Reviewerly/exHarmony huggingface dataset:
Novel Study of Machine Learning Algorithms with Supervised Learning Approach to Solve Real Time Big Data Applications Using Hadoop and Spark

This review focuses on different machine learning algorithms, descriptions, pros and cons of the algorithms. It also includes task of machine learning and supervised learning process, tools, techniques and programming language to build machine learning model. Machine learning is a building computational art craft, which learns through time from experience. Machine learning is learning from data and it turns input data to information (mapping input value to output value using label data). The objective of machine learning is building the algorithm that can learn from past data without the help of human experts. The learning algorithm contains task T, performance measure P and training experience T. Machine learning algorithm categorized as supervised, unsupervised and reinforcement learning. Supervised machine learning has two tasks, which are classification and regression. Hadoop with Spark and Python programing language most commonly used to build machine learning model. Information gain and Gain ratio, Gini index and Random forest algorithm used to measure feature importance. From this study most of supervised learning algorithms used k-fold cross validation techniques to split the data set in to training set and testing set. Standard value of k will be five or ten but it didnâ€™t fixed, because it depends on the size of data set. The machine learning model performance mainly evaluated using classification accuracy, precision, recall, area under the curve and F-measure (F-score). Generally machine learning algorithms depend on the nature of the data, because the performance of the learning algorithms affected by data set. Therefore it is impossible to say the prediction accuracy of one algorithm is best over others. As each machine learning algorithms have pros and cons, designing of hybrid algorithms might be overcome this problem.
Etherless Ethereum Tokens: Simulating Native Tokens in Ethereum

Standardized Ethereum tokens, e.g., ERC-20 tokens, have become the norm in fundraising (through ICOs) and kicking off blockchain-based DeFi applications. However, they require the user's wallet to hold both tokens and ether to pay the gas fee for making a transaction. This makes for a cumbersome user experience, and complicates, from the user perspective, the process of transitioning to a different smart-contract enabled blockchain, or to a newly launched blockchain. We formalize, instantiate, and analyze in a composable manner a system that we call Etherless Ethereum Tokens (in short, EETs), which allows the token users to transact in a closed-economy manner, i.e., having only tokens on their wallet and paying any transaction fees in tokens rather than Ether/Gas. In the process, we devise a methodology for capturing Ethereum token-contracts in the Universal Composability (UC) framework, which can be of independent interest. Our system can be seen as a targeted instance of the more general paradigm put forth—without a formal study—by the Ethereum Gas Station Network (GSN). We have implemented and benchmarked our system and compared it to GSN. In addition to being the first system with a rigorous security analysis, we demonstrate that EETs are far easier to deploy and less gas intensive than the GSN.
Generative adversarial networks in medical imaging

Generative adversarial networks (GANs) are a recently introduced class of state-of-the-art generative models. GANs are characterized by a unique training process that, although unstable, enables them to accurately learn highly complex distributions. While much of the recent attention that GANs have received in the machine learning and computer vision communities is due to their ability to synthesize highly realistic images, this is but one of many potential uses for these models. In this chapter, we survey several recent applications of GANs in medical imaging, highlighting significant developments, and illustrating avenues for future work in this nascent area of research.
Automated Diagnosis of Cyber-Physical Systems

Research on cyber-physical systems has gained importance and we see an increasing number of applications ranging from ordinary cars to autonomous systems. The latter are of increasing interest requiring additional functionality like self-healing capabilities for improving availability. For autonomous systems, it is not only important to detect failures during operation, but also to come up with their causes. In this paper, we contribute to the foundations of diagnosis. We introduce a method for modeling cyber-physical systems considering behavior over time, in order to make use of model-based reasoning for computing diagnosis candidates. In particular, we discuss a thermal model coupled with a controller for keeping temperature within pre-defined values and show how this contributes to the computation of diagnoses given an unexpected behavior. The discussed modeling principles can be used as a blueprint for similar systems where controllers are coupled with a physical system. Diagnosis results obtained when using the thermal model and the observed diagnosis time, which was a fraction of a second, seem to indicate the applicability of the presented approach for industrial applications.
Development of an authoritative file of subject headings

