Reviewerly/exHarmony huggingface dataset:
Script Identification of Movie Titles from Posters

In this work, a new problem of script identification in movie posters has been addressed. Movie posters contain an amalgamation of different types of objects like images of actors, sceneries, different graphic symbols, several texts having disparate fonts, colors, textures, etc. Such a complex set of components makes it a challenging task in the automatic identification of the script of the movie titles for further processing. Before identifying the script of the titles, localization of the texts is very much necessary. Using transfer learning and non-maximum suppression the text localization has been performed followed textural feature-based script identification among Bangla, Devanagari, and Roman. We experimented with these poster images from Tollywood, Bollywood, and Hollywood and obtained the highest accuracy of 90.65%.
SEA: An UML Profile for Software Evolution Analysis in Design Phase

Software evolution is one of the software process activities that occupies a major percentage of software development cost.Since requirements change continually and new technologies emerge, software should be adapted to satisfy these new changes to continue to survive.Despite software evolution being performed after software validation and deployment, software developers should predict at earlier stages how software would evolve in the future to avoid surprises.Although many works focus on how to enhance the program structure to facilitate maintenance tasks, only few works treat software evolution in earlier phases of software development process.In this direction, we propose an UML profile that permits to tackle software maintenance issues at the early phases of software development process.The proposed approach helps software developers to predict in design phase the kind of maintenance tasks that could occur in the future.
Statistical <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi mathvariant="script">EL</mml:mi></mml:math> is ExpTime-complete

We show that the consistency problem for Statistical EL ontologies, defined by Peñaloza and Potyka, is -hard. Together with existing upper bounds, we conclude -completeness of the logic. Our proof goes via a reduction from the consistency problem for EL extended with negation of atomic concepts.
Deep Learning on Superpoint Generation with Iterative Clustering Network

In 3D point clouds, superpoint is a set of points that share common characteristics. Semantically pure superpoints can greatly reduce the number of points while ensuring that the points located in the same superpoint have common semantic information. In this paper, we propose an end-to-end method for generating semantically pure superpoints. Specifically, we first use a light PointNet-liked network to embed low-dimensional point clouds into feature space to obtain semantic information. Next, we use farthest point sampling (FPS) to sample K points as the initial cluster centers. For each center, we cluster the points by jointly considering spatial and feature space. After clustering, we update the feature of each cluster center by simply averaging the point feature in the same cluster. By iteratively clustering and updating the feature of clusters, we obtain coarse superpoints, which contain a few points incorrectly clustered. Finally, to eliminate incorrectly clustered points, we leverage the breadth-first-search (BFS) to find and fuse them to obtain fine superpoints, leading to improvement on semantically pure superpoints. Extensive experiments conducted on S3DIS and ScanNet demonstrate the effectiveness of the proposed method. Furthermore, we achieve the state-of-the-art on both two datasets.
Improving user experience with TOTP hardware tokens by implementing QR codes and HID keyboard emulation

The paper presents a concept of a device that will introduce an effort to minimize user interaction by transferring the one-time password codes from hardware devices to the main systems via alternative methods to avoid manual entry by implementing keyboard emulation and quick response code generation.
Design and Implementation of intelligent car based on MK60

Since the 1970s, intelligent cars have been studied in developed countries and China also start to develop this technology since the 1980s. Up to now, the technology of driverless cars has made a breakthrough in intelligence and feasibility. Road detection and intelligent control are two key factors to ensure the safe operation of driverless cars. This paper takes “Freescale” Cup National Intelligent Car Competition as the background to introduces the design of intelligent car, and intelligent control algorithm.
Generating Stylistic Images by Extending Neural Style Transfer Method

Fine arts have long been considered a reserved mastery for the minority of talented individuals in society. The ability to create paintings using unique visual components such as color, stroke, theme, and other creative aspects is currently beyond the reach of computer algorithms. However, there exist algorithms, which have the capability of imitating an artist's painting style and stamping it on to virtually any image to create a one-of-a-kind piece. This paper introduces the concept of using a convolutional neural network (ConvNet or CNN) to individually separate and recombine the style and content of arbitrary images to generate perceptually striking “art” [2]. Given a content and style image as reference, a pre-trained VGG-16 ConvNet can extract feature maps from various layers. Feature maps hold semantic information about both reference images. Loss functions can be developed for content and style by minimizing the mean-square-error between the feature maps used. These loss functions can be additively combined and optimized to render a stylistic image [6]. This technique is called Neural Style Transfer (NST) originally proposed by Leon Gatys in his 2015 research paper, “A Neural Algorithm of Artistic Style”. This research project attempts to replicate and improve upon the work done by Leon Gatys. The purpose of this research is to experiment using a variety of feature maps and optimizing the loss function to identify visually appealing results. A total variation loss factor is introduced to minimize pixilation and sharpen feature formation. Images generated have been assigned a Mean Opinion Score (MOS) by a group of non-bias individuals to affirm the attractiveness of the results.
Personal Data as a Critical Element of Sustainable Systems—Comparison of Selected Data Anonymization Techniques

