Reviewerly/exHarmony huggingface dataset:
Can Dynamic Widgets Improve Data Entry Efficiency?

Data entry mechanism becomes pervasive in the area of human-computer interaction (HCI) as it is the first interface for user interaction. The available research publications on data entry studies in Indian context are very few in number. Data entry happens in several application areas like medical, tourism, governance, share market, or even in Business Process Outsourcing (BPO), where data accuracy is extremely vital. But it is a tendency of users to make errors during data entry. In this research paper, two diversified application areas-BPOs and healthcare information systems are targeted with respect to Indian context. Data entry is one of the major works carried out in BPOs as well as for Electronic Medical Records (EMR). In both cases, keeping up the quality of the data during entry is a crucial factor. A dynamic widget containing the drop-down split menu for data entry can help in fulfilling such objective. It is expected that the dynamic widgets can significantly increase the performance in both context (BPOs and medical) of data entry. The results of such experimentation indicate the potential of design elements as ‘dynamic intelligent interfaces’ for data entry applications in both BPOs and EMRs.
Telemetry-Based Autonomous Drone Surveillance System

The existing surveillance system comprises monitoring and manual inspection of visual feed which is relatively difficult when the area of coverage is too large and objects in motion. Henceforth, this dilemma needs to be solved through implementing automated surveillance system in mobile drones which would eliminate the disadvantages of stationary surveillance. Also, by implementing the intruder and obstacle detection system to identify object motion and analyze the image data so that we would be managing to integrate a full-fledged non-stationary, mobile surveillance system through drones with cameras.
Enhancing Attention through the Eye Tracking

The contribution describes an implementation of a system capable of measuring and possibly improving the human attention through the use of eye-tracking techniques. The used techniques achieve high accuracy for non-head mounted cameras with low resolution in visible spectrum. In order to achieve this, an analysis of existing methods was carried out. The implementation of the system is created through the utilization of Python-compatible library for face detection as well as using Timm and Barth algorithm for pupil detection. The main focus of the testing phase is the accuracy of the face detection and pupil detection algorithms in various lighting conditions as well as under different view angles. Additional tests are performed on participants with different eye-colors. The experimental solution may be used also for tracking of the attention of students or in various educational games.
Sentiment analysis on product reviews data using supervised learning

With the availability of text data in various forms on social media platforms, text mining and sentiment analysis have received huge attention. The task of deriving information from this volume of data in order to extract knowledge is very complex and expensive because it is usually unstructured and contains noise. Recently, there is a growing need for implementing various approaches and models for efficiently processing this type of data and extracting useful information. This process is known as sentiment analysis, which includes: data gathering, data pre-processing, feature engineering and labelling, finally the application of various natural language processing and machine learning algorithms. This paper provides an overview of the most recent methods used in text mining and sentiment analysis along with their detailed description and a discussion of obtained results.CCS Concepts
DyslexiAR: Augmented Reality Game Based Learning on Reading, Spelling and Numbers for Dyslexia User’s

Augmented Reality Game for based learning has been enhanced the learning experienced and developed the knowledge and skills of the user. The project methodology used in this study is the game development life cycle (GDLC). It includes initiation, pre-production, production, testing, beta testing and release. The purpose of this project is to produce video games for children with dyslexia who have visual and auditory learning difficulties related to memory, time management, speed processing, organization, organization and planning. The objective of this product was to develop Augmented Reality games for dyslexia students, second was to develop Reality-based games on reading, spelling and numbers for dyslexia students using the Unity Game Engine, the third was to test the appropriateness of learning based on reading, spelling games and numbers for dyslexic students. The number of user targets used to test this product are dyslexia students, teachers who teach dyslexia students, expert programmers and designer games and evaluators from the eLearning Carnival & Conference (eLCC 2019). The result of this DyslexiAR learning game is that dyslexic students have a better understanding of learning to read, spell and learn numbers.
Optimal Basis Matrices of a Visual Cryptography Scheme with Meaningful Shares and Analysis of Its Security

