Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Consumers’ Adoption Intentions of Remote Mobile Payments in the United Kingdom: Extending UTAUT with Innovativeness, Risk, and Trust

Mobile payments (MPs) are predicted to be one of the future's most successful mobile services but have achieved limited acceptance in developed countries to date. PCs are still the preferred technology for online shopping in the United Kingdom but the continued growth of mobile commerce (MC) is highly correlated with the success of remote MPs (RMPs). Currently MP research has largely ignored the variations between different MP solutions, and existing MP adoption studies have predominantly utilized Davis’ (1989) Technology Acceptance Model, which has been criticized for having a deterministic approach without much consideration for users’ individual characteristics. Therefore, this study applied the Unified Theory of Acceptance and Use of Technology (UTAUT), extended with more consumer-related constructs, to explore the factors affecting nonusers’ intentions to adopt RMP in the United Kingdom. Quantitative data were collected (n = 268) and structural equation modeling was undertaken. The findings revealed that performance expectancy, social influence, innovativeness, and perceived risk significantly influenced nonusers’ intentions to adopt RMP, whereas effort expectancy did not. Inclusion of MP knowledge as a moderating variable revealed that there was a significant difference in the effect of trust on behavioral intention for those who knew about MP than for those who did not. These findings have important theoretical and practical implications, particularly for the development and marketing of RMP, which will support the long-term success of MC.
Computational Symmetry via Prototype Distances for Symmetry Groups Classification

Symmetry is an abstract concept that is easily noticed by humans and as a result designers make new creations based on its use, e.g. textile and tiles. Images of these designs belong to a more general group called wallpaper images, and these images exhibit a repetitive pattern on a 2D space. In this paper, we present a novel computational framework for the automatic classification into symmetry groups of images with repetitive patterns. The existing methods in the literature, based on rules and trees, have several drawbacks because of the use of thresholds and heuristics. Also, there is no way to give some measurement of the classification goodness-of-fit. As a consequence, these methods have shown low classification values when images exhibit imperfections due to the manufacturing process or hand made process. To deal with these problems, we propose a classification method that can obtain an automatic parameter estimation for symmetry analysis. Using this approach, the image classification is redefined as distance computation to the binary prototypes of a set of defined classes. Our experimental results improve the state of the art in symmetry group classification methods.
DeepFacade: A Deep Learning Approach to Facade Parsing With Symmetric Loss

Parsing building facades into procedural grammars plays an important role for 3D building model generation tasks, which have been long desired in computer vision. Deep learning is a promising approach to facade parsing, however, a straightforward solution by directly applying standard deep learning approaches cannot always yield the optimal results. This is primarily due to two reasons: 1) it is nontrivial to train existing semantic segmentation networks for facade parsing, e.g., Fully-Convolutional Neural Networks (FCN) which are usually weak at predicting fine-grained shapes (J. Long et al., 2015); and 2) building facades are man-made architectures with highly regularized shape priors, and the prior knowledge plays an important role in facade parsing, for which how to integrate the prior knowledge into deep neural networks remains an open problem. In this paper, we present a novel symmetric loss function that can be used in deep neural networks for end-to-end training. This novel loss is based on the assumption that most of windows and doors have a highly symmetric rectangle shape, and it penalizes all window predictions that are non-rectangles. This prior knowledge is smoothly integrated into the end-to-end training process. Quantitative evaluation demonstrates that our method has outperformed previous state-of-art methods significantly on five popular facade parsing datasets. Qualitative results have shown that our method effectively aids deep convolutional neural networks to predict more accurate, visually pleasing, and symmetric shapes. To the best of our knowledge, we are the first to incorporate symmetry constraint into end-to-end training in deep neural networks for facade parsing.
Bounded Reasoning and Higher-Order Uncertainty

