Reviewerly/exHarmony huggingface dataset:
Research on Global Competition and Acoustic Search Algorithm Based on Random Attraction

In view of complex optimization problems with high nonlinearity and multiple extremums, harmony search algorithm has problems of slow convergence speed, being easy to be stalled and difficult to combine global search with local search. Therefore, a global competitive harmonic search algorithm based on stochastic attraction is proposed. Firstly, by introducing the stochastic attraction model to adjust the harmonic vector, the harmonic search algorithm was greatly improved and prone to fall into the local optimum. Secondly, the competitive search strategy was made to generate two harmony vectors for each iteration and make competitive selection. The adaptive global adjustment and local search strategy were designed to effectively balance the global and local search capabilities of the algorithm. The typical test function was used to test the algorithm. The experimental results show that the algorithm has high precision and the ability to find the global optimum compared with the existing algorithms. The overall accuracy is increased by more than 50%.
Escaping the complexity-bitrate-quality barriers of video encoders via deep perceptual optimization

We extend the concept of learnable video precoding (rate-aware neural-network processing prior to encoding) to deep perceptual optimization (DPO). Our framework comprises a pixel-to-pixel convolutional neural network that is trained based on the virtualization of core encoding blocks (block transform, quantization, block-based prediction) and multiple loss functions representing rate, distortion and visual quality of the virtual encoder. We evaluate our proposal with AVC/H.264 and AV1 under per-clip rate-quality optimization. The results show that DPO offers, on average, 14.2% bitrate reduction over AVC/H.264 and 12.5% bitrate reduction over AV1. Our framework is shown to improve both distortion- and perception-oriented metrics in a consistent manner, exhibiting only 3% outliers, which correspond to content with peculiar characteristics. Thus, DPO is shown to offer complexity-bitrate-quality tradeoffs that go beyond what conventional video encoders can offer.
Maven Loss with AGW-Net for Biomedical Image Segmentation

Traditionally, the dice loss compares the similarity of boundaries between ground truths and predictions. However, the result can be unauthentic when it comes to the situation that both ground truth and predictions are too small. The focal Tversky loss is proposed to address the imbalance between positive and negative samples as well as to contribute a better trade-off between precision and recall. In this paper, we introduce a novel loss function named 'Maven Loss' by considering 'specificity' to handle the issue of data disequilibrium and to help achieve weighing both abilities to correctly segment lesion and non-lesion areas. To evaluate our loss function, we also propose an AGW-Net based on the attention U-Net and W-Net by injecting self-reinforced skip connections. Experiment on ISIC 2018 dataset in which lesions occupy 21.4% on average of the whole images shows that maven loss function and the new network architecture improved IOU and F1-score by 4.9% and 3% compared to the standard attention U-Net, respectively.
A Hybrid Approach to Interpretable Analysis of Research Paper Collections

We define and find a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a taxonomy. This generalization lifts the set to a "head subject" in the higher ranks of the taxonomy, that is supposed to "tightly" cover the query set, possibly bringing in some errors, both "gaps" and "offshoots". Our method involves two more automated analysis techniques: a fuzzy clustering method, FADDIS, involving both additive and spectral properties, and a purely structural string-to-text relevance measure based on suffix trees annotated by frequencies. We apply this to extract research tendencies from two collections of research papers: (a) about 18000 research papers published in Springer journals on data science for 20 years, and (b) about 27000 research papers retrieved from Springer and Elsevier journals in response to data science related queries. We consider a taxonomy of Data Science based on the Association for Computing Machinery Classification of Computing System (ACM-CCS 2012). Our findings allow us to make some comments on the tendencies of research that cannot be derived by using more conventional techniques.
A Novel Hybrid Algorithm for Sentiment Analysis via Classifier Ensembles for Online Shops User Using User Generated Contents and Review

Recently, Sentiment analysis and classification on social networking has been becoming popular in recent years. Industry and companies have realized the value of huge data to create a valuable advantage to get more customer. User generated content in online reviews for online shops or social media makes a lot of brand related information for marketing fields. In this paper we proposed a method to classify the sentiment polarities and find customer opinions and feeling about everything to propose product selection for each user in online markets. Our qualitative and quantitative experiment shown the usefulness of using positive, neutral, and negative customer opinion for product recommendation in online markets. By considering different combinations of techniques such as feature hashing, bag of words, and lexicons, and also consider the extensive results that described in the literature for application purposes, we can present the accuracy and precision of our method for online markets users.
A Study on DeLC Hybrid Model for Improvement of Classification Technique on Sentiment Analysis

