Miracle-dz/UniFAR-FTUs huggingface dataset:
Bi-Normal Filtering for Mesh Denoising

Most mesh denoising techniques utilize only either the facet normal field or the vertex normal field of a mesh surface. The two normal fields, though contain some redundant geometry information of the same model, can provide additional information that the other field lacks. Thus, considering only one normal field is likely to overlook some geometric features. In this paper, we take advantage of the piecewise consistent property of the two normal fields and propose an effective framework in which they are filtered and integrated using a novel method to guide the denoising process. Our key observation is that, decomposing the inconsistent field at challenging regions into multiple piecewise consistent fields makes the two fields complementary to each other and produces better results. Our approach consists of three steps: vertex classification, bi-normal filtering, and vertex position update. The classification step allows us to filter the two fields on a piecewise smooth surface rather than a surface that is smooth everywhere. Based on the piecewise consistence of the two normal fields, we filtered them using a piecewise smooth region clustering strategy. To benefit from the bi-normal filtering, we design a quadratic optimization algorithm for vertex position update. Experimental results on synthetic and real data show that our algorithm achieves higher quality results than current approaches on surfaces with multifarious geometric features and irregular surface sampling.
Jointly Modeling Topics and Intents with Global Order Structure

Modeling document structure is of great importance for discourse analysis and related applications. The goal of this research is to capture the document intent structure by modeling documents as a mixture of topic words and rhetorical words. While the topics are relatively unchanged through one document, the rhetorical functions of sentences usually change following certain orders in discourse. We propose GMM-LDA, a topic modeling based Bayesian unsupervised model, to analyze the document intent structure cooperated with order information. Our model is flexible that has the ability to combine the annotations and do supervised learning. Additionally, entropic regularization can be introduced to model the significant divergence between topics and intents. We perform experiments in both unsupervised and supervised settings, results show the superiority of our model over several state-of-the-art baselines.
Research Workshop on Future TPM Functionality : Final Report

The purpose of this short document is to summarise the discussions on future Trusted Platform Module (TPM) functionality held during a Technical Workshop at the First European Summer School on Trusted Infrastructure Technologies. It is hoped that this document will serve as a source of information on possible directions for future research relating to the TPM. It is also hoped that these topics can be discussed further at a future summer school. The discussions at the Technical Workshop covered the following main topics, and a summary of the points raised on each of these topics is given below.
The health literacy divide: user experiences of web-based tools for delivering health information

Health Literacy (HL) is ‘the capacity to acquire, understand and use information in ways which promote and maintain good health’(p.304) (Nutbeam, 2009). Low HL is associated with poor health-related knowledge, illness self-management, health service use, health and survival. Addressing HL is therefore key to caring for the health and well being of people worldwide and has become a pressing international priority. Eighty percent of the adult population are now online. Reports also suggest that 80% of those using the Internet have used it to search for health information (McMullan, 2005). However, there is recognition that some of the most socially deprived and marginalised groups and those with lower education and HL may not be accessing digital and web-based technologies. Even if these groups are accessing online resources, evidence suggests they may not find the content of these accessible or understandable. Whilst webbased and mobile interventions exist, few are designed for people with lower literacy, numeracy or HL (Hou, 2012; Sarkar et al., 2010). With a high proportion of the adult population having low literacy and HL levels this is a challenge for health professionals. The aim of this research was to explore the acceptability of web-based tools for delivering health information to varied and low HL groups and specifically to look at the acceptability of the ‘Healthy Living with Diabetes’ website and the tools contained within it. A review of qualitative studies was conducted to explore the current evidence base for webbased interventions for people with varied levels of education and HL. A thematic approach drawing on grounded theory was used. The purpose of the review was to specifically explore the acceptability of web-based interventions for chronic and acute health conditions for varied health literate groups and to explore which tools and technologies were preferred. This helped to inform the intervention development for the research. The research in this thesis explored how, why, where and when web-based, mobile and digital interventions have been used to support people with various long term and acute conditions. Sixty-five interviews were conducted with adults with type 2 diabetes from five participating countries to explore their views on the ‘Healthy Living with Diabetes’ website, a digital health promotion intervention designed to be accessible to people with lower levels of HL. Particular interest was given to exploring user reactions to the interactive and audiovisual elements of the website. A think aloud interview method was used to elicit user views of the website and findings are reported in this thesis.
Nonlinear glottal flow features in Parkinson's disease detection

