Reviewerly/exHarmony huggingface dataset:
Comparison of a physical model and a machine learning approach for a more accurate assessment of fuel efficiency measures

The paper focuses on a more accurate quantification method of fuel efficiency measures which can be applied to commercial transport airplanes. These measures or modifications can for example affect the engine or aerodynamical efficiency. Full flight data of a commercial transport aircraft before and after a modification will be used to assess the impact of the modification on the fuel efficiency of the aircraft. By using the available full flight data from normal operation, information about the different flight conditions as well as environmental parameter are considered in the assessment. The results of two different algorithms will be discussed and compared with the standard approach, which is using a very limited data frame. One algorithm is using a pure statistical machine learning approach and the other is making use of a algorithm combining a physical model of the aircraft with a gradient-based method to determine the aircraft specific coefficients. Thus the main question is, if the additional information of a physical model can significantly increase the precision of the assessment. What is better - pure data science or a fitted physical model?
Improving Brain Tumor Segmentation with Dilated Pseudo-3D Convolution and Multi-direction Fusion

Convolutional neural networks have shown their dominance in many computer vision tasks and been broadly used for medical image analysis. Unlike traditional image-based tasks, medical data is often in 3D form. 2D Networks designed for images shows poor performance and efficiency on these tasks. Although 3D networks work better, their computation and memory cost are rather high. To solve this problem, we decompose 3D convolution to decouple volumetric information, in the same way human experts treat volume data. Inspired by the concept of three medically-defined planes, we further propose a Multi-direction Fusion (MF) module, using three branches of this factorized 3D convolution in parallel to simultaneously extract features from three different directions and assemble them together. Moreover, we suggest introducing dilated convolution to preserve resolution and enlarge receptive field for segmentation. The network with proposed modules (MFNet) achieves competitive performance with other state-of-the-art methods on BraTS 2018 brain tumor segmentation task and is much more light-weight. We believe this is an effective and efficient way for volume-based medical segmentation.
Metinlerde Duygu Analizi Emotion Analysis in Texts

Emotion Analysis in Texts aims to predict the feeling that the text gives in text-based data sets. The emotion given by the text can be positive or negative polarization shaped, or can be any emotion such as anger, joy, sadness. In this notice, both polarization analysis and four basic emotion (anger, sadness, joy and fear) analysis are examined. As a method K-Nearest Neighbor (KNN), Case Based Reasoning (CBR) and Naive Bayes methods are applied to Zolkepli's English Text Emotion (ETE) dataset. The ETE dataset is a new dataset and there is no published result on that dataset yet. For four basic emotion analysis, the highest success rate on ETE dataset is 96.4% which is obtained by KNN algorithm. For the polarization analysis the highest success rate on the ETE dataset is 99.5% and obtained by KNN algorithm after first applying CBR method to the training set of ETE dataset.
Using Style-Transfer to Understand Material Classification for Robotic Sorting of Recycled Beverage Containers

Robotic sorting machines are increasingly being investigated for use in recycling centers. We consider the problem of automatically classifying images of recycled beverage containers by material type, i.e. glass, plastic, metal or liquid-packaging-board, when the containers are not in their original condition, meaning their shape and size may be deformed, and coloring and packaging labels may be damaged or dirty. We describe a retrofitted computer vision system and deep convolutional neural network classifier designed for this purpose, that enabled a sorting machine's accuracy and speed to reach commercially viable benchmarks. We investigate what was more important for highly accurate container material recognition: shape, size, color, texture or all of these? To help answer this question, we made use of style-transfer methods from the field of deep learning. We found that removing either texture or shape cues significantly reduced the accuracy in container material classification, while removing color had a minor negative effect. Unlike recent work on generic objects in ImageNet, networks trained to classify by container material type learned better from object shape than texture. Our findings show that commercial sorting of recycled beverage containers by material type at high accuracy is feasible, even when the containers are in poor condition. Furthermore, we reinforce the recent finding that convolutional neural networks can learn predominantly either from texture cues or shape.
Tractable Unordered 3-CNF Games

