Miracle-dz/UniFAR-FTUs huggingface dataset:
The South Pacific Meridional Mode as a Thermally Driven Source of ENSO Amplitude Modulation and Uncertainty

This study seeks to identify thermally driven sources of ENSO amplitude and uncertainty, as they are relatively unexplored compared to wind-driven sources. Pacific meridional modes are argued to be wind triggers for ENSO events. This study offers an alternative role for the South Pacific meridional mode (SPMM) in ENSO dynamics, not as an ENSO trigger, but as a coincident source of latent heat flux (LHF) forcing of ENSO SSTA that, if correctly (incorrectly) predicted, could reduce (increase) ENSO prediction errors. We utilize a coupled model simulation in which ENSO variability is perfectly periodic and each El Niño experiences identical wind stress forcing. Differences in El Niño amplitude are primarily thermally driven via the SPMM. When El Niño occurs coincidentally with positive phase SPMM, the positive SPMM LHF anomaly counteracts a fraction of the LHF damping of El Niño, allowing for a more intense El Niño. If the SPMM phase is instead negative, the SPMM LHF amplifies the LHF damping of El Niño, reducing the event’s amplitude. Therefore, SPMM LHF anomalies may either constructively or destructively interfere with coincident ENSO events, thus modulating the amplitude of ENSO. The ocean also plays a role, as the thermally forced SSTA is then advected westward by the mean zonal velocity, generating a warming or cooling in the ENSO SSTA tendency in addition to the wind-forced component. Results suggest that in addition to wind-driven sources, there exists a thermally driven piece to ENSO amplitude and uncertainty that is generally overlooked. Links between the SPMM and Pacific decadal variability are discussed.
Open Problems on Graph Coloring for Special Graph Classes

For a given graph G and integer k , the Coloring problem is that of testing whether G has a k -coloring, that is, whether there exists a vertex mapping c : V → { 1 , 2 , . } such that c ( u ) (cid:54) = c ( v ) for every edge uv ∈ E . We survey known results on the computational complexity of Coloring for graph classes that are hereditary or for which some graph parameter is bounded. We also consider coloring variants, such as precoloring extensions and list colorings and give some open problems in the area of on-line coloring.
Lookup Table-Based Adaptive Body biasing of Multiple Macros for Process Variation Compensation and Low Leakage

A reduced supply voltage must be accompanied by a reduced threshold voltage, which makes this approach to power saving susceptible to process variation in transistor parameters, as well as resulting in increased subthreshold leakage. While adaptive body biasing is efficient for both compensating process variation and suppressing leakage current, it suffers from a large overhead of control circuit. Most body biasing circuits target an entire chip, which causes excessive leakage of some blocks and misses the chance of fine grain control. We propose a new adaptive body biasing scheme, based on a lookup table for independent control of multiple functional blocks on a chip, which controls leakage and also compensates for process variation at the block level. An adaptive body bias is applied to blocks in active mode and a large reverse body bias is applied to blocks in standby mode. This is achieved by a central body bias controller, which has a low overhead in terms of area, delay, and power consumption. The p.
Optic Disc and Cup Segmentation Through Fuzzy Broad Learning System for Glaucoma Screening

—Glaucoma is an ocular disease that causes perma-nent blindness if not cured at an early stage. Cup-to-disc ratio (CDR), obtained by dividing the height of optic cup (OC) with the height of optic disc (OD), is a widely adopted metric used for glaucoma screening. Therefore, accurately segmenting OD and OC is crucial for calculating a CDR. Most methods have employed deep learning methods for the segmentation of OD and OC. However, these methods are very time-consuming. We present a new fuzzy broad learning system-based technique for OD and OC segmentation with glaucoma screening. We comprehensively integrated extracting a region of interest (ROI) from RGB images, data augmentation, extracting red and green channel images, and inputting them to the two separate fuzzy broad learning system-based neural networks for segmenting the OD and OC, respectively, and then calculated CDR. Experiments show that our fuzzy broad learning system-based technique outperforms many state-of-the-art methods.
An Automatic Singing Skill Evaluation Using Pitch Interval Accuracy

This paper presents a method of evaluating singing skills that does not require score information of the sung melody. This requires an approach that is different from existing systems, such as those currently used for Karaoke systems. Previous research on singing evaluation has focused on analyzing the characteristics of singing voice, but were not aimed at developing an automatic evaluation method. The approach presented in this study uses pitch interval accuracy and vibrato as acoustic features which are independent from specific characteristics of the singer or melody. The approach was tested by a 2-class (good/poor) classification test with 600 song sequences, and achieved an average classification rate of 83.5%.
Embodied Persuasion: How Holding Your Smartphone Changes Your Product Perception

