Reviewerly/exHarmony huggingface dataset:
Robust Shortest Path Planning for Aircraft using Bounded Region Voronoi Diagram

Flight safety and fuel consumption are major concerns of airline industry. En route civil aircraft may encounter hindrances such as bad weather patches, prohibited areas, and flight constraints. Robust path planning ensures maximum safety of the aircraft, crew members and passengers in unknown environment such as uncertain wind direction and drag from low pressure areas. This paper presents the robust path generation methodology near to the line of sight from starting aerodrome to the target aerodrome. This methodology confined the search space on the aerospace map to the nearby hindrance from the line of sight and then plan a robust path for the aircraft with the help of voronoi diagram. The voronoi diagram for the airspace map gives cells on the grid map, where each cell is equidistant from the hindrances. For these cells, the shortest robust path is evaluated using Dijkstra algorithm. The simulations for the shortest robust path on the airspace map were carried out for the proposed methodology and voronoi diagram for the full airspace map. The results obtained show that unnecessary clearances from the hindrances are avoided, and the computational time to evaluate the shortest robust path has been considerably reduced.
Development Of Modules Of Neuro-Like Cryptographic Encryption And Decryption Of Data And Their Implementation On FPGA

The requirements are formed and the main stages of synthesis of cryptological encryption and data decryption modules are explained. The tabular-algorithmic method of scalar product calculation has been improved and it is focused on working with floating-point operands. The structures of modules of cryptographic encryption/decryption of data are developed. The design of specialized tools for the modules of neuro-like cryptographic encryption and data decryption has been carried out. The VHDL programming language and the Quartus II development environment (version 13.1) were used for this purpose. The estimation of hardware costs and decryption time of the specialized means of decryption module implemented on FPGA is carried out.
Angular‐spatial analysis of factors affecting the performance of light field reconstruction

As a new VR multimedia format, Light Field (LF) has received more and more attention. LF can support users to move freely within a certain range during the experience. However, the limitation of LF acquisition technology has become the main obstacle to its large-scale application. Since the current hardware technology cannot produce a lens small enough to meet the needs of LF capture, the intensity of the collected LF image is usually insufficient. Although there are many software LF reconstruction algorithms have been proposed to make captured result denser, they all face the problem of poor generalization in the application process. In this work, it is attempted to locate the factors that affect the performance of LF reconstruction from both the angular and spatial domains. The analysis results show that it is affected not only by the edge and texture of the LF content in the spatial domain, but also by the adjacent disparity and the reflection characteristics in the angular domain. An indicator to quantify the impact of these factors on reconstruction performance is also proposed, which will be helpful to design a new generalized adaptive LF reconstruction algorithm in the future.
Modelling and verification of reconfigurable fault-tolerant and self-recovering systems in hybrid Clouds

Hybrid Cloud environments allow the utilization of local resources in private Clouds with resources from public Clouds when needed. Such environments represent systems with high failure rates because they feature heterogeneous components, a large number of servers with intensive workload are built as complex architectures. For these reasons, the availability of such systems could be easily compromised if the failure of these heterogeneous components is not handled correctly, which may cause request rejection and frequent performance degradation. Providing highly reliable Cloud applications, in particular in a hybrid Cloud environment, is a challenging and critical research problem. Therefore, the question we address in this paper is how to provision resources to user requests in the presence of failures in a hybrid Cloud environment. To this end, we propose a reconfigurable formal model of the hybrid Cloud architecture, then we utilize instantiations of this model, simulation and real-time execution runs to estimate different performance metrics related to fault detection and self-recovery strategies in hybrid Cloud. Our approach is based on the combination of the model-based and the probabilistic approaches. • A verification methodology of self-recovery strategies in Hybrid architectures. • Our methodology is based on a formal model built as a network of components. • Our methodology is applied to the analysis of different recovery strategies. • Using our verification methodology, stochastic analysis is also performed.
Development of a Text and Speech Enabled Conversational Agent for Students’ Activities Planning Using Dialog Flow

Conversational agents are now ubiquitous and catching speed as an application of computer communication and are able to respond intelligently like humans. Some of these agents help humans with their everyday tasks. Students perform several activities in school and having to remember all these activities can be stressful thereby increasing lethargy because a user has to surf through multiple pages in order to obtain relevant information. The aim of this project is to automate personal assistants for activities planning most especially for students. This study proposed a machine learning intent classification algorithm provided by Google’s Dialog flow API to make seamless conversations with the user. The proposed system makes use of Node.Js for server-side programming, React.Js for building the user interface and Mongo.Db as its database and most especially Dialogflow API for natural language processing. The results showed that the system could be used in real-time by making use of the aforementioned technologies.
Engineering for Chaos

