Reviewerly/exHarmony huggingface dataset:
Earthquake Time Prediction using CatBoost and SVR

Seismic tremors everywhere throughout the globe have been a noteworthy reason for decimation and death toll and property. The following context expects to recognize earthquakes at a beginning time utilizing AI. This will help individuals and salvage groups to make their errand simpler. The information in this manner comprises of these seismic acoustic signals and the time of failure. The model is then prepared utilizing the CatBoost model and the utilization of Support Vector Machines. This will help foresee the time at which a Seismic tremor may happen. CatBoost Regression Algorithm gives a Mean Absolute Error of about 1.860. The Cross Validation (CV) Score for the Support Vector Machine (SVM) approach is -2.1651. The datasets metrics are not reliable on any outer parameter in this manner the variety of exactness is constrained, and high accuracy is accomplished.
APPLICATION OF SUPPORT VECTOR MACHINE BASED SPEECH RECOGNITION TECHNOLOGY IN HUMAN-COMPUTER INTERACTION TECHNOLOGY

Speech recognition technology, an important part of human-computer interaction technology, has become a research hotspot.This study focuses on the support vector machine (SVM) algorithm based speech recognition technology.Mel frequency cepstrum coefficient (MFCC) algorithm was used to extract feature parameters.In order to make SVM algorithm more reliable, a fuzzy SVM was proposed.Then the algorithm was validated by the collected speech samples.The algorithm was found having shorter training time and higher recognition rate compared to hidden Markov model (HMM) algorithm and SVM algorithm.When the feature dimension was 16, the recognition rate was as high as 88.6%.In the study of anti-noise performance, the fuzzy SVM algorithm was also better than the other two algorithms.When the noise rate reached 50%, the algorithm still had a recognition rate more than 80%, which suggested the high reliability of the algorithm and the application prospect of the technology in human-computer interaction.
Detection and identification of &lt;i&gt;Ascaris lumbricoides&lt;/i&gt; and &lt;i&gt;Necator americanus&lt;/i&gt; eggs in microscopic images of faecal samples of pigs

Parasite egg detection and identification is an important task for diagnosis of many diseases. Manual identification of various types of parasite eggs is time consuming and prone to human error. Automation of this process can help in reducing time, human effort and error rate significantly. In this paper, we proposed a system for detection and identification of parasite eggs from microscopic images of faecal samples of pigs using image processing and machine learning techniques. We considered images of two types of parasite eggs: Ascaris lumbricoides and Necator americanus that are commonly found in pigs. The system segments different objects from the microscopic images and identify each of them as either a parasite egg of specified type or non-egg object. Identification of the segmented objects is done by convolutional neural network (CNN) models that automatically extract relevant features and classify them into three classes: Ascaris egg, Necator eggs and non-egg object.
Indian Sign Language Automated Learning using Region Bound Algorithm

This paper presents a novel technique to Indian sign language detection for automated learning to develop an interface towards elimination of communication gap between vocally disabled and common individual. In the approach of sign language detection, learning approach, decision making and processing overhead are of major concern. In this paper, a representation of sign language symbol for automated sign language detection and decision making is proposed. The proposed system is evaluated on hand based symbols of Indian sign language.
Blockchain-based Multi-Purpose Authentication Method for Anonymity and Privacy

Various applications using smart contract, a leading application technology of blockchain, are being rapidly introduced to the industrial sector. As a result, services in various fields are actively being developed. Currently, most of the services are offered on a variety of platforms, not blockchain-based. If these services are linked to prepaid features that provide anonymity in smart contracts, a more diverse service scenario could be created. In this paper, we propose scenarios that provide certification for various purposes based on smart contracts. It provides a scenario that provides the privacy of the contract signed by the customer while retaining the anonymity provided by blockchain. Smart contracts register keys that do not give a clue to guess the encoding keys and deliver hash functions of the child keys that change each time with authentication parameters.In addition, the master seed that can generate these authentication parameters is designed to be kept only by the user and the service provider to be able to verify them. It is proposed by considering both a single service provider transaction and a smart contract authentication model that is shared with a large number of service providers. To generate these child keys, we proposed a mechanism to use the method of generation of child keys based on the Elastic Curve Cryptography public-key method. Various attack scenarios were analyzed to complement the scenario and the efficiency of the proposed mechanism was analyzed. In addition, differences and excellence were compared by organizing scenarios that had the same purpose as scenarios in the relevant study
Best Paper Selection