In order to facilitate the sharing and dissemination of public data, the open data concept has been developed in recent years. Apart from its unquestionable positive effects, the whole process of opening data can also lead to negative ones. In most of the sectors processing vast amounts of data, such as medical, financial, or environmental, there are also legally protected data, the publication of which has far-reaching pejorative consequences. The article presents selected methods for sharing data containing confidential information in open data systems. The depersonalization of data subject to modifications makes it impossible to identify the subject that has been moved. Anonymization and pseudo-anonymization techniques were used. The most popular methods of data anonymization were compared: masking, permutations, adding noise, k-anonymization, l-diversification, and t-closeness. The risk of data extraction, correlation, or reasoning was determined based on the research.
Bidirectional Boost: On Improving Tibetan-Chinese Neural Machine Translation With Back-Translation and Self-Learning

Despite the remarkable success of Neural Machine Translation system, such challenges as its drawback in low-resourced conditions persist. In recent years, working mechanism of exploiting either one or both source and target side monolingual data within the Neural Machine Translation framework gained much attention in the field. Among many supervised and unsupervised proposals, back translation is increasingly seen as one of the most promising methods to improve low-resource NMT performance. Regardless of its simplicity, the effectiveness of back translation is highly dependent on performance of the backward model which is initially trained on available parallel data. To address the dilemma of back translation practices in low resource scenarios, we propose to employ target-side monolingual data to improve both backward and forward models by step-wise adoption of self-learning and back translation, which we refer to as Bidirectional Boost.Our experiments on a Tibetan-Chinese translation task attested the proposed approach with a result of producing 3.1 and 8.2 BLEU scores, respectively, both on forward and backward models over vanilla Transformers trained on genuine parallel data under supervised settings.
Region-Attentioned Network with Location Scoring Dynamic-Threshold NMS for Object Detection in Remote Sensing Images

Object detection in remote sensing images is of importance in the field of computer vision. Although many advanced methods have succeeded in natural images, the progress in remote sensing images is relatively slow due to the complex backgrounds, vertical views, and variations in kind and size of the objects. To solve these problems, we propose a region-attentioned network with location scoring dynamic-threshold NMS for object detection in remote sensing images. In particular, we first introduce the saliency constraint and propose a region-attentioned network (RANet) to effectively enhance the object regions for better detection. Meanwhile, the proposed network adopts a feature pyramid, which fully uses the low-level and high-level features, to improve the ability for handling the multiscale objects. Then, considering that there are many dense objects in remote sensing images, we propose a novel dynamic-threshold NMS (DTNMS) method for overlap detection elimination, which is more reasonable and efficient than the traditional NMS method. In addition, we further employ the IoU header to obtain the location information of the predicted boxes and propose location scoring dynamic-threshold NMS (LSDTNMS), which can further improve the detection performance. Due to the prediction of the target mask in RANet, we can also obtain the detection results of the rotating bounding box. To verify the effectiveness of the proposed method, we execute comparative experiments on the remote sensing public dataset and the experimental results demonstrate that the proposed method significantly outperforms state-of-the-art methods.
Model to Form Schoolchildren’s Professional Self-Determination within Educational Environment “Secondary School - Military Higher School”

The article considers the process of forming schoolchildren’s professional self-determination within the educational environment “secondary school - military higher school”. The author presents a pedagogical model to form schoolchildren’s professional self-determination on the basis of multi-level cooperation of educational institutions; reveals the pedagogical conditions influencing professional self-determination formation; describes research methods; analyses effects of introducing the model. Scientific originality of the study involves developing a model to form schoolchildren’s professional self-determination within the educational environment “secondary school - military higher school”. The experiment results testify to efficiency of the proposed pedagogical model when forming schoolchildren’s professional self-determination.
Initial Evaluation of System for Visualizing Novices’ Unaware Search Intentions by Analyzing Behaviors of Article Selection

