Miracle-dz/UniFAR-FTUs huggingface dataset:
The Growth of The Anti-Jewish Stereotype

There are three good reasons at the present time to try to arrive at an historical model to explain the development of anti-Jewish stereotyping and prejudice, and in this way, provided it is worked out at a sufficiently high level of abstraction, at an historical model of racism. The first reason is that both the Netherlands and its neighbours are increasingly faced with racism and that for a good line of action it is necessary to collect all kinds of knowledge. Moreover, it is desirable that historians prove willing to co-operate by making their particular contribution to this collection of knowledge. The second reason is that in contemporary thinking about history a tendency seems to have made itself felt that considers the narrative element of history as the only true activity of the historian, so that a hypothetical-deductive, one might say Popperian, approach to the past seems to be wrong. Although I do not want to enter into a methodological discussion, which I am glad to leave to my friend P. H. H. Vries, who has very capably formulated a point of view that I subscribe to, my intention is to show the usefulness of an abstract, partially mathematical, model in this article. By the way, in the framework of an article it is impossible to present an extensive test of the predictions of the model by means of source material. It can only be hinted at. This article is not non-narrative because I want it to be non-narrative, but because of lack of space. A full exposition would need a book. I shall only present in summary what I hope is the logical argument that lies at the basis of the model.
Home diagnosis of sleep apnoeas: A systematic review of the literature

CRD summary This review assessed the accuracy of various types of portable monitors for the diagnosis of sleep apnoea. The authors concluded that the best evidence was for type 3 monitors in sleep laboratory attended settings. The review had a number of methodological limitations, particularly in the literature search, selection criteria and synthesis of the results, making it impossible to draw reliable conclusions.
Phase-Locked Restored Pseudo Heat Flux Thermography for Detecting Delamination Inside Carbon Fiber Reinforced Composites

Thermogram reconstruction methods based on one-dimensional models are widely used in data processing for thermography inspections. However, the surface temperature variances caused by thermal diffusion will be compatible with those caused by delamination whose normalized aspect (diameter-to-depth) ratio is close to 1 when the defect itself is very thin, about 0.15 mm in this research. This phenomenon makes the detection capacity of these methods reduced at such defects with small aspect ratios. The paper proposes a new reconstruction method, phase-locked restored pseudo heat flux (RPHF), for thermography inspection using square-wave optical stimulations. The theoretical analysis shows the independence of the method upon the effect of thermal diffusion blur at defect-free areas. Square-wave thermography tests are conducted on a carbon fiber composite panel with artificial delimitations buried up to 4 mm deep. The method is implemented on a private computer to deconvolute the RPHF kernel from the transformed thermogram data. The data are separated into two sets with a one-period phase shift to each other sequentially; a phase-locked substation is applied between the sets. The global signal-to-noise ratios obtained with the proposed method are compared to those obtained with Busse's lock-in phase images and those with thermographic signal reconstruction. The phase-locked RPHF gives the best global signal-to-noise ratios for normalized aspect ratio at 1.1 when sufficient heat is applied. It's concluded that the thermal diffusion effect at defect-free areas should be considered in thermography inspection for defects with a normalized aspect ratio at 1.1.
FATIGUE AND WORK SATISFACTION OF EMERGENCY NURSES IN BANDUNG, WEST JAVA, INDONESIA