Harsanyi type structures, the device traditionally used to model players’ beliefs in games, generate infinite hierarchies of beliefs. Can the standard framework nevertheless be used to model situations in which players potentially have a finite depth of reasoning? This paper extends the Harsanyi framework to allow for higher-order uncertainty about players’ depth of reasoning. The basic principle is that players with a finite depth of reasoning cannot distinguish states that differ only in players’ beliefs at high orders. I apply the new framework to the electronic mail game of Rubinstein (1989). Coordination on the Pareto-efficient action is possible when there is higher-order uncertainty about players’ depth of reasoning, unlike in the standard case, provided that one player thinks it is sufficiently likely that the other player has a finite (though potentially very high) depth of reasoning. Finally, I construct a type space that allows for bounded reasoning that contains the universal type space (which generates all infinite belief hierarchies) as a subspace, showing that the present framework fully generalizes the Harsanyi formalism. ∗U.C. Irvine and Santa Fe Institute. E-mail: willemien.kets@gmail.com. Phone: +1-505-204 8012. This paper supersedes Kets (2009). I am grateful to Adam Brandenburger, Yossi Feinberg, and Matthew Jackson for their guidance and support. This paper owes much to the input of Adam Brandenburger, Alfredo Di Tillio, Amanda Friedenberg, and Aviad Heifetz. I thank Songzi Du, Matt Elliott, Yossi Feinberg, Alex Frankel, Ben Golub, Joe Halpern, Yair Livne, Martin Meier, Rosemarie Nagel, Andrés Perea, Tomasz Sadzik, Burkhard Schipper, Elias Tsakas, and Takuro Yamashita for stimulating discussions and helpful comments and suggestions. Much of this research was carried out during visits to Stanford University and NYU Stern School of Business, and I thank these institutions for their hospitality.
Using generative artificial intelligence to test hypotheses about animal signal evolution: A case study in an ornamented fish

The sensory drive hypothesis of animal signal evolution suggests that animal communication signals evolve in response to environmental pressures. While classical approaches to testing this hypothesis focus on one aspect of the signal, deep learning techniques like generative models can create and manipulate stimuli without targeting a specific feature. Here, we used a technique called style transfer to experimentally test preferences for colour patterns in a fish. We manipulated how similar or dissimilar male body patterns were to their habitats using the Neural Style Transfer (NST) algorithm. We predicted that males whose body patterns are similar to their habitats are easier to process and thus preferred by conspecifics. Our findings suggest that both males and females tend to be sensitive to habitat congruence in their preferences, but to different extents, requiring additional investigation. Nonetheless, this study demonstrates the potential of deep learning techniques in testing hypotheses about animal communication signals.
Visual discrimination of optical material properties: a large- 2 scale study

22 For elucidation of complex visual processing such as material perception of realistic objects, 23 multiple research approaches should be combined. Shearing stimulus and response data is an 24 effective strategy to make the results of different studies directly comparable, and to assist a wide 25 range of researchers with different backgrounds to jump into the field. We therefore constructed a 26 database of a variety of material images with standard human discrimination performance 27 attached. We created various material images by using physically-based computer graphics 28 techniques and conducted psychophysical experiments both in the laboratory and through 29 crowdsourcing. The observer’s task was material discrimination for six material dimensions (gloss 30 contrast, gloss sharpness, translucent vs. opaque, gold vs. plastic yellow, glass vs. silver, and 31 glossy vs. painted) with several task difficulties controlled by material parameters. We also varied 32 illumination consistency and object geometry. We used a non-verbal procedure (oddity task) for 33 our database to be widely used in diverse studies such as cross-cultural, cross-species, clinical and 34 developmental studies. Results showed that discrimination performance was affected by 35 illumination condition and object geometry, in agreement with previous gloss perception studies, 36 although the pattern of effects was slightly different for some material dimensions. Specifically, 37 we found that discrimination of translucency was easier for rugged shapes than for smooth shapes, 38 while the opposite was the case for gloss perception. We also found that the ability to discriminate 39 the spatial consistency of specular highlights on glossiness perception had a larger individual 40 difference than other tasks. The performance obtained through crowdsourcing was highly 41 correlated with that in the laboratory, which suggests that our database can be used even when the 42 experimental conditions are not strictly controlled under non-laboratory environments. Several 43 projects using our dataset are underway. 44 45 46
Effects of Moon's Thermal State on the Impact Basin Ejecta Distribution