The classic TQBF problem can be viewed as a game in which two players alternate turns assigning truth values to a CNF formula’s variables in a prescribed order, and the winner is determined by whether the CNF gets satisfied. The complexity of deciding which player has a winning strategy in this game is well-understood: it is $$\textsf {NL}$$ -complete for 2-CNFs and $$\textsf {PSPACE}$$ -complete for 3-CNFs. We continue the study of the unordered variant of this game, in which each turn consists of picking any remaining variable and assigning it a truth value. The complexity of deciding who can win on a given CNF is less well-understood; prior work by the authors showed it is in $$\textsf {L}$$ for 2-CNFs and $$\textsf {PSPACE}$$ -complete for 5-CNFs. We conjecture it may be efficiently solvable on 3-CNFs, and we make progress in this direction by proving the problem is in $$\textsf {P}$$ , indeed in $$\textsf {L}$$ , for 3-CNFs with a certain restriction, namely that each width-3 clause has at least one variable that appears in no other clause. Another (incomparable) restriction of this problem was previously shown to be tractable by Kutz.
Towards Accurate Panel Detection in Manga: A Combined Effort of CNN and Heuristics

Panels are the fundamental elements of manga pages, and hence their detection serves as the basis of high-level manga content understanding. Existing panel detection methods could be categorized into heuristic-based methods and CNN-based (Convolutional Neural Network-based) ones. Although the former can accurately localize panels, they cannot handle well elaborate panels and require considerable effort to hand-craft rules for every new hard case. In contrast, detection results of CNN-based methods could be rough and inaccurate. We utilize CNN object detectors to propose coarse guide panels, then use heuristics to propose panel candidates and finally optimize an energy function to select the most plausible candidates. CNN assures roughly localized detection of almost all kinds of panels, while the follow-up procedure refines the detection results and minimizes the margin between detected panels and ground-truth with the help of heuristics and energy minimization. Experimental results show the proposed method surpasses previous methods regarding panel detection F1-score and page accuracy.
Standard monomials and extremal point sets

We say that a set system F⊆2[n] shatters a set S⊆[n] if every possible subset of S appears as the intersection of S with some element of F, and we denote by Sh(F) the family of sets shattered by F. According to the Sauer–Shelah lemma we know that in general, every set system F shatters at least |F| sets, and we call a set system shattering-extremal if |Sh(F)|=|F|. In Mészáros (2010) and Rónyai and Mészáros (2011), among other things, an algebraic characterization of shattering-extremality was given, which offered the possibility to generalize the notion to general finite point sets. Here we extend the results obtained for set systems to this more general setting, and as an application, strengthen a result of Li, Zhang and Dong from Li et al. (2012).
Dimensionality Reduction via Locality Constrained Competitive Sparse Representation by L2-Norm Regularization

Dimensionality reduction is an important scheme for dealing with data of high dimensionality. Subspace learning is extensively studied in pattern recognition due to the fact that it is simple and computational efficient. In this paper, a new dimensionality reduction method is proposed based on sparse representation. First, the representation coefficients are obtained through competitive sparse representation with L2-Norm regularization. To make the coefficients more discriminant, the locality structure is also utilized. Then, the inter-class graph (or the penalty graph) and intra-class graph (or the intrinsic graph) are constructed according to the coefficients. Finally, the optimal projective matrix is calculated by minimizing the intar-class scatter while maximizing the inter-class scatter. The experimental results show that the proposed method exhibits good performances.
Emerging Trends in Korean‐Diaspora and Translated Korean Picture Books

The Reading TeacherVolume 73, Issue 4 p. 399-404 The Inside Track Emerging Trends in Korean-Diaspora and Translated Korean Picture Books Yoo Kyung Sung, Search for more papers by this author Yoo Kyung Sung, Search for more papers by this author First published: 27 December 2019 https://doi.org/10.1002/trtr.1872Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Citing Literature Volume73, Issue4January/February 2020Pages 399-404 RelatedInformation
Linear Algebra on Parallel Structures Using Wiedemann Algorithm to Solve Discrete Logarithm Problem

