Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Korean Poetry Generation Using LSTM

Automatically composing poem is considered a highly challenging task, and it has received increasing attention across various ﬁelds. The computer has to ensure readability, meaningfulness, and vocabulary adequacy as well as the semantics of the poet’s production, which are otherwise realized by their imagination and inspiration. In this study, a model for Korean poem generation based on long short-term memory (LSTM) is proposed with the aim of creating poems that imitate the writing style of four poets who represent Korea’s modern poetry. To do this, 1000 poems by the target poets were collected, and their styles were deﬁned using natural language processing (NLP). Following this, each sentence of the poem was preprocessed, and training was performed using LSTM. When a user selects the desired poet and enters a keyword, the model automatically generates a poem based on that poet’s style. The poems produced showed some errors in syntactic structure and semantic delivery, but they successfully reproduced the characteristic vocabulary and emotions of the poet.
The versatile X-ray beamline of the Munich Compact Light Source: design, instrumentation and applications

The multipurpose X-ray beamline of the Munich Compact Light Source, a compact synchrotron radiation facility based on an inverse Compton X-ray source, is presented including its design and instrumentation. Its wide range of capabilities is discussed based on application examples ranging from spectroscopic as well as phase-contrast X-ray imaging and tomography to X-ray absorption spectroscopy or micro-beam radiation therapy research.
College Attendance Management System with Mobile Phone Detector: A Proposed Framework

Today, all the parents have anxiety about the use of mobile phone, less attendance and lower academic performance of their children. Amali Ismaila, Onche. O, Bello Muhinat and Hassan, Ibrahim (2012), hypothesize that the use of mobile phones in constantly unfair during lectures. This is an indication that mobile phones have become negative influence that affects students’ commitment to their academic work. This would have been the cause of failure of some student in their courses. Unnati A. Patel, Dr. Swaminarayan Priya (2014), shows that, Student’s overall academic performance is affected by the student’s present in his institute therefore Whole world and administrators of Educational institutions’ in our country are concerned about regularity of student attendance Some teacher restrict the students to use of mobile phone during lecture, but illegal users of mobile phones may not be uncovered by teachers during lecture hours, better equipment for detecting unauthorized usage of mobile phones during lecture time is needed. Hence, there is a requirement of computer-based student attendance management system with Mobile Phone detection facility, which will assist the faculty for maintaining attendance of presence and also system will detect the illegal use of mobile phone. In this Paper, researcher review the various computerized system which is being developed by using different techniques for “Attendee System” and “Mobile Phone Detection System”. Based on this review a new approach for “College Attendance Management System with Mobile Detector” is proposed to be used for various colleges or academic institutes.
Improving Shape Design of Auditoriums with Real-Time Feed-back Based on Parametric Modelling, Simulation and Machine Learning

Auditoriums require both acoustic performance and aesthetic values, while the efficiency of current design process should be improved for architects to better realize these two goals. In this paper, a new design process of auditoriums integrating parametric modelling, acoustic simulation and Support Vector Machine is proposed, implemented and verified to provide architects with real-time architectural and acoustic feedback in the early design stage. This paper contains three parts: 1) a parametric model of auditoriums that can generate various auditorium designs automatically; 2) an interface connecting modelling and acoustic simulation software, which facilitate architects' acoustic evaluations on auditorium designs; 3) an acoustic aiding tool that provides architects with suggestions on design revision. The results indicate that the new design process and relevant design tools can provide reliable outcomes within 1 min in most cases. Moreover, it enables architects to revise an acoustically unacceptable shape into a desirable one independently. Thus architects' efforts in attempting infeasible design ideas could be largely saved, and desirable designs are more likely to be achieved.
SCENARIO TSUNAMI SOURCE MODELING AND PROBABILISTIC TSUNAMI HAZARD ASSESSMENT METHOD

