Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and control of UPFC for voltage compensation using ATP/EMTP

This paper presents the analysis and control of a unified power flow compensator (UPFC) for voltage compensation using the Alternative Transients Program/Electromagnetic Transients Program (ATPIEMTP) software. One of the common power quality problems experienced in power systems is the voltage hanging, both amplitude and phase, especially voltage swell. To compensate the voltage sag on the load bus, the UPFC has been proposed by using pq-theory. The system parameters and controller gains are designed under the real power system conditions in Thailand. The simulation results show that the shunt and series part of UPFC can control the amplitude and phase of injection voltage and current on the load bus. These results demonstrate both the benefit and the performance of the proposed methodology which will enhance the performance ofUPFC for voltage compensation.
Research on Lightweight and Fast Image Style Transform Algorithm

Image style transform technology refers to the use of a convolutional neural network to extract the style of a famous painting, thereby converting the input image into a corresponding style image. The current methods are mainly divided into two types, namely, the style transform method proposed by Gatys and the fast image style transform method (FNST) proposed by Li Feifei. The common problem between the two methods is that the hardware requirements are high, which is not conducive to a wide range of popularization and application. In order to use image style transform on more terminals and even mobile phones, research h on lightweight and fast image style transform is of great significance. This paper draws on the deep-wise split convolution in MobileNets to prun the convolutional layer in the loss network. After the pruning, the parameter content is only 11% of the original model; the residual component in Image Transform Net is trimmed. The parameter content was reduced by 96% compared to the original model. However, the amount of parameters in the process of weight reduction is greatly reduced, which may lead to the degradation of the stylized effect. It is proved by experiments that the style degradation of the method is not obvious, that is to say that the performance loss caused by the reduction of the parameter quantity is acceptable.
The benefits of Deceit: a Malicious client in a 5G Cellular Network

As we advance towards smart cities, autonomous vehicles and the avalanche of IoT devices proposed for the future, we need to give careful consideration to how easily compromised devices can impact network state. Current proposals for devices typically use cellular networks as the backhaul or final hop. These devices will leverage existing trust-based client-side channel metrics, such as Channel Quality Indicator (CQI), when the base-station determines scheduling decisions. In this paper, we investigate the scheduling impact of a malicious device when it changes its channel metrics, so as to improve its download rate or even to negate the download rate of others. We utilise real-time 4K ultra-high definition video delivery as an example of high throughput demand application and compare the delivery rates of multiple devices in an open-source 5G simulated NS-3 network. Our results illustrate that when a malicious client deceives the scheduler, the other clients in the network have a noticeable decrease in both viewable quality and underlying delivery rate (25% decrease in the average video quality across the non malicious clients).
Chromospheric Microjets in the Penumbra of a Sunspot

In this thesis chromospheric features called Penumbral Microjets (PMJs) are investigated in a sunspot. Observations of high resolution in space, time and wavelength from the Swedish 1-meter Solar Telescope (SST) are used to probe the Ca II 8542 Å -line. The goal is to obtain detailed spectral signatures of PMJs in the Ca II 8542 Å -line, as well as basic statistical measures such as lifetime and sizes. This is achieved by the implementation of an automated detection scheme employing dimensionality reduction methods and a simple learning algorithm which together reduce and classify the observations from the SST. The advantages of employing an automated numerical detection scheme are exploited to assemble a large sample of objects and extract statistically significant results from it. The obtained statistical values and spectra are then discussed and put in a wider context.
The eta invariant and equivariant index of transversally elliptic operators

We prove a formula for the multiplicities of the index of an equivariant transversally elliptic operator on a G-manifold. The formula is a sum of integrals over blowups of the strata of the group action and also involves eta invariants of associated elliptic operators. Among the applications, we obtain an index formula for basic Dirac operators on Riemannian foliations, a problem that was open for many years.
The Implementation of the Pronunciation Techniques through the Warming Up Stage

After an analysis of the Colombian context in the teaching of English, it was concluded that the pronunciation was not taken into account in the teaching of English in schools and that the warming up stage was used in the wrong way. This classroom project pretends to integrate pronunciation techniques through the warming up stage in order to promote and foster the English pronunciation through the warming up stage in the EFL classroom. This classroom project presents the results in three main parts. First, professional growth; second, linguistic outcomes, and third, students’ responses. After an analysis of the information collected from the implementations in journals and observations formats, each one gave specific results which were discriminated in strengths and challenges in order to facilitate the comprehension of the results. To conclude, it was identified that the project had a positive impact in the promotion of the English pronunciation in the students, and the improvement in the teacher’s professional development. Additionally, this kind of project offers and alternative to develop an English class in Colombian’ context.
Drug Specification Named Entity Recognition Base on BiLSTM-CRF Model

