Reviewerly/exHarmony huggingface dataset:
Digital Future For Man As A Social Being

The purpose of the study is to consider the non-obvious and dangerous consequences of the process of digitalization of social live and to propose some means of their neutralization. The article raises such questions as: What is the ultimate goal of the creators of artificial intelligence: freeing natural intelligence from routine work or replacing the natural intelligence of socio-cultural subjects with artificial intelligence of technical devices? What are the limits of permissible facilitation of intellectual activity? How does the steady replacement of the natural and socio-cultural environment of mankind affect the formation of human intelligence and socio-cultural qualities? Today it is not enough to say that robots should not harm human beings. The leitmotif of humanitarian arguments about the digital future of mankind should be the idea of preserving not only the natural environment, the animal and plant world of the Earth, but also the human being as a biological species and the subject of socio-cultural activities. Really competent specialists for the digital society should receive higher education, which makes it possible for them to understand that social life is a result of activity and a form of existence of socio-cultural subjects, but not of virtual characters that appear and disappear “by the click” of IT-specialists. The objective of higher education for digital society is specialists capable to create such technologies, the use of which requires development of socio-cultural qualities of a person.
Pose and Illumination Invariant Hybrid Feature Extraction for Newborn

Background: Abduction, swapping and mix-ups are the unfortunate events that could happen to Newborn while in hospital premises and medical personnel are finding it difficult to curb this unfortunate incident. Accurate patient identification (ID) is essential for patient safety, especially with our smallest and most vulnerable paediatric patients. The level of security is very crucial issue in maternity ward and the problem of missing and swapping of Newborn is of prime concern to the persons involved and affected. There is a common perception in the society that nothing can be done to prevent this unfortunate tragedy. In comparison to developed nations the developing countries are facing more challenges because of overcrowding and scarcity of medical facilities in the hospital. The face of a newborn baby changes appearance in different lighting conditions in spite of various contrast enhancement techniques. The class of images which are under similar environment conditions (inter-class) may result in more differences than that of the intra-class images under different illumination conditions. This gives rise to the misclassification of images. Objective: The main objective of this paper is perform newborn face recognition in a hybrid approach. Speeded Up Robust Features (SURF) and Local Binary Pattern (LBP). Methods: The scientific contributions of the proposed work are stated below: For face recognition of newborns in a semi controlled environment. Overcoming the pose and illumination challenge. Uses single gallery image. Uses a hybrid approach to improve the results. Conclusion: In this paper new semi supervised technique is used for demonstrate the improve the performance of the newborn face recognition system in different illumination and pose conditions. In our. Results: The average Rank 1 accuracy of the proposed method is 93.65% whereas that of the existing algorithms is quite low. The proposed technique is 12.8% more accurate than LBP, 10.5% more accurate than SURF, 12.8% more accurate than LDA and 18.1% more accurate than PCA for rank1.
Parsimonious online learning with kernels via sparse projections in function space

Despite their attractiveness, popular perception is that techniques for nonparametric function approximation do not scale to streaming data due to an intractable growth in the amount of storage the...
Dry As the Desert? On the Liquidity of a Bitcoin Exchange

We analyze liquidity provision on Kraken, one of the major cryptocurrency exchanges, and compare the results to the foreign exchange (FX) and the stock market, recognizing that Bitcoin has originally been designed as a digital currency and evolved to an investment. In a first step, we characterize liquidity based on traditional models for market microstructure to capture different aspects of the spread. These measures unanimously indicate that the Bitcoin market on Kraken is extremely illiquid compared to the FX markets and usually less liquid than the stocks in our stock market sample. In a second step, we identify liquidity determinants and show that both market specific characteristics (e.g., noise, volatility) and blockchain characteristics play a role in explaining on-exchange liquidity.
An analysis of research papers on English teacher education published in Modern English Education

This paper analyzed 28 articles on English teacher education out of 413 articles published in Modern English Education over the past 10 years since it was first indexed in the Korea Citation Index, to investigate the trend and characteristics of research on English teacher education in the journal. The articles were categorized and analyzed according to subject (teacher status and school level), research methodology, and research theme. The findings are as follows: Pre-service elementary school teachers were less studied than in-service elementary school teachers, pre-service and in-service secondary school teachers; quantitative, qualitative, and mixed methodologies were employed in similar proportions across the studies; among eight research themes including teacher competence, teacher identity, and teacher training program, teacher cognition was the most studied. These findings show that the journal reflects changes in the field of second language teacher education (SLTE) and the key issues in research on SLTE.
A Conceptual Model for Dynamic Access Control in Hadoop Ecosystem

