Reviewerly/exHarmony huggingface dataset:
Image Aesthetics Assessment Using Multi Channel Convolutional Neural Networks

Image Aesthetics Assessment is one of the emerging domains in research. The domain deals with classification of images into categories depending on the basis of how pleasant they are for the users to watch. In this article, the focus is on categorizing the images in high quality and low quality image. Deep convolutional neural networks are used to classify the images. Instead of using just the raw image as input, different crops and saliency maps of the images are also used, as input to the proposed multi channel CNN architecture. The experiments reported on widely used AVA database show improvement in the aesthetic assessment performance over existing approaches.
Comparative Analysis of Traditional Teaching Mode and Participatory Teaching Mode in English Major Teaching

With the continuous reform of China’s higher education and the increasing market demand for English professionals, the traditional college English teaching model has been unable to adapt to the development of the new era. In order to strengthen the training of application-oriented talents for college English majors, it has become an important task to reform the traditional English teaching mode. In the traditional teaching of college English major, it is necessary to add the participatory teaching mode. This paper mainly introduces definitions, characteristics, impacts on teachers and influences on students of two kinds of teaching modes. It also analyzes the differences of two kinds of teaching modes as well as how to combine two teaching modes and how to complement each other in order to achieve the perfect combination, making due contribution for training applied talents for English majors, adapting the time requests and making the college English teaching keep pace with the times.
Social Media and Sentiment Analysis of Nifty 50 Index

Now-a-days, micro blogging social media platforms such as twitter, foursquare, ello etc. are used extensively by a diverse range of users to express their explicit opinions about a diverse range of topics on the internet. This paper aims to collect this freely available data using web scraping techniques and analyse it to assess the general sentiment prevailing in the market regarding the Nifty 50 index. Opinions are collected explicitly from twitter using the twitter application program interface (APl). A total of 154,398 tweets were collected and analysed to calculate the proportion of positive sentiment in the markets between Jan. 1, 2016 and Dec. 31, 2017. For assessing the sentiment of a given sentence, SentiWordNet, a lexical resource for opinion mining, was used. The average sentiment for all the tweets is calculated to assess the general sentiment prevailing in the market about the Nifty 50 index on a particular day. To understand the relationship between both the variables, linear regression analysis is performed, whereby, natural log of Nifty 50 index has been taken as the dependent variable and 10 days Moving Average (10MA) of proportion of positive sentiments as the independent variable. Based on the regression analysis, the coefficient of regression was obtained as -0.602 and its p-value was obtained as 0.00, indicating that there is a significant relationship between the 10MA of proportion of positive sentiments and natural log of Nifty 50 index. As the p-value of the regression is significant, it gives us a scope to do further analysis on the collected data. If significant, traders can have an additional tool in their technical analysis kit.
Binary Search Tree

A binary search tree (BST) is a binary tree in which each node has at most two children, and it facilitates fast search, insertion, and deletion operations. The time complexity of each operation is O(log n), which is considerably faster than linear search. The two main characteristics of the binary search trees are as follows:
Efficiently computing logical noise in quantum error-correcting codes

Quantum error correction protocols have been developed to offset the high sensitivity to noise inherent in quantum systems. However, much is still unknown about the behavior of a quantum error-correcting code under general noise, including noisy measurements. This lack of knowledge is largely due to the computational cost of simulating quantum systems large enough to perform nontrivial encodings. In this paper, we develop general methods for incorporating noisy measurement operations into simulations of quantum error-correcting codes and show that measurement errors on readout qubits manifest as a renormalization on the effective logical noise. We also derive general methods for reducing the computational complexity of calculating the exact effective logical noise by many orders of magnitude. This reduction is achieved by determining when different recovery operations produce equivalent logical noise. These methods could also be used to better approximate soft decoding schemes for concatenated codes or to reduce the size of a lookup table to speed up the error correction step in implementations of quantum error-correcting codes. We give examples of such reductions for the three-qubit, five-qubit, Steane, concatenated, and toric codes.
Methodological Principles for Reproducible Performance Evaluation in Cloud Computing.

