Miracle-dz/UniFAR-FTUs huggingface dataset:
CFD Modeling of a Realistic Turbofan Blade for Noise Prediction. Part 2: Analytical Acoustic Predictions

Noise sources on the rotor of a scale-model turbofan are investigated at approach conditions. The fan geometry considered is the baseline configuration of the “Fan Noise Source Diagnostic Test” (SDT) experimental set-up. Based onReynoldsAveraged Navier-Stokes simulations (RANS) competitive noise source mechanisms, the turbulent interaction noise and the trailingedge noise on the rotor are investigated. The noise estimations are compared to acoustic predictions from Large Eddy Simulations (LES) which consider only the rotor row and include a single blade, and to noise measurement performed by NASA . Focus is made on the modeling of the noise excitations in the analytical predictions.
A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

Network Function Virtualization (NFV) and service orchestration simplify the deployment and management of network and telecommunication services. The deployment of these services requires, typically, the allocation of Virtual Network Function - Forwarding Graph (VNF-FG), which implies not only the fulfillment of the service’s requirements in terms of Quality of Service (QoS), but also considering the constraints of the underlying infrastructure. This topic has been well-studied in existing literature, however, its complexity and uncertainty of available information unveil challenges for researchers and engineers. In this paper, we explore the potential of reinforcement learning techniques for the placement of VNF-FGs. However, it turns out that even the most well-known learning technique is ineffective in the context of a large-scale action space. In this respect, we propose approaches to find out feasible solutions while improving significantly the exploration of the action space. The simulation results clearly show the effectiveness of the proposed learning approach for this category of problems. Moreover, thanks to the deep learning process, the performance of the proposed approach is improved over time.
Principle of Madrasah Head Management in Realizing Effective Madrasah

The ability of the head of the Madrasah to realize effectiveness is closely related to the comparison between the level of achievement of goals and the plans that have been prepared before, or the comparison of tangible results with the planned results. The effectiveness of madrasah management, as well as the effectiveness of education in general, can be seen based on system theory and time dimensions. That is, the criteria for effectiveness must reflect the entire cycle of input-process-output, not only output or results and must reflect the relationship - reciprocity between management and the surrounding environment. Effective Madrasas are Islamic educational institutions that have curricula, strategies, effective teaching and learning and there are interactions with interested parties (students, teachers, parents, the environment and related officials) and produce reliable output.
THE ROLE OF DIGITAL INNOVATION IN MEDIATING DIGITAL CAPABILITY ON BUSINESS PERFORMANCE

The purpose of this study is to elucidate the role of digital innovation in mediating digital capabilities on business performance. The sampling method used is purposive sampling. The number of samples is 75 respondents. Data collection is carried out by distributing questionnaire to SME managers in IT sector in Denpasar City. The data analysis technique used is the Path Analysis and Sobel Test technique. The results of the study show that the digital capability variable has a positive and significant effect on business performance. Likewise, digital innovation has a positive and significant effect on business performance, and digital innovation is able to mediate the effect of digital capabilities on business performance. Therefore, in the future, SMEs in IT sector must always improve their digital capabilities in order to develop digital innovations that have an impact on improving business performance. Article visualizations:
Measuring the Decision Accuracy and Decision Confidence of Air Defence Operators

i ACKNOWLEDGEMENTS . iv LIST OF FIGURES . x LIST OF TABLES . xi GLOSSARY OF TERMS . xiv PART ONE: Literature Review and Background . 1 CHAPTER ONE: . 1 1. The Problem of Air Defence Decision Making . 1 1.
Detecting valvular event times from echocardiograms using deep neural networks

