Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical prediction of absorbing materials via Computational AeroAcoustics

In the context of reduction of the propulsive noise of airplanes, manufacturers are led to generalize the employment of “sound absorbing materials” (or “liners”), and to optimize their use. The aim of the present study is to improve the numerical prediction of noise attenuation by accurately modelling such absorbing materials either for time domain approaches (such as Computational AeroAcoustics) or for frequency domain methods (such as Boundary Element Method). Such a modelling raises several key questions, which are related to various aspects such as the type of flow involved (heterogeneities, turbulent boundary layers, etc.), the sound levels considered (non-linear phenomena), the diffraction effects induced by ruptures of impedance, etc. Besides specific theoretical developments dedicated to the accurate modelling of acoustic liners, the present work relies on specific calculations, to be compared against other results (analytical, numerical, and/or experimental). Therefore, a numerical campaign has been recently initiated, which aim is to numerically duplicate several canonical tests of noise absorption by acoustic liners. These cases will be used as means of validation to improve the consideration of an accurate acoustic impedance boundary condition for Onera's time and frequency domains solvers.
PAGANDA : An Adaptive Task-Independent Automatic Data Augmentation

Data augmentation is a useful technique to enlarge the size of the training set and prevent overfitting for different machine learning tasks when training data is scarce. However, current data augmentation techniques rely heavily on human design and domain knowledge, and existing automated approaches are yet to fully exploit the latent features in the training dataset. In this paper we propose Parallel Adaptive GAN Data Augmentation(PAGANDA), where the training set adaptively enriches itself with sample images automatically constructed from Generative Adversarial Networks (GANs) trained in parallel. We demonstrate by experiments that our data augmentation strategy, with little modelspecific considerations, can be easily adapted to cross-domain deep learning/machine learning tasks such as image classification and image inpainting, while significantly improving model performance in both tasks. Our source code and experimental details are available at https: //github.com/miaojiang1987/ k-folder-data-augmentation-gan/.
Prestazioni A Robust and Versatile MultiView Learning Framework for the Detection of Deviant Business Process Instances

Increasing attention has been paid of late to the problem of detecting and explaining “deviant” process instances —i.e. instances diverging from normal/desired outcomes (e.g., frauds, faults, SLA violations)— based on log data. Current solutions discriminate between deviant and normal instances by combining the extraction of (sequence-based) behavioral patterns with standard classifier-induction methods. However, there is no general consensus on which kinds of patterns are the most suitable for such a task, while mixing multiple pattern families together will produce a cumbersome, redundant and sparse representation of log data that may well confuse the learner and lead to unreliable deviance-prediction models. We here propose an ensemble-learning approach to this process mining tasks, where multiple base learners are trained on different feature-based views of the given log (obtained each by using a distinguished family of patterns). A stacking procedure is then employed to combine the discovered base models into an overall deviance-oriented classifier, which exploits the predictions of the former models, hence implicitly reasoning on different structural features. The induction of such a meta-classifier is performed by using a (scalable and robust) probabilistic method. This way each prediction is equipped with some reliable measure of confidence, which can allow for prioritizing suspicious case, and make the analysis of estimated deviations efficient and feasible even in complex BPM scenarios. Beside possibly taking advantage of all non-structural data that are typically associated with the traces, the approach employs a resampling mechanism to deal with situations where the training set contains far less deviant traces than normal ones. The approach has been implemented as part of a comprehensive framework for detecting and analysing business process deviances, which is also meant to support the analyst in investigating new suspect deviances, and to provide some feedback on the real nature of new traces, which can help improve the discovered deviance-prediction models. A series of experiments on a real-life log proved the validity of the approach, which showed its capability of achieving compelling performances w.r. t. state-of-the-art methods.
Market Properties in an Extended Glosten-Milgrom Model

This paper develops a model of a learning market-maker by extending the GlostenMilgrom model of dealer markets. The market-maker tracks the changing true value of a stock in settings with informed traders (with noisy signals) and liquidity traders, and sets bid and ask prices based on its estimate of the true value. We empirically evaluate the performance of the market-maker in markets with different parameter values to demonstrate the effectiveness of the algorithm, and then use the algorithm to derive properties of price processes in simulated markets. The complexity of our model allows us to capture interesting interactions between different market parameters and price properties. For example, we examine the impact of two different kinds of volatility on the average spread and show that the autocorrelation patterns of raw and absolute returns are similar to real stock market data. Submitted to: Quantitative Finance
BUS ONLY PRIVATE VEHICLES Optional Bus Advance BUS PRIVATE VEHICLES Lane Change BUS BUS