In order to realize automatic recognition and extraction of entities in unstructured medical texts, a model combining language model conditional random field algorithm (CRF) and Bi-directional Long Short-term Memory networks (BiLSTM) is proposed. We crawled 804 drug specifications for treating asthma from the Internet, and then quantized the normalized field of drug specification word by a vector as the input of the neural network. Compared with the traditional machine learning algorithm CRF model, the system accuracy, recall and F1 value are improved by 6.18%, 5.2% and 4.87%. This model is applicable to extract named entity information from drug specification.
An optimization approach to the multi-player pursuit-evasion problem

In this paper a scenario of one evader being chased by multiple pursuers in two speciﬁc simulation environments is studied. The simulation environments are divided into an open area without obstacles and a closed area with obstacles. In the open area a fairly accurate sys-tem of dynamics are implemented for both pursuers and evader. The Virtual Vehicle Approach is used to provide a reference trajectory for the pursuers to follow in order to catch the evader. The main purpose of this thesis is to ﬁnd a decentralized robust control method for the dynamics of the pursuers. In the closed area, the line of sight and ﬁeld of view are introduced and the solution to the Minimum time UGV surveillance problem and the Centroidal Voronoi partitions. Diﬀer-ent capturing strategies, encirclement and one-on-one chase, are both studied and compared. The numerical implementation and the resulting simulation are presented and analyzed. Conclusion on the optimal formation for the multiple pursuers is made.
DEEP LEARNING FOR STOCK MARKET TRADING: A SUPERIOR TRADING STRATEGY?

Deep-learning initiatives have vastly changed the analysis of data. Complex networks became accessible to anyone in any research area. In this paper we are proposing a deep-learning long short-term memory network (LSTM) for automated stock trading. A mechanical trading system is used to evaluate its performance. The proposed solution is compared to traditional trading strategies, i.e., passive and rule-based trading strategies, as well as machine learning classifiers. We have discovered that the deep-learning long short-term memory network has outperformed other trading strategies for the German blue-chip stock, BMW, during the 2010–2018 period.
Implementation of Deep Learning Accelerator Unit

: Machine learning is usually used in cloud administration and apps. Also, as AI's increasing field, deep learning shows brilliant ability to address complicated learning problems. Superior executions of deep learning apps appear to be important in order to offer customers better participation. As a typical manner of speeding up calculations, FPGA uses superior, low energy, Little size and various characteristics. The DLAU Quickening Agent utilizes three performance-enhancing pipeline training devices and utilizes tile procedures to discover territory for machine learning applications. Test results on the best Xilinx FPGA panel indicate that DLAU can reach speeds of up to 36.1x relative to Intel Core2 computers and energy.
Algorithm of weak edge detection based on the Nilpotent minimum fusion

To overcome the shortcoming in traditional edge detection, such as the losing of weak edges and the too rough detected edges, a new edge detection method is proposed in this paper. The new algorithm is based on the Nilpotent minimum fusion. First of all, based on the space fuzzy relation of weak edges, the algorithm makes decision fusion to improve the structure of weak edges by using the Nilpotent minimum operator. Secondly, detect edges based on the fusion results. As a result, the weak edges are detected. Experiments on a variety of weak edge images show that the new algorithm can actually overcome the shortcoming in traditional edge detection, for the results are much better than traditional methods. On one hand, some of the weak edges of complex images, such as medical images, are detected. On the other hand, the edges detected by the new algorithm are thinner.
RANCANGAN LAYANAN WEB DAN APLIKASI SMS GATEWAY E-GAMPONG UNTUK DISEMINASI INFORMASI GAMPONG BERBASIS ANDROID

Desa atau Gampong adalah organisasi masyarakat hukum terkecil yang berwenang dalam batasan wilayah dan sistem tata kelola sendiri. Desa dipimpin oleh seorang kepala desa. Salah satu tugas utama kepala desa yaitu untuk menyampaikan informasi kepada warga. Pada saat ini sistem diseminasi informasi yang ada pada masyarakat gampong masih menggunakan metode konvensional sehingga masih terdapat banyak kekurangan. Salah satu solusi untuk mengatasi masalah di atas adalah dengan memanfaatkan teknologi Short Message Service (SMS) Gateway. Untuk menerapkan teknologi tersebut penulis membuat layanan web yang bersifat RESTful supaya dapat diakses oleh aplikasi SMS Gateway eGampong . Aplikasi SMS Gateway dibangun untuk perangkat Android supaya kepala desa bisa lebih mudah menggunakannya tanpa harus menggunakan perangkat komputer. Kata kunci: SMS Gateway,Android, Web Service, google cloud endpoint
Computer Aided Evaluation of Fir Tree Blade Root Profile Based on Particle Swarm Algorithm

