Reviewerly/exHarmony huggingface dataset:
Integer Klt For Lossless Hyperspectral Image Compression on BeagleBone Black Board

This research concerned with the lossless hyperspectral image compression for satellite imagery using Integer Karhunen–Loeve Transform (KLT) on BeagleBone Black Board. The Integer KLT is selected as the transform for compression due to it showing superior performance in decorrelating the spectral component in hyperspectral images compared to other algorithms. The objective of this research is to develop an Integer KLT algorithm to implement it into the embedded system. The performance of the algorithm on the board in terms of execution time is investigated. The implementation of the Integer KLT algorithm is executed into the BeagleBone Black board and tested for its performance, profiling the execution time and comparing it with other embedded platforms which are a low-power DSP platform. The implementation shows a faster execution time compared to the low-power DSP platform.
A CMOS Random Number Generator with Noise-Coupled Voltage-Controlled Oscillators

This paper presents a hardware random number generator (RNG) based on oscillator circuitry. A reference circuit generates a bias voltage for controlling the frequencies of ring oscillators. Noise is coupled to the bias voltage so that manifest timing jitters are formed at the oscillator outputs. The outputs are multiplexed to a chaotic signal which is subsequently sampled by a faster clock for generating a random bit stream. The RNG circuit is verified by using standard 0.35μm CMOS process. The quality of the randomness is evaluated by using the NIST statistical tests. The circuit requires 0.089nJ for generating a random bit with chip area of 0.049mm2.
Analysis of Employee Satisfaction in Relation to Employee Welfare in Hospitality Industry

The hospitality industry includes lodging, event planning, theme parks, transportation, cruise line and additional fields within the tourism industry. It is a multibillion-dollar industry that depends on the availability of leisure time and disposable income. Work environment acts as a crucial factor to influence the employees’ job satisfaction. There is a strong relationship between working environment (self-realization, workload, conflict and nervousness) and employees’ job satisfaction. A positive work environment is believed to make employees feel good about coming to work and provide the necessary motivation to sustain them through the day. To keep its staff motivated, a hotel should offer benefits like perks and discounts, professional support, family and parenting, vacation and time off policy, insurance, health & wellness, overtime allowance and annual increment.
Hybrid High-order in Graph Attention Layer

As a result of approximating the Eigenbasis of the graph Laplacian proposed by GC-layer of Kipf & Welling [5], the convolution operation is efficiently applied from Euclidean domain to graph domain, and the end-to-end deep graph neural network is widely used and developed. However, fixed neighborhood limits the learning ability of the model, and GAT [7] models global node pairs to avoid information loss. In the form, this modeling is equivalent to only considering the first-order proximity relation of the network, which leads to the indirect and lossy transmission of the higher-order information of the network, even if the multi-layer attention mechanism is used to expand the order of the network. In order to avoid the above situation and obtain higher-order information better, this paper tries to establish the concept of higher-order neighborhood mixed learning of graphs. In our work, unlike the implicit propagation of neighborhood information through activation functions in the past, our model called H-GAT explicitly obtain the information of high-order neighborhood of nodes, and use attention mechanism to model different weights between high-step nodes.
WARNINGS ON CANS OF LIQUID PAINT, NOT MENTIONING THE C*RC*N*G*N WORD &amp; NOT SHOWING THE EXPLODING MAN ICON

Sequential Pattern Mining (SPM) is a popular data mining task with broad applications. With the advent of big data, traditional SPM algorithms are not scalable. Hence, many of the researchers have migrated to big data frameworks such as MapReduce and proposed distributed algorithms. However, the existing MapReduce algorithms assume the data as static and do not handle the incremental database updates. Moreover, they use to re-mine the updated database while new sequences are inserted. In this paper, we propose an efficient distributed algorithm for incremental sequential pattern mining (MR-INCSPM) using the MapReduce framework that can handle big data. The proposed algorithm incorporates the backward mining approach that efficiently makes use of the knowledge obtained during the previous mining process. Also, based on the study of item co-occurrences, we propose Co-occurrence Reverse Map (CRMAP) data structure. The issue of combinatorial explosion of candidate sequences is dealt using the proposed CRMAP data structure. Besides, a novel candidate generation and early prune mechanisms are designed using CRMAP to speed up the mining process. The proposed algorithm is evaluated on both the real and synthetic datasets. The experimental results prove the efficacy of MR-INCSPM with respect to processing time, memory and pruning efficiency.
Competitive Analysis for Machine Learning and Data Science