The number of private vehicles increased dramatically during the last two decades in the island of Cyprus. This phenomenon had a negative effect on the bus transport mode which over the years its use has been considerably reduced. As traffic congestion increased considerably during the same period the need for a high quality public transportation system became clear. This need is also supported by the demand for public transportation by the aging and disadvantaged population. The best way to attract people back to public transportation is to have a reliable bus system with a high quality of service which can compensate for the lack of freedom and flexibility a private vehicle can provide. However such a system should not have a negative effect on the traffic network in terms of congestion and travel times but should coexist serving both the needs of people who do not drive and those who use the public transport mode. The purpose of this paper is to present the development and evaluation of a number of scenarios aiming at attracting commuters back to public transportation. This idea stems from the fact that surveys among the Cypriot commuters show that people will use buses if buses provide a fast and reliable service. For this purpose, microscopic simulation models are developed in order to examine several scenarios of dedicated bus lanes and bus priority schemes so that the buses can provide the desired level of service with the minimal impact on the rest of the traffic.
Image Registration using Discrete Wavelet Transform and Particle Swarm Optimization

The term image registration basically denotes the process of alignment of images . In recent years, the rapid growth in the field of military automatic target recognition remote cartography ,computer vision, image fusion, medical imaging, and astrophotography has established the need for the development of good image registration technique. Image registration is a process in which final information is gained from different data sources. It is a process of aligning two images acquired by same/different sensors, at different times or from different viewpoint. This paper presents the image registration techniques based on extracting interest point area of scene images using Discrete wavelet Transform and PSO. The root mean square error is used as similarity measure for finding out the area of interest . KeywordsDiscrete wavelet Transform (DWT), PSO, Interest Point Area Extraction, Image Registration.
TD-GAN for Chatbot Text Generation

A dialog system is a computer system aims to converse with a human. There are two major structures: retrieval-based model and generative model. Retrieval-based model selects answers from fixed database. In other word, it is grammatical errors and improper wordings free while it is not able to generate contents itself or generate unseen conversational style. Our goal is to train a generative model which generates proper responses with various sentence lengths based on all previous contexts. During pretrain, we constructed a encoder-decoder generator pretrained with seq2seq learning process(1), along with WGAN(2) pretained critic; in adversarial training, we used policy gradient method (3) to collect gradient information from scores for updating generator parameters. During testing, we found our model shows the ability to use diverse words from the vocabulary set. However, due to the limited layer size, limited number of hidden units, and short training time (around 2 days on one gtx1080 gpu), our model is not able to made reasonable response to all input sentences.
Aspect-Based Opinion Mining from Online Reviews

A great number of websites are lending forums feature for consumers for publicizing their personal assessments about products purchased and thus helping spoken communication among various consumers. A lot of crucial issues exist in the evaluation of safety of different drugs. One among such major issues happens to be Automatic Integration pertaining to drug indications. It is not possible to detect adverse impacts of drugs immediately after being administered to patients in prescribed dosage. They become known only after certain given time after usage. From recent findings, it has been shown that blogs, online reviews and dialogue forums about some drugs and certain chronic diseases have been becoming increasingly important resources of support for the patients. Extracting data from such substantial content of texts happens to be challenging and helpful. There are quite a few reviews from patients about medications on internet. Such reviews give some brief outline of methods for aspect mining since they are related to discovery of drugs. Several detrimental drug reactions connected with chronic diseases may not be uncovered during the constrained prior-to-marketing clinical trials; they may possibly be noticed only after the long-term after-marketing investigation about usage of drugs. W e have developed one creative probabilistic aspect mining model (PAMM) toward diagnosing the topics/aspects associated with type labels or definitive meta-data about a corpus. Contrary to several other unmonitored methods or monitored methods, PAMM tends to have one unique feature, namely, it keeps its focus on identifying aspects associated with only one type rather than identifying aspects regarding all the types in every execution simultaneously. This happens to reduce chances of having the aspects produced from mixing the concepts pertaining to various classes. Because of this, aspects that have been identified will be easy-to-interpret for people. Aspects that have been found will also have quality which they may be classdistinguishing. They may be utilized in distinguishing one class from the other ones. One efficient EMalgorithm has been created toward parameter appraisal. The study presents idea for proposing an effective EM algorithm for introducing opinion aspects toward different sets of ages. EM algorithm has been employed to find approximate parameters related to some underlying distribution out of the data set in case it has some missing values.
ToNy: Contextual embeddings for accurate multilingual discourse segmentation of full documents