Background: Emergency nurses are required to always carry out their roles in a variety of situations and conditions. Nursing services in the emergency unit must be done quickly and accurately. Data showed that the number of patient visits in the Emergency Unit of Hasan Sadikin Hospital in August 2017 was 3,059 (73% of patients with category III) with an average visit of 99 people per day. This causes overcrowding in the emergency unit, which lead to fatigue and influence work satisfaction.Aim: The aim of this study was to determine fatigue and job satisfaction of emergency nurses in Hasan Sadikin Hospital in Bandung, West Java, Indonesia.Methods: This was a quantitative descriptive study, which involved 55 respondents. Fatigue was measured using Individual Strength Checklist Instrument of 20-self-reported questionnaire (CIS20R), and job satisfaction was measured using McCloskey / Mueller Satisfaction Scale (MMSS). Results: The mean value of work fatigue was 3.4 with the highest value at the sub variable of physical activity. In general, the mean value of nurse work satisfaction was 2.66. Conclusion: Emergency nurses in the emergency unit of Hasan Sadikin hospital has high level of fatigue and low work satisfaction. The hospital management is suggested to provide better work schedule for better interaction among nurses, provide opportunities for all staff to be involved in research or other scientific work, and schedule family gatherings regularly to improve togetherness between staff and their families.
Secure Control of Nonlinear Systems Using Semi-Homomorphic Encryption

A secure nonlinear networked control system (NCS) design using semi-homomorphic encryption, namely, Paillier encryption is studied. Under certain assumptions, control signal computation using encrypted signal directly is allowed by semi-homomorphic encryption. Thus, the security of the NCSs is further enhanced by concealing information on the controller side. However, additional technical difficulties in the design and analysis of NCSs are induced compared to standard NCSs. In this paper, the stabilization of a nonlinear discrete time NCS is considered. More specifically, sufficient conditions on the encryption parameters that guarantee stability of the NCS are provided, and a trade-off between the encryption parameters and the ultimate bound of the state is shown.
EEG pattern recognition-arousal states detection and classification

We use an electroencephalogram (EEG) to detect the arousal states of humans. The EEG patterns fluctuating between waking and sleep are described as several features extracted from the moving time windows. The significant feature, mean frequency (MF), is used for arousal states detection. The fluctuations of the EEG mean frequency are characterized as hidden Markov models (HMMs), which well estimate the next possible arousal state. In this HMM, single current-to-next state transition probability is considered along with global states transitions. Both local contextual effects (LCE) and global contextual effects (GCE) autoregressive HMMs are used to estimate and detect the transitions from waking to sleep. The validity of our proposed model is verified via a behavior measure-the correct rate of the subject's responses to auditory stimuli. In our study, the estimated values of mean frequency by GCE HMM show high correlation with the behavior measure. This high recognition rate makes arousal states detection practicable. Furthermore, the LCE HMMs are also applicable for artifacts rejection. In real-time arousal states detection, alarms are given whenever the subject's vigilant states turn into drowsy states. This system has many applications, for example, it can be used to maintain long term vehicle driver's arousal.
Ratings Distribution Recommendation Model-based Collaborative Filtering Recommendation Algorithm

In order to solve the problem of the popular item ratings interfering in similarity calculation, we proposed the ratings distribution recommendation model. Based on this model, we designed a new collaborative filtering algorithm. According to ratings distribution, this algorithm firstly get the amount of information carried (The Shannon Entropy). Then, it calculated the rating weights to filter into traditional similarity calculation. The experimental results show that the algorithm can effectively alleviate the above problem and improve the performance of the algorithm.
Bengali Longhand Character Recognition using Fourier Transform and Euclidean Distance Metric

Handwritten Character Recognition (HCR) is widely considered as a benchmark problem for pattern recognition and artificial intelligence. Text matching has become a popular research area in recent days as it plays a great part in pattern recognition. Different techniques for recognizing handwritten letters and digits for different languages have already been implemented throughout the world. This research aims at developing a system for recognizing Bengali handwritten characters i.e. letters and digits using Fourier Transform (FT) and Euclidean distance measurement technique. A dataset with 800 handwritten character texts from different people has been developed for this purpose and these character texts are converted to their equivalent printed version to implement this research. MATLAB has been used as an implementation tool for different preprocessing techniques like cropping, resizing, flood filling, thinning etc. Processed text images are used as input to the system and they are converted to FT. Handwritten character of different person may be of different style and angle. The input dataset is collected from various types of people including age level from 5 to 70 years, from different professions like pre-schooling students, graduate students, doctors, teachers and housewives. So, to match the input image with printed dataset (PDS) each printed data is rotated up to 450 left and right and then their FT is computed. The Euclidean distance among the input image and the rotated 30 images of each printed text are taken as intermediate distance set. The minimum value of Euclidean distance for a character is used to recognize the targeted character from the intermediate set. Wrongly detected texts are not thrown away from the system rather those are stored in the named character or digits file so that those can be used in future for deep learning. By following the proposed methodology, the research has achieved 98.88% recognition accuracy according to the input and PDS.
Palliative Care for Children: Preparing Undergraduate Nursing Students