Big data, huge and varied collections of data, is a blanket concept that is greatly used in recent years. Hadoop has imposed itself as the principal big data platform for storing and processing multiple data types. It is widely used in various sectors which may contain a huge amount of sensitive data. Hadoop provides a highly available, cost-effective, and fault-tolerant ecosystem to deal with sensitive data with high speed. Due to the distributed nature of this platform, privacy and security threats have become a critical concern within Hadoop. For this reason, the proposed Dynamic Data Sensitivity Access Control (D2SAC) framework in [] aims to protect sensitive data contained in Hadoop platform. In this paper, we aim to keep enhancing this framework by presenting a conceptual model of the D2SAC framework, then we provide a formal definition of each component of this conceptual model. In addition, we give a detailed description of the Access Enforcement Module that is responsible for providing the access decision about the user request.
IMPLEMENTASI RAPID APPLICATION DEVELOPMENT DALAM PENGEMBANGAN APLIKASI PELAPORAN KERUSAKAN JALAN

&lt;p class="Abstract"&gt;Proses pengembangan aplikasi merupakan salah satu kondisi yang sangat penting agar mendapatkan aplikasi yang baik. Untuk mengembangan aplikasi terdapat berbagai metode pengembangan yang dapat digunakan. Salah satu metode tersebut yaitu rapid application development (RAD). Metode RAD merupakan metode pengembangan yang melibatkan pengguna atau client. Untuk itu di dalam penelitian ini dilakukan implementasi metode RAD dalam proses pengembagnan aplikasi pelaporan kerusakan jalan. Dimana kebutuhan dari aplikasi ini untuk menjawab permsalahan dalam proses pengaduan kerusakan jalan yang selama ini memiliki mekanisme birokrasi yang panjang. Dengan dibuatnya aplikasi diharapkan mampu menjadi salah satu alternative bagi masyarakat untuk menyampaikan laporan dengan cepat, tepat dan akurat. Hasil dari penelitian ini berupa aplikasi pelaporan yang dibuat dengan dua antarmuka yaitu mobile yang diperuntukkan bagi masyarakat dan web yang diperuntukkan bagi pemerintah. selain itu juga aplikasi yang dihasilkan telah dikembangkan secara terstruktur mengikuti proses dari metode RAD mulai dari perencanaan, desain RAD dan implementasi. Hasil implementasi menunjukkan bahwa proses pelaporan kerusakan jalan dapat dilakukan yang dibuktikan dengan akses pengguna masyarakat dapat melakukan laporan dan akses admin dapat menindak lanjuti laporan tersebut. &lt;/p&gt;&lt;p class="Abstract"&gt; &lt;/p&gt;The application development process is one of the most important conditions in order to get a good application. To develop applications, there are various development methods that can be used. One such method is the rapid application development (RAD). The RAD method is a development method that involves users or clients. For this reason, in this study, the implementation of the RAD method is carried out in the process of developing a road damage reporting application. Where is the need for this application to answer problems in the process of complaints about road damage which so far has a long bureaucratic mechanism. By making the application, it is hoped that it can become an alternative for the public to submit reports quickly, precisely and accurately. The results of this study are in the form of a reporting application made with two interfaces, namely a mobile which is intended for the public and a web which is intended for the government. Besides that, the resulting application has been developed in a structured manner following the process of the RAD method starting from planning, RAD design and implementation. The implementation results show that the road damage reporting process can be carried out as evidenced by the access of community users who can make reports and admin access can follow up on the report.
Tracking Objects Based on Multiple Particle Filters for Multipart Combined Moving Directions Information