The global COVID-19 pandemic has transformed the way we live, learn, and teach - impacting both "how we learn" and "what we learn." Software system resilience has emerged as a critical concept, a departure from historical system objectives obsessed with high performance. In practice, there are many situations when development focused on efficiency, creates a system that is not very resilient. Fortunately, some technology companies have prioritized stability and availability over efficiency in order to deliver to customers a more consistent experience. Governments also value resilience to reliably serve their communities in the face of crises like cyber hacking and COVID-19. System resilience is a topic often neglected in computer science curricula. This paper reports on a recent virtual ACM SPLASH-E Education Symposium panel session held in November 2020 that discussed resilience, efficiency, and the impact of COVID-19 on computer science education. The panel featured Steven Fraser (panel impresario) and panelists Rebecca Mercuri, Landon Noll, Ales Plsek, and Moshe Vardi.
Fair and Tender Data. The FAIRness of Four Databases With Historical Individual Life Course Data Tested

Four databases with data on individual historical life courses are tested for FAIRness: the TRA, Umeå, HSN and IPUMS databases. All databases make their data much more Findable than they were in the original sources. But as databases, they are best findable if their name is a unique acronym, and if different sub-datasets all use that same acronym. Sensitive data have to be protected. Two databases make anonymous data sets or those only containing information on deceased individuals Accessible without any formalities, and other databases could follow this example. To increase Interoperability a large number of tools are offered by the databases. Reusability is among the raisons d’être of these databases.
Digital Environment of Higher Education in Russia: Objective Conditions and Subjective Perception of Distance Learning by Students in a Situation of Increasing Risks

The article introduces a comprehensive study of distance learning in higher education during the pandemic. The authors analyzed the readiness of universities to implementing e-learning platforms. They focused on the legal and organizational aspects of digital environment. The authors studied relevant interviews given by heads of higher educational institutions in various media sources. The research objective was to develop and perform a formalized interview with full-time students of Russian universities. The study made it possible to identify a wide range of issues that affect the passage to distance learning, as well as to define the prospective directions and problem areas of the digital environment at universities as part of domestic digital economy. The scientific novelty of the research lies in the development of tasks for a successful transfer of universities to distance or blended learning.
Estimating Facial Aging Using Light Scattering Photometry

Facial aging is a complex process, and the changes in the inner layers of the skin will affect how the light scatters from the skin. To observe whether a light scattering model parameter is suitable to be used for age classification/estimation, this study investigated and analyzed the relationship between the parameter of an analytical-based light scattering model and skins of various ages using photometry method. Multiple models are used to investigate and compare the relationship between the model parameters and the subject’s age. The results show that all of the models’ roughness parameter representation has a significant positive correlation with age (\(p<0.05\)), making it a suitable choice to be made as a feature for estimating/classifying age. This study proves that the parameter(s) for an analytical-based light scattering model can be used as an alternative method for estimating/classifying a person’s age, provided that we know the light incidence and reflectance angles. In the future, this method can be used to work with other age extractors/estimators/classifiers, for the purpose of designing a more robust age estimation/classification method.
A general and configurable framework for blockchain-based marketplaces

The first generation of blockchain focused on digital currencies and secure storage, management and transfer of tokenized values. Thereafter, the focus has been shifting from currencies to a broader application space. In this paper, we systematically explore marketplace types and properties, and consider the mechanisms required to support those properties through blockchain. We propose a generic and configurable framework for blockchain-based marketplaces, and describe how popular marketplace types, price discovery policies, and other configuration parameters are implemented within the framework by presenting concrete event-based algorithms. Finally, we consider two use cases with widely diverging properties and show how the proposed framework supports them.
STRATEGIC COMMUNICATIONS IN THE SCIENTIFIC AND INFORMATION PROCESS OF MODERNITY

The article considers the peculiarities of the use of strategic information communications in the development of domestic science, in the formation of the national innovation ecosystem and the sphere of innovation for the period up to 2030, the growing importance of these tools for managing the use of domestic science in social practice.
On the string matching with <i>k</i> differences in DNA databases