Segmentation is the first step in building practical discourse parsers, and is often neglected in discourse parsing studies. The goal is to identify the minimal spans of text to be linked by discourse relations, or to isolate explicit marking of discourse relations. Existing systems on English report F1 scores as high as 95%, but they generally assume gold sentence boundaries and are restricted to English newswire texts annotated within the RST framework. This article presents a generic approach and a system, ToNy, a discourse segmenter developed for the DisRPT shared task where multiple discourse representation schemes, languages and domains are represented. In our experiments, we found that a straightforward sequence prediction architecture with pretrained contextual embeddings is sufficient to reach performance levels comparable to existing systems, when separately trained on each corpus. We report performance between 81% and 96% in F1 score. We also observed that discourse segmentation models only display a moderate generalization capability, even within the same language and discourse representation scheme.
A PATIENT ’ S INDOOR POSITIONING ALGORITHM USING ARTIFICIAL NEURAL NETWORK AND SVM 1

This paper proposes a patient’s Indoor Positioning Algorithm using Artificial Neural Network and Support Vector Machine (SVM). The proposed algorithm is ANN-SVM which combines Artificial Neural Network and Support Vector Machine to estimate the user position for IPS. The input data for the algorithm consists of Received Signal Strength Indicator and the location vector which is extracted by Access Point. The output is input weight and output weight. The input and output weight are processed by SVM with Room ID data. The last output is the estimated x and the room ID. According to the result of average class loss rate, SVM and ANN-SVM are 0.45 and 0.4, ANN-SVM has lower class loss rate by 0.05 than SVM. The accuracy rate of SVM and ANN-SVM are 65% and 70%. The ANN-SVM has more accuracy rate by 5% than SVM.
Measurement of sound diffusion coefficients of scattering furnishing volumes present in workplaces

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Measurement of sound diffusion coefficients of scattering furnishing volumes present in workplaces Adil Faiz, Joël Ducourneau, Adel Khanfir, Jacques Chatillon
An automated system for program parameters fine tuning in the cloud

An automated system for program parameters fine tuning in the cloud S. A. Smirnov, A. S. Tarasov Institute for Information Transmission Problems of the Russian Academy of Science, Kharkevich Institute, Bolshoy Karetny per. 19, build.1, Moscow 127051 Russia E-mail: a sasmir@gmail.com Received December 25, 2014 The paper presents a software system aimed at finding best (in some sense) parameters of an algorithm. The system handles both discrete and continuous parameters and employs massive parallelism offered by public clouds. The paper presents an overview of the system, a method to measure algorithm's performance in the cloud and numerical results of system's use on several problem sets.
ECS 455 : Mobile Communications Call Blocking Probability

In this note, we look at derivations of the Erlang B formula and the Engsett formula, both of which estimate the call blocking probability when trunking is used. To do this, we need to borrow some concepts from queueing theory. Moreover, some basic analysis of stochastic processes including Poisson processes and Markov chains is needed. For completeness, working knowledge on these processes is summarized here as well. However, we do assume that the readers are familiar with concepts from basic probability course.
Bayesian Optimal Policies for Asynchronous Bandits with Known Trends

We formalize the asynchronous multi-armed bandits with known trend problem (amabkt) and propose a few empirical solutions, the most efficient one being based on finite-horizon Gittins indices. We designed a GPU-optimized code which allowed us to solve exhaustively the – intractable – Bayesian Optimal policy for up to four arms with reasonable horizons. Our experiments underline the quality of the trend-aware finite-horizon Gittins index as an approximant for the Bayesian optimal policy on stochastic amabkts.
Molecular characterization of selected cultivars of rice, Oryza sativa L. using random amplified polymorphic DNA (RAPD) markers.

