Miracle-dz/UniFAR-FTUs huggingface dataset:
Modularising the Complex Meta-Models in Enterprise Systems Using Conceptual Structures

The development of meta-models in Enterprise Modelling, Enterprise Engineering, and Enterprise Architecture enables an enterprise to add value and meet its obligations to its stakeholders. This value is however undermined by the complexity in the meta-models which have become difficult to visualise thus deterring the human-driven process. These experiences have driven the development of layers and levels in the modular meta-model. Conceptual Structures (CS), described as “Information Processing in Mind and Machine”, align the way computers work with how humans think. Using the Enterprise Information Meta-model Architecture (EIMA) as an exemplar, two forms of CS known as Conceptual Graphs (CGs) and Formal Concept Analysis (FCA) are brought together through the CGtoFCA algorithm, thereby mathematically evaluating the effectiveness of the layers and levels in these meta-models. The work reveals the useful contribution that this approach brings in actualising the modularising of complex meta-models in enterprise systems using conceptual structures.
Deep Distributional Reinforcement Learning Based High-Level Driving Policy Determination

Even though some of the driver assistant systems have been commercialized to provide safety and convenience to the driver, they can be applied for autonomous driving in limited situations such as highways. In this paper, we propose a supervisor agent that can enhance the driver assistant systems by using deep distributional reinforcement learning. The supervisor agent is trained using end-to-end approach that directly maps both a camera image and LIDAR data into action plan. Because the well-trained network of deep reinforcement learning can lead to unexpected actions, collision avoidance function is added to prevent dangerous situations. In addition, the highway driving case is a stochastic environment with inherent randomness and, thus, its training is performed through the distributional reinforcement learning algorithm, which is specialized for stochastic environment. The optimal action for autonomous driving is selected through the return value distribution. Finally, the proposed algorithm is verified through a highway driving simulator, which is implemented by the Unity ML-agents.
Exploiting Recognition of Natural Visual Stimuli for Brain Computer Interfacing

Keywords: Brain computer interface ; Electroencephalography ; Rapid serial visual presentation ; Event related potentials ; P300 ; Image retrieval These Ecole polytechnique federale de Lausanne EPFL, n° 6003 (2013)Programme doctoral Genie electriqueFaculte des sciences et techniques de l'ingenieurInstitut interfacultaire de Bioingenierie (STI)Chaire Fondation Defitech en interface non-invasive de cerveau-machineJury: Prof. P. Frossard, president du jury. Prof. J. D. R. Millan Ruiz, Dr R. Chavarriaga Lozano, directeurs de these. Prof. F. Babiloni, rapporteur. Prof. O. Blanke, rapporteur. Prof. B. Blankertz, rapporteur Public defense: 2013-12-11 Reference doi:10.5075/epfl-thesis-6003Print copy in library catalog Record created on 2013-12-05, modified on 2016-08-09
The Many Variations of Emotion

This paper presents a novel approach for changing facial expressions in images. Its strength lies in its ability to map face images into a vector space in which users can easily control and generate novel facial expressions based on emotions. It relies on two main components. The first one learns how to map face images to a 3-dimensional vector space issued from a neural network trained for emotion classification. The second one is an image to image translator allowing to translate faces to faces with expressing different emotions, the emotions being represented as 3D points in the aforementioned vector space. The paper also shows that the proposed face embedding has several interesting properties: i) while being a continuous space it allows to represent discrete emotions efficiently and hence enables to use those discrete emotions as targeted facial expressions ii) this space is easy to sample and enables a fine-grained control on the generated emotions iii) the 3 orthogonal axes of this space may be mapped to arousal, valence and dominance – 3 directions used by psychologists to describe emotions – which again is highly interesting to control the generation of facial expressions.
Real time NMR data processing