The rapid adoption and the diversification of cloud computing technology exacerbate the importance of a sound experimental methodology for this domain. This work investigates how to measure and report performance in the cloud, and how well the cloud research community is already doing it. We propose a set of eight important methodological principles that combine best-practices from nearby fields with concepts applicable only to clouds, and with new ideas about the time-accuracy trade-off. We show how these principles are applicable using a practical use-case experiment. To this end, we analyze the ability of the newly released SPEC Cloud IaaS benchmark to follow the principles, and showcase real-world experimental studies in common cloud environments that meet the principles. Last, we report on a systematic literature review including top conferences and journals in the field, from 2012 to 2017, analyzing if the practice of reporting cloud performance measurements follows the proposed eight principles. Worryingly, this systematic survey and the subsequent two-round human reviews, reveal that few of the published studies follow the eight experimental principles. We conclude that, although these important principles are simple and basic, the cloud community is yet to adopt them broadly to deliver sound measurement of cloud environments.
A Many-sorted Polyadic Modal Logic

We propose a general system that combines the powerful features of modal logic and many-sorted reasoning. Its algebraic semantics leads to a many-sorted generalization of boolean algebras with operators, for which we prove the analogue of the Jónsson-Tarski theorem. Our goal was to deepen the conne ctions between modal logic and program verification, while also testing the expressiveness of our system by defining a small imperative language and its operational semantics.
SkELL Corpora as a Part of the Language Portal Sõnaveeb: Problems and Perspectives

The paper provides an analysis of the quality and presentation of authentic corpus sentences from Sketch Engine for Language Learning (SkELL) corpora (Baisa & Suchomel 2014), based on the example of Sonaveeb (Wordweb), a new language portal being developed in the Institute of the Estonian Language. Currently Sonaveeb contains a total of 150,000 Estonian headwords; about 70,000 of them have Russian equivalents. Authentic corpus sentences are displayed for both languages. In some cases (e.g. terms, derived forms, compounds and multi-word expressions), corpus sentences are the only source of usage examples that are available on the portal. We describe the parameters of Good Dictionary Examples (GDEX) (Kilgarriff et al., 2008) configurations for Estonian and for Russian used for the compilation of etSkELL 2018 and ruSkELL 1.6 corpora, give an overview of an evaluation of the GDEX configuration for Estonian, and outline the requirements for the user-friendly presentation of SkELL corpora as a part of the language portal.
PENGEMBANGAN BAHAN AJAR TEKS SURAT BERDASARKAN SURAT PRIBADI DAN SURAT DINAS DAN IMPLEMENTASINYA DALAM PEMBELAJARAN DI SMP

This study aims to develop teaching materials for letter texts based on personal letters and letters from agencies in Brebes Regency. This research was carried out because there were still limited effective letter text textbooks that were developed and used in learning for VII graders in junior high school. The purpose of this research is to produce teaching material designs, obtain information about the feasibility of textbooks, and the effectiveness of the use of teaching materials in class VII SMP, and produce the final product of textbooks. To achieve this goal, the Research and Development (R&amp;D) research method is used by using the ADDIE research model. Based on the results of the research that has been carried out it can be seen that the textbooks developed are very feasible and can be effectively used in learning for VII grade students in junior high schools.
Sampling of Surfaces and Learning Functions in High Dimensions

The efficient representation of data in high-dimensional spaces is a key problem in several machine learning tasks. To capture the non-linear structure of the data, we model the data as points living on a smooth surface. We model the surface as the zero level-set of a bandlimited function. We show that this representation allows a non-linear lifting of the surface model, which will map the points to a low-dimensional subspace. This mapping between surfaces and the well-understood subspace model allows us to introduce novel algorithms (a) to recover the surface from few of its samples and (b) to learn a multidimensional bandlimited function from training data. The utility of these algorithms is introduced in practical applications including image denoising.
Digitalization and the Principal-Agent Problem in the Higher Education

The main aim of this article is to study the transformation of social and labor relations in the higher education in the process of the Russian economy digitalization, as well as to identify related opportunities to improve these relations because of solving the principal-agent problem. The methodology of the study is based on the principal-agent theory in with regard to the higher education, where the principal is the employer (the head of the University or structural department), and the agents are the teaching staff and employees of the University. The solution of the principal-agent problem is considered through the prism of the information asymmetry theory and its statements. The research identifies the main development tracks of social and labor relations in the higher education and proposes the digital economy tools, which allow obtaining positive effect in solving the problem of principal-agent. The article includes the conclusion and recommendations where the authors agree that the digitalization of the economy changes the content, nature and forms of the labor activity, increases the role of its individualization, and creates the new design of economic mechanisms in the personnel management system. The results of the study can be used to substantiate the strategic development directions of social and labor relations in the field of education as well as to assess the development effectiveness of these relations in the modern conditions of the Russian economy digitalization, and to develop appropriate regulatory impacts.
Entropy-Based Measure of Statistical Complexity of a Game Strategy