In this paper, we discuss an efficient and effective index mechanism for the string matching with k differences, by which we will find all the substrings of a target string y of length n that align with a pattern string x of length m with not more than k insertions, deletions, and mismatches. A typical application is the searching of a DNA database, where the size of a genome sequence in the database is much larger than that of a pattern. For example, n is often on the order of millions or billions while m is just a hundred or a thousand. The main idea of our method is to transform y to a BWT-array as an index, denoted as BWT ( y ), and search x against it. The time complexity of our method is bounded by O( k · | T |), where T is a tree structure dynamically generated during a search of BWT ( y ). The average value of | T | is bounded by O(|Σ| 2 k ), where Σ is an alphabet from which we take symbols to make up target and pattern strings. This time complexity is better than previous strategies when k ≤ O(log |Σ| n ). The general working process consists of two steps. In the first step, x is decomposed into a series of l small subpatterns, and BWT ( y ) is utilized to speedup the process to figure out all the occurrences of such subpatterns with ⌊ k/l ⌋ differences. In the second step, all the found occurrences in the first step will be rechecked to see whether they really match x , but with k differences. Extensive experiments have been conducted, which show that our method for this problem is promising.
Big Data in Cloud Computing

The huge size of data that has been produced by applications that spans from social network to scientific computing is termed big data. Cloud computing as a delivery model for IT services enhances business productivity by reducing cost. It has the intention of achieving solution for managing big data such as high dimensional data sets. Thus, this chapter discusses the background of big data and cloud computing. It also discusses the various application of big data in detail. The various related work, research challenges of big data in cloud computing, and the future direction are addressed in this chapter.
BERT-based Named Entity Recognition Method for Chinese Recipe Text

In order to extract the information from the massive unstructured recipe text, a named entity recognition method based on BERT pre-trained language model is proposed by this paper. Firstly, after pre-processing the public recipe texts obtained from the web, the entities were classified into three categories of main ingredients, auxiliary ingredients and cooking methods by combining the domain expert knowledge and BIO annotation method to build a large-scale Chinese cuisine domain named entity recognition dataset, and then the BERT-Attention-BiLSTM-CRF(BABC) model proposed in this paper is used to train on this dataset. Finally, we compared the results of other named entity recognition models on this dataset, and the experimental results showed that BABC could identify the entities in the recipe text more accurately.
Low-Delay FPGA-Based Implementation of Finite Field Multipliers

Arithmetic operations over binary extension fields GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) have many important applications in domains such as cryptography, code theory and digital signal processing. These applications must be fast, so low-delay implementations of arithmetic circuits are required. Among GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) arithmetic operations, field multiplication is considered the most important one. For hardware implementation of multiplication over binary finite fields, irreducible trinomials and pentanomials are normally used. In this brief, low-delay FPGA-based implementations of bit-parallel GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) polynomial basis multipliers are presented, where a new multiplier based on irreducible trinomials is given. Several post-place and route implementation results in Xilinx Artix-7 FPGA for different GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) finite fields are reported. Experimental results show that the proposed multiplier exhibits the best delay, with a delay improvement of up to 4.7%, and the second best Area×Time complexities when compared with similar multipliers found in the literature.
Geology, mineralization, geochemistry and fluid inclusion studies of Mashkan copper prospect area, northeastern Sabzevar

Geology, mineralization, geochemistry and fluid inclusion studies of Mashkan copper prospect area, northeastern Sabzevar
A comparative review of extractive text summarization in Indonesian language

Abstract Text summarization has important role in natural language processing. One of text summarization type is extractive summarization. Research on text summarization in Indonesian Language is still rare and not evaluated comprehensively. Each research is only conducted based on subjectivity of researcher. This paper reviewed and evaluated some works on Indonesian Language Text Summarization for obtaining the better method by analysing some aspects. This review also mapped Indonesian text summarization evaluation techniques and obtained its advantages and drawbacks. This research aims to provide a comprehensive review of text summarization in Indonesian Language. Result of this study is a comparative review of some works which showed detailed aspects in summarization method.
An Arabic Mispronunciation Detection System Based on the Frequency of Mistakes for Asian Speakers

