Reviewerly/exHarmony huggingface dataset:
Improving Quality of Data Exchange Files. An Industrial Case Study

In the development of electrical machines users run a batch of command line programs by providing text-based data exchange files as input. The required structure and content of these files is often only informally documented and implicitly enforced by programs. Therefore, users are forced to execute programs without prior syntactic and semantic verification. To improve the quality of data exchange files, users need editor support that allows syntactic and semantic verification using grammar-based analyzers. In order to reduce the effort for creating grammars, we use grammar recovery which analyzes software artifacts and makes the retrieved knowledge visible as a language grammar. The assessment and completion of the extracted grammar requires both knowledge in software-language engineering and in the application domain. This paper examines whether the integration of grammar recovery with domain-specific languages is suitable for creating editor support for data exchange files. In particular, we are interested in whether we can recover (1) a grammar and validation rules from documentation and a corpus of example files. Furthermore, we are interested in whether (2) a domain-specific language (DSL) allows domain experts to provide missing details and evolve grammars. To answer these questions, we conducted an industrial case study on three different types of data exchange files. Results show that about 45% of the grammar rules could be recovered automatically and that the completion of the extracted grammars by end-users is a promising means to provide correct and maintainable grammars for data exchange files.
Learning Restricted Deterministic Regular Expressions with Counting

Regular expressions are widely used in various fields. Learning regular expressions from sequence data is still a popular topic. Since many XML documents are not accompanied by a schema, or a valid schema, learning regular expressions from XML documents becomes an essential work. In this paper, we propose a restricted subclass of single-occurrence regular expressions with counting (RCsores) and give a learning algorithm of RCsores. First, we learn a single-occurrence regular expressions (SORE). Then, we construct an equivalent countable finite automaton (CFA). Next, the CFA runs on the given finite sample to obtain an updated CFA, which contains counting operators occurring in an RCsore. Finally we transform the updated CFA to an RCsore. Moreover, our algorithm can ensure the result is a minimal generalization (such generalization is called descriptive) of the given finite sample.
Two-View Triangulation: A Novel Approach Using Sampson’s Distance

With the increase in the need for video-based navigation, the estimation of 3D coordinates of a point in space, using images, is one of the most challenging tasks in the field of computer vision. In this work, we propose a novel approach to formulate the triangulation problem using Sampson’s distance, and have shown that the approach theoretically converges toward an existing state-of-the-art algorithm. The theoretical formulation required for achieving optimal solution is presented along with its comparison with the existing algorithm. Based on the presented solution, it has been shown that the proposed approach converges closely to Kanatani–Sugaya–Niitsuma algorithm. The purpose of this research is to open a new frontier to view the problem in a novel way and further work on this approach may lead to some new findings to the triangulation problem.
Multi-secret Image Sharing Scheme based on coding method

Image Secret sharing scheme is an effective way to share a secret message by distributing the shares to different owners which has become a relatively independent research field. The classical Shamir's scheme is based on Lagrange interpolation polynomial, and the utility of the scheme is greatly compromised by the complex polynomial operations cost. Visual cryptography is a special image sharing scheme, which uses the human visual system to recover the secret information. Multi-secret shared visual cryptography is an important research part of visual cryptography. It can share several secret images, and have important significance for perfecting the theoretical system of visual cryptography and expanding the application fields of visual cryptography. In this paper, based on the research of the internal relations between the coding theory and the secret sharing, a Multi-secret Image Sharing scheme is proposed, the generation of the shares is realized by the binary Cauchy encoding. It satisfies the general characters of the secret image sharing schemes, and the algorithm complexity is low, which is easy for implementation.
Global Features of Fused Frame Relationships Help Video Classification

ECO [] is a newly proposed efficient video classification network that combines 2D and 3D networks. On this basis, we try to extract the global features composed of inter-frame relationships and fuse the global features with the initial frame feature, which can enhance the learning of video features to achieve better classification results. In this way, the new frame feature not only contains the information of itself but also contains the global feature which is related to other frames., so the module can more easily learn about distinguishing features. The main contribution of this paper is proposing a module for extracting global features to enhance video feature learning. It is very lightweight and basically does not add computation to the model. At the same time, it is flexible and easy to generalize to various network structures. Finally, based on the baseline the classification accuracy is increased by 0.8% on the HMDB51 dataset.
National-patriotic education of future teachers of musical art in the study of the disciplines of the conductor-choral cycle (on the example of the revival of the choral heritage of A. Wedel abroad)