Object tracking is an important procedure in the computer vision field as it estimates the position, size, and state of an object along the video’s timeline. Although many algorithms were proposed with high accuracy, object tracking in diverse contexts is still a challenging problem. The paper presents some methods to track the movement of two types of objects: arbitrary objects and humans. Both problems estimate the state density function of an object using particle filters. For the videos of a static or relatively static camera, we adjusted the state transition model by integrating the movement direction of the object. Also, we propose that partitioning the object needs tracking. To track the human, we partitioned the human into N parts and, then, tracked each part. During tracking, if a part deviated from the object, it was corrected by centering rotation, and the part was, then, combined with other parts.
Methodology for calculating the average time of entry into synchronism of stations of the system of quantum key distribution with sequential polling of fiber-optic line sections with decreasing length by the graphic-analytical method

A method is proposed for calculating the average time of entry into synchronism of stations of the system of quantum key distribution (QKD) with sequential polling of sections of a fiber-optic communication line (FOCL) with decreasing length. A diagram of states and transitions for a sequential search for a photon pulse is constructed. Analytical expressions are obtained for finding the probability of detecting a photon pulse, the average number of steps for entry into synchronism of stations, the variance of the number of steps and the average time for entry into connection. It is noted that when the FOCL is divided into sections with decreasing length, the level of dark current pulses (DCP) significantly decreases from section to section. The latter allows to reduce the probability of false alarms of the photodetector. The analysis of the results obtained showed that the time of entry into synchronism for the proposed algorithm is 3 times less than the time required for entry into communication of stations in the case of an algorithm-analogue. The results obtained indicate the possibility of increasing the length of the FOCL while ensuring the value of the synchronization error probability at the level of 0.01.
Creating Website as a Service using Web Components

In today’s fast pace world, technology is changing rapidly and we are working on technologies that seems to be impossible a decade ago. Web applications and web technologies are changing the world rapidly. Web applications has become the most important feature of any business specially to showcase what they are offering and tools to use their products. To meet current business needs, web applications have become quite complex to develop, the number of files has increased and as the application grows it becomes difficult for the developer to maintain the application and as well as test the application. At the same time, we have web applications that are used as a service by various enterprises, these applications are modified according to the need of the customers, which sometime results in building the application again and modifying the app according to the customer needs, this process sometimes leads to regression which increases the delivery time as developers have to find out the root cause of the issue. To solve this issue what if we divide the whole application into components that are capable of functioning independently, we can add or remove the functionality according to our needs and it would be easier for developers to build the application even from scratch. Now, since the application is divided into components we can maintain and test these components independently allowing developers to reduce the time taken in a release cycle. In this paper my aim is to automate the process of creating a website that can be modified according to the user need without making change on the code level and also to automate the whole process using Web Components.
Light field editing in the gradient domain

This paper presents a new method for light field applications such as content replacement and fusion in the gradient domain. This approach is inspired by successful gradient domain based image and video editing techniques. A necessary and important part of gradient-based solutions is recovering the signal of interest from artificially generated, and typically non-integrable, gradient data. As such, a new algorithm is developed to reconstruct a light field from a given gradient data set. In the algorithm, first, the 4D Haar wavelet decomposition of the light field is obtained from the given gradient data. Then, the light field is obtained from a wavelet synthesis step. This algorithm is intended as a building block for gradient-based light field editing methods, and as such, its performance is analysed on a set of benchmark light field data sets. The proposed reconstruction algorithm is an essential part in developing solutions for two light field problems: light field editing and light field fusion. Results show that processing light fields in the gradient domain offers significant advantages over processing in the intensity domain.
A Retrofitting Model for Incorporating Semantic Relations into Word Embeddings

We present a novel retrofitting model that can leverage relational knowledge available in a knowledge resource to improve word embeddings. The knowledge is captured in terms of relation inequality constraints that compare similarity of related and unrelated entities in the context of an anchor entity. These constraints are used as training data to learn a non-linear transformation function that maps original word vectors to a vector space respecting these constraints. The transformation function is learned in a similarity metric learning setting using Triplet network architecture. We applied our model to synonymy, antonymy and hypernymy relations in WordNet and observed large gains in performance over original distributional models as well as other retrofitting approaches on word similarity task and significant overall improvement on lexical entailment detection task.
Software Pipelining for Quantum Loop Programs

We propose a method for performing software pipelining on quantum for-loop programs, exploiting parallelism in and across iterations. We redefine concepts that are useful in program optimization, including array aliasing, instruction dependency and resource conflict, this time in optimization of quantum programs. Using the redefined concepts, we present a software pipelining algorithm exploiting instruction-level parallelism in quantum loop programs. The optimization method is then evaluated on some test cases, including popular applications like QAOA, and compared with several baseline results. The evaluation results show that our approach outperforms loop optimizers exploiting only in-loop optimization chances by reducing total depth of the loop program to close to the optimal program depth obtained by full loop unrolling, while generating much smaller code in size. This is the first step towards optimization of a quantum program with such loop control flow as far as we know.
Sample Efficient Reinforcement Learning through Learning from Demonstrations in Minecraft.