Online shopping through mobile devices has dramatically increased worldwide. This research investigates the role embodied interactions may play in stimulating virtual product experience in mobile commerce settings. Drawing on research on virtual product experience and embodied cognition, we hypothesize that holding a mobile device in hands (vs. putting the mobile device on the table) is more likely to create an illusion that the products being viewed are actually present in the real world and to stimulate imagery consumption experience, leading to higher purchase intention and choice satisfaction. This effect is more salient for desirable products than for undesirable products. We describe an experiment design for testing the hypotheses, report preliminary data analysis results, and discuss the potential theoretical and practical implications of this study.
Characteristics of Spatial-Temporal Evolution of Meteorological Drought in the Ningxia Hui Autonomous Region of Northwest China

This study focuses on the intensity, duration, and frequency of meteorological droughts in Ningxia, northwest China, from 1960 to 2016. The analysis was conducted using the interannual characteristics of droughts, which were studied by the standardized precipitation evapotranspiration index (SPEI) at different timescales. Additionally, the correlation, mutability, and periodicity of two series in both the time and frequency domains in multi-time scales were scientifically analyzed using the wavelet analysis method. The results showed that the occurrence of drought had increased at different timescales (i.e., SPEI1, SPEI3, and SPEI12) during the past 57 years. The autumn drought had the largest share; spring is the high-occurrence season of extreme drought and the overall drought intensity increases towards the north. The periodical change of droughts was described with the wavelet coefficient real part value, wavelet power spectrum, and wavelet variance. The results showed that the 13- and 8-year time scales had the strongest oscillation and energy, and they were the first and the second major periods of drought. Moreover, the drought mutational fluctuations are in better agreement with that characterized by the SPEI12 and actual conditions. The results help to understand the occurrence and evolution of droughts in Ningxia, and to provide a scientific basis to reduce and prevent the drought disaster.
Using Encryption Technique for Effective Data Storage

Today we are living in a digital world. Data is generated from sensors, social networking sites and satellites at an unbelievable speed. In this digital world, data is generated and collected at a rapid rate that is exceeding the boundary range. According to IBM, 2.5 quintillion bytes of data are generated every day. The data with high speed, high variety and high volume is known as Big Data. Integrity, privacy, security, scalability, analysis and storage of data are main challenges of Big Data. Big Data Storage is a challenging issue in Big Data. Various techniques like MongoDB, SimpleDB, BigTable, Dynamo and Apache Hbase are used for Big Data Storage. In this paper, a new storage method is proposed. A client- server framework is generated in which data is stored on the server after getting splitted and encrypted.
Performance Analysis and Enhancement of Software Quality Metrics using Decision Tree based Feature Extraction.

Software managers routinely face the problem with software projects that contain error or inconsistencies which increases the budget, time limit and reduces the customer satisfaction. By applying the data mining technique for software metrics dataset as a quality prediction model, helps manager to tackle the above problems in an efficient way and improve the quality. In this paper, Decision tree classifier, a data mining technique is used as the software quality prediction model and applied on the reduced dataset for evaluating the overall performance by measuring the Accuracy, Mean Absolute Error(MAE), Root Mean Square Error(RMSE), Precision and Recall. The observations show that the overall accuracy is increased when applied with the reduced dataset than the original data set. Hence we can term that the reduced dataset play a vital role in the improving the software quality by increasing the classifier performance and hence can be said that the attributes in reduced dataset are enough as good predictors for future classification. The dataset used here is the KC2 NASA data set. To evaluate the performance of decision tree classifier, measures such as Accuracy, MAE, RMSE, Precision and Recall are used.
Big Data empowered Agility for dynamic, volatile, and time-sensitive service industries: The case of Tourism sector. International Journal of Contemporary Hospitality

Purpose – Dynamic, volatile, and time-sensitive industries, such as tourism, travel, and hospitality, require agility and market intelligence to create value and achieve competitive advantage. The current study examines the influence of Big Data (BD) on the performance of service organizations, and probes for a deeper understanding of implementing BD, based on available technologies. applications to support organizational decision-making and business performance in the tourism industry. Originality/value – The study responds to scholars’ recent calls for more empirical research with contextual understanding of the use of BD to add value in marketing intelligence within business ecosystems. It delineates factors contributing to BD value creation and explores the impacts on the respective service encounters. The role of stakeholders and necessary resources are explained, and the full potential of big data in tourism and hospitality is revealed. The research presents models of the Big data flows of a tourism and hospitality organization (BDF) and the Big Data Tourism Analytical (BDTA) Framework which shows the data that flows in and out of the organizational boundaries and interactions with key stakeholders. A review of the literature on Big data in tourism and hospitality is presented and a review is provided with the capability issues identified as either emergent or estab-lished.
Mapping with confidence; delineating seagrass habitats using Unoccupied Aerial Systems (UAS)

