Miracle-dz/UniFAR-FTUs huggingface dataset:
Microfacet BSDFs Generated from NDFs and Explicit Microgeometry

Microfacet distributions are considered nowadays as a reference for physically plausible BSDF representations. Many authors have focused on their physical and mathematical correctness, while introducing means to enlarge the range of possible appearances. This article is dedicated to Normal Distribution Functions (NDFs) and the influence of their shape on the rendered material aspect. We provide a complete framework for studying the impact of NDFs on the observed Bidirectional Scattering Distribution Functions (BSDFs). To explore very general NDFs, manually controlled by the user, and including anisotropic materials, we propose to use a piecewise continuous representation. It is derived with its associated Smith shadowing-masking function and importance sampling formulations for ensuring efficient global illumination computations. A new procedure is also proposed in this article for generating an explicit geometric micro-surface, used to evaluate the validity of analytic models and multiple scattering effects. The results are produced with a computer-generated process using path tracing. They show that this generation procedure is suitable with any NDF model, independently from its shape complexity.
Cloudlet capture by transitional disk and FU Orionis stars

After its formation, a young star spends some time traversing the molecular cloud complex in which it was born. It is therefore not unlikely that, well after the initial cloud collapse event which produced the star, it will encounter one or more low mass cloud fragments, which we call “cloudlets” to distinguish them from full-fledged molecular clouds. Some of this cloudlet material may accrete onto the star+disk system, while other material may fly by in a hyperbolic orbit. In contrast to the original cloud collapse event, this process will be a “cloudlet flyby” and/or “cloudlet capture” event: A Bondi–Hoyle–Lyttleton type accretion event, driven by the relative velocity between the star and the cloudlet. As we will show in this paper, if the cloudlet is small enough and has an impact parameter similar or less than GM*/v∞2 (with v∞ being the approach velocity), such a flyby and/or capture event would lead to arc-shaped or tail-shaped reflection nebulosity near the star. Those shapes of reflection nebulosity can be seen around several transitional disks and FU Orionis stars. Although the masses in the those arcs appears to be much less than the disk masses in these sources, we speculate that higher-mass cloudlet capture events may also happen occasionally. If so, they may lead to the tilting of the outer disk, because the newly infalling matter will have an angular momentum orientation entirely unrelated to that of the disk. This may be one possible explanation for the highly warped/tilted inner/outer disk geometries found in several transitional disks. We also speculate that such events, if massive enough, may lead to FU Orionis outbursts.
Using Mouse Movements to Predict Web Survey Response Difficulty

A key goal of survey interviews is to collect the highest quality data possible from respondents. In practice, however, it can be difficult to achieve this goal because respondents do not always understand particular survey questions as designers intended. Researchers have used a variety of indicators to identify and predict respondent confusion and difficulty in answering questions in different modes. In web surveys, it is possible to automatically detect response difficulty in real time. The research to date has focused on response latencies—mostly long response times—as evidence of difficulty. In addition to response latencies, however, web surveys offer rich behavioral data, which may predict respondent confusion and difficulty more directly than response times. This article focuses on one such behavior, mouse movements. We examine a set of mouse movements participants engage in when answering questions about experimental scenarios whose difficulty has been manipulated (as confirmed by respondent ratings). This approach makes it possible to determine which movements are general movements, demonstrating how a person interacts with a computer, and which movements are related to response difficulty. We find not only that certain mouse movements are highly predictive of difficulty but also that such movements add considerable value when used in conjunction with response times. The approach developed in this article may be useful in delivering help to confused respondents in real time and as a diagnostic tool to identify confusing questions.
Human-Centred Web Search

People commonly experience difficulties when searching the Web, arising from an incomplete knowledge regarding their information needs, an inability to formulate accurate queries, and a low tolerance for considering the relevance of the search results. While simple and easy to use interfaces have made Web search universally accessible, they provide little assistance for people to overcome the difficulties they experience when their information needs are more complex than simple fact-verification. In humancentred Web search, the purpose of the search engine expands from a simple information retrieval engine to a decision support system. People are empowered to take an active role in the search process, with the search engine supporting them in developing a deeper understanding of their information needs, assisting them in crafting and refining their queries, and aiding them in evaluating and exploring the search results. In this chapter, recent research in this domain is outlined and discussed. DOI: 10.4018/978-1-4666-2455-9.ch096
Simultaneous fault estimation for uncertain Markovian jump systems subjected to actuator degradation