Sample inefficiency of deep reinforcement learning methods is a major obstacle for their use in real-world applications. In this work, we show how human demonstrations can improve final performance of agents on the Minecraft minigame ObtainDiamond with only 8M frames of environment interaction. We propose a training procedure where policy networks are first trained on human data and later fine-tuned by reinforcement learning. Using a policy exploitation mechanism, experience replay and an additional loss against catastrophic forgetting, our best agent was able to achieve a mean score of 48. Our proposed solution placed 3rd in the NeurIPS MineRL Competition for Sample-Efficient Reinforcement Learning.
Radio Frequency Fingerprinting Driven Drone Identification Based on Complex-valued CNN

Drone detection and identification technique is of great significance both in the military and civilian fields.Radio frequency (RF) fingerprinting of drone is considered as one of promising techniques due to its uniqueness.Deep learning based RF fingerprinting identification technique can extract hidden features in RF data and then achieve excellent performance.Motivated by this idea, this paper proposes a drone identification method using complex-valued convolutional neural network (CNN) algorithm with higher classification accuracy and faster equipment running time.The complex-valued CNN method convolves the complex convolutional kernel and the real and imaginary parts of the data features separately.In order to verify the proposed method, five state-of-the-art recognition algorithms are adopted to compare their recognition performance and equipment efficiency.Simulation results show that our proposed drone identification method can efficiently recognize the signal of various drones within less computation time.
Model Agnostic Information Biasing for VQA

VQA involves generating information rich features from given images and questions based on them. Here we have explored the use of inducing biases and structuring of multi-modal latent spaces using fusion loss regularization. Our loss based strategy is aimed at making the multi-modal representation of the student branch (Image+Question) to be those like that of the teacher branch (Image+Answer), made with the same model. Our main contribution is that we explore a model agnostic approach based on creation of a homogeneous multimodal latent space for image with question and image with answer’s representation. To our best knowledge this is the only work exploring the use of latent space fusion using regularization for VQA.
Mitigation of Multi-Source Modulation Distortions in A-RoF Link by Using Transfer Learning aided ANN

We adopt transfer learning aided artificial neural network (TL-ANN) as the approach of post digital signal process in A-RoF link. The proposed method could mitigate multi-source modulation distortions and decrease training cost of ANN simultaneously.
Design of Information Systems Inventory Inventory In Sinar Sakti Racing Industry Based Web: Design of Information Systems Inventory Inventory In Sinar Sakti Racing Industry Based Web

Inventory Information System Design is a design system that supplies information to many process that consists of stock, inventory and reports. Therefore, it is necessary to make the Inventory Information System applications to support the company's performance manual was originally turned into a computerized system to generate reports that more rinci.Pengembangan applications is done by collecting data, system planning, system analysis, system design and development base data.Pemrograman applications developed in PHP. Development of the database used is MySQL and scripts developed by Sublime.Dapat concluded that the design and implementation of information system in Company Sinar Sakti Inventory Insdustry Racing has its dikembangkan.Dengan designed this system in the Company Sinar Sakti Racing Industry,
SMARIoT: Augmented Reality for Monitoring System of Internet of Things using EasyAR

Technology development delivers the Internet of Things (IoT) to control and monitor the digital system. Augmented Reality (AR) has the ability to display 3D virtual objects into real environments. In general, the interface on IoT monitoring uses a static computer screen, in a dashboard model. This research focuses on integrating interfaces in the IoT monitoring system using augmented reality. Our work includes making the applications for mobile devices using EasyAR and building the connectivity to an online database. We also create the prototype of an IoT device to send data. We measured the system speed displaying the object with AR. Although the data stored online, we find out how effective this system is compared to its predecessor, which is using the static dashboard. We studied and measured the convenience of the use and the user experience.