Sentiment Analysis is that the application of linguistic communication process, linguistics and text process. Sentiment Analysis is the opinion mining task that aims to obtain the writer's or speaker's feelings (“smile”, “anger”, “sorrow”, etc.), attitude (Positive, negative or neutral) towards particular task such as product reviews, movie reviews or overall tonality of document. It is one of the serious issues which can be solved by using Natural Language Processing (NLP) methods and so called text pre-processing. In this paper, focuses on to investigate the classifier to improve the classification process and what kind of NLP methods can be processed. There is a great deal of significant information on Internet that, really, is difficult to utilize. Procedure this information can enable to foresee future items, economy patterns or social certainties by preparing the individuals emotions that are shared on web. In this paper, proposed a hybrid model named DeLC model.
Decentralised Compliant Data Trading for Banks

Banks must comply with a complex set of regulatory and legislative requirements in order to be sure they know who they are doing business with. Current Know Your Customer processes are inefficient and costly, often being repeated by multiple banks. As such, financial institutions have a need to reduce the cost of compliance. Banks can reduce their costs through inter-bank Know Your Customer data trading. In this paper, we build upon an industry-strength, blockchain-based, Know Your Customer data sharing platform to facilitate a novel data marketplace by detailing a method of pricing data such that banks save money and are suitably incentivised to trade their data. The pricing model we detail enables banks to reduce the cost of document acquisition and verification by at least 45%, and even profit by providing documents. In addition, we discuss how decentralised pricing is realised through the use of smart contracts.
Modal Logic and Planarity of Graphs

When a Kripke frame is viewed as a graph, the denotation of a formula is interpreted as a set in a graph and so a modal formula can be used to describe the properties of graphs. This study particularly focuses on planarity of graphs and proposes a syntax of extended modal logic in which planarity is definable. Moreover, we provide semantically complete and decidable axiomatizations of the logics under consideration.
Hierarchical Joint Learning for Chinese Word Embeddings

In recent years, some methods of learning Chinese word embedding based on the characters and sub-characters information have been proposed and achieve state-of-art results. Generally speaking, good embedding of characters and sub-characters can help learn better embedding of words. However, current methods utilizing characters and sub-characters information, such as character-based CWE and sub-character-based JWE, only learn embedding of characters and sub-characters by predicting the target word. We believe it's not enough to improve the quality of characters and sub-characters embedding, so we propose a method called HJWE which predicts the target word, characters and sub-characters in the target word at the same time. The experiment result shows that our method performs best on the word similarity, word analogy and text classification tasks.
Cross-Domain Visual Attention Model Adaption with One-Shot GAN

The state-of-the-art models for visual attention prediction perform well in common images. But in general, these models have a performance degradation when applied to another domain with conspicuous data distribution differences, such as solar images in this work. To address this issue and adopt these models from the common images to the sun, this paper proposes a new dataset, named VASUN, that records the free-viewing human attention on solar images. Based on this dataset, we propose a new cross-domain model adaption approach, which is a siamese feature extraction network with two discriminators and trained in a one-shot learning manner, to bridge the gaps between the source domain and target domain through the joint distribution space. Finally, we benchmark existing models as well as our work on VASUN and give some analysis about predicting visual attention on the sun. The results show that our method achieves state-of-the-art performance with only one labeled image in the target domain and contributes to the domain adaption task.
Feature Extraction of Chinese Characters Based on ASM Algorithm

In Chinese Calligraphy education, the computer-based evaluation on Chinese handwriting is one of the problems in the field of computer intelligent education. In this study, the method of feature comparison is first proposed in the process of computer-based evaluation on Chinese handwriting, focusing on automatically and accurately extracting the features of Chinese characters. Then, the key technologies applied in feature extraction of Chinese character were analyzed. It discussed the representation of features, aligns training samples, and reduces dimensions by principal component analysis, established local gray-scale model, and converged the gray-scale information of target feature points through statistical analysis. The experimental results show that the accuracy of the algorithm is 93.84%.
Exploring Efficient and Tunable Convolutional Blind Image Denoising Networks

We address the problem of building a blind image denoising network that better adapts to user-defined efficiency and performance requirements. CNN-based architectures such as FFDNet as well as classical methods like BM3D provide fast denoising capability but require the user to specify an approximate noise level. Blind denoising networks like DnCNN and CBDNet are appealing due to their ease of use by non-experts but can be slow. Additionally, these networks are not designed to allow for selecting a reliable operating point based on constraints like available compute, affordable latency, and expected quality. To this end, we propose to develop denoising networks that are tunable to achieve a desired balance between image quality and model size. We seek inspiration from architectures that are tuned for classification, detection, and semantic segmentation on mobile phone CPUs. Incorporating recent advances in architectural building blocks and network architecture search and building upon the success of the DnCNN architectures, we present an efficient convolutional blind image denoising network.
La questione della lingua