The article deals with the problems of the national-patriotic education of future teachers of musical art by means of choral art on the example of one of the outstanding representatives of the Ukrainian composer and choirmaster school – Artemy Lukyanovich Vedel. The stages of knowledge of the choral heritage of the Ukrainian composer of the late eighteenth and early nineteenth centuries in Western Europe and America of the twentieth century are considered. Sources from periodicals allow us to analyze various forms of perception of his works by a foreign listener during concert performances. Named the national choir, Ukrainian and foreign performers, who were direct popularizers of the artist’s creativity. The distribution of A. Vedel’s choral heritage in the countries of Western Europe and America of the twentieth century had its own definite place. The problem of popularizing the choral heritage of the outstanding Ukrainian composer Artemii Lukyanovich Vedel is not sufficiently covered in modern scientific research. Domestic educators and culturologists pay more attention to revealing the secrets of biography, outlook and search of little-known works of the composer, therefore the question of widespread distribution of his musical work is now relevant, which helps to highlight the bright personality of the artist. The source of our searches is based on the materials of publications of the national periodicals of the twentieth century. These are, first and foremost, magazine and newspaper articles related to concert performances by well-known domestic choral groups and individual performers of the last century. Available factual materials allow us to conclude that despite all the prohibition on singing the works of this composer in the 19th and the first half of the 20th century, his art proved his vitality in concert performance by choir collectives, in the pedagogical repertoire of educational choir collectives, which was confirmed by modern practice. national musical pedagogy. This topic, with all its obvious features, has a prospect of development in various areas of musical and pedagogical research.
Geothermal Waters of Urganli (Manisa) as a Hydrogeological Model, Western Anatolia, Turkey

Abstract The area of Urganlı is located in the western part of Gediz graben and consists of Paleozoic metamorphic rocks with intercalations of various schists and marbles, Mesozoic limestones and ultrabasic rocks, and Pliocene to recent travertine deposits and alluviums. Mica schists as intercalations in Paleozoic metamorphic rocks form impermeable basement rocks hydrogeochemically. The marble as intercalations in metamorphic rocks is of reservoir rock for geothermal waters. Moreover, these marbles are also aquifer for groundwaters. As impermeable rocks, sedimentary rocks with high quantities of clay contents in Pliocene to recent age play an essential role for enhancement of geothermal waters. Geothermal waters of Urganlı with temperatures of up to 75 °C and reservoir temperatures of up to 180 °C are of Na-HCO 3 type whereas groundwaters display Ca-Mg-HCO 3 type. Geothermal waters of Urganlı were represented as an hydrogeological model within this study. The Kula volcano in the Gediz rift zone with a last eruption age up to 18,000 a is responsible for heating geothermal waters.
Automatic Balance Provider

Abstract Rupee Note is the basic requirement in day-to-day life which are used everywhere. The Rupee Notes are in the denomination ₹10, ₹20, ₹50, ₹100, etc. and the coins are in the denomination ₹1, ₹2, ₹5. It is not sure to get the exact amount for the product while purchasing. It may be a difficult task for the shopkeeper to search the coins and provide the balance to each and every customer. This is the main purpose of designing such a machine. This machine designed for providing the balance amount in coins automatically to the customer. This machine captures the image of the note provided by the customer and the value of the note determined using image processing technique. The Image Processing is done with the help of Python OpenCV.
The Service of Secure Data Publication and Subscription on Cloud Platforms

Abstract With the increase of data volume, the service of data publication and subscription is an efficient method to share massive data. It is impossible to capture and manage data with traditional database tools. Therefore, the cloud platform becomes the most suitable platform for data publication and subscription due to its huge storage, strong computing power and good economical utility. In the process of data publishing and subscription, cloud server as a third party is semi-honest, which will bring serious privacy problems. In this paper, a secure data publishing and subscription scheme based on cloud platform is proposed, which can protect the privacy of data and subscriber interest, and achieve efficient and accurate data publishing and subscription. It can realize that only data subscribers who meet the requirements of access rights can obtain and decrypt the data, and the matching of data subscriber interest and data label can be realized at the same time. In addition, this scheme also supports user cancellation, it can cancel dishonest users in the system.
On Realizing Distributed Deep Neural Networks: An Astrophysics Case Study