With the increasing diversity and complexity of the Web, skills for online exploration have become more and more important. People typically obtain such skills by gaining experience with classifying and organizing acquired Web pages, creating search queries. However, it is difficult for unskilled people to get the opportunity for meaningful experience. In this research, we aim to develop a mechanism for utilizing the intentions of inexperienced searchers to cultivate exploration skills in realistic way. To achieve this goal, we focused on the attention paid by a searcher to specific pages during the search process. After analyzing this behavior, we developed several methods for extracting clues that embody the searcher's unaware intentions for use in further exploration. Then, with these methods as a basis, we designed a system for visualizing unaware intentions to promote the experience of exploration in novices. Experimental results using a prototype demonstrate the effectiveness of our methods.
High Capacity Blind Information Hiding Based on Autoencoder

Abstract To realize image information hiding, we train a multi-layers autoencoder includes an encoder and a decoder. Using encoder architecture, we input the secret color image and generate the cover image where the size stays the same directly. In addition, we reconstruct the secret color image by inputting the stego images passing on the decoder which accomplish the secret image blind extraction. Our method realizes a high resolution and high payload color image steganography. Experimental results show that we can realize the high capacity blind specified secret images information hiding automatically by our method.
Real-Time Video Processing System for Fixed-Wing UAV

A real video surveillance is the most effective and powerful way to monitor a particular remote area and fixed-wing Unmanned aerial vehicles (UAVs) are the most popular UAV to complete this job. This paper proposed a hardware integration method of the vision system with a communication setup, which is mounted on our fixed-wing UAV. During the practical implementation of real time video surveillance systems, a lot of challenges like delay, image quality, range, etc., are encountered. Handling of these challenges are discussed in this paper.
Game-Theoretic Analysis on CBDC Adoption

As an important blockchain application, CBDC (Central Bank Digital Currency) has received significant worldwide attention as it can restructure financial market, affect national currency policies, and introduce new regulation policies and mechanisms. It is widely predicted that CBDC will introduce numerous digital currency competitions in various aspects of the global financial market, and winners will lead the next wave of digital currency market. This paper applies the game theory to study the competitions between different countries, in particular to analyze whether they should adopt the CBDC program. We propose two game-theoretic models for CBDC adoption, both analyzing whether to adopt the CBDC program via the Nash equilibrium. Both game-theoretic models draw the same conclusion that each country should adopt the CBDC program regardless of the choices of other counties. In other words, current currency leaders should adopt CBDC because it may lose the premier status, and other countries should adopt CBDC otherwise they risk of getting even further behind in the digital economy. According to our game-theoretic models, the current market leader who has 90% of market shares may lose about 19.2% shares if it is not the first mover.
An Advanced Signature Scheme Based on Non-abelian Group Over Algebraic Technique

In general, digital signatures are part of cryptosystem. Here, signature scheme is based on non-abelian property of groups. It proposes an advanced signature scheme constructed over non-abelian group above algebraic technique in this paper. It is defined a scheme of signature algorithm through generation and verification signature. Here, algorithm is verified and confirmed by illustrations. To strengthen the algorithm, security analysis is given in this paper.
Chinese Zero Pronoun Resolution Based on Biaffine Attention Mechanism

To solve the task of Chinese zero pronoun resolution, this paper proposes a Chinese zero pronoun resolution based on biaffine attention mechanism. First, Bert is employed to get representation of the text, and then build a candidate antecedent set for the target zero pronoun, and then get the representation of the local information and global information respectively for each antecedent candidate. Finally, biaffine attention mechanism is used in classification of the zero pronoun resolution. The Experiments on Chinese OntoNotes5.0 dataset show that our proposed approach can effectively improve the effect of Chinese zero pronoun resolution, and the F1 value reaches 59.7%.
Autonomous Provisioning of Preemptive Instances in Google Cloud for Maximum Performance Per Dollar

Cloud computing and its popularity has boomed over the last decade, enabling anyone to rent computing power on demand. Cloud providers such as Amazon and Google rent out surplus computing power for a discounted price according to demand in their data centers, but with the trade off that it is revocable and can only be rented for a short amount of time.This paper investigates the use of surplus computing power in order to reduce the cost of batch computing. We rely on a simple economical principle, the most cost-efficient Virtual Machine (VM) in a public cloud is the one that offers the highest performance per dollar. Therefore by rescheduling the workloads to the most cost-efficient location in terms of performance per dollar our solution dynamically provisions preemptible VMs in Google Cloud while continuously monitoring the performance per dollar of all available resources in every region. The algorithm automatically relocates workloads to a less expensive location if any appears and handles revoked access of the resources. Our algorithm views the cost reduction problem as a linear optimization problem with constraints and solves it using a greedy procedure. In the experiment we spawn Docker containers to mine cryptocurrency. The experimental results show that 67% of the cost is saved compared to renting on-demand VMs. The system can readily be extended to containers processing similar types of workloads and more generally to applications where the performance per dollar is easy to measure.
Forecasting of Credit Card Default Based on Incremental Random Forest