In this note, we introduce excess strategic entropy-an entropy-based measure of complexity of the strategy. It measures complexity and predictability of the (mixed) strategy of a player. We show and discuss properties of this measure and its possible applications.
How Do Neural Networks Estimate Optical Flow A Neuropsychology-Inspired Study

End-to-end trained convolutional neural networks have led to a breakthrough in optical flow estimation. The most recent advances focus on improving the optical flow estimation by improving the architecture and setting a new benchmark on the publicly available MPI-Sintel dataset. Instead, in this article, we investigate how deep neural networks estimate optical flow. A better understanding of how these networks function is important for (i) assessing their generalization capabilities to unseen inputs, and (ii) suggesting changes to improve their performance. For our investigation, we focus on FlowNetS, as it is the prototype of an encoder-decoder neural network for optical flow estimation. Furthermore, we use a filter identification method that has played a major role in uncovering the motion filters present in animal brains in neuropsychological research. The method shows that the filters in the deepest layer of FlowNetS are sensitive to a variety of motion patterns. Not only do we find translation filters, as demonstrated in animal brains, but thanks to the easier measurements in artificial neural networks, we even unveil dilation, rotation, and occlusion filters. Furthermore, we find similarities in the refinement part of the network and the perceptual filling-in process which occurs in the mammal primary visual cortex.
Development and Optimization of Check-in System Based on Face Recognition Technology

Abstract Face recognition technology is widely used. By comparing the speed and accuracy of Face++ and Baidu AI face recognition, the face sign-in service was collected in the mobile phone or pad APP, which realized the mobile check-in attendance that can be used for college students or training institutions. The system was characterized by accuracy, fast and anti-cheating ability. The experimental results show that Baidu AI’s face detection and recognition performance is better, its speed is more than twice faster than Face++, the recognition score is higher than Face++, and includes live detection, which can effectively prevent static picture cheats. However, Baidu AI’s face detection time (about 271.2ms) was much higher than the commercial face detection algorithm (about 25ms), so the Adaboost face detection algorithm was studied, and the Adaboost algorithm based on OpenCV was transplanted to Android. The platform enables real-time detection of faces. After the system optimization, the face detection time is about 10ms, which effectively improves the face detection speed.
Hand Gesture Recognition Using Computer Vision Applied to Colombian Sign Language

In this document we describe a hand gesture classification system of the Colombian Sign Language for both dynamic and static signs, based on Computer Vision and Machine learning. The proposed processes sequence is divided in four stages: acquisition of RGB-D image, extraction of the blob closest to the sensor, detection and validation of the hand, and classification of the sign entered. The results obtained are for multi-class classifiers with a self-captured dataset of 3.600 samples. As a conclusion we found that the best choice for descriptor-classifier according to sign type are HOG-SVM for static signs with an accuracy of $$98\%$$, and SVM classifier besides the trajectory-based descriptor with an accuracy of $$94\%$$.
Face Expression Recognition Based on Deep Learning

Abstract Nowadays, the information technology such as artificial intelligence (AI) and the cloud classroom have been increasingly introduced to improve the quality of modern education. It is well known that teachers can adjust the teaching mode through the interactive performance of students in the classroom. For example, if many students show surprise expressions on their faces, it means that students may not understand what the teacher is talking about, and the teacher needs to slow down the rhythm and explain the knowledge point carefully. For another example, if students present positive expressions, it means that the teacher has mobilized the atmosphere of the classroom well and should keep this good teaching style to make students be interested in this subject. All in all, face expression recognition is quite useful in the modern society.
Real -time Fall Detection and Alert System Using Pose Estimation

One of the most prevalent events in elderly persons is falling and not only it can cause mental and physical injuries but also it can cost too much. Consequently, due to the vital importance of developing an intelligent surveillance system to detect fall events and inform the family or the caregivers, we presented a fast and robust approach that can immediately detect falls and inform the caregivers by SMS so they can provide immediate help and consequently, the amount of injury and treatment costs will be reduced. Our approach includes two networks, pose estimation and MLP classifier. At first, we gathered a dataset that includes landmarks of the body at 500 poses including lying and non-lying positions with different illumination settings and then, MLP net is used to classify these poses. The accuracy and loss reached 92.5% and 0.3 respectively, on the validation dataset. The speed of the process is nearly 3 frames per second on core i7-6500U CPU, hence it can be used in real-time.
ROS Based Multimode Control of Wheeled Robot