There is growing interest in the use of Unoccupied Aerial Systems (UAS) for mapping and monitoring of seagrass habitats. UAS provide flexibility with timing of imagery capture, are relatively inexpensive, and obtain very high spatial resolution imagery compared to imagery acquired from sensors mounted on satellite or piloted aircraft. However, research to date has focused on UAS applications for exposed intertidal areas or clear tropical waters. In contrast, submerged seagrass meadows in temperate regions are subject to high cloud cover and water column turbidity, which may limit the application of UAS imagery for coastal habitat mapping. To test the constraints on UAS seagrass mapping, we examined the effects of five environmental conditions at the time of UAS image acquisition (sun angle, tidal height, cloud cover, Secchi depth and wind speed) and five site characteristics (eelgrass patchiness and density, presence and density of non‐eelgrass submerged aquatic vegetation, sediment tone, eelgrass deep edge and site exposure) at 26 eelgrass (Zostera marina) monitoring sites in British Columbia, Canada. Eelgrass was delineated in UAS orthomosaics using object‐based image analysis, combining image segmentation with manual classification. Each site was ranked according to the analysts’ confidence in the delineated eelgrass. Robust Linear Regression revealed sun angle and ‘theoretical visibility’ (an aggregate of tidal height, Secchi depth, and eelgrass deep edge conditions) to be the most important variables affecting mapping confidence. In general, ideal environmental conditions to obtain high confidence eelgrass mapping included: (1) sun angles below 40°; (2) positive theoretical visibility with Secchi depths >5 m; (3) cloud cover conditions of <10% or >90%; and (4) wind speeds less than 5 km h−1. Additionally, high mapping confidence was achieved for sites with dense, continuous, and homogeneous eelgrass meadows. The results of this analysis will guide implementation of UAS mapping technologies in coastal temperate regions.
Optimal Aggregation of Consumer Ratings : An Application to Yelp

Consumer review websites such as Yelp.com leverage the wisdom of the crowd, with each product being reviewed many times (some with more than 1000 reviews). Because of this, the way in which information is aggregated is a central decision faced by consumer review websites. Given a set of reviews, what is the optimal way to construct an average rating? We offer a structural approach to answering this question, allowing for (1) reviewers to vary in stringency (some reviewers tend to leave worse reviews on average) and accuracy (some reviewers are more erratic than others), (2) reviewers to be influenced by existing reviews, and (3) product quality to change over time. We apply this approach to reviews from Yelp.com to derive optimal ratings for each restaurant (in contrast with the arithmetic average displayed by Yelp). Because we have the history of reviews for each restaurant and many reviews left by each reviewer, we are able to identify these factors using variation in ratings within and across reviewers and restaurants. Using our estimated parameters, we construct optimal ratings for all restaurants on Yelp, and compare them to the arithmetic averages displayed by Yelp. As of the end of our sample, a conservative finding is that roughly 25-27% of restaurants are more than 0.15 stars away from the optimal rating, and 8-10% of restaurants are more than 0.25 stars away from the optimal rating. This suggests that large gains could be made by implementing optimal ratings. Much of the gains come from our method responding more quickly to changes in a restaurant’s quality. Our algorithm can be flexibly applied to many different review
Decision Trees for Probability Estimation: An Empirical Study

Accurate probability estimation generated by learning models is desirable in some practical applications, such as medical diagnosis. In this paper, we empirically study traditional decision-tree learning models and their variants in terms of probability estimation, measured by conditional log likelihood (CLL). Furthermore, we also compare decision tree learning with other kinds of representative learning: Naive Bayes, Naive Bayes tree, Bayesian network, K-nearest neighbors and support vector machine with respect to probability estimation. From our experiments, we have several interesting observations. First, among various decision-tree learning models, C4.4 is the best in yielding precise probability estimation measured by CLL, although its performance is not good in terms of other evaluation criteria, such as accuracy and ranking. We provide an explanation for this and reveal the nature of CLL. Second, compared with other popular models, C4.4 achieves the best CLL. Finally, CLL does not dominate another well-established relevant measurement AUC (the area under the curve of receiver operating characteristics), which suggests that different decision-tree learning models should be used for different objectives. Our experiments are conducted on the basis of 36 UCI sample sets that cover a wide range of domains and data characteristics. We run all the models within a machine learning platform $Weka
Robust speech recognition in noise: an evaluation using the SPINE corpus