As a methodology for automatic detection of Parkinson's disease (PD), it is proposed the estimation of the different glottal flow features considering nonlinear behavior of the vocal folds. This paper evaluates the discrimination capability of set with eight different Nonlinear Dynamic (NLD) features. The experiment presented considering the five Spanish vowels uttered by 50 People with PD (PPD) and 50 Healthy Controls (HC). According to the results, it is possible to achieve accuracy rates of up to 75.3% when only the vowel |e| is considered.
Improving DVFS in NoCs with Coherence Prediction

As Networks-on-Chip (NoCs) continue to consume a large fraction of the total chip power budget, dynamic voltage and frequency scaling (DVFS) has evolved into an integral part of NoC designs. Efficient DVFS relies on accurate predictions of future network state. Most previous approaches are reactive and based on network-centric metrics, such as buffer occupation and channel utilization. However, we find that there is little correlation between those metrics and subsequent NoC traffic, which leads to suboptimal DVFS decisions. In this work, we propose to utilize highly predictable properties of cache-coherence communication to derive more specific and reliable NoC traffic predictions. A DVFS mechanism based on our traffic predictions, reduces power by 41% compared to a baseline without DVFS and by 21% on average when compared to a state-of-the-art DVFS implementation, while only degrading performance by 3%.
Structurally refined feature matching for multi-source images

In this paper we consider the problem of matching feature points between multi-source images. Straightforwardly comparing visual features such as SIFT may result in desirable local feature correspondences for single-source images. However, when it comes to matching feature between multi-source images, e.g. between an RGB color image and an infrared image, the feature based matching scheme tends to yield certain mismatches. To address this shortcoming, we develop a structural constraint for refining the multi-source feature matching performance. Our structural refinement rejects the small number of mismatches which contradict the spatial consistency that the large number of correct matches follow. The key to the effectiveness of our method is the observation that correct matches turn out to form a tight cluster in a subspace spanned in the light of the spatial relationships of feature points; on the other hand, the mismatches are generally located far away from the cluster as outliers in the subspace. Experimental results validate that our method not only properly refines the feature correspondences between multi-source images, but also outperforms alternative state-of-the-art cross-field matching methods.
School-Based Psychosocial Intervention Programs-A Framework for the Treatment of Children Exhibiting Sub-Clinical Symptoms of ADHD: A Systematic Narrative Review

Objective: Symptoms of hyperactivity, impulsivity and inattention can lead to significant academic issues for children, with few treatment options readily accessible for concerned families. This review investigates the benefits of psychosocial interventions programs in school-aged children exhibiting sub-clinical symptoms of ADHD. Method: Pubmed, Scopus, Cochrane Library, and CINAHL were searched up to July 2017 for trials meeting inclusion criteria. Results: Three studies met inclusion criteria for this review. The research reported improvements in self-control and impulsiveness (p<0.01) as well as reduced hyperactivity (p<0.01), conduct issues (p<0.05) and increased attention (p<0.01) in non-clinical child and adolescent samples. Effect sizes were mixed between programs. Discussion: This area of research highlights the significance of the child’s proximal environment and its impact on learning in a social context. The results reported here contribute to the applicability of psychosocial programs in improving behavioural symptoms of sub-clinical ADHD. However, consistent replication research is required to confirm their validity. Conclusion: Psychosocial interventions are a valid and reliable intervention for improving the cognitive and behavioural outcomes in school-aged children. This is the first review to investigate the effects of psychosocial intervention programs framed within the context of sub-clinical ADHD. This review highlights the therapeutic benefits of these programs for children exhibiting symptoms of ADHD. Despite the reported significance of each study, the varied program designs made direct comparison difficult. The lack of reproducibility in this area may indicate the complex nature of the population being studied.
Adaptive color snake model for real-time object tracking

Motion tracking and object segmentation are the most fundamental and critical problems in vision tasks such as motion analysis. An active contour model, snake, was developed as a useful segmenting and tracking tool for rigid or non-rigid objects. Snake is designed on the basis of snake energies. Segmenting and tracking can be executed successfully by energy minimization. In this research, two new paradigms for segmentation and tracking are suggested. First, because the conventional method uses only intensity information, it is difficult to separate an object from its complex background. Therefore, a new energy and design schemes should be proposed for the better segmentation of objects. Second, conventional snake can be applied in situations where the change between images is small. If a fast moving object exists in successive images, conventional snake would not operate well because the moving object may have large differences in its position or shape, between successive images. Snake's nodes may also fall into the local minima in their motion to the new positions of the target object in the succeeding image. For robust tracking, the condensation algorithm was adopted to control the parameters of the proposed snake model called "adaptive color snake model (ACSM)". The effectiveness of the ACSM is verified by appropriate simulations and experiments.
Optimal training for block transmissions over doubly-selective fading channels