Random Amplified Polymorphic DNA (RAPD) analysis was performed to assess the genetic diversity in ten selected cultivars of rice, Oryza sativa L. using 30 decamer random primers. Out of 30, 25 RAPD primers revealed polymorphism while the remaining 5 primers showed no reaction. The primers produced a total of 428 bands of which 363 were polymorphic (85.02%). The number of polymorphic fragments for each primer varied from 7 to 23 with an average of 14 polymorphic fragments. The primer OPB-17 produced the maximum number of polymorphic bands. The RAPD data was analyzed to determine the genetic similarity coefficients which ranged from 0.46 to 0.81. Cluster analysis was performed using Unweighted Paired Group of Arithmetic Means (UPGMA) using the Jaccard’s similarity coefficient. The UPGMA dendrogram resolved the selected rice cultivars into two major clusters.
A Tool for Retrieving Meaningful Privacy Information from Social Networks

The use of social networking services (SNSs) such as Facebook, Flickr, or MySpace has grown explosively in the last few years. People see these SNSs as useful tools to find friends and interact with them. SNSs allow their users to share photos, videos, and express their thoughts and feelings. Even though users enjoy the capabilities that these SNSs offer, they have became aware of privacy issues. The public image of a subject can be affected by photos or comments posted on a social network. Therefore it is important for SNS users to control what others can see in their profile. Recent studies demonstrate that users are demanding better mechanisms to protect their privacy. An appropriate approximation to solve this problem is a tool that automatically suggests a privacy policy for any item shared on a SNS. The first step for any mechanism to recommend and predict privacy policies is to retrieve meaningful privacy information from the SNS, such user communities and the relationships of them. Most SNSs rely on groups to help users specify their privacy policies. Therefore, a basic functionality of such a mechanism is to group the user’s friends automatically. Although SNSs treat all of the friends of a user the same, without taking into account different degrees of the friendship, this is not a realistic approach. Hence, another factor to consider when defining a privacy policy is the type of relationship between the owner of the item being shared and its potential viewers. In this work, we present a tool called Best Friend Forever (BFF) that automatically classifies the friends of a user in communities and assigns a value to the strength of the relationship ties to each one. We also explain the characteristics of BFF and show the results of an experimental evaluation.
Novel Video Steganography Algorithm Based on Secret Sharing and Error-Correcting Code for H.264/AVC

In this paper, we analyze the video steganography technique, which is used to ensure national security and the confidentiality of the information of governmental agencies and enterprises. Videos may be used to transmit secrets and conduct covert communication. As such, we present an algorithm based on a secret sharing scheme and an Error-Correcting Code (ECC), which combines Grey Relational Analysis (GRA) with a partition mode in video compression standard H.264/AVC. First, we process secret information by secret sharing, and then use an ECC to process the obtained information. Moreover, we choose the Discrete Cosine Transform (DCT) blocks using GRA, and then use rules to hide the pretreated information in DCT coefficients of the video frames. Experimental results indicate that our algorithm has good invisibility, better robustness, good anti-steganalysis ability, and little influence on the bit rate of the video carrier. In addition, the bit error rate is low after attacks such as noise, filtering, or frame loss in the simulation environment.
A Meta Understanding of Meta-Learning

Recent years have witnessed an abundance of new publications and approaches on meta-learning. This community-wide enthusiasm has sparked great insights but has also created a plethora of seemingly different frameworks, which can be hard to compare and evaluate. In this paper, we aim to provide a single principled, unifying framework that draws a close connection between meta-learning and traditional supervised learning. By treating pairs of task-specific data sets and trained models as (feature, label) samples, we can reduce many meta-learning algorithms to instances of supervised learning. This view not only unifies meta-learning into an intuitive and practical framework but also allows us to transfer insights from supervised learning directly to improve meta-learning. For example, we obtain a better understanding of generalization properties, and we can readily transfer well-understood techniques, such as model ensemble, pre-training, joint training, data augmentation, and even nearest neighbor based methods. We provide an intuitive analogy of these methods in the context of metalearning and show that they give rise to significant improvements in model performance.