Children with life-threatening illnesses rarely benefit from palliative services. Because nurses spend more time with dying children than any other healthcare professional does, it is essential that they are prepared to address the complicated physical, psychological, legal, ethical, and spiritual issues associated with terminal illness and death. The authors discuss a course that provides students with the knowledge, skills, and attitude needed to provide comprehensive care for pediatric patients and their families.
Human Brain Function

Principles and methods: Linking brain and behaviour, C. Frith Analyzing brain images - principles and overview, K. Friston Registering brain images to anatomy, J. Ashbumer, K. Friston Characterizing brain images with the general linear model, A. Holmes et al Making statistical inferences, J. Poline et al Characterizing distributed functional systems, K. Friston Characterizing functional integration, C. Buechel, K. Friston A taxonomy of study design, K. Friston et al. Functional anatomy: Dynamism of a PET image - studies of visual function, S. Zeki Mapping somatosensory systems, E. Paulesu, R. Frackowiak Functional organization of the motor system, R. Passingham The cerebral basis of functional recovery, R. Frackowiak Functional anatomy of reading, C. Price Higher cognitive processes, C. Frith, R. Dolan Human memory systems, R. Dolan et al Measuring neuromodulation with functional imaging, R. Dolan et al Brain maps - linking the present with the future, J. Mazziotta et al Functional imaging with magnetic resonance, R. Turner et al.
A Fast Converging RLS Equaliser

SUMMARY It is well known that based on the structure of a transversal filter, the RLS equaliser provides the fastest convergence in stationaryenvironments. This paper addresses an adaptive transversal equaliser which has the potential to provide more faster convergence than the RLS equaliser. A comparison is made with respect to computational complexityrequired for each update of equaliser coefficients, and computer simulations are demonstrated to show the superiorityof the proposed equaliser.
Advances in Region-of-Interest Video and Image Processing

The advent of cheaper and more powerful devices with the ability to play, create, and transmit video content has led to a dramatic increase in the multimedia content distribution on both wireline and wireless networks. Also, the reduction of cost of digital video cameras along with the development of user-generated video sites (e.g., iTunesTM, YouTubeTM) stimulated a new user-generated video content sector and made unprecedented demands for high-quality and low-delay video communication. The Region-of-Interest (ROI) is a desirable feature in many future scalable video coding applications, such as mobile device applications, which have to be adapted to be displayed on a relatively small screen; thus, a mobile device user may wish to extract and track only a predefined ROI within the displayed video. At the same time, other users having a larger mobile device screen may wish to extract other ROIs to receive higher video stream resolution. Therefore, to fulfill these requirements, it would be beneficial to simultaneously transmit or store a video stream in a variety of ROIs, as well to enable efficiently tracking of the predefined Region-of-Interest. This chapter presents recent advances in Region-of-Interest video and image processing techniques for multimedia applications, while making a special emphasis on a scalable extension of the H.264/AVC standard. The detailed observations and conclusions, which are presented in this chapter, are supported by authors’ personal experience in this field, thereby presenting a variety of experimental results. DOI: 10.4018/978-1-4666-2660-7.ch004
O TRABALHO EM EQUIPE