High data rates give rise to frequency-selective propagation, while carrier frequency-offsets and mobility-induced Doppler shifts introduce time-selectivity in wireless links. To mitigate the resulting time- and frequency-selective (or doubly-selective) channels, an optimal training strategy is designed in this paper for block transmissions over doubly-selective channels, relying on a basis expansion channel model. The optimality in designing our PSAM parameters consists of maximizing a tight lower bound on the average channel capacity, that is also shown to be equivalent to the minimization of the minimum mean-square channel estimation error. Numerical results corroborate our theoretical designs.
Fuzzy Optimal Control for Robot Manipulators 59 Fuzzy Optimal Control for Robot Manipulators

This chapter deals with the design of a Fuzzy Logic Controller based Optimal Linear Quadratic Regulator (FC-LQR) for the control of a robotic system. The main idea is to design a supervisory fuzzy controller capable to adjust the controller parameters in order to obtain the desired axes positions under variations of the robot parameters and payload variations. In the advanced control of robotic manipulators, it is important for manipulators to track trajectories in a wide range of work place. If speed and accuracy is required, the control using conventional methods is difficult to realize because of the high nonlinearity of the robot system. In control design, it is often of interest to design a controller to fulfil, in an optimal form, certain performance criteria and constraints in addition to stability. The theme of optimal control addresses this aspect of control system design. For linear systems, the problem of designing optimal controllers reduces to solving algebraic Riccati equations , which are usually easy to solve and detailed literature of their solutions can be found in many references . Nevertheless, for nonlinear systems, the optimization problem reduces to the so-called Hamilton-Jacobi (HJ) equations, which are nonlinear partial differential equations. Different from their counterparts for linear systems, HJ equations are usually difficult to solve both numerically and analytically. Improvements have also been carried out on the numerical solution of the approximated solution of HJ equations. But few results so far can provide an effective way of designing optimal controllers for general nonlinear systems. In the past, the design of controllers based on a linearized model of real control systems. In many cases a good response of complex and highly non-linear real process is difficult to obtain by applying conventional control techniques which often employ linear mathematical models of the process. One reason for this lack of a satisfactory performance is the fact that linearization of a non-linear system might be valid only as an approximation to the real system around a determined operating point. However, fuzzy controllers are basically non-linear, and effective enough to provide the desired non-linear control actions by carefully adjusting their parameters. In this chapter, we propose an effective method to nonlinear optimal control based on fuzzy control. The optimal fuzzy controller is designed by solving a minimization problem that minimizes a given quadratic performance function. Both the controlled system and the fuzzy controller are represented by the affine TakagiSugeno (T-S) fuzzy model taking into consideration the effect of the constant term. Most of the research works analyzed the T-S model assuming that the non-linear system is linearized 4
Structural and Functional Investigation of Flavin Binding Center of the NqrC Subunit of Sodium-Translocating NADH:Quinone Oxidoreductase from Vibrio harveyi

Na+-translocating NADH:quinone oxidoreductase (NQR) is a redox-driven sodium pump operating in the respiratory chain of various bacteria, including pathogenic species. The enzyme has a unique set of redox active prosthetic groups, which includes two covalently bound flavin mononucleotide (FMN) residues attached to threonine residues in subunits NqrB and NqrC. The reason of FMN covalent bonding in the subunits has not been established yet. In the current work, binding of free FMN to the apo-form of NqrC from Vibrio harveyi was studied showing very low affinity of NqrC to FMN in the absence of its covalent bonding. To study structural aspects of flavin binding in NqrC, its holo-form was crystallized and its 3D structure was solved at 1.56 Å resolution. It was found that the isoalloxazine moiety of the FMN residue is buried in a hydrophobic cavity and that its pyrimidine ring is squeezed between hydrophobic amino acid residues while its benzene ring is extended from the protein surroundings. This structure of the flavin-binding pocket appears to provide flexibility of the benzene ring, which can help the FMN residue to take the bended conformation and thus to stabilize the one-electron reduced form of the prosthetic group. These properties may also lead to relatively weak noncovalent binding of the flavin. This fact along with periplasmic location of the FMN-binding domains in the vast majority of NqrC-like proteins may explain the necessity of the covalent bonding of this prosthetic group to prevent its loss to the external medium.
rddensity : Manipulation Testing in Stata