At present, the formation of authoritative files system is an obligatory condition for organizing and managing electronic catalogues of large scientific libraries. SPSTL SB RAS is managing its own authoritative file of subject headings, the work on which began in 2008, and is actual until today. The paper describes the results of creating and managing an authoritative file of subject headings of SPSTL SB RAS in the period of 2011 – 2020: the history of the file creation is mentioned; the importance of linguistic support of the electronic catalogue for conducting information search is marked; the necessity for analyzing the current state of affairs and evaluating the authoritative file quality is pointed out. The analysis is based on the results of implementing the statement of work for creating the authoritative file; the program of step-by-step works is reproduced and the results of accomplishing each stage, the complexity of the tasks are evaluated. The article indicates the need to edit subject headings in accordance with the requirements of the method of objectification; to pair information search languages: library and bibliographic indices of classification and subject headings. It also indicates the problem of filling the authoritative file with new subject headings, reveals the necessity of checking the vocabulary by alphabetical index. The conclusion forms the notion about the current state of the authoritative file, the terms of work on implementing the statement of work. The main problem of completing the creation of an authoritative file of subject headings of SPSTL SB RAS is indicated; ways to solve this problem are suggested.
Android-Based Cacao Bean Solar Drying Monitoring System With SMS Notification

Android Based Cacao Bean Solar Drying Monitoring System with SMS Notification aimed to monitor the temperature and relative humidity inside the drying chamber using DHT 11 Sensor, and the soil moisture in monitoring the moisture of the cacao beans inside the chamber. The system application would gather the data from sensors which can be viewed in the android monitoring system. The developed android based monitoring application was being programmed in android studio application and uses java programming language. When the drying process was already done it would send SMS Notification to the owner of the dried cacao bean. The developed prototype with the attached sensors gathers the data to be able to create real time monitoring in system server side and passed it to the android based monitoring application. The consolidated mean of the descriptive ratings for functionality of the hardware of the study weighted 3.40 that mean’s “Agree”. The consolidated mean of the descriptive ratings for reliability of the software of the study weighted 3.53 that mean’s “Very Agree”. The consolidated mean of the descriptive ratings for usability of the software weighted 4.00 that mean’s “Very Agree”. The overall ratings of descriptive level of the effectiveness of Android Based Cacao Bean Solar Drying Monitoring System with SMS notification in terms of Functionality, Reliability and Usability had a consolidated mean of 3.64 that mean’s “Very Agree”. Based on its objectives the proponent where able to identify that several factors including moisture sensor needs to be further investigated where it was used in this type of study.
Teacher Educators Teaching Competency of West Bengal towards ICT

Teacher education today is an integral part of any educational system. ICT has taken a key role in this system of the present knowledge society. Now ICT has become important part of Indian school curriculum as well as teacher education. The importance of ICT in empowering teachers and learners, and enhancing teaching and students’ achievement has been highlighted in several studies. This study assessed Teaching competency of teacher educators of West Bengal towards ICT. To achieve the goal of the study, the researcher developed and standardized 5 point Likert type scale, “Scale for teaching Competency of Teacher Educators towards ICT”, which is highly reliable (α = 0.776) and. A survey was conducted on 180 (One hundred and eighty) samples selected from 30 (Thirty) teacher education colleges in West Bengal, through simple random sampling technique. Collected data were analyzed through SPSS 22.0 Version and significance of t values and F values were tested at 0.05 level. The results Show that most of the teacher educators are competent towards ICT. No significant difference was established between male and female teacher educators towards ICT.
Annoyed to Discontinue: Factors Influencing (Dis)Continuance of Using Activity Tracking Wearables

What influences the continued usage of activity tracking wearables? Several studies investigated the acceptance of activity tracking technologies, but the reasons for a continued usage or abandonment of this technology are rarely addressed. This study is focusing on current and former users of activity tracking wearables and their different perceptions of the technology. For this purpose, an online survey was conducted and a convenience sample of 235 valid cases was obtained. Factors potentially influencing the continued usage included age, gender, community feeling, motivation, design, or perceived data sensitivity.
Design of Natural Human-Computer Interaction for Unmanned Delivery Vehicle Based on Kinect