The extended visual cryptography scheme (EVCS) proposed by Ateniese et al. is one of variations of the visual cryptography scheme such that a secret image is recovered by superimposition of certain qualified collections of shares, where cover images are visible on respective shares. In this paper, we give a new definition of the EVCS for improving visibility of the recovered secret image as well as the cover images. We formulate the problem to construct the basis matrices of the EVCS with the minimum pixel expansion as an integer programming problem. We solve the integer programming problem for general access structures with less than or equal to five participants and show that basis matrices with a smaller pixel expansion can be obtained for certain cases. We also analyze security of the EVCS meeting the new definition from an information-theoretic viewpoint. We give a condition under which any forbidden collection of shares does not reveal any additional information on not only a secret image but also the cover images that are not visible on the other shares.
Parametric audio quality estimation models for broadcasting systems and web-casting applications based on the Genetic Programming

The COVID-19 pandemic has been one of the biggest disruptions to education that the world has ever experienced, affecting the most of the world student population. Many countries turned to online based distance education to ensure that learning never stops. As a consequence, throughout the globe there has been an increasing trend among the students to use different broadcasting systems and web-casting applications for the purpose of online learning. However, the video or audio quality that these various applications offer will be the key factor for their acceptance, i.e. whether or not the students will be willing to use those systems for online learning. Therefore, a machine learning technique, i.e. Genetic Programming, is used in this work for the purpose of assessing audio quality using an objective approach. A design and performance evaluation of the parametric models estimating the audio quality perceived by the end user of broadcasting systems and web-casting applications are presented in this paper. To estimate the quality of audio broadcasting systems and web-casting applications, a set of parameters influencing the quality is used as an input for the developed parametric quality estimation models. The results obtained by the developed parametric audio quality estimation models have validated Genetic Programming as a powerful technique, providing a good accuracy and generalization capabilities. This makes it a possible candidate for the estimation of audio quality perceived by the end user in the context of the broadcasting systems and web-casting applications.
The Impact of Question Type and Topic on Misinformation and Trolling on Yahoo! Answers

Trolling and misinformation are ubiquitous on social media platforms, such as Yahoo! Answers. Yet, little is known about the impact of question type and topic on the extent of trolling and misinformation in answers on these platforms. We address this gap by analyzing 120 transactions with 2000 answers from two Yahoo! Answers categories: Politics & Government and Society & Culture. We found that trolling and misinformation are widespread on Yahoo! Answers. In most cases, trolling in questions was echoed by more trolling in answers, and misinformation in questions with more misinformation in answers. We also found that 1) more misinformation and more trolling were found in answers to conversational questions than to informational questions; 2) more misinformation occurred in answers to questions in politics than answers to questions in culture; and 3) trolling significantly differed between politics and culture.
Android-based physics learning-media apps on circular motion

Abstract In recent years, the use of Android smartphones in the Indonesia community has increased very fast. Along the way, its use in the Educational aspect rises accordingly. The increase improved significantly during the period of the COVID-19 pandemic burst. While this fact has brought pain and sorrow for many, we looked at it differently. It was a kind of a blessing in disguise. Besides many inconveniences, it also opens plenty of opportunities. Instead of giving up, we prefer to develop android-based physics learning-media apps which is now needed more than before. The increase of the online portion on the learning processes during the COVID-19 pushed our spirit to complete the development of learning media apps. This apps has been tried out by high school students and has gained positive response from its users.
Blood phenotyping and estimation of hemoglobin content using image processing

At present era, nearly 75% of the diseases are identified by analysing blood samples. There are many parameters like RBC and Platelets present in blood, of which hemoglobin is an important one. Hemoglobin is the significant thing estimated for medical procedures, during traumatic conditions, pregnancies and so on. This paper is based on blood phenotyping by processing of images procured during the slide test and also for the valuation of hemoglobin by image processing using MATLAB. It combines both the process in a single device and is handled easily. This reduces the time required to know the blood type and hemoglobin content. Another main advantage is that there is no need of skilled personnel.
A Performance Measurement and Optimization Mechanism for Blockchain Mining Pool System

Pooled mining is the de facto mining pattern in public, PoW (Proof of Work) based blockchain systems like Bitcoin and Ethereum. Mining pool aggregates discrete miners’ computation power to improve probability of getting the right block, and stabilize miners’ rewards. Miners reach consensus among pools, and maintain the whole blockchain network's stability. Due to architecture constrains across underlying transmission mechanism, computation power differences among nodes, and consensus algorithms, mining pools are facing increasing performance challenges, which lead to wastes of computing resources and loss of miners’ rewards. Based on analysis of mining pool architecture, this paper proposed a numerical model MENM (Miner Efficiency Numerical Model) to measure miner's computing efficiency, and a performance tuning mechanism DDCT (Dynamic Difficulty Calculation and Tuning) to dynamically adjust individual miner's difficulty in a pool based on MENM value, to improve mining pool's efficiency. The proposed tuning mechanism is also tested against a small experimental PoW mining pool to validate its effect.
Evaluation of the photovoltaic power prediction performance of a neural network based on input data