Abstract In this paper, the incremental random forest algorithm is proposed for the classification and prediction problem of dynamically increasing data. Traditional batch machine learning algorithms perform modeling at one time and cannot allow newly generated samples to participate in learning, which leads to too much model deviation. This paper combines incremental learning with random forest and proposes incremental random forest. Applying this algorithm to the problem of predicting credit card customer default behavior can help banks control risks and reduce losses. It is important to conduct card issuance audits on card issuers and early warning of risks to cardholders. The algorithm performed better in the experiment of predicting the default behavior of credit card customers based on a batch of credit card holder data of a bank in Taiwan. Compared with random forest, decision tree, logistic regression, naive bayes, BP neural network, and support vector machine, it has relatively better performance in our experiment.
Modelling 4-Dimensional Tonal Pitch Spaces with Hopf Fibration

The question of how to arrange harmonically related pitches in space is a historical research topic of computational musicology. The primitive of note arrangement is linear in 1-D, in which ordered ascending pitches in one direction correspond to increasing frequencies. Euler represented harmonic relationships between notes with a mathematical lattice named Tonnetz, which extends the 1-D arrangement into 2-D space by reflecting consonances. Since then, mathematicians, musicians, and psychologists have studied this topic for hundreds of years. Recently, pitch-space modelling has expanded to mapping musical notes into higher-dimensional spaces. This paper aims to investigate existing tonal pitch space models, and to explore a new approach of building a pitch hyperspace by using the Hopf fibration.
A Novel Solution to Cloud Data Security Issues

Cloud Computing is one of the fastest emerging technology which is in a massive growth to transform the definition of computing with its service oriented paradigm. It has overcome the issue related accruing the own IT infrastructure by proving the service through pay and use model. As a resultant reduce the cost of ownership, headache of manageability and saving of time of or execution of the process. However, the cloud services could be deterring due to the security breaches. Data security might be the major issue in cloud as the entire service is provided through internet only. Current data encryption mechanism might not be so strong to provide true privacy and reliability. Here in this paper we proposed a novel solution to data security with data encryption along with salted password hashing technique. Efficacy of the proposed method is validated through comparison with the current traditional security approach on the basis of the security parameters.
Story-Making for School: Essence, Notion, Educational Potential

The purpose of the study is to determine possibility of using socio-cultural phenomenon of story-making, popular in online space, as a pedagogical tool in a modern school. The article examines essence of socio-cultural phenomenon of story-making; describes features of culture of “co-participation”, which determine, among other things, formation of new pedagogical tools; justifies appropriateness of using story-making as a pedagogical tool in a modern school. Scientific novelty of the research lies in identifying characteristic features of socio-cultural phenomenon of story-making, substantiating multi-functionality of phenomenon of story-making, broadening understanding of the potential that phenomenon of story-making carries through its use as a pedagogical tool. As a result, it is proved that story-making can be used in a modern school as a pedagogical tool.
A Multi-engine Aspect-Oriented Language with Modeling Integration for Video Game Design

Video game programming is a diverse, multi-faceted endeavor involving elements of graphics programming, systems programming, UI, HCI, and other software engineering disciplines. Game programmers typically employ a new “codebase” per software artifact which often means a unique choice of game engines and scripting languages. Non-portable code is exacerbated by a lack of a shared language and a lack of translation utilities between languages. Meanwhile, many game programming tasks occur time and time again. Aspect-oriented programming was largely developed to assist software engineers in decoupling tasks while maintaining software reuse. GAMESPECT is a language that promotes software reuse through aspects while also providing a platform for translation of software artifacts which has enabled it to be used in multiple game engines across multiple projects. Code reuse on these projects has been high and our methodologies can be summarized by discussing three tenants of GAMESPECT: 1) composition specifications, which define source to source transition properties 2) pluggable aspect interpreters and 3) high level language constructs and modeling language constructs (MDAML) which encourage designer friendly terminology. By comparing accuracy, efficiency, pluggability, and modularity, these three tenants are demonstrated to be effective in creating a new game programming language.