Reviewerly/exHarmony huggingface dataset:
A Study of Emerging Areas in Adoption of Blockchain Technology and it’s Prospective Challenges in India

Blockchain technology is a decentralized distributed system meant for secure computation and information sharing platform free from any central authority that enables multiple authoritative domain which do not trust each other, to cooperate, coordinate and collaborate in rational decision-making process. Bitcoin is pioneer crypto currency platform that uses blockchain technology but over the time it has moved beyond Bitcoin and has successfully bypassed to other application domain. Beside Bitcoin, Ethereum, Hyperledger and Corda, Quorum have emerged as a successful distributive computing platform. Major consortium like Enterprise Ethereum Alliance, Hyperledger and R3 are formed with aim to come up with business solution. Today we have different blockchain application in public sector, finance, supply-chain, healthcare and IoT. In many industries people think blockchain will be transformative. Financial sector is most enthusiastic in adopting blockchain followed by supply chain management. Blockchain is closely looked at in India too. In 2018 Reserve Bank of India (RBI) has issued a white paper in order to identify the potential application areas of blockchain in Indian banking. NITI Aayog is working on national strategy for blockchain which will identify the area where country can implement blockchain. Blockchain is still new and has issues like scalability, security and privacy breaches, limited transaction loads, and high (computational) costs, besides its technical problem, adopting blockchain into Indian market system has its own specific challenges. This paper discusses the prospect of blockchain technology in India, its current position and likely problem in its adoptation to Indian market place. This paper also give overview about blockchain and its fundamental technologies and prominent blockchain platform, it also gives account of application area's in which blockchain is implemented successfully around the world.
HSSM: High Speed Split Multiplier for Elliptic Curve Cryptography in IoT

Security of data in the Internet of Things (IoT) deals with Encryption to provide a stable secure system. The IoT device possess a constrained Main Memory and Secondary Memory that mandates the use of Elliptic Curve Cryptographic (ECC) scheme. The Scalar Multiplication has a great impact on the ECC implementations in reducing the Computation and Space Complexity, thereby enhancing the performance of an IoT System providing high Security and Privacy. The proposed High Speed Split Multiplier (HSSM) for ECC in IoT is a lightweight Multiplication technique that uses Split Multiplication with Pseudo-Mersenne Prime Number and Montgomery Curve to withstand the Power Analysis Attack. The proposed algorithm reduces the Computation Time and the Space Complexity of the Cryptographic operations in terms of Clock cycles and RAM when compared with Liu et al.,'s multiplication algorithms [1].
SOFTWARE ASSURANCE: THE THINGS A MANAGER NEEDS TO KNOW

Given the stakes in a world where losses to cyber-attacks are becoming unsustainable, every organization’s software inventory should be ensured by a substantive, properly documented, and rational process. Software assurance is the explicitly designed and implemented function that ensures that the specific security goals of the organization will be maintained. The development and instantiation of a software assurance process is essentially a matter of good management. The problem though, is that the process itself is often a black box for managers. This will explain the role and function of software assurance as well as the major factors that should be kept in mind when developing and overseeing the process.
Computer-animated scientific visualizations in the immersive art installation NOISE AQUARIUM

In the collaborative project NOISE AQUARIUM, visualizations of tiny plankton species were created to present them as large as they are important for Earth’s ecosystems. Tomographic scanned microscopic organisms are presented in immersive art installations including Virtual and Augmented Reality features. The main challenge was to transfer the visual data of the scans into the interactive virtual world. In the context of art and science collaborations, the described practice was found to offer opportunities to convey important topics in a memorable way.
Sentiment Analysis Tool in Website Comments

Understanding the profile and analysis of individuals’ consumption behavior in order to identify opinions about products and services available on websites has been an increasingly necessary research area. The Behavioral Perspective Model (BPM) provides conditions to conduct consumer behavior analysis by investigating antecedent variables (consumer behavior profile and learning history) and consequences (reinforcements and punishments, utilitarian and informational). This article presents the development stages of the SentimentALL tool, which aims to extract and prospect user comments from an online traveler service booking platform. The system is based on the sentiment analysis algorithm (user opinion) to quantify “positive feelings” and “negative feelings” in relation to the restaurants and entertainment attractions of the places visited and the accommodations used by users on their travels.
Predicting translation behaviorsby using Hidden Markov Model