This paper investigates simultaneous estimations of states, system fault, and sensor fault for a class of Markovian jump systems, resorting to the design of a robust observer, in which both the external disturbance and actuator degradation are taken into consideration. The loss‐of‐effectiveness, stuck, and outage fault cases are involved, and the considered Markovian jump system is assumed to possess generally uncertain transition rates, which can generalize the traditional bounded uncertain transition rates and partially unknown transition rates. In particular, an augmented system whose states cover the original states, system fault, and sensor fault is constructed. Then, a novel adaptive observer is presented with time‐varying gain matrices and parameter accommodation being injected to deal with the disturbance and actuator degradation. Finally, a practical example is shown to demonstrate the high efficiency of the proposed method as well as its advantages over some existing results.
Learning robust, real-time, reactive robotic grasping

We present a novel approach to perform object-independent grasp synthesis from depth images via deep neural networks. Our generative grasping convolutional neural network (GG-CNN) predicts a pixel-wise grasp quality that can be deployed in closed-loop grasping scenarios. GG-CNN overcomes shortcomings in existing techniques, namely discrete sampling of grasp candidates and long computation times. The network is orders of magnitude smaller than other state-of-the-art approaches while achieving better performance, particularly in clutter. We run a suite of real-world tests, during which we achieve an 84% grasp success rate on a set of previously unseen objects with adversarial geometry and 94% on household items. The lightweight nature enables closed-loop control of up to 50 Hz, with which we observed 88% grasp success on a set of household objects that are moved during the grasp attempt. We further propose a method combining our GG-CNN with a multi-view approach, which improves overall grasp success rate in clutter by 10%. Code is provided at https://github.com/dougsm/ggcnn
Coordinating vertical and horizontal scaling for achieving differentiated QoS

The growth in popularity of cloud computing, along with the rapid development of internet based technologies, has led to a paradigm shift in the way computing resources are provisioned. Computing resources are to a larger extent offered as services, and exposed via the internet in models as pay-as-you-go and on-demand. Cloud service providers are trying to reduce their operating costs while offering their services with higher quality by resorting to the concept of elasticity. However, this is challenging because of heterogeneous applications running on their infrastructure with arbitrary requirements in terms of resources. This thesis investigates how this problem can be addressed by designing and implementing an autonomic provisioning controller based on elements from control theory to coordinate between real-time and non-realtime applications. Traditional approaches perform elasticity-decisions either based on monitoring the resource usage or merely based on the QoS of, solely, latency-critical applications, particularly web servers. Nevertheless, a broad class of applications have different SLA-constraints that does not fall under the same class of latency-critical applications. For instance batch processing is becoming more popular and posses QoS requirements that are defined in different manner than latency-critical applications. The novelty of this study is the focus on how to coordinate resource allocation between real-time and non-real-time applications. Furthermore, our approach is hybrid as it relies on both resource usage and QoS-requirements in the same time to drive the elasticity decisions. Another novelty of our work is orchestrating both vertical and horizontal scaling under the same framework.
Application of Variational Mode Decomposition and Permutation Entropy for Rolling Bearing Fault Diagnosis

Rolling bearings are the key components of rotating machinery. However, the incipient fault characteristics of a rolling bearing vibration signal are weak and difficult to extract. To solve this problem, this paper presents a novel rolling bearing vibration signal fault feature extraction and fault pattern recognition method based on variational mode decomposition (VMD), permutation entropy (PE) and support vector machines (SVM). In the proposed method, the bearing vibration signal is decomposed by VMD, and the intrinsic mode functions (IMFs) are obtained in different scales. Then, the PE values of each IMF are calculated to uncover the multi-scale intrinsic characteristics of the vibration signal. Finally, PE values of IMFs are fed into SVM to automatically accomplish the bearing condition identifications. The proposed method is evaluated by rolling bearing vibration signals. The results indicate that the proposed method is superior and can diagnose rolling bearing faults accurately.
Task-based fast multipole method for clusters of multicore processors

Most high-performance, scientific libraries have adopted hybrid parallelization schemes - such as the popular MPI+OpenMP hybridization - to benefit from the capacities of modern distributed-memory machines. While these approaches have shown to achieve high performance, they require a lot of effort to design and maintain sophisticated synchronization/communication strategies. On the other hand, task-based programming paradigms aim at delegating this burden to a runtime system for maximizing productivity. In this article, we assess the potential of task-based fast multipole methods (FMM) on clusters of multicore processors. We propose both a hybrid MPI+task FMM parallelization and a pure task-based parallelization where the MPI communications are implicitly handled by the runtime system. The latter approach yields a very compact code following a sequential task-based programming model. We show that task-based approaches can compete with a hybrid MPI+OpenMP highly optimized code and that furthermore the compact task-based scheme fully matches the performance of the sophisticated, hybrid MPI+task version, ensuring performance while maximizing productivity. We illustrate our discussion with the ScalFMM FMM library and the StarPU runtime system.
Speech recognition based on a compound kernel support vector machine