Statistical machines learn from regularity in data and are often designed for stationary or even independent and identically distributed (IID) processes. However, in most real-world applications it is not known how close the data process is to being IID. Moreover, this is impossible to learn when past data may be misleading or otherwise unrepresentative of the future. An adversarial data process, on the other hand, is not subject to probabilistic constraints; instead an adversary can deterministically attempt to mislead or otherwise confuse the machine. Of course designing for an adversary has its own limitation: that of being pessimistic. Despite the disparity between IIDness and adversarialism, for a given application, it may not be known which will better approximate the data. Fortunately, in many supervised settings, learning from data that is generated by an adaptive adversary is not much harder (statistically) than if it were generated by a static distribution. More precisely, the minimax expected regret values differ by a constant factor. In that case, a machine optimally designed for an adversary is necessarily competitive with any other, even when the data process is IID.
Radio transmitter identification using manifold embedding

In this paper, a novel fingerprint feature extraction method is proposed for identifying radio transmitters. The square integral bispectra (SIB) transformation is firstly utilized to recast the problem of radio transmitter identification into the form of measuring similarity between points in its metric space, and then the fingerprints of radio transmitters are extracted by our proposed orthogonal local spline discriminant embedding (O-LDE). The experimental results on the real-world data sets demonstrate the effectiveness of our proposed method.
New statistics to test log-linear modeling hypothesis with no distributional specifications and clusters with homogeneous correlation

Traditionally, the Dirichlet-multinomial distribution has been recognized as a key model for contingency tables generated by cluster sampling schemes. There are, however, other possible distributions appropriate for these contingency tables. This paper introduces new statistics capable of testing log-linear modeling hypotheses with distributional unspecification, when the individuals of the clusters are possibly homogeneously correlated. An estimator for the intracluster correlation coefficient, valid for different cluster sizes, plays a crucial role in the construction of the goodness-of-fit test-statistics.
A Framework with Ciphertext Attribute-Based encryption for data security in the cloud

Security of outsourced data is the primary concern in the cloud computing environment. Cloud can be made more credible and admissible by effective implementation of access control, confidentiality, integrity, and data recovery. In this paper, first discussed practical cryptographic approaches, and then analyze Cipher Text Attribute Based Encryption (CPABE) under security challenges, access control, revocation, and application. Also, proposed a framework with implementation, which is based on multiple attributes authorities to ensure the safety and privacy of data. Here CPABE, Advanced Encryption Standard (AES), proxy server, Proof of Reliability (POR) and Third Party Auditor (TPA) are jointly used to attain access control, confidentiality, reduced user's overhead, data backup, and integrity checks respectively.
CityRec — A Data-Driven Conversational Destination Recommender System

In today's age of information, recommender systems have evolved to a must have feature for many platforms, especially in the area of travel and tourism. Destination recommender systems is a challenging domain, since unlike restaurants and points of interests, the items are not so well defined and no reliable rating information is available. Thus, we propose a data-driven characterization of cities, which is then directly used in a conversational recommender system. Through this, we overcome the costly elicitation of expert-based destination characterization, as well as the cold start problem of recommender systems. The recommender system can be used at http://cityrec.cm.in.tum.de/ and the source code has been published.
The Security Challenge of Disruptive Technologies

Dynamic changes that characterize the modern security environment have been significantly driven and shaped by the rapid pace of technological development. Of particular importance is the emergence and development of technologies that revolutionary change the character of the security environment - disruptive technologies.The paper analyzes disruptive technologies aimed at the automation, acceleration and autonomy of the data processing process, decision-making abilities, and actions independent of human factor. These technologies affect all spheres of social life and subsequently security.Unlike previous disruptive technologies that influence security driven by the states and their defense sectors, the recent technologies are predominantly developing in private sector. This fact leads the countries to dependency on the private sector, and face them with significant challenges in identifying threats, its content and possible responses. Technologically less developed countries are particularly vulnerable.Based on the hypothesis of how disruptive technologies influence the design of security environments, an analysis of strategic security documents of Croatia, the USA, Russia, China, NATO, EU, and the states from the Croatian neighborhood has been conducted to determine if these countries recognize disruptive technologies as a security threat and how they approach them.The research has shown that Croatia does not recognize disruptive technologies as a specific threat. The analysis also indicates that, in the case of technologically less developed countries, such as the case of Croatia, security communities such as the EU or NATO provide a platform for responding to those threats.
Решение прямой задачи химической кинетики для процессов получения синте-тических каучуков в условиях неопределенности кинетических данных