We investigate how different temperature gradients of the Moon affect the ejection of lithic and molten materials for impact basin several hundred kilometers in diameter to quantify the thickness and melt content of ejecta blanket as a function of radial distance. We find, by means of numerical modeling, that the ejecta thickness and melt content, similar to the basin formation, is sensitive to the thermal properties of the target. For two similar impact scenarios, the ejecta thickness with radial distance is proportional to a power law, but for a “warm” target, it declines faster than for a “cold” target. In addition, the impact on the warm target produces more molten ejecta than in the case of the cold target. The thermal effects on the ejecta thickness distribution can be testified by the topographic variations around Imbrium and Orientale basins, which were thought to be formed on a warm and cold Moon, respectively. Our study demonstrates that the thermal effect needs to be taken into account to estimate the ejecta thickness distribution for large‐scale impact basins on airless planetary surfaces.
Cooperative Control for Ocean Sampling: The Glider Coordinated Control System

The glider coordinated control system (GCCS) uses a detailed glider model for prediction and a simple particle model for planning to steer a fleet of underwater gliders to a set of coordinated trajectories. The GCCS also serves as a simulation testbed for the design and evaluation of multivehicle control laws. In this brief, we describe the GCCS and present experimental results for a virtual deployment in Monterey Bay, CA and a real deployment in Buzzards Bay, MA.
Women August Meeting and the Quest against Discrimination of OSU in South-East, Nigeria

AbstractThis paper examined the utility of yearly August meeting of Igbo women as a vehicle for denouncingthe orthodox belief that sees the Osu as less humans in the South-East, Nigeria. The paper contends that this annual women meeting holds great promise in this direction in that, it will help to educate and create awareness against the treatment of the Osu as inferior human beings a situation that had caused more alienation among Igbo stock. The paper utilized qualitative data sourced through key respondents’ interviews of chairmen of the August women meeting, leaders of both Osu and Diala extractions purposively selected from two communities each in Enugu and Imo states. Imo and Enugu States were selected because they still have some communities that treat Osu as inferior human beings. While Umuoka community was selected in Imo state, Umuode community was selected in Enugu Sate. The data were analysed using descriptive and content analysis. Results of the analysis showed that discrimination against the Osus in the South-East was based on mere belief and ignorance transferred from one generation to the other and that patterns and levels of discrimination against the Osus took the form of rejection, open abuse, planned attacks, divorce, isolation, separation and applying of poison. The results showed that in spite of the useful potentials of the August meeting, little has been done by women in this meeting to nip the Osu caste system in the bud. The study concluded that effective and rapid mitigation of the Osu caste system in the South- East demands a multi-faceted approach in its implementation as it affects both men and women in the region.
Improving grid performance by dynamically deploying applications

Grid applications are normally deployed on computing nodes beforehand, which may cause the undesirable situation that some of these nodes (with hot applications deployed) are always busy whereas the others are consistently idle. Therefore, the overall performance (e.g. throughput and load balancing) of such a Grid system would be seriously degraded. In this paper, we present the idea of Hierarchical and Dynamic Deployment of Application (HDDA) in Grid to improve the system performance. With HDDA, an application can be dynamically deployed and undeployed when necessary. In order to reduce the overhead caused by HDDA, the Average Latency Ratio Minimum (ALR-MIN) replacement strategy is also proposed. It deploys applications to nodes with minimum ALR of Node (NALR), and evicts applications with minimum increment of ALR. The results of the experiment we conducted on ChinaGrid show that HDDA can achieve 10 and 24p less average complete time (ACT) than the schemes of non-HDDA and Static Deployment of Application (SDA), respectively. Additionally, throughput and load balancing of HDDA are also better than the other two schemas. Results of the simulation performed on a simulator particularly developed for this research show that our ALR-MIN replacement strategy results in 17p less relative delay-time of jobs than the well-known Least Recently Used (LRU)-based strategies in a typical setting. Copyright © 2010 John Wiley & Sons, Ltd. Part of this paper has been published in the proceeding of ScalCom 2009.
Length-weight relationship of a hill-stream fish, Glyptothorax telchitta (Ham.) from Saptakoshi River of Nepal

The length-weight relationship of a hill-stream fish, Glyptothorax telchitta from Saptakoshi River of Nepal was analysed using the formula W=aLᵇ. The exponential values computed for total length and standard length in relation to body weight were 2.991 and 2.888 respectively.
A Generic Application-Level Protocol Analyzer and its Language

