, while meridional isentropic transport of air from the tropical upper troposphere to the extratropical lower stratosphere is also possible.