Miracle-dz/UniFAR-FTUs huggingface dataset:
Biomechanical Factors That Influence the Bone-Implant-Interface

• Page 1 of 14 • Citation: Elsayed MD (2019) Biomechanical Factors That Influence the Bone-Implant-Interface. Res Rep Oral Maxillofac Surg 3:023. doi.org/10.23937/2643-3907/1710023 Accepted: June 24, 2019; Published: June 26, 2019 Copyright: © 2019 Elsayed MD. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Impact of National Cultures on Automotive Sector After Sales Services Perception

The automobile industry is one of the most global relevant industries. Consequently, ample literature has dealt with it. Nevertheless, not every aspect of the value chain has been researched with the same or required effort. Traditionally, sales, product design and operational issues are the most intensely emphasized topics, which is most likely a result of the car manufacturer’s dominant status. Manufacturers of original equipment (OEMs) are considered as system leaders (DiezandReindl, 2005, p. 61), which for long have been predominantly engaged in producing and selling. The focus, in the industry, has migrated actually towards services – and likewise services research – has become increasingly important (Diez, 2009, pp. 109; Reichhuber, 2010, pp.89-103;; Saccani, Songini, and Gaiardelli, 2006, p. 262).
Efficacy of Modified Observational Sampling Regimes for Use in Zoological Parks

Margulis and Westhus 1 Efficacy of Modified Observational Sampling Regimes for Use in Zoological Parks 1 Susan W. Margulis 1,2,3 and Eric Westhus 2 1 Chicago Zoological Society, 3300 Golf Road, Brookfield IL 60513 USA 3 2 University of Chicago, Committee on Evolutionary Biology, Chicago, IL 60637 USA 4 3 Lincoln Park Zoo, 2001 N. Clark St., Chicago IL 60614 USA 5 6 Direct all correspondence to: 7 Susan Margulis 8 Curator of Primates 9 Lincoln Park Zoo 1
Voice Quality and Between-Frame Entropy for Sleepiness Estimation

Sleepiness monitoring and prediction has many potential applications, such as being a safety feature in driver-assistance systems. In this study, we address the ComparE 2019 Continuous Sleepiness task of estimating the degree of sleepiness from voice data. The voice quality feature set was proposed to capture the acoustic characteristics related to the degree of sleepiness of a speaker, and between-frame entropy was proposed as an instantaneous measure of the speaking rate. An outlier elimination on the training data using between-frame entropy enhanced the system robustness in all conditions. This was followed by a regression system to predict the degree of sleepiness. Utterances were represented using i-vectors computed from voice quality features. Similar systems were also developed using mel-frequency cepstral coefﬁcients and the Com-ParE16 feature set. These three systems were combined using score-level fusion. Results suggested complementarity between these feature sets. The complete system outperformed the base-line system which used the ComParE16 feature set. A relative improvement of 19.5% and 5.4% was achieved on the development and the test datasets, respectively.
PREDICTION OF THE TIME-DEPENDENT STATIC YIELD STRESS FOR AN OIL BASED DRILLING FLUID UNDER REST THROUGH THE STORAGE MODULUS EVOLUTION OVER THE AGING TIME

Drilling fluids are suspensions of bentonite particles, which provide non-Newtonian behavior to the fluid. When at rest, drilling fluids build up a gel-like structure so as to prevent precipitation of cuttings, which could lead to bit obstruction. However, the static yield stress rises up with the aging time, which affects the pressure required to start the fluid flow: the longer is the time under rest, the higher is the pressure required. This paper presents an innovative approach to predict the value of the static yield stress after different times under rest. An oscillatory time sweep was performed, and it was observed that the storage modulus rises up exponentially until reaching an equilibrium state. The growth rate of the static yield stress evaluated after different aging times is consistent with the growth rate of the storage modulus, leading to a correlation with a deviation lower than 10% between the normalized storage modulus and the normalized yield stresses. These results allows field engineers to predict the static yield stress by using only one oscillatory time sweep and only one start-up test. Thereby, this method avoids the need for performing several time-demanding tests to evaluate the critical stress after different periods of rest. Key-Words: Storage Modulus, Drilling Fluid, Yield Stress
Regularization methods and gradient check Data Science 2

In this section, we will motivate the reason why dropout is regarded as a regularization technique. We will consider some simple neural networks where a better understanding of dropout is available and infer this interpretation to other deep networks. In addition to this, we will describe “batch normalization” to enhance training speed in deep networks. Finally, we will present gradient checking as a technique to validate backpropagation gradients. This method is important to verify manual implementations of the optimization procedure, or novel functions not present in the framework’s library.
Dynamic Road Surface Detection Method Based on 3D Lidar