Eichstaedt JC, Smith RJ, Merchant RM, Ungar LH, Crutchley P, Preotiuc-Pietro D Asch DA, Schwartz HA. Facebook language predicts depression in medical records. Proc Natl Acad Sci U S A 2018;115(44):11203-8 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6217418/ Parr SK, Shotwell MS, Jeffery AD, Lasko TA, Matheny ME. Automated mapping of laboratory tests to LOINC codes using noisy labels in a national electronic health record system database. J Am Med Inform Assoc 2018;25(10):1292-300 https://academic.oup.com/jamia/article-abstract/25/10/1292/5075874?redirectedFrom=fulltext Xiao C, Ma T, Dieng AB, Blei DM, Wang F. Readmission prediction via deep contextual embedding of clinical concepts. PLoS One 2018;13(4):e0195024 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5890980/
Feasibility of Convolutional Neural Networks (CNN) for Content Based Image Retrieval of Images from Hadoop

In search engines, though feature-based query is provided, Content Based Image Retrieval (CBIR) still results in less sensitivity and specificity. It is because the conventional approach is based on feature extraction and inherent parameters in conventional feed forward networks. Performance of the system is strongly dependent on the extracted features. Hence it is necessary to develop a CBIR system that retrieves the similar images without explicit feature extraction. Convolutional Neural Networks are the recent neural network architectures which accept images as input and perform both feature extraction and classification. The proposed work aims at using the conventional architectures of VGGNET, RESNET, and DENSENET for flower classification in CBIR. Performance is measured in terms of accuracy of classification.
Mapping and Lithological discrimination using digital image processing and radioactive investigations of Wadi Um Gheig area, Central Eastern Desert, Egypt

The present study aims to create the geological map of Wadi Um Giegh area by applying different image processing techniques for Landsat-8 ETM+ satellite, airborne gamma ray spectrometric survey and geological investigations to discriminate between the differently exposed lithological units.The mapping approach developed and applied in this study integrates resulted images from processed Landsat-8 ETM+ data and field data sets to produce a map showing the alteration zones at study area.The processed digital data of Landsat-8 ETM+ covering the study area have been used.Several products of Landsat-8 ETM+ digital data such as (7, 4 and 1 in RGB), false color composite images (8, 6, 1 in RGB) and (3, 2, 1 in RGB).Band rationing images (4/2, 5/4, 7/5), (6/7, 6/2, 4/2) and (5/7) are generated.Products of image processing improve lithological discrimination especially of serpentines, volcanics, metagabbro and granitic rocks and different varieties of intrusive rocks.The analysis of the gamma-ray spectrometric data, reveals that, there is an increase in the eTh content from 1.34 to 15.68 ppm.Whilst, the eU content increases from 0.17 to 7.84 ppm, the K%-content with an average of about 1.36 %, eU/eTh from 0.09 to 0.64 with an average 0.4 and eTh/eU from 1.57 to 11.41 with an average of 2.80.So, the different rock units of Wadi Um Giegh area are enriched in uranium.The eU-(eTh/3.5)reaches 3.43 ppm over the rhyolite rocks and diminishes to (-0.40) ppm at the same rocks.This indicates that, the uranium migration (leach out) in these rocks less than the (leaching in).The granites at Wadi Um Giegh area show low degree of fractionation suggesting a source for further hydrothermal or supergene concentrations.Therefore, this study area is considered to be of low Ufavourablty.The calculated dose rates in the study area, ranging between 0.1 and 1.1 mSv/year, which indicate that the study area as a whole remains in the safe side and under the maximum permissible safe radiation dose rate without harm to the individual, except over the rhyoitic rocks which exceeds the safe limit (1.0 mSv/y).
A Block Chain Based Framework to Enhance Security against Cyber Attacks