This paper aims to show the influence of the data size and the number of meteorological data used in the prediction of the output power of a photovoltaic installation with neural networks. We trained with different input data with the 2019a MATLAB Neural Network Start (NNS) tool, three feedforward networks. To train these networks, we used the algorithm of Levenberg-Marquardt and as data meteorological data such as wind speed at 10m from the ground, air temperature at 2m from the ground, position of the sun, direct radiation on an inclined plane and diffuse radiation on an inclined plane downloaded in the PVGIS database for a period from January 1, 2005 to December 31, 2016 for Natitingou city in the Republic of Benin. The first network was trained with wind speed at 10m, air temperature at 2m and sun position as input, the second network with wind speed at 10m, air temperature at 2m, sun position and direct radiation on an inclined plane and the third network with wind speed at 10m, air temperature at 2m, sun position, direct radiation on an inclined plane and diffuse radiation on an inclined plane. For the three networks we took the best results from 10 trainings. Thus, we obtained for the three networks respectively as mean square error 6186, 191 and 0.46 and as regression values 0.95, 0.998 and 0.999 respectively. In descending order according to the data used, the best performance was obtained with: • wind speed at 10m, air temperature at 2m, position of the sun, radiation, direct on an inclined plane and diffuse radiation on an inclined plane; • wind speed at 10m, air temperature at 2m, position of the sun, and the direct radiation on an inclined plane; • wind speed at 10m, air temperature at 2m and position of the sun.
Point-of-interest recommendation using extended random walk with restart on geographical-temporal hybrid tripartite graph

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Assessing the Learning Administration of Higher Education Institutions by Graduates’ Turn-Out and Learning Outcomes: Theory and Practice

Introduction. This article describes how universities are fulfilling their training assignments. Its relevance is determined by the growing need of teachers to correlate the learning results of specialists-to-be with learning assignments. The aim of the article is to describe a method for assessing the learning management system of higher education institutions by analysing learning outcomes and graduates’ turn-out. Materials and Methods. The study relies on the collection and analysis of the results of specialists’ training between 2015 and 2019. The joint use of the obtained data and methods of normative forecasting and interpolation allowed the authors to calculate and measure the intermediate results relative to the final ones, to build educational trajectories, which made it possible to evaluate the learning management system by dealing with training results and graduates turn-out. Results. For the first time the authors of the article showed that the measured results demonstrated not only the scope of specialists’ training and graduation, but they evaluated management of the educational process and developed necessary management decisions to regulate standards and measures for specialists’ training and graduation according to the specified final results. Discussion and Conclusion. The conclusions contribute to the development of the concept of learning management systems, provided that there are specified final results of the specialists’ graduation. Findings are of interest to the scientific and pedagogical community, professors, teaching staff and leadership of educational institutions.
A Video Copyright Transaction Traceability Method Based on Mother-Child Blockchain

The paper proposes a video copyright transaction traceability method (TTMC) based on the parent-child chain. The video production process records and the video traceability source code records that can uniquely identify the video are recorded on the private blockchain (child chain). The alliance blockchain (parent in the chain), the nodes that are authenticated by the member nodes can conduct video media resource copyright information transactions and traceability. First, the parent chain member node is authenticated, and after the authentication, the copyright transaction and the traceability system can be accessed; secondly, the member node stores each step of information in the child chain when making the video for rights certification; then the video is watermarked and obtained Video fingerprint, calculate the video ID for each video, smart contract automatically uses the matching algorithm based on the mother-child chain distributed architecture to automatically complete the transaction. Simulation experiments show that the method proposed in this paper is effective in tracing the source of video copyright transactions.
The implementation of Blockchain framework in MOOCs to support a freedom of learning in Indonesia