Baseline removal by subtraction, in which each second-excitation RF pulse changes its polarity, can be successfully used in a variety of data processing applications in real time by appropriately coding the odd and even excitations. The authors demonstrate this technique in: (1) fast fat/water imaging utilizing phase modulation of the spectral components and (2) the detection of lactate in vivo by proton saturation transfer. They achieved more than 80% fat or water suppression in imaging of the knee and obtained lactate signal with an SNR (signal-to-noise ratio) of 10:1 after 6 min of data acquisition. A large number of other MR (magnetic resonance) applications can also use this simultaneous data acquisition/processing technique. <<ETX>>
Adaptive gray-level digital watermark

An adaptive watermarking technique based on gray-level digital watermarks is proposed. By still image decomposition technique, a gray-level digital watermark is decomposed into a series of binary digital images. The decomposed products can be used as usual two-dimensional binary digital watermarks. Experimental results show that the proposed technique is robust to attacks such as Joint Photographic Expects Group (JPEG) lossy compression.
Optimization of K-means Clustering by Feature Weight Learning

The performance of K-means clustering algorithm depends on the selection of distance metrics. The Euclidean distance is usually chosen as the similarity measure in the conventional K-means clustering algorithm, which usually relates to all attributes. When feature weight parameters are introduced to the distance formula, the performance will depend on the weight values and accordingly can be improved by adjusting weight values. Since K-means algorithm is iterative, it is difficult to optimize clustering results by giving weight values directly. An indirect learning feature weight algorithm is introduced to improve the clustering result. Mathematically it corresponds to a linear transformation for a set of points in the Euclidean space. The numerical experiments prove the validity of this algorithm.
Flexible Resource Allocation in Device-to-Device Communications Using Stackelberg Game Theory

In underlaid device-to-device (D2D) cellular networks, severe radio interference can be typical for cellular and D2D users, which causes each D2D user’s quality-of-experience (QoE) to degrade significantly. Thus, in this paper, a dynamic Stackelberg game is formulated with a single-leader (base station) and multiple-followers (D2D pairs). The leader reduces interference within the network by charging a price to followers, whereas followers react to this price and compete to find optimal transmit power and resource block allocation. To enhance D2D user QoE, D2D users are categorized into one of three application classes with each class mapped to a different utility function. To address this, we propose a crucial innovation where the several different utility functions, available to followers, are solved using a non-scalarized approach, as a scalarized approach to this multi-criteria optimization problem is generally infeasible in real-time D2D cellular communications. To solve the game, a distributed algorithm is proposed, and it is shown to converge to a sub-game perfect Stackelberg equilibrium across all users. Simulation results show that the proposed approach reduces transmit power effectively and increases throughput, giving a Pareto-efficient outcome, while guaranteeing best social welfare and satisfaction across all D2D users.
Biodiversité fongique du raisin au vin : impact de l'activité anthropique

Les effets de differentes activites anthropiques (vignoble, cuverie) sur les populations fongiques du raisin au vin ont ete etudies. Afin de caracteriser ces effets, il etait necessaire d’avoir acces a la diversite globale des populations (pyrosequencage et IR-TF) mais egalement a la diversite intraspecifique (IR-TF). La spectroscopie a ainsi ete validee pour sa capacite a caracteriser la population globale et a discriminer les souches pour trois especes de levures non-Saccharomyces (NS). Pour la premiere fois, il est demontre que la baie de raisin constitue une source limitee pour les levures NS alors que la cuverie semble constituer une source importante; l’air etant un vecteur important de dissemination de ces levures. De plus, la persistance et la reimplantation des levures NS dans le mout l’annee suivante ont ete demontrees. Les activites anthropiques etudiees modifient la diversite fongique. Une biodiversite plus faible pour les raisins de la modalite biologique a ete mesuree pour les 3 millesimes consideres. Les populations fongiques sont ensuite fortement remaniees par l’etape de pressurage/clarification et l’influence de la flore de cuverie est confirmee. L’ajout de SO2 modifie les dynamiques des populations et favorise la domination de l’espece S. cerevisiae. L’analyse chimique non-ciblee des vins montre pour la premiere fois que ces derniers peuvent etre discrimines a la fin de la fermentation alcoolique en fonction de la protection phytosanitaire, que ces fermentations aient eu lieu en presence ou non de SO2. Ainsi, l'existence dans les vins de signatures de diversite chimique et microbiologique liees au mode de protection au vignoble est mise en evidence.
Electroencephalography: Basic Principles, Clinical Applications and Related Fields