In 1591, Giovanni Giacomo Gastoldi published a modest collection of light homophonic partsongs with fa la refrains: <italic>Balletti a cinque voci</italic>. Gastoldi’s collection brings together three important stylistic points of reference: homophony, an emerging monodic style, which had always been entwined with homophony, and social dance. The print was an instant success, garnering ten Venetian reprints, eleven editions in other European cities, and a handful of homages and translations, including Thomas Morley’s <italic>First Booke of Balletts to Five Voyces</italic> (London, 1595). When Morley, and eventually German editors and composers, adopted and adapted Gastoldi’s infectious musical style, they not only translated Gastoldi’s texts, but also his musical idioms. In this process, they participated in a rich tradition of nationalistic identity formation already underway in the robust field of literary translation.
Clone detection in student programs based on lexical analysis of source codes

Abstract This article presents the development of automatic clone detection for verification students programs - the Detector of Clones. The Levenshtein Distance is used for the initial assessment of the similarity of the two token sequences which is calculated by the Wagner-Fisher algorithm. Verification of two programs is carried out by the fingerprint method and by the winnowing method. To improve the results of plagiarism assessment, an additional calculation was added to the implementation of the winnowing method: the detector compares not only the labels themselves, but also the sequences near the matching labels. The detector of clones is formed for the cycle of disciplines Object Oriented Programming, Object Oriented Analysis and Design on the Moodle platform. These tools support C-like languages (C++ and C#). In general, the work of the detector undoubtedly made it possible to increase the consistency in verification of student programs.
Integration of Agile Methodologies and Model-Driven Development

Agile methods (AM) and model-driven engineering (MDE) are two principal domains of software development. AM proposes best practices in information programming, while MDE focuses on technical part of software development. Both of these domains are in the way of improvement and evolution in order to facilitate the development of IT projects. However, these areas evolve separately despite the great number of researches that focus on improving development project' techniques. Thus, in this chapter the authors present an overview of their approach “Scrum with MoDAr-WA”, that aims to improve Scrum Agile methodology by combining two variants of MDE: model-driven architecture and mode-based testing with the V development lifecycle used to deal with sprint development in Scrum methodology. Then they present a comparison study between the standard Scrum process and Scrum with MoDAr-WA approach in order to highlight the authors' contribution to improve agile methodologies.
COMPACT KNN: Developing an Analogy-Based Cost Estimation Model for CubeSats Jet Propulsion Laboratory, California Institute of Technology Pasadena, CA 91109

The “CubeSat Or Microsat Probabilistic and Analogies Cost Tool”, or COMPACT, is a NASA Headquarters funded effort to fill the gap in cost estimating capabilities for CubeSats, as well as other microsat spacecraft. The COMPACT team has focused mainly on CubeSats to date, and has collected technical, programmatic and cost data on dozens of flown CubeSats missions led by NASA, research labs, and universities. The data covers a large range of costs for a variety of different CubeSat designs and mission types. With this collected data, the COMPACT team has completed a prototype cost modeling approach using k-nearest neighbors (KNN). This approach can be used to produce an early ballpark cost estimate for new CubeSat concepts that is grounded by historical actual costs and designs. This paper will demonstrate the use of KNN with the COMPACT data, and will preview our work to develop a full parametric cost model for CubeSats.
Knowledge-Based Networks for Artificial Intuition

The ability to carry out automated decision making from large data sets with potentially large quantities of parameters presents a major challenge in decision making systems. Moreover, this involves the need for deeper analysis to allow their investigation of their corresponding scenarios in other to perform knowledge discovery, which is usually associated with high computational complexity. In this article, we discuss a knowledge-based network approach to facilitate the identification and extraction of information for a novel Artificial Intuition approach which is currently being developed by the authors.
Quantum Entanglement-based Deep Convolutional Neural Networks

The paper presents a quantum-based approach for modeling deep neural networks. Quantum entanglement-based neural network model is described, that utilizes several quantum phenomena applied to the connections of the network structures. The concept of tensor networks and their relation to the quantum neural network modeling is presented. A quantum entanglement-based deep convolutional neural network model is suggested based on the classical VGG16 model. The network architecture, the possibilities for its implementation using quantum logic circuits and through simulation on classical computer as well as the quantum neural network training are also discussed in the paper.
Headline-based visualization to prioritize events