The timing of cardiac events is essential for the analysis of certain components of myocardial function [36]. Finding an algorithm that detects these timings has therefore been the subject of several studies[36]. In this project, deep neural networks were used to detect the valvular event times from echocardiography sequences. Three classes of neural network algorithms were tested: fully convolutional architectures [48], VGG [47] inspired architectures and recurrent neural networks. Temporal information was also incorporated by feeding the network the relative time passed since the last QRS peak. It was found that incorporating temporal information was necessary for detecting the valvular event times with an acceptable accuracy. The model providing the highest performance metrics was a VGG inspired architecture with both an RNN head and the relative time since the QRS peak. A version of this model that did not include the relative times was visualised using both guided backpropagation [48] and image occlusion [54], which demonstrated that the position and movement of the valves were important for correctly predicting valvular events. The best model achieved a 93% test accuracy and correctly detected all the valvular events in 7 out of 11 test series with a mean error of 1.03 frames. This is not satisfactory for clinical use. However, it does indicate that deep neural networks applied to echocardiography are a promising approach for automatic valvular event time detection.
Do It Like a Syntactician: Using Binary Gramaticality Judgements to Train Sentence Encoders and Assess Their Sensitivity to Syntactic Structure

Do it like a syntactician, using binary grammaticality judgements to train sentence encoders and assess their sensitivity to syntactic structure. by Pablo González Mart́ınez Adviser: William Sakas The binary nature of grammaticality judgments and their use to access the structure of syntax are a staple of modern linguistics. However, computational models of natural language rarely make use of grammaticality in their training or application. Furthermore, developments in modern neural NLP have produced a myriad of methods that push the baselines in many complex tasks, but those methods are typically not evaluated from a linguistic perspective. In this dissertation I use grammaticality judgements with artificially generated ungrammatical sentences to assess the performance of several neural encoders and propose them as a suitable training target to make models learn specific syntactic rules. I generate artificial ungrammatical sentences via two methods. First by randomly pulling words following the n-gram distribution of a corpus of real sentences (I call these Word salads). Second, by corrupting sentences from a real corpus by altering them (changing verbal or adjectival agreement or removing the main verb). We then train models with an encoder using word embeddings and long short term memory (LSTMs) to discriminate between real sentences and ungrammatical sentences. We show that word salads can be distinguished by the model well for low order n-grams but that the model does not generalize well for higher orders. Furthermore, the word salads do not help the model in recognizing corrupted sentences. We then test the contributions of pre-trained word embeddings, deep LSTM and bidirectional LSTM. We find that the biggest contribution is adding pre-trained word
Joint Source-Channel Coding for Image Transmission over Underlay Multichannel Cognitive Radio Networks

The increasing prominence of wireless applications exacerbates the problem of radio spectrum scarcity and promotes the usage of Cognitive Radio (CR) in wireless networks. With underlay dynamic spectrum access, CRs can operate alongside Primary Users, the incumbent of a spectrum band, as long as they limit the interference to the Primary Users below a certain threshold. Multimedia streaming transmissions face stringent Quality of Services constraints on top of the CR interference constraints, as some packets in the data stream have higher levels of importance and are the most vulnerable to packet loss over the channel. This raises a need for Unequal Error Protection (ULP) for multimedia streams transmissions, in which the channel encoder assigns different amount of error correction to different parts of the data stream, thereby protecting more the most valuable parts of the stream from packet loss problems. This research presents an end-to-end system setup for image transmission, utilizing ULP as part of a Joint Source-Channel Coding scheme over a multichannel CR network operating through underlay dynamic spectrum access. The setup features a Set Partitioning in Hierarchical Trees (SPIHT) source encoder, and Reed-Solomon forward error correction channel coding, and uses their properties to devise an ULP framework that maximizes the quality of the received image.
3D Gaussian Descriptor for Video-based Person Re-Identification