As the work of the principal authors and 23 other leading authorities, this book is a landmark in the field. Of the 44 chapters, the senior author, Dr Niedermeyer, who is electroencephalographer-in-chief at The Johns Hopkins Hospital, Baltimore, wrote a third himself. Together with such standard topics as the physiological and pathophysiological basis of the EEG, also considered are changes with maturation and aging, normal and abnormal EEGs in a great variety of conditions (epileptic, vascular, metabolic, and degenerative), activation techniques, and depth recordings, the newer areas of sleep disorders, and clinical applications of potentials evoked by sensory stimulation. Laboratory organization, electronics, and recording techniques are also considered. Some chapters are in themselves virtual monographs (eg, Niedermeyer's chapter on epileptic seizure disorders, Lombroso's on neonatal EEG, and Lopes da Silva's on EEG data recording and computer processing). To a large measure, the many topics are well organized, well covered, well
Adaptive Prediction of Channels with Sparse Features in OFDM Systems

A time domain channel prediction method exploiting features of sparse channel is proposed for orthogonal frequency division multiplexing (OFDM) systems. The proposed predictor operates in the time domain on each channel tap and separates the negligible taps from significant channel taps before performing prediction. We also compare the proposed prediction method with the classical frequency domain method realized at each OFDM subcarrier and demonstrate that our method increases the prediction accuracy and reduces the computational complexity. Simulations on the physical channel model verify the performance of the proposed method.
On Supporting Cancer Grading Based on Histological Slides Using a Limited Number of Features

A computer assisted cancer diagnosis approach based on histopathological images is outlined in the current study. It mainly detects the lighter components that appear in each slide and computes simple morphological features for them, as well as basic statistic about these. This relatively short numerical data set is subsequently fed to a classifier for learning the correspondence between the features and their classes. The classification accuracy reaches outputs similar to other more complex methodologies and therefore encourages future investigation.
Robust control strategies on the optimization of a wind turbine pumping system

Wind Turbine water pumping system (WTWPS) has been an important area of research during these last years. The control of WTWPS has attracted researchers from different fields to improve the effectiveness of water pumping systems. In this paper, we focus on the study of a pumping system composed by a wind turbine, permanent magnet synchronous generator, an induction motor (IM), and a centrifugal pump. Our purpose is to keep the water pumping in optimum conditions and maximize the extracted wind power. In a first step, we analyze the operation of WTWPS at variable speed with pitch angle control to ensure the required power without mechanical failure. The principle source and the dynamic load were connected via a direct current bus. On the other hand, to achieve the feeding of the moto-pump with a good quality of electrical energy, an analytic equation is developed which adapts the set point rotation speed of induction machine regardless the state of wind velocity. A nonlinear DTC control strategy is adopted based on space vector pulse width modulation. Simulation results are performed in MATLAB environment and show the performance and capability of the proposed control scheme of WTWPS.
Sentiment Classification of Reviews Based on BiGRU Neural Network and Fine-grained Attention

