Reviewerly/exHarmony huggingface dataset:
An Efficient Fragment Reconstruction Using RANSAC Algorithm

This paper, we presented an efficient fragment reconstruction when the original image can be accessed. By using RANSAC algorithm, the computational time can be distinctly reduced. First the SIFT algorithm is used to extract and match the feature points between each piece of the puzzle and the original image. Then, the RANSAC is used to eliminate incorrect matching pairs. By using the result of RANSAC, the transformation matrix (TM) between the piece of puzzle and the original image can be obtained. The TM contains the coordinates and the rotation angle information. Finally, the TM parameters are applied to the robotic arm to solve the puzzle. However, due to the working space of the robotic arm is limited; we design a pre-rotation algorithm of the robotic arm to reduce running time. Compared with the method of the histogram based full search, the feature point matching method can greatly reduce almost 88.5% computation time.
Constructing of vulnerability prevention secure model for the cloud computing

Clouds provide a powerful computing platform is in focus, the security of the current loss of confidence in cloud computing. This study based on the traditional ISO 27001 artificial audit practice, into the waterproof wall in the cloud “information security management model”, with cloud computing development faces serious security problems. In this paper, this study based on system dynamics for information security policy to simulate the cloud computing security scenario and illustrate the cloud computing security prevention procedure. Finally, the simulation results show that the Audit system can provide preliminary prevention of data centers and endpoint host side's breach in the framework of cloud computing. The main contribution has the following three points: 1. The traditional online information security standards, through intelligent automated audit, will gradually update the manual audit mode. 2. Through the introduction of the WcsM waterproof wall concept in this study, the traditional security protection mechanisms (Firewalls, IPSs, IDSs, etc.) will have more proactive measures to ensure cloud information security. 3. The concept of reducing the capital investment by means of automated audits will further promote the importance of SMEs-based small and micro-enterprises.
Uyghur and China in The American Media Discourse: A Critical Discourse Analysis of CNN News Articles

This paper analyzes the textual aspects in Critical Discourse Analysis (CDA) Norman Fairclough model of CNN news discourse about Uyghur issue. When this research is conducted, there are only at least five articles specifically discussing Uyghur issue that are 29th December 2011, 29th February 2012, 29th February 2012, 30th October 2013, and 5th September 2014 publications. The research focuses on analyzing the text representation and the relation between participants in the discourse. Objectives of the paper are: 1) to analyze the text representation of news in the news channel of CNN related to the Uyghur case, and 2) to analyze the relation between participants in the news channel of CNN related to the Uyghur case. The subject of this paper is Uyghur issue as reflected in the news articles of CNN International. The paper applies qualitative descriptive method. As a consequence: CNN often put formality features and a vague vocabulary to block and obscure the negative value from the readers to China. The relation here is presented by CNN to China rather than CNN to Uyghur. It is reflected by the power of the status of China.
Towards the Information Security Governance for Institutions of Higher Education: Harmonization of Standards

Institutions of Higher Education have been continually threatened by the lack of direction and control from the perspective of information security in the context of information technology governance. The ISO/IEC 27014:2013 standard represents an opportunity to govern information security; however, it suffers from a clear alignment that allows it to articulate its activities with the IT governance and provide visibility to the organizational government. This exploratory and document-level study has carried out a harmonization process between the ISO/IEC 27014:2013 and ISO/IEC 38500:2015 standards with the purpose of identifying overlapping problems and strongly related elements that contribute to a consistent model of information security governance at three levels: principles (responsibility, performance, strategy, risk analysis, compliance and human behavior), objectives and indicators. As a result, the components of the information security governance model have been defined as strongly related to information technology governance. This work contributes to the knowledge and collaboration of decision-makers in the strategic steering and information security control committees of Ecuador’s higher education institutions. Future work will focus in the relation of substantives components of law of higher education, the factorial analysis of components of the model with the participation of actors from the institutions, in order to consolidate it towards what the institutions cannot do without.
Understanding Musical Predictions With an Embodied Interface for Musical Machine Learning