This paper propose a road surface classification method based on 3D-lidar that is not required to be high-resolution. Lidar range data is transformed with static calibration result, and the near-ground points are extracted with a ground elevation estimation method. After filling the scanning gaps with interpolation, the continuity of each scan lines at each point is examined by its polar interpretation. The smoothness is analyzed in the same interpretation with simple linear regression to evaluate the estimated angle feature. The road boundaries defined by the two criteria form the road surface with region growing method. The proposed method has been verified through actual road scenario to show its robustness and efficiency on two different vehicle mounting configurations.
Cross-Lingual, Multi-Speaker Text-To-Speech Synthesis Using Neural Speaker Embedding

Neural network-based model for text-to-speech (TTS) synthesis has made significant progress in recent years. In this paper, we present a cross-lingual, multi-speaker neural end-to-end TTS framework which can model speaker characteristics and synthesize speech in different languages. We implement the model by introducing a separately trained neural speaker embedding network, which can represent the latent structure of different speakers and language pronunciations. We train the speech synthesis network bilingually and prove the possibility of synthesizing Chinese speaker’s English speech and vice versa. We explore different methods to fit a new speaker using only a few speech samples. The experimental results show that, with only several minutes of audio from a new speaker, the proposed model can synthesize speech bilingually and acquire decent naturalness and similarity for both languages.
HOW BORROWING CONSTRAINTS SHAPE EDUCATIONAL SYSTEMS

This paper provides a theory of how the wealth distribution of an economy affects the optimal design of its educational system. The model features two key ingredients. First, agents are heterogeneous both in their ability and wealth levels, neither of which is observable. Second, returns to schooling depend on the ability-composition of agents attending each school tier, for example, because of choices of common curricula. An educational system is characterized by an assignment rule of agents to schools and by endogenous sizes of tiers. I find that a benevolent planner seeking to maximize economic efficiency implements “elitist” educational systems in economies with poor, borrowingconstrained, agents. Compared to the first best, the optimal solution features (i) relatively low-ability, rich agents selecting into higher education and (ii) higher education schools with less capacity. The same qualitative results obtain when only two commonly used instruments are available to the planner: school fees and exams. In addition, I show that economies with relatively tighter borrowing constraints rely more extensively on exams, and that agents performing better on exams are rewarded with lower school fees.
An Improved Intrusion Detection System for MANETs using Digital Signature

In recent years Mobile Ad-Hoc Networks(MANETs) have become a very popular research topic. By providing communications in the absence of a fixed infrastructure MANETs are an attractive technology for many applications such as rescue operations, tactical operations, environmental monitoring, conferences and the like. However, this flexibility introduces new security risks. Since prevention techniques are never enough, Intrusion Detection Systems(IDS) which monitor system activities and detect intrusions, are generally used to complement other security mechanisms. Intrusion Detection for MANET is a complex and difficult task mainly due to the dynamic nature of MANETs, their highly constrained nodes, and the lack of central monitoring points. Conventional IDS are not easily applied to them. New approaches need to be developed or else existing approaches need to be adapted for MANETs. Our work outlines issues of conventional Intrusion Detection methods like watchdog and Two Ack schemes. We propose and implement a new Intrusion-Detection System using cryptographic technique like Digital Signatures and results will demonstrate positive performances against conventional schemes in the cases of receiver collision, Limited transmission power and false misbehavior report. In this paper we propose an enhanced intrusion detection scheme By combining ACK,S-ACK,MRA and digital signature and compare it with watchdog and TWOACK.
Track HPPMS ( Cr 1x Al x ) N + WS y Coatings for the Application in Dry Cold Forging of Steel : Synthesis and Raman Characterization

Lubricants are applied to reduce friction between workpieces and forming tools in cold forging processes. There is a strong demand to avoid lubricants due to economic, ecological and legislative aspects. PVD coatings took over the tasks of lubricants in numerous applications in the recent years. They may enormously reduce tool and workpiece wear in cold forging or deliver special functions even in the absence of lubricants. However, the abdication of lubricants goes along with the requirement that the dry tribological system has to withstand the increased tribological loads in dry metal cold forging of steel. The combination of (Cr1-xAlx)N and WSy within a PVD coating by means of high power pulsed magnetron sputtering (HPPMS) to gain outstanding tribological properties of WSy with increased abrasive wear resistance of (Cr1-xAlx)N is a promising candidate in order to replace lubricants on highly loaded tools in dry cold forging. HPPMS technology offers outstanding advantages in terms of dense morphology and high hardness. Using HPPMS-technology, complex-shaped forging tools with surfaces oriented non-parallel to the target can be coated as well. The aim of the current contribution is the investigation of the HPPMS (Cr1-xAlx)N+WSy coatings by means of Raman spectroscopy. These coatings were deposited on 1.2379 tool steel using an industrial scale PVD unit. Tribological model tests were conducted using a Pin-on-Disc tribometer (PoD) which simulates dry contacts in cold forging. The analysis of tribological properties included the determination of friction coefficients and wear rates. The contact region was analyzed by Raman spectroscopy after the tribological tests. The investigated (Cr1-xAlx)N+WSy coating is a promising candidate to avoid to use of lubricants for the application in dry cold forging process of steel.
Training needs of school counseling site supervisors in the Pacific Northwest : An exploration via the construct of self-efficacy