The tsunami caused by the 2011 off the Pacific Coast of Tohoku Earthquake on March 11, 2011 (the 2011 Tohoku Tsunami) led to a severe accident at the Fukushima Daiichi Nuclear Power Plant (NPP) of Tokyo Electric Power Company. The accident revealed the underestimation of the tsunami design level of the power plant, its unpreparedness for tsunamis exceeding the design level, and the importance of awareness of tsunami risks to NPPs. The tsunami design levels of NPPs in Japan have been estimated based on a report by the Japan Society of Civil Engineers (JSCE). In the JSCE report, a basic tsunami source models which can reproduce tsunamis in the past are established and various tsunami source models based on the basic tsunami source models are provided by changing the positions, strikes, dips, and so on. This estimation method has been applied to address the uncertainty of possible tsunamis in the future. It, however, failed to anticipate a tsunami height comparable to that of the 2011 Tohoku Tsunami. Consequently, the conventional method based on the greatest magnitude of earthquakes in the past proved inadequate in establishing the magnitude of possible earthquakes for the tsunami assumption. After the 2011 Tohoku Tsunami, Sugino et al.[1] compiled findings on tsunami sources triggered by Mw9-class earthquakes in Japan and abroad (including the 2011 Tohoku Tsunami) to improve scenario tsunami source modeling in future forecasts. The authors then proposed a characterized source model for tsunamis triggered by inter-plate earthquakes by adopting a different concept from the conventional tsunami assumption method based on the greatest scale of tsunami in the past while pointing out the undeniable possibilities of even greater tsunamis in the future. To improve the probabilistic tsunami hazard assessment method used in an evaluation of the tsunami risks at NPPs, this study applies the characterized source model by Sugino et al.[1] as a new scenario tsunami model to present practical examples of tsunami for the inter-plate earthquakes along the Chishima Trench and Japan Trench. The results are compared with tsunamis assumed by the conventional model (hereinafter called the “old scenario tsunami model”) in order to examine the impact of the different approaches in handling uncertainties for tsunami assumption in terms of the results of the probabilistic tsunami hazard assessment. As a result, the difference of the tsunami hazard curve before the 2011 Tohoku Tsunami was significantly large in a range smaller than 10 (annual exceedance probability) in case of water level rising. Comparison of the annual exceedance probability in terms of the peak water level based on the simulation analysis of the 2011 Tohoku tsunami demonstrated that the result based on the new scenario tsunami model (about 10) is roughly 10 times greater than the result based on the old scenario tsunami model (less than 10). This report is a summary of the studies of Sugino et al.[1,2], which were published in the Japan Association for Earthquake Engineering (JAEE) journal in Japanese.
STANDING ON THE SHOULDERS OF GIANTS: AN ETHICAL LEADERSHIP AGENDA FOR EDUCATIONAL REFORM IN MAURITIUS

The increasing economic needs of Mauritius to position itself as an intelligent nation state in the front line of global progress and innovation, mounting pressures to improve the quality of schools, and the shortcomings of various educational reforms over the past two decades have prompted Mauritian education authorities to seek ways to raise educational standards. Reflecting the Mauritian government’s ‘quality’ agenda and its focus on the work of school leaders, this paper reports the findings of research exploring Mauritian principals’ views about how a leadership focus on systems thinking could create major changes for school improvement processes directly impacting teaching and learning. Whilst confirming the lack of a systemic focus in Mauritian schools, the paper brings new thinking to understanding the critical role of the school leader for systemic improvement of the total educational system in Mauritius with social justice as the platform for transformation. The paper further reviews an empirically developed and theoretically sound framework that builds on a set of values and ethics that underpin Mauritian principals’ leadership practices. The central claim is that this framework could be applied in realizing and sustaining the vision of transformed learners for quality education. Significantly, the paper discusses school leadership from an ethical perspective, often silenced in the literature, and is a serious attempt to relate morality and ethics in educational leadership.
Field and satellite observations on the seasonal variability of the surface chlorophyll-a in the Bay of La Paz, Gulf of California, Mexico

As the pigment present in all photosynthetic phytoplankton species, chlorophyll-a (CHLA) is the most common indicator of the phytoplankton biomass in any aquatic ecosystem, and thus the quantification of its seasonal variability is key to understanding the dynamics of the marine ecosystem. Based on in-situ observations systematically gathered during four seasons in different years, this paper reports the surface CHLA concentrations in the central part of the Bay of La Paz, Gulf of California, Mexico. The in-situ data are compared and supported by satellite observations at 1 km/pixel of resolution from the Moderate Resolution Imaging Spectroradiometer. The results show clear seasonal variability, with the highest values recorded during winter time, while the lowest values were recorded during summer. Spring and fall represented seasons of transition. Satellite observations were consistent with the variability observed in in-situ data. The seasonal variability observed could be related to different mechanisms including the local atmospheric forcing, which induces wind-driven mixing and changes in the heating/cooling of the water column. The results presented here contribute to our understanding of the phytoplankton population dynamics in the Bay of La Paz, a highly productive region that supports different species with great importance both ecologically and economically.
PREDICTING ACCURACY OF PLAYERS IN THE CRICKET USING MACHINE LEARNING