In this paper, methodologies for effective sp eech recognition are considered along with evaluations of an NRL speech in noise corpus entitled SPINE. When speech is produced in adverse conditions that include high levels of noise, workload task stress, and Lombard effect, new challenges arise concerning how to best improve recognition performance. Here, we consider tradeoffs in (i) robust features, (ii) front-end noise suppression, (iii) model adaptation, and (iv) training and testing in the same conditions. The type of noise and recording conditions can significantly impact the type of signal processing and speech modeling methods that would be most effective in achieving robust sp eech recognition. We considered alternative frequency scales (M-MFCC, ExpoLog), feature processing (CMN, VCMN, LP-vs-FFT MFCCs), model adaptation (PMC), and combinations of gender dependent with gender independent models. For the purposes of achieving effective speech recognition performance, computational speed and availability of adaptation data greatly impacts final recognition performance. In particular, while reliable algorithm formulations for addressing specific types of distortion can improve recognition rates, these algorithms cannot reach their full potential wit hout proper front-end algorithm data processing to direct compensation. While parallel banks of speech recognizers can improve recognition performance, their significant computational requirements can render the recognizer useless in actual speech applications.
When dynamic cracks meet disorder: A journey along the fracture process zone

The onset of earthquakes along crustal faults, the triggering of slab avalanches or the break of a wine glass are all serious problems driven by the propagation of a dynamic rupture front. Our physical understanding of this ubiquitous phenomenon is however still far from being wellestablished. While Linear Elastic Fracture Mechanics (LEFM) became a successful theoretical framework to describe the stability and the slow growth of flaws within materials, it still fails at describing the dynamics of fast and sudden rupture fronts. Several experimental studies recorded the propagation of dynamic crack fronts and revealed the origin of this discrepancy. As the rupture speed approaches the one of the elastic waves, the crack stops being the simple planar object pictured by the theory. Indeed, dynamic instabilities start interfering with the crack propagation, while the fracture surface roughens and reveals the permanent interplay between the rupture front and the heterogeneities emerging from the material microstructure. In this context, the objective of the present Ph.D. work is precisely to bring novel insights in these complex and unstable dynamics using the great potential of modern computational methods. The originality of its approach consists in looking at the scale of the intimate interaction existing between the rupture front and material heterogeneities, where the crack tip spreads over some distance called the fracture process zone. To this aim, it relies on two “homemade” implementations of the elastodynamic equations running on modern computing cluster facilities. This Ph.D. work consists mainly in two parts. The first one presents a fundamental study of the interplay between a crack front and microscopic heterogeneities. This work reveals the direct impact of the heterogeneities on the local speed of the rupture front, which can even exceed the admissible values predicted for homogeneous conditions. The simulations further allow to connect the progressive roughening of the fracture surface to the “relativistic” contraction of the process zone observed when the crack speed approaches the speed of elastic waves. In the second part of the manuscript, the same formalism is applied to the study of frictional interfaces, for which the microscopic heterogeneities correspond to the scattered topography emerging from the contact between two rough surfaces. This study notably proposes a new estimation of the part of fracture energy entering the seismic energy budget. In spite of being a rather fundamental study of heterogeneous crack dynamics, this Ph.D. work finds direct implications in a large range of domains, from earthquake science to the design of more resilient materials.
Object-Oriented Design Frameworks : Formal Speci cation and Some Implementation Issues

In component-based software development, object-oriented design (OOD) frameworks are increasingly recognised as better units of reuse than objects. This is because OOD frameworks are groups of interacting objects, and as such they can better re ect practical systems in which objects tend to have more than one role in more than one context. In this paper, we show how to formally specify OOD frameworks, and brie y discuss their implementation and con guration management.
Physical and Digital in Games and Play

Pervasive games break the boundary between digital and physical to make use of elements in the real world as part of the game. One form of pervasive games are locative mobile games, which utilize physical movement as game control. To facilitate eyes-free interaction during play, these games benefit from exploring sound-based content. However, it is currently unclear what type of sound-based interaction is feasible to the general audience. Another consideration is which sound design strategies best support the goal of situated experiences , and how to design sound that supports game experiences drawing upon location-awareness, and intermixing virtual content with physical reality. A first generation of locative mobile games is already commercially available. The present contribution analyzes seven commercially available locative games ( Ingress ; Shadow Cities ; Zombies, Run! ; Inception the App ; The Dark Knight Rises Z+ ; CodeRunner ) and summarizes the sound design strategies employed to contextualize game content in real-world. Comparison to current themes in contextualized audio research indicates similarities but also challenges some assumptions regarding audio-heavy gameplay. The findings illustrate the need for simplicity regarding audio challenges, but generally confirm the view of audio-based gameplay as a facilitator of mobility. Sound is also centrally involved in shaping contextualized experiences, forging links between the physical and digital world, and indexing game content to context through functionality, verbal references, spatialization, and remediation . The
Fact-Free Learning ∗

People may be surprised by noticing certain regularities that hold in existing knowledge they have had for some time. That is, they may learn without getting new factual information. We argue that this can be partly explained by computational complexity. We show that, given a database, ﬁ nding a small set of variables that obtain a certain value of R 2 is computationally hard, in the sense that this term is used in computer science. We discuss some of the implications of this result and of fact-free learning in general. ∗ Earlier