approved: ________________________________________________________________________ Dr. Dale-Elizabeth Pehrsson School counseling site supervisors provide a critical contribution to the professional development of master’s program school counseling interns; however, their training needs remain unidentified in the literature. To that end, the purpose of this quantitative study was to explore the training needs of site supervisors of master’s program school counseling interns in the Pacific Northwest via the construct of self-efficacy. To initiate this exploration, the Site Supervisor Self-Efficacy Survey (S4) was developed. This 28 item web-based survey investigated respondents’ (N = 147) perceived self-efficacy in relation to supervision as well as hours of supervision training received. Results (82% return rate) indicate that many site supervisors have little or no supervision training, and that supervisor self-efficacy appears relatively strong—consistently so for those with over 40 hours of training. A partial correlation indicates a slightly positive relationship (r = .202, p < .009, one-tailed) between the hours of supervision training received and perceived self-efficacy regarding supervision. Implications regarding site supervisor training and suggestions for future research are offered. © Copyright by Lorraine DeKruyf June 13, 2007 All Rights Reserved Training Needs of School Counseling Site Supervisors in the Pacific Northwest: An Exploration via the Construct of Self-Efficacy
Advanced Practice Nursing in the Community

PART ONE: OVERVIEW Book Overview and Community Health Nursing Review PART TWO: THEORETICAL FOUNDATIONS OF COMMUNITY HEALTH NURSING Theoretical Framework for a Community Focus PART THREE: COMMUNITY ASSESSMENT Economic Forces Influencing Community Health Environmental Forces Influencing Community Health Social and Cultural Influences on Community Health Community Assessment Community Analysis and Community Diagnoses PART FOUR: COMMUNITY PLANNING AND INTERVENTIONS Planning Community Interventions Planning for Diffusion and Maintenance of Community Changes Community Intervention - Using Mass Media and the Political Process Community Interventions - Empowerment, Coalition Building and Involving Lay and Professional Members and Organizations Community Interventions - Nursing Centers, Mass Clinics, Partnerships, School Services Applying the Interventions in the Rural Area - Jeri W Dunkin PART FIVE: COMMUNITY EVALUATION Evaluation of Community Interventions
Sentiment Detection Using PPM

Sentiment Detection Using PPM. This paper reports on our work in the DEFT 2015 French Text Mining Challenge. The topic of this challenge was opinion mining, sentiment analysis and emotion detection in tweets written in French. The task was solved by a system that used the PPM (Prediction by Partial Matching) compression algorithm based on an n-gram statistical model. We submitted two runs; character-based PPMC algorithm with normalization and without. The results in the experiments on character based PPMC algorithm were better than word-based. The normalisation method applied in the process of classification in order to overcome the imbalanceness of the data was not appropriate in this case and did not help in improving the results. Mots-clés : fouille d'opinion, analyse de sentiments, détection d'émotion, Prédiction par correspondance partielle.
Neural Network Distillation on IoT Platforms for Sound Event Detection

In most classification tasks, wide and deep neural networks perform and generalize better than their smaller counterparts, in particular when they are exposed to large and heterogeneous training sets. However, in the emerging field of Internet of Things memory footprint and energy budget pose severe limits on the size and complexity of the neural models that can be implemented on embedded devices. The Student-Teacher approach is an attractive strategy to distill knowledge from a large network into smaller ones, that can fit on low-energy lowcomplexity embedded IoT platforms. In this paper, we consider the outdoor sound event detection task as a use case. Building upon the VGGish network, we investigate different distillation strategies to substantially reduce the classifier’s size and computational cost with minimal performance losses. Experiments on the UrbanSound8K dataset show that extreme compression factors (up to 4.2 · 10−4 for parameters and 1.2 · 10−3 for operations with respect to VGGish) can be achieved, limiting the accuracy degradation from 75% to 70%. Finally, we compare different embedded platforms to analyze the trade-off between available resources and achievable accuracy.
Interpreting and Improving Deep Neural SLU Models via Vocabulary Importance