Discrete logarithm problem, popularly known as DLP has been the heart of many Public Key Infrastructures that are being used today. DLP belongs to the category of hard problems. Many ideas have been proposed to solve DLP. The best-known methods are Number Field Sieve (NFS) class algorithms. The Function Field Sieve (FFS) algorithm is one of the NFS class algorithms to solve DLP in an extension field, i.e. Galois field GF(2n). This FFS involves three phases such as Function Sieving, Filtering, and Linear Algebra. Linear Algebra phase involves solving huge sparse matrices/system of linear equations. The most popular methods to solve sparse matrices are Lanczos and Wiedemann method. Recently, Wiedemann method got attention to solve the linear system of equations such as Ax = 0. This paper studies Wiedemann algorithm to solve AX≡0 mod 2n−1 based on the factors of 2n−1; since the DLP is defined over GF(2n). The different cases considered in this paper are (1) 2n−1 is a prime of size less than n bits, (2) 2n−1 is a composite number with one factor of size more than n bits, and (3) 2n−1 is a composite number with more than one factors of n bits. The naive Wiedemann is considered in case 1, paralleled version of Joux and Pierrot [“Nearly sparse linear algebra and application to discrete logarithms computation,” preprint Contemporary Developments in Finite Fields and Applications, 2016.] is considered in case 2 and the client-server model along with the paralleled version of Wiedemann for case 3. Algorithms for all the three cases of solving AX≡0 mod 2n−1 are designed, analysed, experimented, and tested under the conditions based on density and size of matrices. The experiments are carried out on the matrices obtained from filtering step of FFS. The results are analysed and reported. From the results, it is shown that the communication cost between master and the slaves is to be considered and minimum density in the matrix is to be maintained in the previous step of linear algebra of FFS, i.e. filtering step to avoid trivial solutions. The configuration of the system used for experiments is 64 bit Intel (R) Xeon (R) CPU E5-2650 v3 @ 2.30 GHz with 40 cores and Intel(R) Core i7-6500U CPU @ 2.50 GHz.
Breeding Unicorns: Developing Trustworthy and Scalable Randomness Beacons

Randomness beacons are services that periodically emit a random number, allowing users to agree on the same random outcome without trusting anyone: ideally, the randomness beacon is secure (cannot be influenced) and transparent (can be monitored by users). Hence, such randomness beacons can serve as an important primitive for smart contracts in a variety of contexts. In this paper we aim to bridge the gap between theory and practice of public beacon design inspired by the unicorn protocol of Lenstra and Wesolowski using verifiable delay functions. We first present a structured security analysis, based on which we design, implement, and evaluate a trustworthy and efficient randomness beacon allowing users to join at any time. We then compare different implementation and deployment options on distributed ledgers, and report on a Ethereum smart contract-based lottery using our beacon.
PROFESSIONALLY ORIENTED TASKS IN THE SYSTEM OF FUTURE TEACHER’S MATHEMATICAL TRAINING (BY THE EXAMPLE OF INFORMATICS TEACHERS’ TRAINING)

Mathematical disciplines are an integral component in the preparation of future teachers. The article presents a set of professionally oriented tasks based on the material of mathematical disciplines aimed to form future teachers’ professional competencies. These tasks correspond to students’ area of training, can be performed individually or in a group and allow assessing the quality of educational process. Students’ involvement in solving professionally oriented tasks is a necessary condition for the realization of the competence-based education model, a means to stimulate students’ professional self-development.
Multi-Objective Configuration Sampling for Performance Ranking in Configurable Systems