Abstract A freedom of learning program has been released by the Indonesian Ministry and Culture this year 2020. There are three ways for students to earn their credits, namely take the subject course in face-to-face based class, virtual based class or under Massive Open Online Courses (MOOCs). MOOCs is a model that is developed to help people to learn about certain skills through the online platform, without any limitation in the audience. MOOCs aim to enhance broad collaboration between individuals in creating learning environments that have high scalability and can be accessed by anyone and anywhere. The complexity arises when students undertake a subject course through MOOCs, how to certify the completion of their program in which the certification can be gained easily, and the last how secure the obtained certificate? Blockchain technology can help to improve the quality of MOOCs by providing control of academic records as evidence that someone has completed a learning process on MOOCs. Academic records generated will be stored in one place forever and safely stored in the Blockchain environment. This article will explore how the possible to implement the Blockchain framework in MOOCs to support a freedom of learning in Indonesia.
Modified Continuous-Time Particle Filter Algorithm Without Overflow Errors

The modification for the continuous-time particle filter algorithm is offered. The developed modification that is based on the well-known strategy such as modeling trajectories to numerically solve stochastic differential equations provides the lack of overflow errors during the calculation of particle weights. To implement such an idea practically particle weights should be expressed in terms of logarithms with an additional customization of exponents. The effectiveness of the modified algorithm is demonstrated when solving the tracking problem to find coordinates and velocities of an aircraft executing a maneuver in the horizontal plane.
Stabilization of Floor Projection Image with Soft Unmanned Aerial Vehicle Projector

By mounting a projector on a soft unmanned aerial vehicle (UAV) called “S-CLOVD”, one can freely move and show visual information on the floor. One practical problem with the UAV-projector is the distortion of the projected image caused by UAV disturbance. In this paper, we propose a geometry-based method of stabilizing the floor projected image using the flight information of the UAV (e.g altitude, roll, pitch, yaw). By visual comparison of the stabilized images, its stabilization performance is shown much improved. In this paper, The UAV-projector system and stabilization algorithm are proposed. And, the performance of proposed idea is validated with experimental data.
A Multi-task Learning Approach to Text Simplification

We propose a multi-task learning approach to reducing text complexity which combines text summarization and simplification methods. For the purposes of this research, two datasets were used: the Simple English Wikipedia dataset for simplification and the CNN/DailyMail dataset for summarization. We describe several experiments with reducing text complexity. One experiment consists in first training the model on summarization data, then fine-tuning it on simplification data. Another experiment involves training the model on both datasets simultaneously while augmenting source texts with a task-specific tag that shows the model which task (summarization or simplification) needs to be performed on a given text. Models with a similar architecture were also trained on each dataset separately for comparison. Our experiments have shown that the multi-task learning approach with task-specific tags is more effective than the fine-tuning approach, and the models trained for both tasks simultaneously can perform as good at each of them as the models that were trained only for that specific task.
Comparative Study of Faster Region-Based Convolutional Neural Networks with Inception V2 and Single Shot Detector with Inception V2 on Their Signature Detection Capabilities

Signature detection and verification in offline handwritten signature printed document is a problem when solved helps to target forgery and reduce man hours. With challenges like stray marks, several methods were proposed and eventually with the rise of vision recognition methods and Deep Learning, application program interfaces have been developed to detect signatures. Using TensorFlow APIs, images that are converted to radio frequency (RF) format are processed to detect signatures. This paper intends to evaluate two different object detection algorithms, faster region-based convolutional neural networks and single shot detector with inception V2, on signature dataset. Various standard metrics such as train and test time, loss rate and precision at area under curve etc. are used to compare their performance and fitment for the problem statement.
The Cost of Path Information: Routing in Anonymous Communication

Anonymity is an essential asset for a variety of communication systems, like humans' communication, the internet of things, and sensor networks. Establishing and maintaining such communication systems requires the exchange of information about their participants (called subjects). However, protecting anonymity reduces the availability of subject information, as these can be leveraged to break anonymity. Additionally, established techniques for providing anonymity often reduce the efficiency of communication networks. In this paper, we model four mechanisms to share routing information and discuss them with respect to their influence on anonymity and efficiency. While there is no “one fits all” solution, there are suitable trade-offs to establish routing information complying with the technical capabilities of the subjects. Distributed solutions like decentralized lookup tables reduce routing information in messages at the cost of local memory consumption; other mechanisms like multi-layer encrypted path information come with higher communication overhead but reduce memory consumption for each subject.