Despite being often considered less challenging than image-based person re-identification (re-id), video-based person re-id is still appealing as it mimics a more realistic scenario owing to the availability of pedestrian sequences from surveillance cameras. In order to exploit the temporal information provided, a number of feature extraction methods have been proposed. Although the features could be equally learned at a significantly higher computational cost, the scarce nature of labelled re-id datasets encourages the development of robust hand-crafted feature representations as an efficient alternative, especially when novel distance metrics or multi-shot ranking algorithms are to be validated. This paper presents a novel hand-crafted feature representation for video-based person re-id based on a 3-dimensional hierarchical Gaussian descriptor. Compared to similar approaches, the proposed descriptor (i) does not require any walking cycle extraction, hence avoiding the complexity of this task, (ii) can be easily fed into off-shelf learned distance metrics, (iii) and consistently achieves superior performance regardless of the matching method adopted. The performance of the proposed method was validated on PRID2011 and iLIDS-VID datasets outperforming similar methods on both benchmarks.
An automated method for the selection of complex railway lines that accounts for multiple feature constraints

The automatic selection of railway marshaling lines is an important procedure in the generalization of road network data. The line selection results must be geometrically consistent with the topological features of the external/internal structural and topological connectivity of the railway stations. When current methods are used to process the lines at railway marshaling stations, which exhibit crisscrossing, dense, non‐hierarchical, and complex characteristics, the selected lines tend to be topologically and structurally erroneous. In this article, we propose an automated method for the selection and generalization of complex railway lines that accounts for multiple feature constraints. First, we identify four types of line structures in accordance with the spatial and topological features of railway stations. Then, based on length and spacing thresholds specified for the generalization process, line selection is performed while accounting for constraints regarding structural robustness and topological extensibility. Finally, we report the validation of our method on a dataset relating to Chengdu Railway Station, China. The results indicate that our method successfully preserves the external and internal structural features and connectivity of railway stations.
Pencil Code improves learners' computational thinking and computer learning attitude

In the current implementation of K‐12 education, computational thinking is considered an important part of developing students' key abilities. Many studies have shown that computational thinking can be improved by programming, for example, using Scratch, App Inventor, and Python. However, different programming tools have different forms of programming that affect the development of learners' computational thinking. This study aimed to test the effects of different programming tools with the same teaching content on learners' computational thinking and computer learning attitude in real classroom teaching. Ninety‐nine participants took part in an eight‐week teaching intervention in the Visual Basic (text‐based) and Pencil Code (block‐based) programming environments. T tests showed that students learning in a mixed text‐based and block‐based Pencil Code programming environment not only had significantly improved computational thinking but also became more self‐confident and enjoyed learning programming. Furthermore, our interviews after the eight‐week teaching intervention found that students showed more communication and interaction behaviors learning with Pencil Code.
Spatio-temporal human action detection and instance segmentation in videos

We address the problem of spatiotemporal human action detection in videos. Our proposed model is able to classify and localise (in space and time) multiple co-occurring action instances in temporally untrimmed videos. Action detection in video has several practical real world applications such as human-robot interaction, autonomous driving, autonomous robotic assistant surgeon, VR (virtual reality) gaming to mention a few. In this context action detection reduces to solving three different sub-problems: 1) action classification, 2) spatial detection and 3) temporal detection. Given an input video the detection model is supposed to classify each action instance, localise them spatially using bounding boxes and detect the temporal extent of each action instance by inferring the start and end times. Inspired by the overwhelming success of R-CNN (regions with CNN features) object detectors [1], previous approaches [2, 3] adopted the R-CNN framework for action detection. Although the use of R-CNNs triggered a significant performance improvement, the downsides of relying on them are that: 1) this is computationally expensive, 2) it requires multistage training and 3) relies on unsupervised region proposal algorithms such as Selective Search [2] or EdgeBoxes [3]. Unlike [4, 5], R-CNNs do not share computation and perform a separate forward pass for each object proposal, which is expensive in terms of both time and computing resources. For example, during training this requires to execute 2000 conv forwards passes for 2000 Selective Search proposals, amounting to relatively longer training time and more GPU resources than [4,5]. Besides, the R-CNN framework follows a multistage training pipeline which includes (a) finetuning a CNN, (b) extracting CNN features, (c) caching features to disk, and (d) training a number of one-vs-all SVMs, resulting in a very computationally expensive pipeline. Furthermore, the detection accuracy of R-CNNs is limited by their relying on unsupervised region proposal algorithms [2,3] which, besides being resource-demanding, cannot be trained for a specific detection task and are disconnected from the overall classification objective. To overcome these issues we propose a novel action detection pipeline [6] which adopts, instead, the Faster R-CNN [7] network. Faster R-CNN is computationally less expensive than R-CNN as it shares computation during the forward pass, avoid multistage training and thus eliminating the need for feature extraction, caching and SVM training. In addition, it uses a Region Proposal
O padrão de uso de drogas por grupos em diferentes fases de tratamento nos Centros de Atenção Psicossocial Álcool e Drogas (CAPS-AD)