The problem of performance ranking in configurable systems is to find the optimal (near-optimal) configurations with the best performance. This problem is challenging due to the large search space of potential configurations and the cost of manually examining configurations. Existing methods, such as the rank-based method, use a progressive strategy to sample configurations to reduce the cost of examining configurations. This sampling strategy is guided by frequent and random trials and may fail in balancing the number of samples and the ranking difference (i.e., the minimum of actual ranks in the predicted ranking). In this paper, we proposed a sampling method, namely MoConfig, which uses multi-objective optimization to minimize the number of samples and the ranking difference. Each solution in MoConfig is a sampling set of configurations and can be directly used as the input of existing methods of performance ranking. We conducted experiments on 20 datasets from real-world configurable systems. Experimental results demonstrate that MoConfig can sample fewer configurations and rank better than the existing rank-based method. We also compared the results by four algorithms of multi-objective optimization and found that NSGA-II performs well. Our proposed method can be used to improve the ranking difference and reduce the number of samples in building predictive models of performance ranking.
Multi-label Image Set Recognition in Visually-Aware Recommender Systems

In this paper we focus on the problem of multi-label image recognition for visually-aware recommender systems. We propose a two stage approach in which a deep convolutional neural network is firstly fine-tuned on a part of the training set. Secondly, an attention-based aggregation network is trained to compute the weighted average of visual features in an input image set. Our approach is implemented as a mobile fashion recommender system application. It is experimentally show on the Amazon Fashion dataset that our approach achieves an F1-measure of 0.58 for 15 recommendations, which is twice as good as the 0.25 F1-measure for conventional averaging of feature vectors.
A Setup-Free Threshold Encryption Scheme for the Bitcoin Protocol and Its Applications

Let us consider a situation where someone wants to encrypt his/her will on an existing blockchain, e.g. Bitcoin, and allow an encrypted will to be decryptable only if designated members work together. At a first glance, such a property seems to be easily provided by using conventional threshold encryption. However, this idea cannot be straightforwardly implemented since key pairs for an encryption mechanism is additionally required. In this paper, we propose a new threshold encryption scheme in which key pairs for ECDSA that are already used in the Bitcoin protocol can be directly used as they are. Namely, a unique key pair can be simultaneously used for both ECDSA and our threshold encryption scheme without losing security. Furthermore, we implemented our scheme on the Bitcoin regtest network, and show that it is fairly practical. For example, the execution time of the encryption algorithm Enc (resp., the threshold decryption algorithm Dec) is 0.2sec. (resp., 0.3sec.), and the total time is just only 3sec. including all the cryptographic processes and network communications for a typical parameter setting. Also, we discuss several applications of our threshold encryption scheme in detail: Claiming priority of intellectual property, sealed-bid auction, lottery, and coin tossing service.
Generating Adaptive Learning Games to Multiple Mobile Platforms

The main objective of this paper is to present the new architecture of the educational learning game authoring tool “eAdventure” in order to generate an adaptive Ubiquitous learning game for multiple mobile platforms using corona SDK Cross platform mobile Framework. Mobile technologies are various and market demands are pushing for the continued development of new technologies and features that represents a big challenge in terms of time and development costs. Moreover Ubiquitous Learning Game (ULG) over the last decade has played an important role in technology-enhanced learning. By using mobile technologies and profiting from their functionalities, learner can learn anywhere and anytime without being restricted by time and space. On the other hand creating an interesting game for different learner profile requires the addition of the learner model during the game design phase.
Dual Intercommunication Network: Enabling Interhemispheric Communications in Hemisphere-Inspired ANNs

The human brain has been a main source of inspiration for designing deep learning models. Recently, inspired by the specialized functions of two cerebral hemispheres in processing low and high spatial frequency information, some dual-path neural networks with global and local branches have been proposed to deal with both coarse- and fine-grained visual tasks simultaneously. However, in existing works, the interhemispheric communication mechanism, which is responded by the corpus callosum, the largest white matter structure in the human brain that connecting the left and right cerebral hemispheres, is still not fully explored and exploited. This paper aims to explore how the corpus callosum can inspire us to enable transfer and integration of information between global and local branches in hemisphere-inspired artificial neural networks, such that one branch can leverage the other's learned knowledge and benefit each other. To this end, we propose a gated intercommunication unit to selectively transfer useful knowledge between the two branches via attention mechanisms to alleviate the negative transfer. Experiments on sb-MNIST and two pedestrian attribute datasets show that the proposed method outperforms the compared ones in most cases.
School-driven mobiquitous invisible paths management for smart territories (Jmagine application)