Block chain has drawn major attention recently in the area of cyber security. Till to date many detection techniques and protection mechanisms have been proposed to enhance the security. In this paper we have proposed an overview of distributed ledger framework, block chain to defense against the cyber attacks. We here present a comprehensive overview of block chain architecture and some major algorithms used in different block chains. The future trends of block chain technology in the applications such as IoT security, E-voting, Banking sector has also coined out in this paper.
Blockchains toolbox

Understanding a technology means understanding its principles, so let us explore the basic features of blockchain together. Broadly defined, blockchain is a distributed, and decentralized ledger maintained through a peer-to-peer network. Blockchain is… code, and from that code results trust. Although the ecosystem is a moving target, one may identify two central pillars to blockchain technology, i.e., encryption and immutability. I explore them in turn (Section 1). With that in mind, I then move on to exploring the distinct types of blockchains and consensus systems to get a grasp of the ecosystem’s diversity (Section 2).
KEMUNGKINAN IMPLEMENTASI SISTEM PENGENDALIAN INTERNAL BERBASIS COSO FRAMEWORK PADA UNIT LAYANAN PENGADAAN POLITEKNIK NEGERI BATAM

This research describes the evaluation of the implementation of an internal control system based on the COSO framework in the Batam State Polytechnic procurement unit. By using the COSO framework as outlined in the Internal Control Questioner based on 5 components and broken down into 17 COSO principles; (1) Control environment, (2) Control activities, (3) Risk assessment, (4) Information and communication, (5) Monitoring. Based on the research that has been done, there are several conclusions drawn. First, in the Control environment component, the procurement service unit is quite possible to implement a COSO-based control system. Secondly, the Risk assessment component of the procurement service unit is quite possible to implement a COSO-based control system. Third, the Control activities component of the procurement service unit is quite possible to implement a control system based on COSO. Fourth, the procurement service unit's Information and communication component is quite possible to implement a COSO-based control system. Fifth, the Monitoring component of the procurement service unit is not possible to implement a COSO-based control system. So as a whole it can be concluded that the Batam State Polytechnic procurement service unit is quite possible to implement a COSO-based control system.
Neural Named Entity Transliteration for Myanmar to English Language Pair

Named entity (NE) transliteration is mainly a phonetically based transcription of names across languages using different writing systems.This is a crucial task for various downstream natural language processing applications, such as information retrieval, machine translation, automatic speech recognition and so on.Robust transliteration of named entities is still a challenging task for Myanmar language because of the complex writing system and the lack of data.In this paper, we proposed our Myanmar-English named entity terminology dictionary and experimented on transformer-based neural network model.Furthermore, we evaluated the performance of neural network-based approach on the transliteration tasks using BLEU score.Different units in the Myanmar script, i.e., character units, sub-syllable units and syllables units are compared in the experiments.
Off-Topic Essay Detection: A comparative study on the Portuguese language

Advances in automated essay grading over the last sixty years enabled its application in real scenarios, such as classrooms and high-stakes testing. The recognition of off-topic essays is one of the tasks addressed in automated essay grading. An essay is regarded as off-topic when the student does not develop the expected prompt-related concepts, sometimes purposely. Off-topic essays may receive a zero score in high-stake tests. An off-topic essay detection mechanism may be used in parallel or embedded in an automated essay grading system to improve its performance. In this context, the main goal of this study is to evaluate the existing approaches for automated off-topic essay detection. A previous systematic review of the literature showed some deficiencies in the state of the art, including: the low accuracy of current approaches, the use of artificial validation sets, and the lack of studies focused on the Portuguese language. In this study, the approaches found in the literature, originally proposed for the English language, were adapted for the Portuguese language and compared in an experiment using a public corpus of 2164 essays related to 111 prompts. The experiment used a set of artificial off-topic examples and the best performing algorithm achieved higher accuracy than that found in the literature for the English language (96.76% vs. 94.75%). The results presented suggest the application of off-topic essay detection mechanisms in the Brazilian educational context in order to benefit the student, with computer generated feedback, and educational institutions, regarding automated essay grading. Some suggestions for future research are presented, including the need to address the task of off-topic essay detection as a multiclass problem, and to reproduce the experiment with a larger and more representative set of real off-topic essay examples.
Underwater Image Enhancement using HOG and SIFT Method