Text sentiment analysis is part and parcel of natural language processing. The task of sentiment classification is actually the process of feature extraction through models. The comment text of commodities is very different from the ordinary text. The comment text has no fixed grammar and writing format and the sentiment feature information is scattered in various places of text. Due to these factors, model learning of sentiment classification is becoming increasingly complex. The paper aims at establishing a fine-grained feature extraction model based on BiGRU and attention. Firstly, the vocabulary is vectorized by means of the skip-gram model. Then, according to the pre-trained word vector, the sentiment words list can be reached and noise filtering would be conducted by Naive Bayes algorithm. Finally, the model extracts features using BiGRU and fine-grained attentions. Based on the hypothesis that a long review may lead to feature differentiation, a fine-grained attention model is proposed. In this model, the attention layer is design to focus on the feature in different level such as word level, sentence level and paragraph level. This paper validate the proposed model on two sentiment corpus JD reviews and IMDB. Empirical results show that the FGAtten-BiGRU model achieves state of the art results on sentiment analysis tasks.
PENGEMBANGAN ALAT EKSPERIMEN CEPAT RAMBAT BUNYI DALAM MEDIUM UDARA DENGAN MENGGUNAKAN METODE TIME OF FLIGHT (TOF) DAN BERBANTUAN SOFTWARE AUDACITY

Proses pembelajaran di dalam fisika memerlukan eksperimen untuk memperoleh pemahaman yang baik tentang konsep-konsep yang terkandung di dalamnya. Metode eksperimen dimaksudkan untuk mempelajari serta mengamati langsung fenomena-fenomena yang terjadi di dalam fisika. Tujuan dari penelitian ini adalah mendesain rancang bangun alat eksperimen dan menentukan menentukan nilai cepat rambat bunyi dalam medium udara dengan metode Time Of Flight (ToF) dengan bantuan software audacity . Alat dan bahan yang digunakan dalam eksperimen ini yaitu laptop, garpu tala, 2 buah microphone, mistar, adaptor, dan software audacity . Eksperimen ini dilakukan dengan memukul garpu tala sehingga suara ditangkap oleh microphone yang nantinya akan ditransfer ke laptop dan terbaca oleh software audacity. Selanjutnya data yang terekam dalam audacity dianalisis dengan metode regresi linear. Dari hasil penelitian dapat disimpulkan bahwa alat layak digunakan dalam praktikum dan memiliki kemampuan untuk membaca dan menangkap gelombang bunyi dengan baik. Nilai cepat rambat bunyi di udara yang diperoleh sebesar v = (335,27±5,80) m/s dengan presentase ralat relatif sebesar 1,3 %. Hasil percobaan kurang mendekati nilai secara teori (v teori = 340 m/s), ini dikarenakan oleh perbedaan suhu dan keadaan ruang yang bising yang menimbulkan adanya tumbukan frekuensi sehingga pembacaan suara garpu tala terganggu. The learning physics needs the experiments to take good understanding of the concept. The experimental method for studying and observing directly the phenomena in physics. This study aims to design Experimental tools and determine of velocitu in air medium with Time of Flight (ToF) method with audacity. The istruments and the materials are computer, tuning fork, two microcphone, ruler, adaptor, and audacity softwere. This experiments done by hitting a tuning fork so the sound captured by a microphone, then be transferred to the computer and read by audacity softwere. Then, the data recorded in audacity analyzed by linear regression method. From the results of this study concluded that the tools fit for use in practicum and have ability to read and capture the sound waves well. The velocity in air medium is v = (335,27±5,80) m/s with error relative 1,3 %. Results of the experiment less approaching the theoretical value (v teori = 340 m/s), because of differences in temperature and state of the noisy room which gave rise to the collision frequency of the tuning fork so that the reading voice interrupted.
Transfemoral Catheter Embolization of Intracranial Meningiomas