Os Institutos Federais atuam na formacao e qualificacao profissional de jovens e adultos para o mundo do trabalho e, desenvolver habilidades e competencia no campo de trabalho em equipes, e um requisito presente nas exigencias atuais para a empregabilidade. Nesse sentido, essa oficina objetiva desenvolver um exercicio pratico de trabalho em equipe para percepcao e reflexao sobre as diferentes reacoes e comportamentos presentes nessa atividade. A oficina sera composta por dois exercicios praticos de trabalho que deverao desenvolver em equipes, com tempo e tarefas pre estabelecidas, oportunizando depois de cada tarefa uma reflexao sobre as sensacoes que cada um teve ao realizar a tarefa, identificacao dos diferentes comportamentos presentes durante a realizacao das tarefas trabalhos em equipe e vantagens que possam identificar em como desenvolver as tarefas em grupo para obterem um melhor resultado. A oficina destina-se a um grupo de 20 pessoas, tera duracao de 2 horas e trinta minutos e os materiais necessarios a sua execucao sao: sala de aula, baloes, cartolinas, tesouras, colas, regua. Palavras-chave : comportamentos, lideranca, solidariedade, equipe.
Integrating Social Network Services with Vehicle Tracking Technologies

paper gives design, and implementation of a newly proposed vehicle tracking system, that uses the popular social network as a value added service for traditional tracking system. The proposed tracking system make use of Google maps service to trace the vehicle, each vehicle has an account that contains a posts of Google maps that display the vehicle location on real time mode. A hardware module is inside the vehicle that uses Global Positioning System (GPS) - to detect vehicle location- and Global system for mobile communication (GSM) - to update vehicle location in vehicle account on social network -. System uses the well-known Arduino microcontroller to control GSM-GPS Modem. The proposed system can be used for a broad range of applications such as traffic management and vehicle tracking/ anti theift system, and finally traffic routing and navigation. it can be applied in many business cases, like public transportation, so passengers can track their buses, trains, by following the vehicle account on social network. It also can be used in private business sector as an easy and simple fleet tracking and management system , or can be used by anyone who wants to track his car, or to find his way in case he get lost. KeywordsTracking, GSM, GPS, Microcontrollers, Twitter, Google maps.
Liver ultrasound is more sensitive in assessing the severity of nonalcoholic fatty liver disease with homeostasis model assessment-insulin resistance

Objective To evaluate the association between different grades of nonalcoholic fatty liver disease (NAFLD) as assessed by a liver ultrasound scan and homeostasis model assessment-insulin resistance (HOMA-IR) and also to investigate the possibility of using a noninvasive objective method to both diagnose and predict the severity of NAFLD in Egyptian obese nondiabetic patients. Methods The present study examined 57 obese, nondiabetic participants, aged 18–57 (34.3±10.5) years. Their BMI ranged from 32 to 48.3 (37.9±4.6) kg/m2. Complete medical history, anthropometric measurements, biochemical studies, and abdominal ultrasonography were carried out for each participant. HOMA-IR was calculated using the formula fasting plasma glucose (mmol/l)×fasting insulin (mIU/l)/22.5. Liver steatosis was evaluated using an ultrasound scan and categorized into four grades of severity according to an international guideline. Results NAFLD diagnosed using an ultrasound scan was associated significantly with a high level of insulin resistance expressed by HOMA-IR in obese nondiabetic patients as compared with participants with normal liver (10.54±4.99 vs. 3.303±0.90, P=0.000). HOMA-IR showed a significant positive correlation with the severity of fatty liver as diagnosed and graded by a liver ultrasound scan (7.14±4.3, 10.62±4.57, 11.93±4.98, and 14.69±5.35 in grade 1, 2, 3, and 4 steatosis, respectively). Conclusion NAFLD and its different grades of severity as assessed by liver ultrasound shows a strong positive association with a high level of insulin resistance expressed by HOMA-IR in obese nondiabetic Egyptian patients. The combination of HOMA-IR and liver ultrasound is a highly sensitive and specific method not only in diagnosis of NAFLD but also in predicting its severity.
Improved forecasting considering dynamic properties within the time series of customer demands