To improve the learning and generalization ability of the machine-learning model, a new compound kernel that may pay attention to the similar degree between sample space and feature space is proposed. In this paper, used the new compound kernel support vector machine to a speech recognition system for Chinese isolated words, non-specific person and middle glossary quantity, and compared the speech recognition results with the SVM using traditional kernels and with using RBF network. Experiments showed that the SVM performance with the new compound kernel is much better than with traditional kernels and has higher correct recognition rates than ones of using RBF network in different SNRs, and is of shorter training time.
Decision Tree Creation Methodology Using Propositionalized Attributes

Abstract The aim of the article is to analyse and thoroughly research the methods of construction of the decision trees that use decision tree learning with statement propositionalized attributes. Classical decision tree learning algorithms, as well as decision tree learning with propositionalized attributes have been observed. The article provides the detailed analysis of one of the methodologies on the importance of using the decision trees in knowledge presentation. The concept of ontology use is offered to develop classification systems of decision trees. The application of the methodology would allow improving the classification accuracy.
Non-Extensive Statistical Analysis of Energetic Particle Flux Enhancements Caused by the Interplanetary Coronal Mass Ejection-Heliospheric Current Sheet Interaction

In this study we use theoretical concepts and computational-diagnostic tools of Tsallis non-extensive statistical theory (Tsallis q-triplet: qsen, qrel, qstat), complemented by other known tools of nonlinear dynamics such as Correlation Dimension and surrogate data, Hurst exponent, Flatness coefficient, and p-modeling of multifractality, in order to describe and understand Small-scale Magnetic Islands (SMIs) structures observed in Solar Wind (SW) with a typical size of ~0.01–0.001 AU at 1 AU. Specifically, we analyze ~0.5 MeV energetic ion time-intensity and magnetic field profiles observed by the STEREO A spacecraft during a rare, widely discussed event. Our analysis clearly reveals the non-extensive character of SW space plasmas during the periods of SMIs events, as well as significant physical complex phenomena in accordance with nonlinear dynamics and complexity theory. As our analysis also shows, a non-equilibrium phase transition parallel with self-organization processes, including the reduction of dimensionality and development of long-range correlations in connection with anomalous diffusion and fractional acceleration processes can be observed during SMIs events.
Unscented summation information‐weighted consensus filter for distributed sensor networks with incomplete information

In order to improve network scalability and fault tolerance, the distributed sensor networks are desirable. However, the distributed state estimation becomes challenging when some sensors have insufficient information due to restricted observability, and/or have imparity information due to unequal measurement‐noise covariances. Centralized summation information‐fusion (CSI) model is presented which performs weighted least‐squares estimation for all measurement information to achieve the optimal centralized state estimation. The CSI model revises the initialization and covariance propagation in the original information‐weighted consensus filter (ICF). Since centralized information fusion is a summation mode and is approached by the average consensus protocol, all the covariances involved in the CSI model contain the information regarding the total number of nodes. The artificially preset initial values are considered as measurement information and fused in accordance with the CSI model. By combining the CSI model with unscented transform, distributed unscented summation information‐weighted consensus filter (USICF) is proposed. USICF realizes the nonlinear estimation in the context of highly incomplete information. Theoretical analysis and experimental verification showed that USICF achieves better performance than UICF that is based on ICF.
DRBox-v2: An Improved Detector With Rotatable Boxes for Target Detection in SAR Images

Convolutional neural network (CNN)-based methods have been successfully applied to SAR target detection. Different from prevalently used detection approaches with rectangle bounding box, rotatable bounding box (RBox)-based methods, such as DRBox-v1, can effectively reduce the interference of background pixels and locate the targets more finely for geospatial object detection. Although DRBox-v1 has achieved impressive detected performance, there still exist some remaining problems and room for improvement. In this paper, an improved RBox-based target detection framework is proposed to boost precision and recall rates of detection, and we refer to the method as DRBox-v2 and apply it to target detection in SAR images. The main improvements of DRBox-v2 as well as the contributions of this paper are fourfold. First, a multi-layer prior box generation strategy is designed for detecting small-scale targets. Since shallow layers lack strong sematic information, the feature pyramid network (FPN) module is applied. Second, a modified encoding scheme for RBox is proposed for more precisely estimating the position of RBox and orientation of targets. Third, a focal loss (FL) combined with hard negative mining (HNM) technique is proposed to mitigate the issue of the imbalance between positive and negative samples, which produces better results than solely employing either one. Fourth, comprehensive ablation studies are conducted to reveal the effect of each improvement on detected results. The results of the target detection on three data sets are illustrated and our method obtains 0.135, 0.081, 0.115 gains in average precision compared with three state-of-the-art methods, respectively.
Desert Seismic Low-Frequency Noise Attenuation Based on Approximate-Message-Passing-Based Complex Diffusion