– The most important and decisive task in any sport is selection of players. Evaluation of individual performance and selection of the players in cricket is most critical job. The player’s performances depend on numerous factors such as the location where the match being play, past records, his current form, average rate, strike rate, run scored at a particular venue, number of inning played against the opposition teams etc. The member of selection board, the coach and the captain of the team is conscientious for player selection. They explore special statistic, records and characteristic of the players to select the finest playing11 for each match. Throughout selection process of the players the batsman and bowlers are rated on basis of the batting and bowling average correspondingly. However in the game like cricket it is always important the situation in which it is responsibility of the batsmen to scores at most runs and bowlers to claims wickets. In this project we attempt to predict the performance of the players. For the prediction model these 2 problem are taken into account as goal as classification problem where the number of ‘runs’ and number of ‘wickets’ are classified into dissimilar range using different classifier algorithm. We exploit ‘Decision tree’, ‘Naive Bayes’, ‘Random Forest’ and ‘Multiclass SVM’ classifiers which produce the useful model to Predict for these 2 problems. Out of these 4 classifier algorithm the random forest classifier produce more accurate result then compared to other classifier and SVM produce least useful result.
Synthesis and characterization of physically cross-linked GrapheneOxide(GO)/collagen hydrogels for tissue engineering

Collagen is the most abundant protein of the ECM and, for this reason, collagen hydrogels provide an in-vivo like three-dimensional environment suitable for creating tissue equivalents and studying cell-matrix interactions. Nevertheless, collagen hydrogels exhibit limited mechanical properties if compared to native collagen tissues. Chemical cross-linking is a common method to synthesize hydrogels but cross-linking agents are usually cytotoxic against cells. For this reason, physically cross-linked hydrogels are considered an attractive alternative for cell culture applications. Graphene oxide (GO) has been recently attracting attention due to its unique properties such as large surface area, flexibility, high mechanical strength, high dispersibility, hydrophilicity, biocompatibility and ability to promote cell proliferation. In this study, physically cross-linked GO-collagen nanocomposite hydrogels at increasing concentrations of GO up to 5% w/w% were fabricated by self-assembly of collagen at 37°C, without the use of chemical agents. The effect of GO on the secondary structure of collagen was investigated through circular dichroism (CD) spectroscopy. Spectra confirmed that the helical structure of collagen was preserved even in presence of GO. Overall, a concentration of GO equal to 0.5% turned out to be the most effective amount of oxide in terms of cross-linking density, mechanical properties and cell culture suitability of the resulting GO-collagen nanocomposite hydrogels. GO was successfully sterilized and found to be cytotoxic for concentrations above 2%.
A Novel Hybrid Image Denoising Technique based on Trilateral Filtering and Gaussian Condition Random Field Model

- In digital imaging systems it is found that the acquisition methods and hardware systems adds various types of noises and artifacts. So denoising becomes more important in comparison to other processed involved in image processing as well as in applications. Saving the data or information of an image and eliminating the random noise is the final aim of image denoising systems. Not only the noisy image originates the undesirable visual quality but even downgrades the visibility of dim contrast entities and objects. Gaussian noise is normal form of noise which gets added up in the digital images through the digital systems. Some known techniques improved the visual quality of image by degrading the noise level. But sometimes in an image the noise levels are not same at all regions. Also some techniques perform blurring as well as remove the details present in the image. So there is a need of filtering technique that can remove the Gaussian noise as well as keep the detail present in the image. In this research work, an interactive hybrid Gaussian image denoising technique which is based on trilateral filtering and Gaussian condition random field approach is proposed. The trilateral filter is edge preserving Gaussian filter and Gaussian condition random field use deep neural network to deal with different noise levels. This dissertation provides the improvements to the existing algorithms of denoising in digital images to obtain better results. The proposed algorithm will be compared with the existing state of art Gaussian image denoising methods. The parameters for objective evaluation are peak signal to noise ratio, mean square error of the digital images. For subjective evaluation graph of these parameters will be observed for both input and output images.
Making innovation flow: solving the trade-off between lean and innovation

Can innovation processes be “leanified”? Lean may be the recipe to improve R&D productivity but its tenets seem in contradiction with the nature of innovation, e.g. standardization versus creativity. 42 interviews with managers of a high-tech company help identify challenges and solutions to achieve both art and science in innovation.
Energy-Efficient Quality Adaptation for Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are the deep learning models which are considered the state-of-the-art for sequence modeling and machine translation tasks. One of the most used RNN architectures is the Encoder-Decoder network, which consists of two networks, one encoding the input sequence to a fixed length representation and a second one which then decodes it to produce a new sequence. These models, which lead to high accuracy results and are able to handle sequences of different length, come with increased computational complexity and memory requirements compared to other machine learning models. This complexity, in turn, translates into high energy consumption. This additional overhead makes the execution of such models impossible on embedded devices or edge nodes, which are not equipped with hardware powerful enough to sustain the heavy computations required. However, researchers have shown that executing deep learning inference on such devices, if made possible, would provide several benefits in terms of responsiveness, total energy consumption and security. In order to make the inference more energy efficient on edge nodes, this work proposes an algorithm that improves the energy efficiency of the EncoderDecoder RNNs and proves its effectiveness by measuring the real energy saving on an embedded device. In particular, this work implements a dynamic Beam Search algorithm, which varies the Beam Width (BW) according to the input processed and the translation complexity. The value for this parameter, which is directly tied to the complexity of the network, will be determined by using a new discrimination criterium which yields on the two datasets tested a reduction of the execution time above 20%. This time reduction also affects the energy consumption, reducing it significantly while keeping the same translation quality.
Maturation of Pseudo-Nucleus Compartment in P. aeruginosa, Infected with Giant phiKZ Phage