Deep Learning architectures are extensively adopted as the core machine learning framework in both industry and academia. With large amounts of data at their disposal, these architectures can autonomously extract highly descriptive features for any type of input signals. However, the extensive volume of data combined with the demand for high computational resources, are introducing new challenges in terms of computing platforms. The work herein presented explores the performance of Deep Learning in the field of astrophysics, when conducted on a distributed environment. To set up such an environment, we capitalize on TensorFlowOnSpark, which combines both TensorFlow's dataflow graphs and Spark's cluster management. We report on the performance of a CPU cluster, considering both the number of training nodes and data distribution, while quantifying their effects via the metrics of training accuracy and training loss. Our results indicate that distribution has a positive impact on Deep Learning, since it accelerates our network's convergence for a given number of epochs. However, network traffic adds a significant amount of overhead, rendering it suitable for mostly very deep models or in big Data Analytics.
A feature selection approach combining neural networks with genetic algorithms

Value Feature selection is an effective method to solve the curse of dimensionality, which widely employs Evolutionary Computation (EC), such as Genetic Algorithms (GA), by regarding feature subsets as individuals. However, it is impossible for EC based feature selection approaches to possess big p opulation sizes because of very long and infeasible computational time. We have proposed a method screening individuals by estimating their classification performances rapidly instead of deriving theirs with a certain classifier dilatorily. Consequently, aiming at improving classification accuracies, we propose an approach named as FS-NN-GA (Feature Selection approach based on Neural Networks and Genetic Algorithms) in this work. The proposed approach employs the neural networks trained with some randomly generated individuals, and their actual classification accuracies to estimate individuals’ classification accuracies and screens them in each round of GA. The individuals with low estimated accuracies are directly eliminated. Only a small number of individuals with high estimated accuracies are reserved, evaluated by deriving their accuracies with a certain classifier, and participate GA operations to be explored emphatically. As a result, big population sizes become feasible, and a huge number of individuals can be considered by GA in acceptable and feasible time, which improves performances of GA and derives high accuracies. We perform the experiments with 10 data sets in comparison with 11 available approaches. The experimental results show that FS-NN-GA outperforms other approaches on most data sets.
FDFuzz: Applying Feature Detection to Fuzz Deep Learning Systems

In the past years, many resources have been allocated to research on deep learning networks for better classification and recognition.These models have higher accuracy and wider application contexts, but the weakness of easily being attacked by adversarial examples has raised our concern.It is widely acknowledged that the reliability of many safety-critical systems must be confirmed.However, not all systems have sufficient robustness, which makes it necessary to test these models before going into service.In this work, we introduce FDFuzz, an automated fuzzing technique that exposes incorrect behaviors of neural networks.Under the guidance of the neuron coverage metric, the fuzzing process aims to find those examples to let the network make mistakes via mutating inputs, which are then correctly classified.FDFuzz employs a feature detection technique to analyze input images and improve the efficiency of mutation by features of keypoints.Compared with TensorFuzz, the state-of-the-art open source library for neural network testing, FDFuzz demonstrates higher efficiency in generating adversarial examples and makes better use of elements in corpus.Although our mutation function consumes more time to generate new elements, it can generate 250% more adversarial examples and save testing time.
Unified Software Platform for Discrete Tomography Reconstruction Algorithms

In this paper a unified software platform DTR (Discrete Tomography Reconstruction) is introduced created for discrete tomography reconstruction algorithms.
AAT: An Efficient Model for Synthesizing Long Sequences on a Small Dataset

This work represents an alternative model for speech synthesis, which addresses some major disadvantages of current end-to-end models. Current state-of-the-art models still have some troubles while dealing with long sentences and the size of the dataset. Our proposed Adaptive Alignment Tacotron (AAT) model, however, has achieved impressive results for a very small dataset of the Vietnamese language. By leveraging the nature of diagonal alignment between phoneme and acoustic sequences, we address the issue with long sequences and the proposed model can also be trained efficiently on the small dataset (5 h of recording). The proposed model consists of the following components: a stacked convolutional encoder, a local diagonal attention module, a decoder with schedule teacher forcing to produce a coarse mel-spectrogram prediction, and a converter to transform the mel-spectrogram to linear-spectrogram. Experimental results show that the proposed model achieves faster convergence speed and higher stability than the baseline model and open a feasible approach for speech synthesis on languages with small dataset.
A History Based Logic for Dynamic Preference Updates

History based models suggest a process-based approach to epistemic and temporal reasoning. In this work, we introduce preferences to history based models. Motivated by game theoretical observations, we discuss how preferences can dynamically be updated in history based models. Following, we consider arrow update logic and event calculus, and give history based models for these logics. This allows us to relate dynamic logics of history based models to a broader framework.
A Data-Driven Approach to Automatic Extraction of Professional Figure Profiles from Résumés