Abstract The translation process can be studied as sequences of activity units. The application of machine learning technology offers researchers new possibilities in the study of the translation process. This research project developed a program, activity unit predictor , using the Hidden Markov Model. The program takes in duration, translation phase, target language and fixation as the input and produces an activity unit type as the output. The highest prediction accuracy reached is 61%. As one of the first endeavors, the program demonstrates strong potential of applying machine learning in translation process research.
Cooperative Pursuit Guidance to Surround Intruder Swarms Using Collision Cones

This paper addresses the problem of unmanned aerial vehicles (UAVs) pursuing a swarm of target UAVs moving on a plane. The target UAVs are assumed to be flying together as a flock. The flock is initially modeled as a circle whose radius can be time varying, and this is subsequently generalized to arbitrarily shaped swarms that may change in size. Capture is said to occur when the pursuing UAVs have successfully enclosed the target swarm within their convex hull. In subsequent scenarios, the pursuing UAVs are assumed to be carrying a rope line in an open-chain configuration, and capture is said to occur when the pursuing UAVs have (in addition to enclosing the target UAV swarm within their convex hull) also ensured that their formation approaches the target at an appropriate orientation that corresponds with the open end of the rope line. The guidance laws are developed based on a collision cone framework, using which the pursuing UAVs can cooperatively steer the velocity vector of any point in their convex hull, to intercept the target. Analytical expressions of these nonlinear cooperative pursuit guidance laws that govern the acceleration commands of the UAVs are determined. Simulations are performed using kinematic models, and they demonstrate that the pursuing UAVs are able to successfully surround the target UAV swarm.
The Research of Dynamic Encryption &amp; Decryption Methods Based on the Binary Random Hash

This paper proposes a new method of encryption and decryption based on stream cipher. The method completely abandoned the traditional mathematical calculation method. Its main principle is to using the key to get a binary random Hash mapping; the plaintext will be evenly dispersed in a known binary random Hash, and then mapped to the cipher space. This encryption method has a very strong randomness and collision resistance. The algorithm analysis shows that the time and space complexity of the method is the same as the traditional stream cipher algorithm, both of which are o (n). Finally, we test the time efficiency of the method, and the experimental results show that block and multithreading technology will greatly improve the encryption efficiency for large files.
The most optimal performance of the Levenberg-Marquardt algorithm based on neurons in the hidden layer

Abstract The training algorithm is the main driver in artificial neural networks. The performance of the training algorithm is influenced by several parameters including the number of neurons in the input and hidden layers, epoch maximum, activation function, and the size of the learning rate (lr). One of the benchmarks for optimizing the performance of training algorithms can be viewed from the error or MSE (mean square error) produced. The smaller the error, the more optimal the performance. The test conducted in the previous study obtained information that the most optimal training algorithm based on the smallest MSE produced was Levenberg-Marquardt (LM) with an average MSE=0.001 at the level of α=5% and using 10 neurons in the hidden layer. Therefore, this study aims to test the LM algorithm using several variations in the number of neurons in hidden layers. In this study, the LM algorithm was tested using 5 neurons in the input layer, and 2, 4, 5, 7, 9 neurons in hidden layers, and the same parameters as previous study. This study uses a mixed method that is developing computer programs and quantitative testing of program output data using statistical test. The results showed that the LM algorithm had the most optimal performance using 9 neurons in the hidden layer at the level of lr=0.5 with the smallest error of 0.000137501±0.000178355.
Action recognition model of athletes at the scene of the game based on SVM and multitarget tracking algorithm

This article has been retracted, and the online PDF has been watermarked “RETRACTED”. A retraction notice is available at https://doi.org/10.3233/JIFS-219218.
Persona studies: an introduction

The notion of persona is gaining in relevance and immediacy in a time when the public deployment of the self is transforming sociality across geopolitical borders. It is in light of these transform...
Two-photon statistics of nonclassical radiation in the dissipative finite-size Dicke model

The nonclassical feature of photons in the open finite-size Dicke model is investigated via the two-photon correlation function. The quantum dressed master equation combined with the extended coherent photonic states is applied to analyze the dissipative dynamics of both the photons and qubits. The anti-bunching to bunching transition of photons is clearly observed by tuning the qubit-photon coupling strength. The optimal qubits number is unraveled to enhance the two-photon correlation function. Moreover, the temperature bias of thermal baths induces significant two-photon bunching signature with deep strong qubit-photon interaction.
Mapping and Conversion between Relational and Graph Databases Models: A Systematic Literature Review