Over the last few decades, the field of artificial intelligence and machine learning has evolved. Due to the advancement in these fields, much work has been done to assist language learning with the help of computers called Computer-Assisted Language Learning (CALL). Mispronunciation detection is one of the significant tasks of the CALL system. An efficient mispronunciation detection model has a positive impact on the life of second language learners by providing phoneme level feedback. In this paper, we introduce the phone grouping technique for mispronunciation detection that is based on mistakes probability. We consider mispronunciation detection as a classification problem, traditionally for this purpose, a separate classifier is trained for each phoneme mistake that requires a lot of memory and time. Instead of training a separate classifier, we group the phoneme based on their mistakes probability that helps in reducing the number of the classifiers to be trained and also saves memory and time. We use the Support Vector Machine (SVM) classifier and test the results on the Arabic dataset (28 Phonemes). The performance of our proposed method is evaluated by using accuracy. The results of the model are evaluated using the confusion matrix and gives an accuracy of 88%. Our approach outperforms the existing systems developed for Arabic phonemes in terms of accuracy and is also time/memory efficient.
Zero-Shot Entity Recognition via Multi-Source Projection and Unlabeled Data

Abstract With the widespread of transfer learning, great success has been achieved in named entity recognition on languages without labeled data. Prior works can be mainly classified into two categories: direct model transfer and annotation projection. However, these works only utilize part of available data, including monolingual word embeddings, labeled data in multiple source languages, and unlabeled data in the target language. To make full use of these data and extract more information, we propose to train multiple teacher models on multiple source languages via the annotation projection method. Then a student model is trained on the unlabeled data in the target language with supervision from all labels obtained from multiple teacher models. Extensive experiments are conducted on three benchmark datasets, and the experimental results prove the effectiveness of the proposed method.
Multi-Class Multi-Instance Count Conditioned Adversarial Image Generation

Image generation has rapidly evolved in recent years. Modern architectures for adversarial training allow to generate even high resolution images with remarkable quality. At the same time, more and more effort is dedicated towards controlling the content of generated images. In this paper, we take one further step in this direction and propose a conditional generative adversarial network (GAN) that generates images with a defined number of objects from given classes. This entails two fundamental abilities (1) being able to generate high-quality images given a complex constraint and (2) being able to count object instances per class in a given image. Our proposed model modularly extends the successful StyleGAN2 architecture with a count-based conditioning as well as with a regression subnetwork to count the number of generated objects per class during training. In experiments on three different datasets, we show that the proposed model learns to generate images according to the given multiple-class count condition even in the presence of complex backgrounds. In particular, we propose a new dataset, CityCount, which is derived from the Cityscapes street scenes dataset, to evaluate our approach in a challenging and practically relevant scenario. An implementation is available at https://github.com/boschresearch/MCCGAN.
Diagnosis Ranking with Knowledge Graph Convolutional Networks

The automatic diagnosis of a medical condition provided the symptoms exhibited by a patient is at the basis of systems for clinical decision support, as well as for applications such as symptom checkers. Existing methods have not fully exploited medical knowledge: this likely hinders their effectiveness. In this work, we propose a knowledge-aware diagnosis ranking framework based on medical knowledge graph (KG) and graph convolutional neural network (GCN). The medical KG is used to model hierarchy and causality relationships between diseases and symptoms. We have evaluated our proposed method using realistic patient cases. The empirical results show that our knowledge-aware diagnosis ranking framework can improve the effectiveness of medical diagnosis.
Face completion with pyramid semantic attention and latent codes

Face completion, which is to reproduce the missing region of an incomplete face image, has achieved promising performance due to the adoption of generative adversarial network (GANs) and the development of GPU. However, current network frameworks always ignore that face images usually have strong semantic correlation and symmetry, failing to recover some semantically plausible details especially for filling in large contiguous holes. To produce visually realistic and semantically correct results, we propose a two-stage adversarial framework, the first stage is to produce coarse images and works as a prior for searching the most similar latent codes in reference sets, which are combined by composing their intermediate feature maps. Besides, the second stage captures texture information with our novel pyramid semantic attention block for fully using semantic information and embed the learned structure features into the inpainting process. Our attention layer considers not only the known contents but also our reconstructed parts, so that we can improve the realism of reconstructing parts, then apply this attention layer into a novel pyramid structure. In addition, we add weights in the loss function around the predicted boundary for encouraging our model to generate clearer contour lines and better interpolation properties. Empirically, the experiment on CelebA dataset shows our proposed method is effective to fill in large contiguous holes on the face images. Especially, the SSIM score of our model is nearly higher 0.1 than context encoder model.
Binary LCD Codes Having an Automorphism of Odd Prime Order

The structure of binary LCD codes invariant under a permutation of odd prime order p is described. A systematic construction of such codes when 2 is a multiplicative root modulo p is presented. As an example, binary linear codes with complementary dual (or LCD codes) whose automorphism groups have orders divisible by 5 are constructed.