Machine-learning models of music often exist outside the worlds of musical performance practice and abstracted from the physical gestures of musicians. In this work, we consider how a recurrent neural network (RNN) model of simple music gestures may be integrated into a physical instrument so that predictions are sonically and physically entwined with the performer's actions. We introduce EMPI, an embodied musical prediction interface that simplifies musical interaction and prediction to just one dimension of continuous input and output. The predictive model is a mixture density RNN trained to estimate the performer's next physical input action and the time at which this will occur. Predictions are represented sonically through synthesized audio, and physically with a motorized output indicator. We use EMPI to investigate how performers understand and exploit different predictive models to make music through a controlled study of performances with different models and levels of physical feedback. We show that while performers often favor a model trained on human-sourced data, they find different musical affordances in models trained on synthetic, and even random, data. Physical representation of predictions seemed to affect the length of performances. This work contributes new understandings of how musicians use generative ML models in real-time performance backed up by experimental evidence. We argue that a constrained musical interface can expose the affordances of embodied predictive interactions.
Cellular Automata Based Image Authentication Scheme Using Extended Visual Cryptography

Most of the Visual Cryptography based image authentication schemes hide the share and authentication data into cover images by using an additional data hiding process. This process increases the computational cost of the schemes. Pixel expansion, meaningless shares and use of codebook are other challenges in these schemes. To overcome these issues, an authentication scheme is proposed in which no embedding into the cover images is performed and meaningful authentication shares are created using the watermark and cover images. This makes the scheme completely imperceptible. The watermark can be retrieved just by superimposing these authentication shares, thus reducing the computational complexity at receiver's side. Cellular Automata is used to construct the master share that provides self-construction ability to the shares. The meaningful authentication shares help in enhancing the security of the scheme while size invariance saves transmission and storage cost. The scheme possesses the ability of tamper detection. Experimental results demonstrate the improved security and quality of the generated shares of the proposed scheme as compared to existing schemes.
The Autonomous Vehicle CELiNA as Educational Platform on Final Works in Computer Science

This work introduces the design and construction of the autonomous vehicle CELiNA (Free Electric Car with Autonomous Navigation) qualitatively analyzing its use on educational experiences that have been carried out with Computer Science course students of the University of the Southwest of Bahia (UESB). As a result of its use, several works have been carried out in the course conclusion activity, which include the construction of the CELiNA prototype and other studies regarding robotic vision system and the control of CELiNA, itself. We discuss the educational gains that these UESB students have had at the end of the course, by way of using CELiNA in their completion work.
Focus on Practical Disciplines as a Method of Developing Entrepreneurial Mindset

The purpose of the study is to develop a model for optimizing the practical component of the educational process from the perspective of developing entrepreneurial competences. The research methods include analysis, inductive and deductive approach, comparative and systematic approach, methods of generalizing pedagogical experience regarding the organization of entrepreneurial education and approaches to the development of entrepreneurial competences. Within the framework of the study, a comparative analysis of the entrepreneurial education system in Russia and foreign countries was carried out; the shortcomings of the educational process organization in the context of the entrepreneurial competence development were identified; a model for optimizing the practical component of the educational process from the perspective of developing entrepreneurial competences was developed. The practical relevance of the research is an attempt to develop a model for optimizing entrepreneurial education in former command economies. Changes in the education system can contribute to the solution of socio-economic problems: employment of graduates, mismatch between education and labor market requirements, as well as insufficient development of dual education. The materials and results of the study can be used to optimize educational curricula, normative and legal regulation in the sphere of science and education, as well as to develop standards for entrepreneurial education in Russia.
Intelligent Radio: When Artificial Intelligence Meets the Radio Network

The articles in this special section provide a comprehensive overview on the recent development of the intelligent radio. The advances in wireless communications have continuously been pushing the limit of radio technologies. Nowadays, radio networks can provide extremely high data rate, ultra-low latency, and high reliability to serve communication needs of sectors that could not be imagined before. However, radio technologies have become highly complex and call for new solutions. The recent advances in artificial intelligence (AI), including machine learning (ML), data mining, and big data analysis, bring significant promise for addressing hard problems in radio networks. It has been the increasing trend to move the intelligence beyond the spectrum access, which is primarily targeted by cognitive radio, to address various challenges in radio networks, including, but not limited to, channel modeling, modulation, beamforming, radio resource allocation, and network management. Radio technologies are on the way evolving to the intelligent radio, in which AI/ML frameworks and algorithms are applied to learn from environments and explore hidden characteristics of networks for new capacity, performance, and services. We believe the intelligent radio will be the prominent feature of next generation wireless networks. It calls for interdisciplinary research to integrate the advances in AI/ML, communications, computing, and cloud technologies. Both theoretical and applied breakthroughs are expected in this new area.
Economic and Organizational Problem Research Accompanying the Processes of Replacing Labor Resources in the Condition of Incomplete Information