Increasing complexity and dynamics of today's markets involve volatile customer demands. These conditions complicate a company's sales forecast. However, high-quality data on future sales is of eminent importance for a substantiated long- and middle-term planning of production programs, schedules and resources. Common forecasting methods are mostly based on quantitative and stochastic characteristics. Methods of nonlinear dynamics enlarge the supply of classical and specialized methods by considering deterministic structures within the customer-related time series. The paper at hand presents the research approach of a project which links quantitative and qualitative characteristics of customer demands with the selection and application of suitable forecasting methods emphasizing methods of nonlinear dynamics.
Performance of Python runtimes on a non-numeric scientific code

The Python library FatGHol (FatGHoL) used in (Murri2012) to reckon the rational homology of the moduli space of Riemann surfaces is an example of a non-numeric scientific code: most of the processing it does is generating graphs (represented by complex Python objects) and computing their isomor- phisms (a triple of Python lists; again a nested data structure). These operations are repeated many times over: for example, the spaces M0;6 and M1;4 are triangulated by 4'583'322 and 747'664 graphs, respectively. This is an opportunity for every Python runtime to prove its strength in optimization. The purpose of this experiment was to assess the maturity of alternative Python runtimes, in terms of: compatibility with the language as implemented in CPython 2.7, and performance speedup. This paper compares the results and experiences from running FatGHol with different Python runtimes: CPython 2.7. 5, PyPy 2.1, Cython 0.19, Numba 0.11, Nuitka 0.4. 4 and Falcon.
Optimal designs for segmented polynomial regression models and web-based implementation of optimal design software

Stony Brook University Libraries. SBU Graduate School in Applied Mathematics and Statistics. Lawrence Martin (Dean of Graduate School), Wei Zhu –Dissertation Advisor Professor Department of Applied Mathematics and Statistics, Nancy Mendell - Chairperson of Defense Professor Department of Applied Mathematics and Statistics, Stephen Finch Professor Department of Applied Mathematics and Statistics, Weng Kee Wong Professor of Biostatistics University of California, Los Angeles.
Masters Thesis: Learning State Representations for Robotic Control

Representation learning is a central topic in the field of deep learning. It aims at extracting useful state representations directly from raw data. In deep learning, state representations are usually used for classification or inferences. For example, image embedding that provides similarity metrics can be used for face recognition. Recent success on deep learning has stimulated the interest of applying state representation learning to control. This problem is very different from deep learning in computer visions and language modelling. Control tasks usually require pose information about the task relevant object in the scene. Applying state representation learning to control requires such features to be embedded. This is a difficult problem since there is generally no explicit supervision for extracting pose information from the data. A problem of state representation learning for control is that raw data can contain much irrelevant information for the control task. For example, colors, textures and shapes of taskrelevant objects are not as important as poses of objects. This suggests that the appearance and pose information should be disentangled in learned representations and the pose representations should be used for the control task. Furthermore, we usually need a system dynamic model in order to perform model-based optimal control. The prediction ability of the model is crucial for planning since it has to evaluate future trajectories and optimizes the actions. Thus we need to learn a decent dynamic model on extracted representations. To address these problems, we first propose to use Variational Auto-encoders to disentangle the pose and appearance representations. The benefit of preserving both appearance and pose representations is that we can predict pose representations conditioned on actions. From appearance and predicted pose representations, we can reconstruct and predict future image frames. For this purpose, we introduce the Long Short-Term Memory network to learn a prediction model on the pose representations. We further intend to apply multi-modal state representation learning for control. Leveraging the idea of sensor fusions, we want to verify whether multi-modal sensory data lead to better representations for control. To test our hypothesis, an object poking simulation is prepared in the Gazebo simulator where training and testing data-sets are collected. We validate disentangled representations and prediction performances with a few baseline models. Experiment results show that information disentangling can be achieved without explicit supervisions. The prediction model can Master of Science Thesis Wuyang Duan