Given the current mass of information and, considering that such information is increasingly related, \the use of a graph model to represent Storing these can make it easier to identify information that would be hard to see when using a relational model. The purpose of this study is to characterize the existing techniques about the mapping and conversion process between relational and graph-oriented database models. For this, a systematic literature review was performed in the Scopus and IEEE Xplore databases. We validated 11 articles that were included in the period from 1 January 2013 to 31 May 2019. The results showed that most studies try to perform the mapping and migration process with different algorithms and data structures and each one has a point of failure, such as data loss, test execution distributed environments and other Relational DBMSs. The contribution of this research is to situate the state of the art of the conversion process between relational and graph-oriented databases, highlighting the positive and negative points of the existing techniques, with the objective of developing algorithms that combine the best of each technique, improving the existing failures.
An activity-based approach to up-skilling teachers who teach engineering of software products

Abstract Effective up-skilling teachers who are involved in teaching software engineering should take into account the challenges of teaching practice. Despite the long practice of organizing the teaching of software engineering and computer science in educational organizations in Russia, relevant up-skilling courses are not methodologically supported. The paper introduces an active, practice-oriented approach to organizing up-skilling courses for teachers who teach software engineering and development. The authors developed the level model for the advanced up-skilling teachers interested in mastering and delivering modern technologies of Agile software engineering and development. Authors implemented the model for organizing teachers’ up-skilling courses in intensive schools for engineering, development, design and management of software products
A Better Infected Hosts Detection Combining Ensemble Learning and Threat Intelligence

Ensemble learning techniques have been successfully proposed and used to improve threats detection in cybersecurity. These techniques usually improve the detection results by combining algorithms that together have less errors. However there has not been any ensemble learning algorithm used to classify network flows when several methods are used to give individual detections for each of the flows. The state of the art in the use of ensemble learning techniques was analyzed to find an alternative for the current intrusion detection mechanisms. This research proposes to incorporate ensemble learning to the Stratosphere Linux IPS (SLIPS), a behavioral-based intrusion detection and prevention system that uses machine learning algorithms to detect malicious behaviors. Our ensembling method is used to obtain better results, taking advantage of the benefits of SLIPS’s classifiers and modules. A contribution of our method is to extend the ensembling techniques by considering Threat Intelligence blacklists feeds as part of the detections. We present the results of the first stage of this project, i.e. ensemble learning algorithms to classify individual flows when they have multiple labels. on the other hand we also present the results corresponding to the second stage of our project, i.e. the detection of groups of flows going to the same destination IP.
Combining Wristband Display and Wearable Haptics for Augmented Reality

Taking advantages of widely distributed hardware such as smartphones and tablets, Mobile Augmented Reality (MAR) market is rapidly growing. Major improvements can be envisioned in increasing the realism of virtual interaction and providing multimodal experiences. We propose a novel system prototype that locates the display on the forearm using a rigid support to avoid constraints due to hand-holding, and is equipped with hand tracking and cutaneous feedback. The hand tracking enables the manipulation of virtual objects, while the haptic rendering enhances the user’s perception of the virtual entities. The experimental setup has been tested by ten participants, that expressed their impressions about usability and functionality of the wrist-mounted system w.r.t. the traditional hand-held condition. Subjects’ personal evaluations suggest that the AR experience provided by the wrist-based approach is more engaging and immersive.
The Space Bender: Supporting Natural Walking via Overt Manipulation of the Virtual Environment

Manipulating the appearance of a Virtual Environment to enable natural walking has so far focused on modifications that are intended to be unnoticed by users. In our research, we took a radically different approach by embracing the overt nature of the change. To explore this method, we designed the Space Bender, a natural walking technique for room-scale VR. It builds on the idea of overtly manipulating the Virtual Environment by "bending" the geometry whenever the user comes in proximity of a physical boundary. Our aim was to evaluate the feasibility of this approach in terms of performance and subjective feedback. We compared the Space Bender to two other similarly situated techniques: Stop and Reset and Teleportation, in a task requiring participants to traverse a 100 m path. Results show that the Space Bender was significantly faster than Stop and Reset, and preferred to the Teleportation technique, highlighting the potential of overt manipulation to facilitate natural walking.
A Risk Assessment Method for Enterprise Cloud Accounting

The widespread application of "Internet+" mode makes cloud accounting become the development trend of enterprise accounting informatization. However, in the process of implementing cloud accounting, due to the internal factors of enterprises and the factors of cloud service providers, there are also problems and risks such as quality of service, information and data security, enterprise financial system, personnel operation, and so on. According to the characteristics of cloud accounting, this paper establishes the evaluation index system of enterprise cloud accounting risk from the two dimensions of supplier and enterprise, and puts forward the evaluation method of enterprise cloud accounting risk based on AHP and fuzzy comprehensive evaluation method. Finally, taking an enterprise cloud accounting project as an example, the risk status of cloud accounting is analyzed and evaluated.
glossoVR - voice emission and public speech training system