There are more and more application scenarios for unmanned delivery vehicles, and traditional human-computer interaction methods can no longer meet different task scenarios and user needs. The primary purpose of this paper is to apply natural human-computer interaction technology to the field of unmanned delivery, changing the traditional mode that users can only operate unmanned delivery vehicles through the touch screen to complete tasks. The task scenarios of unmanned delivery vehicles are classified through the concept of context. Participatory design and heuristic research are used to allow users to define interactive gestures. Two groups of gesture interaction set that meet different task scenarios and can be accepted and understood by general users are designed. Based on Kinect’s deep imaging and bone tracking technology, large number of preset gesture samples are collected, and the Adaboost algorithm is used for machine training to realize gesture interaction. Through recognition and detection, it is proved that the gesture recognition achieved by this method has a high recognition rate and responding speed. Finally, based on Unity3D, the task scene of the real unmanned delivery vehicle is simulated in the virtual scene. Through the usability test of the human-computer interaction system, it is concluded that this interaction mode guarantees the task efficiency to a certain extent and improves the user experience.
The Presumed Happiness of the Smiling Pile of Poo – How Emojis are Perceived by People

Emojis are widely used in digital communication, especially on phones. This makes them an important part of our multimedia experience. While most emojis are equally perceived among people, some are not. Using such emojis in UIs might lower the user experience. Furthermore, they should be used with care in communication. To get a better understanding of how people perceive emojis, we performed a survey on 15 emojis that potentially cause misunderstandings in digital communication. The responses from 1,811 participants help to interpret emojis used in online texts, as well as in text-based communication. In this article, we provide an experience report about the creation and performance of our survey as well as the results. The detailed discussion of 15 emojis provides future directions for research on how these are used. This forms the foundation for more detailed and specific future analyses aimed at exploring the potential causes of digital miscommunication.
3D Object Detection and Tracking Using Monocular Camera in CARLA

Vehicle 3D extents and trajectories are crucial cues for many autonomous driving tasks such as path planning, motion prediction, etc [1]. This paper proposes a novel online deep learning framework to tackle the 3D object detection and track problem using a monocular camera. The framework can estimate the complete 3D information from a sequence of 2D images and associate the objects over time. By obtaining continuous frames from the front camera, our network robustly tracks the 3D bounding boxes for each observation and provides its location P with orientation θ, dimension D and 2D projection of its 3D center c. The training dataset is generated from the CARLA simulator and trained using Faster R-CNN [2] on a 2080 Super GPU. The 2D vehicles and centers test accuracy reaches 95% and 3D tracking performance can reach 81% MOTP [3] in CARLA [4] environment.
Applications of Video See-Through HMD only with General Smartphone and Cardboard to Game

As Virtual Reality has the advantage that maximizes immersion and presence, it has been widely used for various contents such as video games, education, and therapy. In addition, the developments of see-closed HMDs such as GearVR, DayDream and Occulus Quest 2 accelerate these widespread. However, it still has the disadvantage that its users cannot see the physical world. Thus, the users can fall into dangerous situations that collide with one of the physical objects or fall. When the players use the see-through HMDs, they can avoid these dangers because the physical world is visible, and they can avoid the object. The see-through HMDs can use an optical-based and a video-based method. The optical-based HMD has advantages in that the physical objects can be directly seen but has a cost problem. The video-based HMD has advantages in the cost aspect and various virtual effects can be added to the raw images from the camera. For example, the virtual ball can be drawn in the real environment. To utilize these advantages for the game at a low cost, we developed the video see-through HMD only with the general smartphone and the affordable cardboard. We also developed a simple game to test the developed HMD. As a result of the affordable video see-through HMD, more users can play the MR-based games.
SAVE-T: Safety Analysis Visualization and Evaluation Tool

Traffic crashes are one of the biggest issues which constitute a threat to lives of the motorists and disrupt operations of the transportation system. To reduce the number of crashes and alleviate their impacts, it is necessary to scrutinize the factors contributing to the risk of traffic crashes. Lately, visual analytics tools become very popular for data exploration and obtaining insights from the data. In this paper, a new web-based data visualization tool called Safety Analysis Visualization and Evaluation Tool (SAVE-T) was introduced. This tool enables users to interactively create queries and visually explore the results. By utilizing an online crash database, it offers various innovative functionalities for analysis and visualization of the crash data such as custom query development module and a subway-like map for easily visualizing the accident on the roadway segments. This tool provides an effective and efficient way to transportation agencies and professionals for traffic safety analyses and visualizations.
A Novel Indoor Object Detection Algorithm Under Complex Conditions