High-quality seismic exploration data are the basis of geological exploration. However, due to the increasingly complex environment in the exploration area, the signal-to-noise ratio (SNR) of seismic data is getting lower and lower. Eliminating noise is the most direct mean to improve the SNR and quality of seismic data. The Tarim Basin with a desert surface in China is rich in energy, but due to the variability of surface conditions, the desert noise has nonstationary, nonlinear, spatial directivity, low frequency, and other characteristics, which make noise attenuation very difficult. Aiming at this problem, we develop an extension of complex diffusion filter, namely approximate-message-passing-based complex diffusion, which is used to eliminate noise in desert seismic exploration data and enhance effective signals. Compared with the traditional complex diffusion, this letter adds a noise estimate in its diffusion processing, which improves the algorithm’s ability to eliminate desert noise. The experimental results of synthetic and field seismic data show that the proposed method is more effective than other methods. Not only it can remove the noise and surface wave in the desert seismic record but also can preserve the effective signal. Therefore, we believe that the proposed method can provide a new idea for desert seismic signal processing.
On low-Prandtl-number convection in an inclined layer of liquid mercury

This paper reports on a theoretical analysis of convection in an inclined layer of mercury, a common low-Prandtl-number fluid ( $Pr=0.025$ ). The investigation is based on the standard Oberbeck–Boussinesq equations, which are explored as a function of the inclination angle $\unicode[STIX]{x1D6FE}$ and for Rayleigh numbers $R$ in the vicinity of the convection onset. Along with the conventional Galerkin methods to study convection rolls and their secondary instabilities, we employ direct numerical simulations for fluid layers with quite large aspect ratios. It turns out that, even for small inclination angles $\unicode[STIX]{x1D6FE}\lesssim 6^{\circ }$ , the secondary instabilities of the basic rolls lead either to oscillatory three-dimensional patterns or to stationary ones, which appear alternately with increasing $\unicode[STIX]{x1D6FE}$ . Due to the competition of these instabilities the patterns may show a complex dynamics.
Fast and Secure Message Authentication Protocol Using Hmac for Vanets

Vehicular ad hoc networks (VANETs) adopt the Public Key Infrastructure (PKI) and Certificate Revocation (withdrawal) Lists (CRLs) for their security. In any PKI system, the authentication of a received message is performed by checking if the certificate of the sender is included in the current CRL, and verifying the authenticity of the certificate and signature of the sender.
A Recognition Method for Main Characters Name in Korean Novels

The main characters play leading roles in novels. In the previous studies, they recognize the main characters in a novel mainly based on dictionaries that built beforehand. In English, names begin with upper cases and are used with some words. In this paper, we propose a recognition method for main characters name in Korean novels by using predicates, rules and weights. We first recognize candidates for the characters name by predicates and propose some rules to exclude candidates that cannot be characters. We assign importances for candidates, considering weights that given by the number of candidates appeared in a sentence. Finally, if the importance of the character is more than a threshold, we decide that the character is one of main characters. The results from the experiments for 300 novels show that an average accuracy is 85.97%. The main characters name may be used to grasp relationships among characters, character's action and tendency.
EFEKTIVITAS PENGGUNAAN LAYANAN HOMECARE DALAM PELAYANAN KESEHATAN PADA PUSKESMAS BAROMBONG KOTA MAKASSAR

Penyelenggara pelayanan publik kurang mampu memberikan pemahaman kepada publik tentang mekanisme pelayanan yang diselenggarakan sehingga pengguna layanan lebih banyak merasa kecewa. Seiring dengan perkembangan ilmu pengetahuan, pengguna pelayanan (costumer) semakin memiliki daya kritis yang tinggi terhadap kebijakan pemerintah, serta didukung oleh kesadaran yang tinggi atas haknya. Tuntuntan publik ini mau tidak mau harus direspon secara positif dengan cara menyuguhkan pelayanan yang mampu membuat publik puas. Karena berhasil tidaknya sebuah kebijakan diukur dari tingkat kepuasan public. Penelitian ini bertujuan: (1) Untuk mengetahui efektivitas penggunaam media layanan Homecare; (2) Untuk mengetahui tingkat kepuasan dalam pelayanan homecare. Penelitian ini menggunakan metode penelitian deskriptif kuantitatif yakni mendeskripsikan atau menggambarkan data yang dikumpulkan melalui penyebaran kuisioner kepada pengguna layanan Homecare yakni masyarakat. Peneliti menggunakan teknik pengambilan sampel cluster sampling dengan berdasarkan daerah populasi yang telah ditetapkan. Hasil penelitian menunjukkan bahwa penggunaan layanan homecare dianggap efektif, dilihat dari 5 faktor yaitu kehandalan, ketanggapan, jaminan, empati dan bukti langsung yang diberikan petugas. Adapun kepuasan dari layanan homecare, masyarakat merasa puas dengan layanan tersebut.