Aiming at the problems of inefficiency and inaccuracy in the evaluation of fir tree blade root profile, a method of computer aided profile evaluation based on particle swarm algorithm is proposed. The evaluation process is automated by this method, the theoretical profile and the coordinates of the measured points are read automatically, the measured points and the theoretical profile are aligned automatically, the profile deviation is calculated, and whether the blade root is qualified is determined. The mathematical model and calculation method of profile evaluation are given, the measured points and theoretical profile are aligned precisely according to the principle of least square by making use of the particle swarm optimization algorithm with adaptive inertia weight(APSO) after coarse alignment, and the influence of position error between design reference and measurement reference on the results of profile evaluation is eliminated. Simulation experiment and practical application manifest better accuracy and stability can be obtained by using APSO algorithm compared with other algorithms when evaluating the fir tree blade root profile. The computer aided profile evaluation method proposed in this paper could totally replace the traditional manual method.
Differentiable Neural Architecture Search with an Arithmetic Complexity Constraint

Deep learning in general and convolutional neural networks (CNN) in particular have demonstrated spectacular success on a variety of tasks becoming a de facto standard in machine learning. Deep learning techniques allow to train the parameters of the network completely automatically from the data. However, architectural choices such as the number and the specific topology of the layers are usually designed by hand. In fact, the most successful CNN architectures in use today were designed by trial and error. Neural Architecture Search (NAS) aims at automating the design of neural network architectures for a given task. Being part of a larger automated machine learning (AutoML) trend, it promises to alleviate the scarcity of machine learning experts needed to design custom architectures, and do this task better than humans. While being a very popular tool in modern machine learning arsenal, CNNs are typically very demanding computationally at inference time. One of the main ways to alleviate this burden is quantization relying on the use of low-precision arithmetic representation for the weights and the activations, leading to more efficient arithmetic operations. The resulting reduction of computational complexity depends on the exact hardware platform, but is typically significant. One of the big questions in designing a quantized neural network is how to allocate bit widths to different filters achieving optimal tradeoff between computational complexity and loss in accuracy. Another popular method that has so far been studied independently is the pruning of the number of filters in each layer. The present study is an attempt to formulate optimal bit allocation and pruning as a NAS problem, searching for the best configuration of allocated bits satisfying a computational complexity budget while maximizing the model accuracy. While conventional NAS methods apply evolution or reinforcement learning over a discrete and nondifferentiable search space, recently, a differentiable search method has been proposed. The method is based on the continuous relaxation of the architecture representation, allowing an efficient search of the architecture using gradient descent. We introduce a differentiable NAS method allowing to find superior heterogeneous architectures, i.e., CNNs in which each filter can be quantized with a different bitwidth or each layer can have a different number of filters, and evaluate its performance. 1 Technion Computer Science Department M.Sc. Thesis MSC-2019-12 2019 2 Technion Computer Science Department M.Sc. Thesis MSC-2019-12 2019 Abbreviations and Notations NAS : Neural Architecture Search CNN : Convolutional Neural Network AutoML : Automated Machine Learning DL : Deep Learning GPU : Graphics Processing Unit NN : Neural Network QNN : Quantized Neural Network RL : Reinforcement Learning BOPs : Bit Operations MAC : Multiply And Accumulate 3 Technion Computer Science Department M.Sc. Thesis MSC-2019-12 2019 4 Technion Computer Science Department M.Sc. Thesis MSC-2019-12 2019 Chapter
Scalable exploration of population-scale drug consumption data