Abstract One of the most characteristic features of modern theory and practice of production is a systematic approach used in considering a wide variety of economic and organizational problems. To a large extent, this relates to optimization problems and this is the reason why a number of authors single out the principle of optimization among the principles of the system approach. The article deals with the cost optimization for the reservation of labor resources and provides an algorithm for finding the costs of reservation.
The smart technologies application for the product life-cycle management in modern manufacturing systems

Perspective directions of development of information technologies in the field of improvement of programs of universal artificial intelligence for tasks of the organization of production are Considered. Modern trends of application in production are considered and advantages to companies are analyzed. The concept of building a production process using smart technologies and other artificial intelligence approaches is proposed.
Visual Descriptor Learning from Monocular Video

Correspondence estimation is one of the most widely researched and yet only partially solved area of computer vision with many applications in tracking, mapping, recognition of objects and environment. In this paper, we propose a novel way to estimate dense correspondence on an RGB image where visual descriptors are learned from video examples by training a fully convolutional network. Most deep learning methods solve this by training the network with a large set of expensive labeled data or perform labeling through strong 3D generative models using RGB-D videos. Our method learns from RGB videos using contrastive loss, where relative labeling is estimated from optical flow. We demonstrate the functionality in a quantitative analysis on rendered videos, where ground truth information is available. Not only does the method perform well on test data with the same background, it also generalizes to situations with a new background. The descriptors learned are unique and the representations determined by the network are global. We further show the applicability of the method to real-world videos.
HDA Video Transmission Scheme for DASH

In recent years, with the development of Internet communication technology, more and more people tend to get information through the network. And people prefer multimedia video streaming services. Traditional streaming media technology cannot meet people's needs because of its limitations. Recently, HTTP-based Dynamic Adaptive Streaming Over HTTP (DASH) has emerged as a new approach. In this paper, we consider two hybrid digital and analog video transmission schemes for DASH and propose three methods of enhancing the video quality of experience (QoE) of DASH users. First of all, considering the importance of data to video quality, we propose an energy-aware recombination method of reorganizing and packaging data and define the priority of the generated DASH layer in transmission. Second, we design an adaptive bitrate allocation algorithm to improve bandwidth utilization and video quality. Finally, we propose a retransmission mechanism to address the situation in which the digital stream at the receiving end cannot be decoded due to channel distance errors. Experiments show that our two schemes are superior to traditional DASH schemes.
STDC‐Flow: large displacement flow field estimation using similarity transformation‐based dense correspondence

In order to improve the accuracy and robustness of optical flow computation under large displacements and motion occlusions, the authors present in this study a large displacement flow field estimation approach using similarity transformation‐based dense correspondence, named STDC‐Flow approach. First, the authors compute an initial nearest‐neighbour field by using the STDC‐Flow of the consecutive two frames, and then extract the consistent regions as the robust nearest‐neighbour field and label the inconsistent regions as the occlusion areas. Second, they improve a non‐local total variation with the L 1 norm optical flow model by using the occlusion information to modify the weighted median filtering optimisation. Third, they fuse the robust nearest‐neighbour field and the computed flow field of the improved variational optical flow model to construct the final flow field by using the quadratic pseudo‐boolean optimisation fusion algorithm. Finally, the authors compare the proposed STDC‐Flow method with several state‐of‐the‐art approaches including the variational and deep learning‐based optical flow models by using the MPI‐Sintel and KITTI evaluation databases. The comparison results demonstrate that the proposed STDC‐Flow method has a high accuracy for flow field computation, especially the capacity of dealing with large displacements and motion occlusions.
Technical Interoperability to Solve Cross-Domain Issues Among Federation Systems

Digital identity management with the metamorphosis of web services enforces new security challenges. A set of identity management systems exists to deal with these identities, alongside the goal of improving user experience and gain secure access. Nowadays, one faces a large number of heterogeneous identity management approaches. This study treated several identity management systems. The federated system makes proof of it eligibility for the identity management. Thus, the researcher interest is on the federated model. Since it consists of the distribution of digital identity between different security domains. The base of security domains is a trust agreement between the entities in communication. Federated identity management faces the problem of interoperability between heterogeneous federated systems. This study is an approach of a technical interoperability between the federations. The authors propose an approach that will permit inter-operation and exchange identity information among heterogeneous federations.
Linear Cryptanalysis of Reduced-Round Simon Using Super Rounds