Spoken language understanding (SLU) is a crucial component in virtual personal assistants. It consists of two main tasks: intent detection and slot filling. State-of-the-art deep neural SLU models have demonstrated good performance on benchmark datasets. However, these models suffer from the significant performance drop in practice after deployment due to the data distribution discrepancy between training and real user utterances. In this paper, we first propose four research questions that help to understand what the state-of-the-art deep neural SLU models actually learn. To answer them, we study the vocabulary importance using a novel Embedding Sparse Structure Learning (SparseEmb) approach. It can be applied onto various existing deep SLU models to efficiently prune the useless words without any additional manual hyperparameter tuning. We evaluate SparseEmb on benchmark datasets using two existing SLU models and answer the proposed research questions. Then, we utilize SparseEmb to sanitize the training data based on the selected useless words as well as the model re-validation during training. Using both benchmark and our collected testing data, we show that our sanitized training data helps to significantly improve the SLU model performance. Both SparseEmb and training data sanitization approaches can be applied onto any deep learning based SLU models.
Resonance Detection Strategy for Multi-Parallel Inverter-Based Grid-Connected Renewable Power System Using Cascaded SOGI-FLL

The increasing use of multi-parallel grid-connected inverters introduces both high-quality and high-capacity power, while it tends to cause a resonance instability problem. A resonance damper can virtualize a resistor at resonant frequency to suppress the instability effectively, but the resonant frequency should be detected primarily. However, the resonant current or voltage is severely distorted and oscillating, which will lead to the resonant frequency extraction being more difficult. To address it, this paper proposes a resonance detection strategy based on the cascaded second-order generalized integrators (SOGI) and the normalized frequency locked loop (FLL). The cascaded structure ensures the accuracy by completely filtering the fundamental component from the detected voltage or current, and the normalization accelerates the frequency detection. The proposed method can be used as a crucial unit of the resonance damping controller. Finally, the performance of the proposed method is verified by the MATLAB-based simulation and Hardware-in-the-Loop (HIL)-based emulation results.
Performance measures for the Malaysian construction industry development board

The structure and roles of the Malaysian Construction Industry Development Board (CIDB), as the lead organisation responsible for the implementation of the Construction Industry Master Plan 2006-2015, has become even more critical to the success of the master plan and the construction industry in general. Each division of the CIDB has taken on the responsibility of spearheading various strategic thrusts in collaboration with other government agencies and industry partners. The performance measures of the CIDB have been identified, and a set of ten has been proposed for reporting to the government and stakeholders. The roles and responsibilities of each division and its corresponding performance measures are also defined. The concept of cascading these performance measures is introduced to ensure organisational alignment for all its objectives and initiatives.
The Impact of US Dairy Component Exports on the All-Milk Price Information

It seems to have become an article of faith that US dairy product exports have a large impact on farm milk prices. This is reflected in the attention paid to export data, ongoing trade negotiations or disputes (for example with China, Mexico, Japan), and the reaction of the futures markets when the trade dispute with China arose in 2018. The value of exports is often discussed in terms of the proportion of solids exported—sometimes differentiated between the fat and non-fat solids—but also in terms of the total value of dairy product exports. The pattern of US dairy exports from 2000 to 2018 is similar whether expressed in the total volume of components or the proportion of components in US farm milk that is exported (Figure 1). Both have grown substantially, especially since 2005, and the percentage exported of combined fat and nonfat components has exceeded 15% in recent years. A goal of 20% of total US milk components exported has been promoted by some organizations.
ERP Signal Analysis with Temporal Resolution Using a Time Window Bank

In order to study the cognitive processes underlying speech comprehension, neuro-physiological measures (e.g., EEG and MEG), or behavioural measures (e.g., reaction times and response accuracy) can be applied. Compared to behavioural measures, EEG signals can provide a more ﬁne-grained and complementary view of the processes that take place during the unfolding of an auditory stimulus. EEG signals are often analysed after having chosen spe-ciﬁc time windows, which are usually based on the temporal structure of ERP components expected to be sensitive to the experimental manipulation. However, as the timing of ERP components may vary between experiments, trials, and participants, such a-priori deﬁned analysis time windows may signiﬁcantly hamper the exploratory power of the analysis of components of interest. In this paper, we explore a wide-window analysis method applied to EEG signals collected in an auditory repetition priming experiment. This approach is based on a bank of temporal ﬁlters arranged along the time axis in combination with linear mixed effects modelling. Crucially, it permits a temporal decomposition of effects in a single comprehensive statistical model which captures the entire EEG trace.
MAJORITY VOTING IMAGE BINARIZATION

This paper presents a new binarization technique for text based images. The proposed method combines several state of the art binarization algorithms through a majority voting scheme and applies a post processing effect to improve the results, more specifically, the edge map of the grayscale image is used in combination with the image resulted through the voting process in order to ensure a more accurate determination of the image’s characters. Compared individually to each algorithm used, the binarization result proves to be quite promising, surpassing every other algorithm for certain images containing machine-written characters.