This research work mainly presents the design and development of a small-scaled wheeled robot, which can be controlled using multiple controlling interfaces using some new technological trends.Raspberry Pi 3 as the main controller, Python as the programming language integrated with the Robot Operating System (ROS) and Virtual Network Computing (VNC) for screen sharing are utilized in order to control the robot.Laptop Keys, Graphical User Interface (GUI) on PC, Virtual Network Computing on smart phones, Joystick connected to Laptop using python integrated with Robot Operating System, Voice commanding through an android app integrated with python and ROS and Internet of Things using a customized webpage with Node MCU as the controller are the dominant control interfaces.A Local Area Network is established in order to communicate with the micro-controllers wirelessly.The robot can be controlled with multiple interfaces by using its corresponding commands.The robot is made to survive in five different paths, controlled by all the six methods and the time for each response obtained was appraised and illustrated properly.
Pedestrian Detection Based on Panoramic Depth Map Transformed from 3D-LiDAR Data

Object detection is a crucial task of autonomous driving. This paper addresses an effective algorithm for pedestrian detection of the panoramic depth map transformed from the 3D-LiDAR data. Firstly, the 3D point clouds are transformed into panoramic depth maps, and then the panoramic depth maps are enhanced. Secondly, the grounds of the 3D point clouds are removed. The remaining point clouds are clustered, filtered and projected onto the previously generated panoramic depth maps, and new panoramic depth maps are obtained. Finally, the new panoramic depth maps are jointed to generate depth maps with different sizes, which are used as input of the improved PVANET for pedestrian detection. The 2D image of the panoramic depth map applied to the proposed algorithm is transformed from 3D point cloud, effectively containing the panorama of the sensor, and is more suitable for the environment perception of autonomous driving. Compared with the detection algorithm based on RGB images, the proposed algorithm cannot be affected by light, and can maintain the normal average precision of pedestrian detection at night. In order to increase the robustness of detecting small objects like pedestrians, the network structure based on the original PVANET is modified in this paper. A new dataset is built by processing the 3D-LiDAR data and the model trained on the new dataset perform well. The experimental results show that the proposed algorithm achieves high accuracy and robustness in pedestrian detection under different illumination conditions. Furthermore, when trained on the new dataset, the model exhibits average precision improvements of 2.8–5.1 % over the original PVANET, making it more suitable for autonomous driving applications.
Optimization and Design of a Gripper Mechanism for Autonomous Unmanned Aerial Vehicle Perching

The design, optimization, development, and testing of a prototype Unmanned Aerial Vehicle (UAV) gripper mechanism is presented. The gripper is designed to be mounted to an omni-directional multi-rotor used for aerial perching allowing the UAV to service structures it lands on. Design cues from robotic hands were used to design a gripper that can grasp a wide range of objects while keeping the mechanism lightweight but structurally strong enough to be used as landing gear. Two prototype grippers were built and successfully tested.
New problems of eventreleted digital management of economics and state

We introduced new structurally complex objects of management in economics and State. We proposed new problems with logical-probabilistic (LP) risk models for managing different types of systems: socio-economic, with risk models based on statistical data, with a large number of indicators and logical combination of several systems. The following problems are considered in detail: managing the development of systems, assessing the quality of management systems, managing a safe living space, taking into account the effect of repeated events on the accuracy of assessing the safety and quality of a system, planning development programs for complex systems with different subsystem outcomes. Calculations on LP-model of State set, what reforms are needed in economics. The role of the digital economics in the active inclusion of the personnel of state institutions, enterprises, companies and every person in managing of State and economics is shown.
Blockchain: The Facts and Fiction

Abstract If you look at any indicator of what the data management world is interested in right now, blockchain will be at the top of the list, writes Ron Ballard, database consultant and author.
Viewpoint-aware Segment Streaming for Free Navigation of Light-field Images

Virtual reality (VR) is drawing a lot of attention as a future killer application that may surpass the era of two-dimensional video. However, VR still has many technical bottlenecks and challenges that should be overcome for wide application. Image quality and interaction should be improved to increase the immersion level for users. This drastically increases the amount of image data needed for rendering. At the same time, the image should be transmitted without intermittence in a network environment having limited bandwidth. This paper proposes a streaming scheme supporting free navigation in a light field–based VR system. The proposed viewpoint-aware streaming of light-field data focuses on the fact that image data can be reused in active exploration of spaces, in contrast to passive video watching. The quantity of data transmission may be significantly decreased by employing a cache in user devices.