The potential for knowledge discovery is currently underutilized on pharmacoepidemiologic data sets. A big dataset enables finding and assessing rare drug consumption patterns that are associated with adverse drug reactions causing hospitalization, or death. To enable such exploration of big pharmacoepidemiology data, four key issues need so be addressed. First, to ingest, transform, preprocess and analyze population scale data, we require large computation power and storage capabilities, and therefore a distributed computing framework. Second, to expose patterns between drug consumption and end-points such as hospitalization, we need to develop feature extraction and preprocessing algorithms which represents the drug consumption and hospitalization in a numerical format. Third, to detect these patterns, we require models from libraries for statistics and machine learning. To interpret performance metrics, we also require visualization libraries. Fourth, to enable rapid development of data exploration methods, we require an interactive system thatmakes the frameworks, libraries andmethods for explorative analyses available in a single, cohesive environment. We make three contributions. First, we present the design and implementation of a system with a live coding environment, which enables use of Apache Spark, our choice of big data framework. It provides Scikit-learn and Tensorflow with Keras for machine learning, and matplotlib and Plotly for visualization. All libraries and frameworks are made available by the interactive environment, which enables rapid development, and Spark enables workloads to scale. Second, to enable machine learning methods, we provide algorithms for feature extraction of drug consumption. We observe drug consumption in hospitalized and unhospitalized patient groups, and label them according to their group. This results in a data set that we use in supervised learning. Third, we assess the performance in prediction of hospitalization on the data set. We also estimate over-represented drugs in hospitalized patients.
Domain-Level Observation and Control for Compiled Executable DSLs

Executable Domain-Specific Languages (DSLs) are commonly defined with either operational semantics (i.e., interpretation) or translational semantics (i.e., compilation). An interpreted DSL relies on domain concepts to specify the possible execution states and steps, which enables the observation and control of executions using the very same domain concepts. In contrast, a compiled DSL relies on a transformation to an arbitrarily different target language. This creates a conceptual gap, where the execution can only be observed and controlled through target domain concepts, to the detriment of experts or tools that only understand the source domain. To address this problem, we propose a language engineering architecture for compiled DSLs that enables the observation and control of executions using source domain concepts. The architecture requires the definition of the source domain execution steps and states, along with a feedback manager that translates steps and states of the target domain back to the source domain. We evaluate the architecture with two different compiled DSLs, and show that it does enable domain-level observation and control while increasing execution time by 2× in the worst observed case.
Spatial Path-Following Control of Underactuated AUV With Multiple Uncertainties and Input Saturation

This paper studies the problem of spatial path-following control of underactuated autonomous underwater vehicles (AUVs) with multiple uncertainties and input saturation taken into account. Initially, the reduced-order extended state observes (ESOs) are introduced to estimate and compensate all lumped uncertainties due to the model parameters perturbations, unmodeled dynamics, environmental disturbances, and nonlinear hydrodynamic damping terms. Furthermore, the spatial path-following control strategy is established by combining with backstepping, integral sliding mode control, and estimator to cope with the position, attitude, and linear velocity tracking of the AUV. Specifically, the dynamic surface control (DSC) technique is utilized to achieve satisfactory differential performance and avoid the “explosion of complexity. ” Additionally, the auxiliary dynamic compensator is presented to analyze the effect of input saturation, and the states of the auxiliary design system are used to develop the controller. It is easily proved that the proposed control scheme can guarantee that all signals of the closed-loop system are globally stable through the Lyapunov theorem, with the tracking errors converging to an arbitrarily small neighborhood of zero. Finally, the numerical simulation results are carried out to illustrate the effectiveness of the proposed controller.
A Model to Investigate the Effect of Work Ethic Culture on Dynamics of Rework in Management of Projects

The present study aims to investigate the effect of work ethic culture on rework in construction projects through a review of literature, surveys, and interviews with project managers. The main research question is what mechanisms can project manager use to balance the costs of personnel training, financial and language incentives, and implementation costs to finish the project with more profit and less rework. In this regard, modelling and data analysis is done using System Dynamics methodology. The results show that by considering work ethic, rework in the project is reduced from 46 % to 39 %. However, the project has been completed with 10 % lower cost and 26 % lower variance. Integrating the issue of rework with the culture of work ethic in the field of project management by at least one exogenous parameter has been studied, while in this study qualitative parameters have been converted to quantitative parameters using fuzzy inference system. The change in management approach to the issue of work ethics and the formulation of human resource strategies of large projects by human resource managers is one of the applications of this study.
A design method of CPR for wide voltage design

In this paper, we proposed an improved design method of critical path replica (CPR) for wide voltage design. Timing accuracy of CPR in wide operating voltage is improved by applying load matching and transistor-level static timing analysis (TSTA). We applied proposed method to 100 critical paths of iscas ’ 95 benchmark circuits, the results of simulation experiments in SMIC 55nm shows that the CPR designed by proposed method can operating between 0.3V – 1.2V with only 0.25% delay error (DE).