We present attacks on 21-rounds of Simon 32/64, 21-rounds of Simon 48/96, 25-rounds of Simon 64/128, 35-rounds of Simon 96/144 and 43-rounds of Simon 128/256, often with direct recovery of the full master key without repeating the attack over multiple rounds. These attacks result from the observation that, after four rounds of encryption, one bit of the left half of the state of 32/64 Simon depends on only 17 key bits (19 key bits for the other variants of Simon). Further, linear cryptanalysis requires the guessing of only 16 bits, the size of a single round key of Simon 32/64. We partition the key into smaller strings by focusing on one bit of state at a time, decreasing the cost of the exhaustive search of linear cryptanalysis to 16 bits at a time for Simon 32/64. We also present other example linear cryptanalysis, experimentally verified on 8, 10 and 12 rounds for Simon 32/64.
ERROR ANALYSIS ON USING SIMPLE PAST TENSE IN WRITING RECOUNT TEXT AT THE EIGHTH GRADE OF SMP BINA BERSAUDARA 1 MEDAN

This research was discussed about error analysis on using simple past tense in writing recount text. The objective of the study were to find out the types of errors, to describe the students error, and to find out the difficulties of students on using simple past tense in writing recount text. The approach of this research is qualitative research. The instrument of this research were writing a recount text, choose the right sentence or word from a recount text, and interview. The subjects of this research were 25 students of eighth grade students of SMP BINA BERSAUDARA 1 Medan. The data was collected by students’ answer sheet. The collected data was identified, calssified, analysed and interpreted based on the types of errors using theory corder’s were 237 errors. The finding revealed that there are 4 types of errors. They are omission, addition,selection, and ordering. Selection was the dominant errors with 151 errors (64%), followed by ordering with 37 errors (16%), addition with 31 errors (13%), and the last was addition wirh 18 errors (7%). Based on the data analysis above, the most of students’ errors was selection and the least of students’ errors was addition.
Transformation optimization and image blending for 3D liver ultrasound series stitching

We propose a consistent ultrasound volume stitching framework, with the intention to produce a volume with higher image quality and extended field-of-view in this work. Directly using pair-wise registrations for stitching may lead to geometric errors. Therefore, we propose an approach to improve the image alignment by optimizing a consistency metric over multiple pairwise registrations. In the optimization, we utilize transformed points to effectively compute a distance between rigid transformations. The method has been evaluated on synthetic, phantom and clinical data. The results indicate that our transformation optimization method is effective and our stitching framework has a good geometric precision. Also, the compound images have been demonstrated to have improved CNR values.
Synthetic Ground Truth for Presegmentation of Known Objects for Effortless Pose Estimation

We present a method for generating synthetic ground truth for training segmentation networks for presegmenting point clouds in pose estimation problems.Our method replaces global pose estimation algorithms such as RANSAC which requires manual fine-tuning with a robust CNN, without having to hand-label segmentation masks for the given object.The data is generated by blending cropped images of the objects with arbitrary backgrounds.We test the method in two scenarios, and show that networks trained on the generated data segments the objects with high accuracy, allowing them to be used in a pose estimation pipeline.
Edge Computing for Mixed Reality

Mixed reality, or augmented reality, where the real and the virtual worlds are combined, has seen an increase in interest in recent years with the release of tools like Google ARCore and Apple ARki ...
Entornos virtuales de aprendizaje: usabilidad y alcance en la formación de competencias profesionales del área educativa

Given the paradigm of Education 4.0, higher education institutions transit to the incorporation of technologies into the teaching-learning process to increase the educational quality of the programs. In this process, it is important to consider the educational implications of the use of Technologies and their scope as a mediating tool for professional learning. In the present investigation, the usability and scope of the Virtual Learning Environments (VLE) in the formation of professional competencies in the undergraduate and postgraduate students of the educational area were analyzed. No experimental, cross-correlational research was designed, the instrument was developed ad hoc on Google Forms digital platform. 43 students enrolled in undergraduate and postgraduate education programs participated. The quantitative results indicate a statistically significant moderate correlation between the degree of mastery of virtual learning environments and the degree of consolidation of professional skills. Also, the qualitative analysis of the questionnaire indicates a positive scope in the following dimensions: a) obtaining scientific information, b) improvement in teacher-student, student-student communication, c) improvement in professional training and job opportunities, d) tools for research and academic projects. The data are discussed about the usability of virtual learning environments in the context of the training of professional skills of the 21st century.