The Shield project relied on application protocol analyzers to detect potential exploits of application vulnerabilities. We present the design of a second-generation generic application-level protocol analyzer (GAPA) that encompasses a domain-specific language and the associated run-time. We designed GAPA to satisfy three important goals: safety, real-time analysis and response, and rapid development of analyzers. We have found that these goals are relevant for many network monitors that implement protocol analysis. Therefore, we built GAPA to be readily integrated into tools such as Ethereal as well as Shield. GAPA preserves safety through the use of a memorysafe language for both message parsing and analysis, and through various techniques to reduce the amount of state maintained in order to avoid denial-of-service attacks. To support online analysis, the GAPA runtime uses a streamprocessing model with incremental parsing. In order to speed protocol development, GAPA uses a syntax similar to many protocol RFCs and other specifications, and incorporates many common protocol analysis tasks as built-in abstractions. We have specified 10 commonly used protocols in the GAPA language and found it expressive and easy to use. We measured our GAPA prototype and found that it can handle an enterprise client HTTP workload at up to 60 Mbps, sufficient performance for many end-host firewall/IDS scenarios. At the same time, the trusted code base of GAPA is an order of magnitude smaller than Ethereal.
Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.
Provenance and tectonic setting of Early Eocene Sohnari Member of Laki Formation from southern Indus Basin of Pakistan

The current study is carried out the bulk mineralogical and geochemical investigation of the Early Eocene Sohnari coal‐bearing sedimentary rocks from the southern Indus Basin in Pakistan. The sediments of the Sohnari Member of Laki Formation are composed of shale and sandstone. Their provenance and tectonic setting can well constrain the evolution of the Indus Basin and help solve the controversy regarding the timing of the India–Eurasia collision. X‐ray diffraction results confirm the existence of abundant quartz, zircon, and hematite. The assemblages of hematite, calcite, halite, and gypsum indicate the depositional history of Early Eocene time of southern Indus Basin had with dry, low temperature, and high salinity conditions. The geochemical results demonstrate that the Sohnari sediments are dominated by mature sublitharenites derived predominantly from quartz sedimentary and felsic rocks with minor arc‐related mafic inputs. The chemical index of alteration and plagioclase index of alteration of the Sohnari Member indicate the sediments experienced low to high weathering conditions in the southern Indus Basin during Early Eocene time. The provenance characteristics suggest that the Early Eocene sediments were deposited in a passive margin, instead of a foreland basin. Therefore, we propose that India might still have not collided with Eurasia in the Ypresian of Early Eocene (56–47.8 Ma).
Evaluation of Air Pollution Tolerance Index and Anticipated Performance Index of Plants and their Role in Development of Green Belt along National Highway-22

The blooming growth in Indian economy in terms of industrialization, urbanization and modernization has lead to increase in various economic activities, where road development has become an integral part, ameliorating the efficient connectivity, financial and social welfare of the people. But such activities go hand in hand causing damage to the natural assets of the environment to a greater or a lesser extent by disturbing the existing ecosystem. Air pollution due to expansion and vehicular emissions has become one of the most serious problems and has resulted in huge threat to both the environment and the health of living organisms like plants, animals and humans (Kaur and Nagpal, 2017). The combustion activities also adds toxic gases like SOX, NOX, CO and particulate matter into the air which includes soot particles as well as smaller quantities of toxic metals, organic molecules and radioactive isotopes (Bhattacharya et al., 2013). Plants growing along the roadsides get affected at the maximum as they are the primary recipients to different air pollutants and show varied levels of tolerance and sensitivity and also plays an important role in monitoring and maintaining the ecological balance by actively Plants play an important role in mitigating air pollution. In such a scenario where problems due to air pollution are faced worldwide screening of sensitive and tolerant plants which acts as a bio indicator and sinks for air pollution is extremely significant. Therefore, present study was designed to evaluate the Air Pollution Tolerance Index (APTI) of six roadside plant species namely Cassia fistula, Grevillea robusta, Grewia optiva, Leucaena leucocephela, Toona ciliate and Woodfordia floribunda growing alongside a selected stretch of National Highway 22 (Datiyar to Solan) in Himachal Pradesh, India. The Anticipated Performance Index (API) of these plant species was also calculated by considering their APTI values with biological and socio-economic characters (plant height, canopy structure, type of plant, laminar characteristics, texture, hardiness and economic value). The study indicated that Cassia fistula with highest APTI value of 23.5 and highest API score is best for green belt development. K e y w o r d s