In robot vision, it is generally required to recognize indoor objects with special colors and shapes quickly and accurately. However, the existing object detection algorithms based on deep learning generally require powerful computing resources, which leads to high power consumption and high cost of robot system. To solve this problem, this paper proposes a low power consumption, low cost indoor object detection algorithm based on color and shape information, with real-time and high accuracy. However, the performance of the indoor object detection with color and shape informations has a substantial decline under the complex indoor conditions. In this paper, we propose a faster and more robust algorithm. Our hierarchical detection scheme is composed of color thresholding and two-stage Maximally Stable Extremal Regions (MSER). Because of the complementarity of the two used algorithms, our method get the state-of-the-art in the field of object detection under the complex indoor conditions. Experiments are performed on our collected indoor object detection sets to validate the effectiveness of the proposed approach, and the experimental results show that this algorithm is superior to other color object detection algorithms.
Formation of the Digital Collection “Books of the Imperial House of Romanovs” in the National Electronic Library

The article is devoted to the creation of the collection “Books of the Imperial House of Romanovs” in the National Electronic Library (NEB) within the framework of the project “Book Monuments”. The general approach to the designation of imperial and grand ducal collections is based on the analysis of presence of the books in the collections of the Russian State Library (RSL). To justify the decision, the author considers the history of private book collections that belonged to the Romanovs before 1917 and their fate after the revolution. The book collections of representatives of the family in historical and scientific literature are divided into imperial and grand ducal collections. After the revolution, the significant part of the libraries located in the residences of St. Petersburg and its suburbs were moved to the Winter Palace. Some collections remained in the owners’ palaces and were distributed and moved to different organizations. After the decision was made to allocate the books to different organizations, the significant part of them was sent first to the Rumyantsev Museum, and then to the V.I. Lenin State Library of the USSR (now the Russian State Library). After registration, the books were distributed between different parts of the holdings without taking into account the ownership of the previous owners; and at present, it is not possible to restore them as a single complex. Based on the study and analysis of the archival documents of the Russian State Library, which had covered the receipt and allocation of the books in quite a detail, it was decided to combine all the books into a single electronic collection, since only this decision will allow getting together the copies of imperial and grand ducal books on a single resource and making them available for general access and study. The NEB’s capabilities allow providing information to specialists in the humanities and a wide range of users.
Study on the Image Overlay Approach to AR Navigation System for Transsphenoidal Surgery

In this study, we propose a system for assisting doctors with transsphenoidal surgery by understanding the positions of tumors during surgery. Transsphenoidal surgery requires examining endoscopic images to assess the situation depicted for surgery; determining the positions of tumors and organs is more difficult for transsphenoidal surgeries compared to other more general procedures. Under the proposed system, a three-dimensional (3D) model is created based on the patient’s preoperative MRIs, and a superimposed image is displayed in real time. This system is expected to assist the surgeon in understanding the situation around the operating field. Markers are used to obtain the data necessary to create an image overlay. The markers are affixed to an operating table and to the end of an endoscope. The patient’s head is held in place during the intraoperative period. The positions of features within the patient’s cranium relative to the operating table marker are obtained through a camera installed on the operating table. The positions of tumors and organs are then estimated from the data obtained and the data from the 3D model created from the patient’s MRIs. The relative position from the marker at the end of the endoscope to the tip of the endoscope is obtained as well, allowing the position of the endoscope tip to be estimated even if the endoscope has been inserted into the body’s interior during the intraoperative period; the tip cannot be seen from the exterior. The relative position of the endoscope tip and the patient’s tumor is calculated, and a 3D model created from the MRI image combined with the current endoscopic image is displayed. The optimum number of simultaneous recognition markers for improving the accuracy of the measurements of the endoscope’s position and orientation was verified, and results of a trial run of the image overlay system conducted using a simplified model were reported.
Exploiting the 3D Structures Observed in 2D Video Sequences for Motion Compensation

Video coding standards widely use 2D translational and affine motion models to compensate for the motion between frames. Noting that most 2D videos are initially captured in a 3D space may lead us to a new direction: using 3D scene geometry for 2D motion compensation. We introduce a method for rendering virtual Reference Pictures (RPs) based on the 3D information extracted from 2D video sequences. The synthesized RPs are offered to the Versatile Video Coding (VVC) encoder to serve as motion compensation references. The 3D information (camera poses and 3D scene geometry) is estimated at the encoder and transmitted to the decoder as an overhead bitstream. Multiple techniques are discussed to minimize this overhead. Simulation results show up to 5% coding gain compared to our anchor (VTM 10.0), with an acceptable increase in decoding time.