Preoperative embolization has been performed between 1 and 7 days prior to surgery to reduce blood loss during surgical excision of 12 intracranial meningiomas, which possessed predominant external carotid vascularization; 5 convexity, 3 falx, 2 sphenoidal ridge and 2 parasagittal meningiomas. Cerebral catheterizations were performed through the retrograde transfemoral route. The embolization in all cases was effected with pieces of gelfoam in two different sizes, measuring 1×1×1 mm and 2×2×5 mm. The smaller pieces of gelfoam were firstly delivered through a catheter, followed by the larger ones. On computed tomography (CT) scanning after embolization, reduction of contrast enhancement was demonstrated in 9 cases, which coincided with the cases of successful occlusion of the main feeding arteries. On pathological examination of the surgical specimens, fragments of the gelfoam were demonstrated in the arterial lumen of the dura mater attached to the tumor, but no fragments were found in the fine vessels deep within the tumors. There were local or extensive hemorrhagic infarctions with an infiltration of inflammatory cells and acute ischemic necrotic lesions of the tumors with an infiltration of foamy cells. Macroscopic hemorrhage around the tumor was also found in a case. There were marked ischemic lesions in 5 cases and moderate lesions in 3 cases. These pathological findings tended to be more remarkable in the cases, in which main feeders had been occluded completely and the reduction of contrast enhancement had been shown on CT scanning. The pathological evaluation of the tumors suggests the possibility of hemorrhage within and around the tumors after embolization. The results of embolization evaluated on the basis of operative findings were effective in 10 cases. From this study, it was concluded that embolization of the intracranial meningiomas can facilitate the surgery by reducing the great risk of hemorrhage during operation, and that surgical excision of the tumor should be performed as early as possible after embolization.
An Evaluation of Computer-Supported Backtracking in a Hierarchical Database

This study was designed to investigate whether computer-supported backtracking tools reduced navigation time over manual backtracking and to compare navigation times among a subset of four backtracking tools. Each tool was evaluated in the context of an experimental, hierarchical, direct-manipulation database. Trials consisted of an information retrieval task requiring subjects to answer multiple-choice questions about the contents of the database. The independent variables included the backtracking tool and the backtrack navigation Task Length. The dependent measures included navigation time, the frequency with which the computer tool was selected and used over manual backtracking (a Table of Contents), and questionnaire responses. Backtracking with any of the four computer-supported tools resulted in a significantly reduced navigation time over manual backtracking using the Table of Contents. When provided with a history list, subjects had significantly smaller navigation times when backtracking at the higher of two levels in the database hierarchy. There were no differences between computer tools in rated efficiency, ease of use, or objective or subjective preference measures.
Task-level time-optimal collision avoidance trajectory planning for grinding manipulators

A computational framework that can plan the task-level time-optimal collision avoidance trajectory (TOCAT) of grinding manipulators is constructed based on the improved simulated annealing algorithm. When the workpiece surface has a plurality of discrete non-connected areas that need to be polished by grinding manipulators, the planning of TOCAT for a given grinding task is crucial, because it has a direct impact on the processing efficiency and intelligence of the automatic grinding system. Although many planning algorithms can be used to plan collision avoidance trajectories between any two points, the planning of the task-level TOCAT with multiple collision avoidance sub-trajectories is more difficult because it involves the permutation of the collision avoidance sub-trajectories for connecting several grinding areas. This paper proposes a task-level TOCAT planning framework based on the improved simulated annealing algorithm. Its key point is to plan the time-optimal sub-trajectory between any two points based on the trajectory evaluation mechanism. Its innovation lies in that the simulated annealing algorithm generates new solutions based on the combined stochastic perturbation method. The experimental results show that this framework can effectively solve the task-level TOCAT planning problem in multiple grinding areas, and the duration of the task-level collision avoidance trajectory is not only less discrete but also approximately globally optimal.
Introduction to Time Series Analysis and Forecasting: With Applications of SAS and SPSS

From the Publisher: This introductory exploration of the principal approaches to time series and forecasting processes explains theories and applications for the social science student. Professor Yaffee devotes significant space to a discussion of how we conceptualize, model, and forecast. He also examines text, model, and forecast evaluation and comparison, and he emphasizes time series programming techniques. Readers can investigate examples from public opinion research, policy analysis, political science, economics, and sociology by using SAS ® and SPSS ® program syntax and data sets available at a free Web site. For those interested in understanding the past or predicting future possibilities and probabilities, this book reveals the power of time series analysis.
Test Resource Partitioning For System On A Chip

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System identification for self-adaptive control

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