A new VR application for voice and speech training has emerged from a problem observable in everyday life: an anxiety of public speaking. In the design process, we incorporated both domain knowledge of experts as well as research with end-users in order to explore the needs and the context of the problem. Functionalities of the prototype are the effect of user-centered process in order to suit best their needs and the way they interact with VR environment.
Automatic QoE evaluation for asymmetric encoding of 3D videos for DASH streaming service

The paper is based on the study of the performance of a Dynamic Adaptive Streaming over HTTP (DASH) system in the context of 3D video streaming, using different scenarios and network conditions, specifically with bandwidth variations. The objective is the development of a framework for the evaluation of QoE in 3D adaptive video streaming scenarios, which allows to analyze the impact on the user's Quality of Experience (QoE) using different bandwidth variation patterns (switching frequency, range and type of variation), among other aspects. A set of subjective tests will be carried out, with the aim of identifying the correlation between the quality of the user experience and the frequency, type, range and temporal location of the bandwidth switching events. The proposed framework allows performance measurements to be carried out in an automated and systematic way for the evaluation of DASH systems in 2D and 3D video streaming service. We have used Puppeteer, the Node.js library developed by Google, which provides a high-level API, to automate actions on Chrome Devtools Protocol, such as starting playback, causing bandwidth changes and saving the results of quality change processes, timestamps, stalls and so on. From this data, a processing is made to allow the reconstruction of the visualized video, as well as the extraction of quality metrics and the users’ QoE assessment using the ITU-T P.1203 recommendation. Categories and Subject Descriptors C.2.1 [Computer Systems Organization]: Computer Communications Networks– Network Architecture and Design, wireless communication
Analysis of Security Mechanisms to Mitigate Hacker Attacks to Improve e-Commerce Management in Ecuador

The problem that was chosen for the elaboration of this scientific article, is the distrust on the part of the Ecuadorians to carry out electronic commerce before the growing cyber-attack by the hackers, through which they steal personal information and money, as well as the poor security offered by digital sites and platforms to protect information. The general objective established in the investigation was to analyze the appropriate security mechanisms to mitigate hacker attacks and thus improve electronic commerce in Ecuador. The question that was born from the investigation is: How can hacker attacks be mitigated to improve electronic commerce in Ecuador? The deductive method was used, and a scheme was obtained that allows cybersecurity, ethical hacking and multi-factor authentication to mitigate hacker attack. It was concluded that for electronic commerce to increase in Ecuador, it is necessary to guarantee information security, since, as access to new Information and Communication Technologies (ICT) increases, it is necessary for companies to increase levels security to counteract possible hacker attacks.
An Observation of Vision Based Underwater Object Detection and Tracking

The detection of an object or moving object is a comprehensive goal in the research domain in the era of computer vision. Numerous scientists are quick to think about the underwater world and the living articles in the deep ocean and they are incompletely effective to their objective. In this research work, , we observe the potentiality in underwater by a vision based object detection sphere by applying various previous work for highlighting the arising consequences in various underwater situation.. We have collected the dataset from various source of water such as clean water, turbid water, bubbles water etc. We used OpenCV and Python programming language to detect the object. Finally the detection of object in underwater is studied for connecting with the basic color based HSV(Hue, Saturation, Value) segmentation.
MeteorologyAR: A Mobile AR App to Increase Student Engagement and Promote Active Learning in a Large Lecture Class

We present a study using a mobile AR (Augmented Reality) App to enhance large lecture classes both inside and outside the classroom. When students scan the target images using the App as directed by the instructor during the class, animated instructive materials, such as different cloud formation, are presented directly on individual students’ mobile screens. When students study independently outside the classroom, audio instruction and sound effects features of the App can be used along with the visual presentation. Unity and Vuforia were used for the cross-platform (iOS and Android) development of the App. The App was deployed and tested in an Introductory Meteorology class through in-class instructor led demo and students independent usage outside classroom. A survey was administered to collect feedback on students’ attitudes, engagement and satisfaction level, app performance, and other learning and user experiences toward the App. Responses from 95 students were received and analyzed. The survey results indicate that majority students have found the App to be engaging, satisfactory, and considered it to be a promising learning tool. Through the study, AR mobile App is shown to have great potential to be utilized as an active learning tool in large lecture class settings where it’s difficult for instructors to engage with individual students.