The giant phiKZ phage infection induces the appearance of a pseudo-nucleus inside the bacterial cytoplasm. Here, we used RT-PCR, fluorescent in situ hybridization (FISH), electron tomography, and analytical electron microscopy to study the morphology of this unique nucleus-like shell and to demonstrate the distribution of phiKZ and bacterial DNA in infected Pseudomonas aeruginosa cells. The maturation of the pseudo-nucleus was traced in short intervals for 40 min after infection and revealed the continuous spatial separation of the phage and host DNA. Immediately after ejection, phage DNA was located inside the newly-identified round compartments; at a later infection stage, it was replicated inside the pseudo-nucleus; in the mature pseudo-nucleus, a saturated internal network of filaments was observed. This network consisted of DNA bundles in complex with DNA-binding proteins. On the other hand, the bacterial nucleoid underwent significant rearrangements during phage infection, yet the host DNA did not completely degrade until at least 40 min after phage application. Energy dispersive x-ray spectroscopy (EDX) analysis revealed that, during the infection, the sulfur content in the bacterial cytoplasm increased, which suggests an increase of methionine-rich DNA-binding protein synthesis, whose role is to protect the bacterial DNA from stress caused by infection.
Neural Technologies in Electrical Drive Control

Introduction. Main properties of neural networks When surveying the genesis of artificial neural networks (ANN) as a scientific branch, one is bound to start with paper [1]. Then he will certainly mention Rosenblatt’s perceptron [2], which used to be a great hope of cyberneticists and is unfairly forgotten now. The revival of interest in ANN in the eighties is connected with such names as J.Hopfield, who suggested a network of original cyclic (full-connected) structure [3], and D.Rumelhart, who rediscovered a network training algorithm known as the Backpropagation Algorithm [4]. What is ANN? In the simplest case, it constitutes an assembly of threshold elements arranged in layers (Fig. 1), similar to a biological neuron net. Elements of different layers are linked in such a way that every neuron of the next layer receives signals from all the neurons of the previous layer. Synapse function (synapse is the place where the axon of a previouslayer neuron joins a dendrite of a next-layer neuron) is simulated by changing synaptic weights, which enhance or attenuate the transmitted signal. Input signals i x received by neu-
Nonlinear  H Control for Uncertain Flexible Joint Robots with Unscented Kalman Filter

Todays, use of combination of two or more methods was considered to control of systems. In this paper is presented how to design of a nonlinear H ∞ (NL-H ∞ ) controller for flexible joint robot (FJR) based on bounded UKF state estimator . The UKF has more advantages to standard EKF such as low bios and no need to derivations. In this research, based on spong primary model for FJRs, same as rigid robots links position are selected as differential equations variables. Then this model was reformed to   H NL differential equations. The results of simulations demonstrate that mixed of   H NL controller and UKF estimator lead to conventional properties such as stability and good tracking. Also, Simulation results show the efficiency and superiority of the proposed method in compare with EKF .
Unsupervised Weighed Graph-Based Terrain Filtering Using Terrestrial Laser Scanning Data

— In this paper we establish a simple method for ﬁltering terrestrial laser scanning data. The ﬁltering problem is modeled as graph connected components problem. The proposed method uses weighted k -nearest neighbor graph( knn ), it consists of four steps: construction of the knn graph, edge weigh assignment, edge thresholding and ﬁnding the graph connected components. In the ﬁrst step, a knn graph is build using the 3D point cloud. In the second step, we introduce a new scheme for labeling the edges of knn graph. Thresholding is then used to remove edges with certain weights from the knn graph, this leads to disconnecting terrain points from object points. In the last step, a graph connected component algorithm is used to ﬁnd the connected components and classify the points into either terrain points or object points. We validate our method using visual observation as well as a manually labeled datasets.
Salt-marsh initiation during the Holocene transgression: the example of the North Norfolk marshes, England

Evidence from a variety of sources is presented to show the dates of inception of twelve salt-marshes on the North Norfolk coast. Sources include air-photographs, maps, radiocarbon dates and archaeological evidence. Marsh inception is shown to range from 2000+ years ago to the present day. Three major periods of marsh inception are recognized which maybe tentatively related to minor fluctuations in the Holocene transgression.