The process of selecting and interviewing suitable candidates for a job position is time-consuming and labour-intensive. Despite the existence of software applications aimed at helping professional recruiters in the process, only recently with Industry 4.0 there has been a real interest in implementing autonomous and data-driven approaches that can provide insights and practical assistance to recruiters. In this paper, we propose a framework that is aimed at improving the performances of an Applicant Tracking System. More specifically, we exploit advanced Natural Language Processing and Text Mining techniques to automatically profile resources (i.e. candidates for a job) and offers by extracting relevant keywords and building a semantic representation of résumés and job opportunities.
Self-Confidence Estimation on Vocabulary Tests with Stroke-Level Handwriting Logs

A user's self-confidence is as important as the correctness of the answer. Giving feedback based on self-confidence will enable the user to review unacquired knowledge more efficiently. In this paper, we propose a self-confidence estimation method based on stroke-level handwriting behavior. We consider two different handwriting answer formats: the format with character writing box and the format without it (free format). We have conducted the experiments to compare the performance on the dataset recorded with these two formats. We successfully built a method that can estimate the user's self-confidence with maximum 80% accuracy in the dataset with character writing box, 79% in the dataset in the free format.
Demo Application for LETO: Learning Engine Through Ontologies

The massive amount of multi-formatted information available on the Web necessitates the design of software systems that leverage this information to obtain knowledge that is valid and useful.The main challenge is to discover relevant information and continuously update, enrich and integrate knowledge from various sources of structured and unstructured data.This paper presents the Learning Engine Through Ontologies (LETO) framework, an architecture for the continuous and incremental discovery of knowledge from multiple sources of unstructured and structured data.We justify the main design decision behind LETO's architecture and evaluate the framework's feasibility using the Internet Movie Data Base (IMDB) and Twitter as a practical application.
Generating Adversarial Examples by Adversarial Networks for Semi-supervised Learning

Semi-Supervised Learning (SSL) has exhibited strong effectiveness in boosting the performance of classification models with the aid of a large amount of unlabeled data. Recently, regularizing the classifier with the help of adversarial examples has proven effective for semi-supervised learning. Existing methods hypothesize that the adversarial examples are based on the pixel-wise perturbation of the original samples. However, other types of adversarial examples (e.g., with spatial transformation) should also be useful for improving the robustness of the classifier. In this paper, we propose a new generalized framework based on adversarial networks, which is able to generate various types of adversarial examples. Our model consists of two modules which are trained in an adversarial process: a generator mapping the original samples to adversarial examples which can fool the classifier, and a classifier that tries to classify the original samples and the adversarial examples consistently. We evaluate our model on several datasets, and the experimental results show that our model outperforms the state-of-the-art methods for semi-supervised learning. The experiments also demonstrate that our model can generate adversarial examples with various types of perturbation such as local spatial transformation, color transformation, and pixel-wise perturbation. Moreover, our model is also applicable to supervised learning, performing as a regularization term to improve the generalization performance of the classifier.
Applying Lagrange Model to Fill Data During Big Data Streaming

Advancements in technology have significantly reshaped the social and economic environment. Businesses are coming up with new strategies to uncover hidden information from data in order to support better prediction and analysis. Data continues to grow at a rapid rate and it has become necessary to process the quality data. In mission critical applications, streaming of data plays a very important role. Discontinuity in data stream is unaffordable as it consumes more time and money. This paper proposes a technique through Lagrange’s Interpolation, which could avoid discontinuity in data streams when large data is being processed.
Lifelong learning in costly feature spaces

An important long-term goal in machine learning systems is to build learning agents that, like humans, can learn many tasks over their lifetime, and moreover use information from these tasks to improve their ability to do so efficiently. In this work, our goal is to provide new theoretical insights into the potential of this paradigm. In particular, we propose a lifelong learning framework that adheres to a novel notion of resource efficiency that is critical in many real-world domains where feature evaluations are costly. That is, our learner aims to reuse information from previously learned related tasks to learn future tasks in a feature-efficient manner. Furthermore, we consider novel combinatorial ways in which learning tasks can relate. Specifically, we design lifelong learning algorithms for two structurally different and widely used families of target functions: decision trees/lists and monomials/polynomials. We also provide strong feature-efficiency guarantees for these algorithms; in fact, we show that in order to learn future targets, we need only slightly more feature evaluations per training example than what is needed to predict on an arbitrary example using those targets. We also provide algorithms with guarantees in an agnostic model where not all the targets are related to each other. Finally, we also provide lower bounds on the performance of a lifelong learner in these models, which are in fact tight under some conditions.