Objective The purpose of the study was to analyze the socio-demographic characteristics and the pattern of drug use in patients of CAPS-AD in Curitiba, Parana. Methods A descriptive cross-sectional study was carried out in a sample of 268 individuals, of which 184 in early phase and 84 in the third month of treatment. Between April and September of 2012, the data was collected with the application of a questionnaire that contained sociodemographic questions and information about characteristics of drug use as substances, time and frequency of use. For data analysis, the programs of Excel and Epi-Info were used. Averages and chi-square tests were calculated in order to compare the two groups. Results The predominant characteristics in both groups were: male, white color, low education, low income, reside with family or friend and own their house. Legal drugs were the most used. Marijuana was the illicit drug most used in life. Considering the last year and last month, crack was the most used. Inhalants and hallucinogens were the least used. Patients in the third month of treatment were significantly older, married and lifetime use of drugs was lower compared to the other group. Also, use of marijuana, cocaine and crack were lower in the last month. Nonetheless, they made more frequent use of alcohol and, over a third had never used illicit drugs in life. Conclusion The findings may help to guide new strategies to increase access to treatment of vulnerable groups and also to remain in treatment in the CAPS-AD.
Assessment of microbiological critical control points in Food processing and serving plant

Restaurant chains are considered attractable places to people around the world; however prepared meals may be contaminated intentionally or accidentally. The examined samples (200) were collected from stages of preparation of grilled chicken (60), casserole beef and hawawshi (50 of each) as well as swabs from cutting boards, knives, mincer and worker hands (10 of each) in a food serving plant. The collected samples were bacteriologically examined for monitoring of APC, Enterobacteriaceae count, Coliform count, Salmonellae, S.aureus, E.coli and Cl. perfringens. The obtained results revealed that the most contaminated product was casserole beef followed by hawawshi and grilled chicken. The incidence of food borne pathogens were S.aureus (32%), Salmonellae (30.2%), E.coli (26.6%) and the lowest one was Cl.perfringens (8.8%).
Supervised learning‐based DDoS attacks detection: Tuning hyperparameters

Two supervised learning algorithms, a basic neural network and a long short‐term memory recurrent neural network, are applied to traffic including DDoS attacks. The joint effects of preprocessing methods and hyperparameters for machine learning on performance are investigated. Values representing attack characteristics are extracted from datasets and preprocessed by two methods. Binary classification and two optimizers are used. Some hyperparameters are obtained exhaustively for fast and accurate detection, while others are fixed with constants to account for performance and data characteristics. An experiment is performed via TensorFlow on three traffic datasets. Three scenarios are considered to investigate the effects of learning former traffic on sequential traffic analysis and the effects of learning one dataset on application to another dataset, and determine whether the algorithms can be used for recent attack traffic. Experimental results show that the used preprocessing methods, neural network architectures and hyperparameters, and the optimizers are appropriate for DDoS attack detection. The obtained results provide a criterion for the detection accuracy of attacks.
Prevalence, Risk Factors and Associated Cardiovascular Complications of Peripheral Arterial Disease in Type 2 Diabetic Patients in Eastern Taiwan