In this paper, we present the Jmagine APPLICATION for schools and smart territories under development at MBDS innovation laboratory of University of Côte d'Azur (UCA) and deployment in Southern France and abroad (Cambodia, Haïti).
A 3D Convolution Accelerator Implemented on FPGA Using SDSoC

Convolutional Neural Networks (CNNs) have become a popular and useful deep learning algorithm. However, the requirements of computation have also increased. As implementation of CNNs in Embedded Systems or Cyber-Physical Systems (CPS) is required, the need of efficient technologies of computation like FPGAs arise substantially. In this paper, we present a 3D convolution accelerator implemented on a Xilinx ZCU102 FPGA board. It achieves 32.08 GOP/s of performance and an efficiency of 3.58 GOP/s per Watt. This accelerator has been developed in Xilinx SDSoC environment.
Slot Filling Using En-Training

The performance of slot filling is crucial for spoken language comprehension. Aiming at the problem of low filling accuracy, an En-training model for slot filling is proposed based on the idea of ensemble learning. The structure completes the task of slot filling by constructing and combining multiple classifiers. Experiments are carried out on ATIS data sets, the results show that the En-training adaptive network structure proposed in this paper has a significant improvement in accuracy, recall rate and F1 value.
A Hybrid Machine Learning Method for the De-identification of Un-Structured Narrative Clinical Text in Multi-center Chinese Electronic Medical Records Data

The premise of the full use of unstructured electronic medical records is to maintain the fully protection of a patient's information privacy. Presently, in prior of processing the electronic medical record date, identification and removing of relevant information which can be used to identify a patient is a research hotspot nowadays. There are very few methods in de-identification of Chinese electronic medical records and their cross-center performance is poor. Therefore we develop a de-identification method which is a mixture of rule-based methods and machine learning methods. The method was tested on 700 electronic medical records from six hospitals. Five-fold cross test was used to evaluate the results of c5.0, Random Forest, SVM and XGBOOST. Leave-one-out test was used to evaluate CRF. And the F1 Measure of machine learning reached 91.18% in PHI_Names, 98.21% in PHI_MEDICALID, 95.74% in PHI_OTHERNFC, 97.14% in PHI_GEO, 89.19% in PHI_DATES, and 91.49% in PHI_TEL. And the F1 Measure of rule-based methods reached 93.00% in PHI_Names, 97.00% in PHI_MEDICALID, 97.00% in PHI_OTHERNFC, 97.00% in PHI_GEO, 96.00% in PHI_DATES, and 89.00% in PHI_TEL.
Stochastically Controlled Compositional Gradient for Composition Problems

We consider composition problems of the form (1/n)∑i = 1n Fi (1/m)∑j = 1m Gj(x) , which are important for machine learning. Although gradient descent and stochastic gradient descent are straightforward solutions, the essential computation of G (x) = (1/m)∑j = 1mGj(x) in each single iteration is expensive, let alone for large m . In this article, we devise a stochastically controlled compositional gradient algorithm. Specifically, we introduce two variants of stochastically controlled technique to estimate the inner function G(x) and the gradient of the objective function, respectively. The computational cost is largely reduced. However, the natural needs of two stochastic subsets D1 and D2 form direct barriers to guarantee the convergence of the algorithm, especially the theoretical proof of the convergence. To this end, we present a general convergence analysis by proving | D1|=min{1/ϵ,m} and | D2|=min{1/ϵ,n } , through which the proposed method significantly improve composition algorithms under low target accuracy (i.e., 1/ϵ << m or n ) in both strongly convex and nonconvex settings. Comprehensive experiments demonstrate the superiority of the proposed method over existing methods.