Capturing underwater images can be considered as form of art and collecting data of underwater environment which plays major role in the field of marine biology research, marine zoology and ecology study. It also plays a significant role in scientific missions such as analyzing marine life species, taking census of population and monitoring underwater biological environment. Underwater imaging also offers many other attractions such as aquatic plants and animals, different types and species of fishes, shipwrecks, coral reef and beautiful landscapes. But the captured images lack contrast and are hazy due to absorption and scattering. Thus the quality of the image gets deteriorated. The degraded underwater images can be enhanced using different methods. This research work proposed the white balancing technique and image blending process to improve the degraded images. The image blending can be done using the methods of feature matching. The features can be matched using HOG and SIFT methods. The proposed technique is implemented in MATLAB and results are analyzed in the terms of PSNR and MSE.
IMPLEMENTATION OF ADVANCED ENCRYPTION STANDARD (AES) FOR HIGH-SECURITY APPLICATIONS.

Advanced encryption standard is a steganographic algorithm that will be used to protect the confidential data.In this project we use a Symmetric key, i.e. same key expansion algorithm is used for both encryption and decryption.The AES can be programmed in software or built in with hardware implementations (like FPGA).The numbers of rounds that has to be performed depends on the size of the key.As we are using 128-bit key which consists of 10 rounds, this makes more complex to crack the information.As the key size increases, the number of rounds also increases and complexity also increase.So, it plays a major role in high-security applications.This algorithm is used in many high-security applications like Secure socket layer, OFTP, HTTPS, FTPS, SFTP, AS2, WebDAV, Online financial Transactions, E-Business Code.
Facet Analysis of Anime Genres: The Challenges of Defining Genre Information for Popular Cultural Objects

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Exploratory Study of Smart Healthcare Services (e-Health & m-Health Applications) in the UAE- Exploring Related Literature & Websites Contents

This study highlights the importance and the growing trend of e-health in the modern world, with special emphasis on Dubai-UAE.The effectiveness and usability of mobile application tools and devices have also been described in this study.Moreover, the different components and features of a Health service for providing a service facility to the patient and to maintain and manage the health sector information and data have been discussed.However, the shortcomings and the weaknesses along with the disadvantage of e & m-Health application are illustrated in this study.The purpose and usability of mobile application and the electronic devices for health tracking and monitoring are reported in this study.In addition, the future trend and the expectations of individuals and the patients related to the advancement in the e-health service are explained.
EFEKTIVITAS IMPLEMENTASI STRATEGI ENJOYABLE LEARNING BERBANTUAN MEDIA AUDIO VISUAL TERHADAP HASIL BELAJAR BAHASA INGGRIS PERMULAAN PESERTA DIDIK SEKOLAH DASAR