We introduce two Stata commands implementing automatic manipulation tests based on the results in Cattaneo, Jansson, and Ma (2015b). These tests exhibit demonstrably better size properties (and more power under additional assumptions) than other conventional approaches currently available in the literature. The first command, rddensity, implements manipulation tests based on a novel local polynomial density estimation technique that avoids pre-binning of the data (improving size properties) and allows for restrictions on other features of the model (improving power properties). The second command, rdbwdensity, implements several bandwidth selectors specifically tailored for the manipulation tests discussed herein. Companion R functions with the same syntax and capabilities as rddensity and rdbwdensity are also provided.
Retrieval-Based Learning

Retrieval is the key process for understanding learning and for promoting learning, yet retrieval is not often granted the central role it deserves. Learning is typically identified with the encoding or construction of knowledge, and retrieval is considered merely the assessment of learning that occurred in a prior experience. The retrieval-based learning perspective outlined here is grounded in the fact that all expressions of knowledge involve retrieval and depend on the retrieval cues available in a given context. Further, every time a person retrieves knowledge, that knowledge is changed, because retrieving knowledge improves one’s ability to retrieve it again in the future. Practicing retrieval does not merely produce rote, transient learning; it produces meaningful, long-term learning. Yet retrieval practice is a tool many students lack metacognitive awareness of and do not use as often as they should. Active retrieval is an effective but undervalued strategy for promoting meaningful learning.
A hierarchical-structured dictionary learning for image classification

This paper proposes a hierarchical-structured discriminative dictionary learning algorithm for image classification. Hierarchical structure of the overall dictionary is learned such that the upper-level dictionaries are specific in representing patterns common across a wide set of class images while lower-level dictionaries are specific in representing patterns localized to a narrow set of class images. Therefore the root dictionary can represent patterns common to all classes, while the leaf dictionaries can represent patterns specific only to a single distinct class. The learned dictionary is efficient in its use of the bases, and leads to a more discriminative representation than that led by previous dictionaries which is devoid of any structure and contains redundant bases. This hierarchical-structured dictionary is learned by solving a constraint optimization problem that minimized reconstruction error of a given image while using dictionaries in the hierarchical structure pertaining only to the class of the image. Sparse representation is pursued in addition, and it acts a regularizer to improve generalization. The representation is as distinct as the paths to each of the class in the hierarchical structure are divergent. To evaluate the effectness of the hierarchical-structured dictionary, classification is performed on three benchmark datasets: Extended Yale B database, Caltech 101 and Caltech 256 dataset, and based on a common features, the proposed algorithm performs better than other state-of-the-art dictionary learning algorithms.
Research of sludge compost maturity degree modeling method based on classify support vector machine for sewage treatment

Because of the complicated interaction of the sludge compost components, it makes the judging system of sludge compost maturity degree appear the non-linearity and uncertainty. According to the physical circumstances of sludge compost, a sludge compost maturity degree modeling method based on support vector machine (SVM) is presented. We select the index of sludge compost maturity degree and take the high temperature duration, moisture content, volatile solids, the value of fecal bacteria, and germination index as the judgment parameters. We construct the structure of SVM network that used for the maturity degree judgment of sludge compost, and use the genetic algorithm (GA) to optimize SVM parameters. With the ability of strong self-learning and well generalization of SVM, the modeling method can truly judge the sludge compost maturity degree by learning the index information of sludge compost maturity degree. The experimental results show that this method is feasible and effective.
An energy efficient TLB design methodology

This paper researches translation look-aside buffer (TLB) of embedded processor. Based on an analysis of design-related factors: power, area, critical path and performance of the research model - Godson-I, a low-power TLB design is proposed without sacrifice of performance and timing. Using this method, the following results are achieved: power of TLB-RAM reduces 92.7% and area of TLB-RAM reduces 50%. Compared with other methods, the hit rate of this design is much higher and the accessing conflict to RAM between ITLB and DTLB is much reduced. Although our work targets to Godson-I, the proposed methodology should be applicable to other designs.