Command and control systems seldom, if ever, make the big picture immediately apparent. These information systems are often a collection of specialized tools for analysts and planners but miss the opportunity to consolidate the message. Thus many of those who may be involved in responses may be unaware of the evolving situation. This issue will likely worsen for multi-domain operations centers that must deal with a wider array of issues. This paper will cover software called C Portal that provides scoring methodologies and human-computer interface concepts that provide high-level situation awareness and assists in prioritizing responses. The scoring is based on the Analytic Hierarchy Process that sums up weighted criteria. For the purposes of C Portal, the highest level criteria includes threat impact, urgency and uncertainty. C Portal's visualization leverages treemapping technology and the time-proven concept of newspaper headlines to call attention to the highest priorities. The uncluttered visualization and logical navigation could be suitable for smartphone-sized displays, wall displays for large groups, or anything in between. A preliminary version of C Portal was transitioned to the National Space Defense Center as part of a Joint Emergent Operational Need and benefited from iterative operator engagements.
Vehicle Tracking in Clutter Environment using High Resolution Radar

Vehicle tracking using automotive radar is an essential part of autonomous driving. With the resolution of radar increasing, a vehicle generates multiple measurements per time and should be considered as an extended target. By using multiple radar measurements, the tracking algorithm can estimate the kinematic state and extension of vehicle. In this paper, we propose a method to track the vehicle in clutter environment by using extended vehicle model and probabilistic data association with Markov chain Monte Carlo technique. Simulation results show the effectiveness and performance our approach for vehicle tracking under clutter environment.
An Encryption Method Based on “Zipper” Stochastic Dynamic Hashing

Abstract This paper studies the theoretical problems and implementation methods of stream ciphers, discusses some deficiencies of conventional stream cipher algorithms, designs methods and rules for random Dynamic Hash Mapping and Bits Scrambling, applies nonlinear feedback shift register (NLFSR) technology, and a new stream cipher method with higher security strength and suitable for encryption of short information is proposed. The purpose is to apply the proposed new method of stream cipher to obtain higher encryption strength and code uniformity, and make some theories and algorithms in the research results have beneficial effects on information security in the era of big data.
Generalization Property of Fuzzy Classification Function for Tsallis Entropy-Regularization of Bezdek-Type Fuzzy C-Means Clustering

In this study, Tsallis entropy-regularized Bezdek-type fuzzy c-means clustering method is proposed. Because the proposed method reduces to four conventional fuzzy clustering methods by appropriately controlling fuzzification parameters, the proposed method is considered to be their generalization. Through numerical experiments, this generalization property is confirmed; in addition, it is observed that the fuzzy classification function of the proposed method approaches a value equal to the reciprocal of the cluster number.
Performance of Gaussian encodings for classical communication on correlated quantum phase-noise channels

We study the problem of transmitting classical information on a quantum channel in the absence of a shared phase reference. This problem is relevant for long-distance communications in free space and optical fiber, where phase noise is typically considered as a limiting factor. Previous analyses considered phase noise that acts independently on each communication mode, thus completely decohering successive signals and making it impossible to establish a phase reference. In the present work we analyze instead the realistic case in which the phase reference is lost only after m uses of the transmission line, due to a finite decoherence time. In this setting, focusing on the simplest case m = 2, we analyze two communication strategies using coherent states of the electromagnetic field and show that it is not beneficial to employ the total energy to establish a reference frame but rather to spread out the energy on all the modes and directly transmit information using their relative degrees of freedom.
An Overview of Knowledge Representation With Frames

Knowledge can be represented in different formats. At present, knowledge representation as ontologies is the mainstream, while much less is heard about frames. In order to understand the state-of-the art of knowledge frames application, the authors overviewed recent research work in IEEE Xplore Digital Library, SpringerLink, ScienceDirect, and ACM Digital Library from 2000 till 2020. The overview touched such aspects as knowledge acquisition techniques, constituent parts of frames, the actual state of technologies used, capabilities of frames integration with other formats, and limitations. The results showed that native limitations of knowledge frames lead to creation of hybrid knowledge bases. However, hybrid systems also have known issues in performance and inconsistency of knowledge due to a conflict between paradigms. Moreover, a large part of technologies mentioned is not supported nowadays. The results can be useful for researchers who investigate whether to use knowledge frames for managing knowledge.