This research is based on the low learning outcomes of English language learning in elementary schools especially after the implementation of the 2013 curriculum where the time of learning English is much reduced from 4 hours a week in the Education Unit Level Curriculum (KTSP) to only 2 hours a week in the 2013 curriculum. To overcome these problems, it is considered important to implement the Audio-Visual Media-Assisted Enjoyable Learning Strategy. Pre-experimental research One Group Pretest-Posttest Design. The subjects in this study were grade 2 students in 8 elementary schools who had just implemented the 2013 curriculum in the 2018/2019 learning year in Buleleng sub-district. The data in this study were obtained by interview methods and field notes. With the random sampling technique, the subjects of this study were selected, namely grade 2 students in SD N 5 Banyuning, amounting to 27 students as a control group, and second grade students in SD N 2 Banjar Tegal, amounting to 25 students as the experimental group. Based on the results of the calculation of the tcount of 5.34. To find out the value of t table with degrees of freedom (dk) = 44 significance level (α) = 0.05 obtained the value of t table = 2.023. By comparing the value of tcount and ttable, the ratio of tcount&gt; ttable is obtained, meaning H0 is rejected and Ha is accepted. And from the average, it was known that the experimental group was 20.86 and the control group was 15.18. This means experiment&gt; control group. Thus, the Enjoyable Learning strategy assisted by audio visual media has a significant effect on the learning outcomes of English in Class II SD students in the 2013 Curriculum Application in the District of Buleleng, Buleleng Regency, Academic Year 2018/2019.
VR Hackfest

We built the future of the web — today! Our four-person eLibrary team designed an afternoon workshop and corresponding network-connected public exhibit centered around two cutting-edge internet technologies: IPFS and A-Frame.
The Effectiveness of Knowledge Management and Business Intelligence for Increasing Financial Performance. (A Case Study: Petroleum Development Oman)

The purpose of this study is to know about the effectiveness of integrating knowledge management and business intelligence for increasing the financial performance of Petroleum Development Oman Company. Based on the impact of this integration to increase the organization’s performance, the data has been collecting through primary and secondary sources. The main objective of this study is the integration between knowledge management and business intelligence and its effect on the financial performance of the company. In this research, two methods were discussed as direct influence and indirect influence. In the direct influence method, the company will use information that is generated by the process of knowledge management system and prepare the reports through business intelligence system for the company’s financial plans and activities. After that company can make appropriate financial decision to improve and develop the current financial plan and strategies. Another way is the indirect influence of the integration through focuses on intellectual capital and human resources development through education and training, enhancing innovation and using their capacities and skills in work. However, both influences contribute to increase efficiency, productivity, sales, profits, and reduced cost. Thus improving the company's performance and its financial performance, which contributes to sustainable the company in its business field market and maintains its competitive advantage.
Co-Training an Observer and an Evading Target

Reinforcement learning (RL) is already widely applied to applications such as robotics, but it is only sparsely used in sensor management. In this paper, we apply the popular Proximal Policy Optimization (PPO) approach to a multi-agent UAV tracking scenario. While recorded data of real scenarios can accurately reflect the real world, the required amount of data is not always available. Simulation data, however, is typically cheap to generate, but the utilized target behavior is often naive and only vaguely represents the real world. In this paper, we utilize multi-agent RL to jointly generate protagonistic and antagonistic policies and overcome the data generation problem, as the policies are generated on-the-fly and adapt continuously. This way, we are able to clearly outperform baseline methods and robustly generate competitive policies. In addition, we investigate explainable artificial intelligence (XAI) by interpreting feature saliency and generating an easy-to-read decision tree as a simplified policy.
Lightweight Blockchain to Improve Security and Privacy in Smarthome

Internet of Things (IoT) growing at a rate of exponential numbers in recent years has received extensive attention with BlockChain (BC) technology which provide trust to IoT with its immutable nature, decentralization in computing, resource constraints, security and privacy. The distributed ledger of transactions in BC is path leading technology for addressing Cyber Threats in the form of data theft; it provides secure application architecture which has proven track of record for securing data. IoT devices using BC enabled to communicate between objects, share data, decide based on business criteria and act as a medium to securely transmit information. This work provides lightweight BlockChain with two prominent consensus mechanism PoW – Proof of Work and PoS – Proof of Stake for smart IoT devices. Next, Smart Home Device (SMD) is ensures providing best-in-class Security and Privacy for smart home Appliances. Further provides future advances in the Approach.