Objective: The purpose of this study was to evaluate the risk factors, including albuminuria, for peripheral arterial disease (PAD), and associated complications in patients with type 2 diabetes. Materials and Methods: All patients with type 2 diabetes 40 years old and over, were recruited consecutively from diabetic clinics at a medical center in eastern Taiwan. Information regarding each participant's sociodemographic characteristics and medical history was gathered from the patients and their medical records. A clinical examination which included anthropometric measurements and a direct ophthalmoscope check-up was performed. After an overnight fast, serum lipids, uric acid, fasting plasma sugar, A1C and urinary albumin concentration were measured. The anklebrachial index (ABI) was calculated by Doppler examination in both legs. PAD was diagnosed if the ABI in one of the legs was less than 0.9. Results: A total of 309 patients were recruited. PAD was present in 38 of the 309 diabetic subjects studied (12.3%; 12.7% of the men and 11.9% of the women). Of all subjects with PAD, 92.1% had a history of hypertension, 15.8% had stroke, 13.2% had coronary heart disease, and 47.4% had albuminuria. For those who completed the direct ophthalmoscope examination, 41.9% of patients with PAD had diabetic retinopathy. In univariate analysis, the significant associative risk factors for PAD were age, tobacco smoking, history of hypertension/stroke, duration of diabetes, insulin therapy, usage of angiotension-converting enzyme inhibitors/angiotension-receptor blockers (ACEI/ARB) and albuminuria. Multiple logistic regression analysis identified age as the most significant risk factor for PAD. The higher the age of the patient, the greater the likelihood of PAD, with a 1.11-fold increase in risk incurred for every 1-year increment. Cigarette smoking was also a significant factor with smokers incurring a 4.77-fold higher risk than non-smokers. Individuals suffering from macroalbuminuria were more likely to have PAD compared to those without albuminuria. Other significant risk factors included insulin usage. Conclusions: The occurrence of PAD in type 2 diabetic patients is associated with the age of the patient, smoking, insulin usage and albuminuria. Incorporation of albuminuria assessment and a smokingcessation program are recommended in clinical practice.
A Mm-Wave Wideband MIMO RX With Instinctual Array-Based Blocker/Signal Management for Ultralow-Latency Communication

This article demonstrates a wideband mm-Wave multiple-input–multiple-output (MIMO) receiver (RX) system with cascadable array-based high-order autonomous spatial filters (ASFs) for closed-loop frontend beamforming, achieving “instinctual” (“Instinctual” is defined as an autonomous operation for sensing, controlling, processing signals with ultralow-latency and no backend digital signal processing assist.) management of multiblockers/signals with ultrafast response time. Over a full field-of-view (FoV), the ASFs achieve automatic blocker suppression and desired signal beamforming without prior knowledge on the angle-of-arrival/frequency/modulation and without beam-searching using backend computations. In particular, the ASFs realize frontend blocker suppression and substantially relax the dynamic range requirement of down-stream analog-to-digital converters (ADCs) in digital arrays. Moreover, cascading multiple ASFs can sequentially suppress multiple blockers, essentially achieving the “iterative source localization” DSP algorithm by the frontend hardware. A proof-of-concept 27–41-GHz four-element MIMO RX array is implemented in a 45-nm CMOS silicon-on-insulator (SOI) process to support multistandard 5G links in the future congested/contested electromagnetic (EM) environments. With autonomous cancellation of multiple co-channel wideband blockers, a wideband desired signal can be received with high signal-to-interference-plus-noise ratio (SINR), achieving −28.64-dB error vector magnitude (EVM) for 3-Gb/s 64QAM and −32.73-dB EVM for 0.8-Gb/s 256QAM. The measured MIMO beamforming response time for beam scanning, localization, and computation is <1.75 $\mu \text{s}$ /stage, enabling 5G and beyond-5G ultralow-latency mm-Wave links.