A direct problem of chemical kinetics is the calculation of the composition of a multicomponent reactive mixture and the reaction rate on the basis of a mathematical model with known parameters, such as rate constants and inital concentrations. As a rule, the kinetic parameters are in a range of possible values, since they are determined on the basis of the obtained set of experimental data, which has a certain inaccuracy. However, when modeling chemical reactions, it is customary to use average values of kinetic constants, which does not guarantee the mode of functioning that occurs during operation. Thus, when solving the direct problem of chemical kinetics, there is a need to apply methods of interval analysis. The influence of uncertainty in the kinetic parameters on the solution of the direct problem is considered. The kinetic data is submitted in the form of intervals and is treated as an object of interval analysis. The computing algorithm of receiving interval estimates of direct process modeling is constructed. The computing experiment on the example of process of receiving butadiene rubber on creation of bilateral estimates of solutions of the differential equations including interval parameters is made. The algorithm and software complex implemented in the Embarcadero Delphi environment has been developed, which allows solving the problem of direct modeling of polymerization processes under conditions of uncertainty of kinetic data.
Mandarin Vocabulary Memorization Using Drill Sandwich Method on Android

Mandarin language is one of the most spoken languages over the world up to this day. Flashcards are often used to aid learners to memorize a new vocabulary of a certain language. Nowadays, many digital flashcards offer more help and efficiency to learners compared to traditional flashcards method. However, without any kind of memorizing method in digital flashcards tend to hamper the overall process of vocabulary learning. Therefore, by using Drill Sandwich as a method of memorizing vocabulary words in digital flashcards will certainly help learners to memorize new words. Drill Sandwich method works by inserting known words in a set of unknown words to help raise retention level of learners towards unknown words. For each word that can be answered correctly for two times or more, the word will become known, if a word cannot be answered correctly by at least two times, the word will become unknown. Learners will be given a set of card consisting of ten cards in total in which the cards will be randomly shuffled for each round but with the same pattern. Application has been successfully designed and built using Drill Sandwich method to assign the pattern of given questions to the user and application achieved the usability scale score of 82.
MENINGKATKAN BUDAYA LITERASI MELALUI PENDEKATAN SAINTIFIK (PROBLEM BASED LEARNING) BERFALSAFAH TRI HITA KARANA MENUJU SISWA BERKARAKTER

The school literacy movement was developed based on nine priority agendas (Nawacita) aimed at growing and developing reading and writing skills. A person is said to be literate if he is able to understand something because reading the right information and doing something based on his understanding of the contents of the reading. Sensitivity or literacy in a person certainly does not just appear. No human is literate from birth. Creating literate generation requires a long process and conducive means. This process starts from a small and family environment, then is supported or developed in schools, social settings, and work environments. Literacy culture is also strongly associated with learning patterns in schools and the availability of reading material in libraries. In this case, the scientific approach to the philosophy of Tri Hita Karana is able to direct students to think inductively so that it is expected to be able to develop creativity and improve literacy culture in schools. By instilling the concept of Tri Hita Karana in scientific learning, it can be expected that students will be able to think critically and have a strong character in establishing good communication with its creator, nature, and others.
EFEKTIVITAS KARBON AKTIF JERAMI SEBAGAI ADSORBEN UNTUK MENURUNKAN KADAR MANGAN (Mn) AIR SUMUR GALI (Studi di Puskesmas Krian pada Ruang UGD Kabupaten Sidoarjo 2019)

The ground water used is dug well water. In order to meet health requirements it needs processing in quality. Some well water contains high minerals in the form of manganese, manganese content of more than 0.5 mg/L can interfere with human health, it is necessary to process the adsorption process. Straw as an adsorbent contains cellulose which can bind metal ions. The study aimed to determine the effectiveness of straw activated carbon as an adsorbent material in reducing manganese content in dug well water.This type of research is one group pretest-posttest design that compares the decrease in manganese levels before and after the adsorption process was carried out using adsorbent straw activated carbon. The study was carried out in 7 treatments with controls, and variations in the mass of activated carbon straw were 5 gr, 10 gr, 15 gr, 20 gr, 25 gr, 30 gr. The treatment was carried out 4 the results of the replication were examined in the Laboratory. Data analysis using One Way Anova test.The results showed that the adsorption of straw activated carbon could reduce manganese levels in well water digging the initial level 1.119 mg/L to 1.161 mg/L mass 0 gr, 1.127 mg/L mass 5 gr, 1.132 mg/L mass 10 gr, 0.984 mg/L mass of 15 gr, 0.972 mg/L mass of 20 gr, 0.977 mg/L mass of 25 gr, and 0.855 mg/L mass of 30 gr. Based on the results of the One Way Anova test and the LSD follow-up test obtained by the treatment group which has the greatest influence on the average in reducing the levels of manganese water in the well, namely the mass of 30 gr with an efficiency of 23.59%.It was concluded that the adsorption of straw activated carbon was able to reduce manganese (Mn) levels but was not yet effective. The mass that has a large influence on average in reducing manganese levels is 30 gr. Further research is needed to be able to achieve adsorption efficiency and find an effort to reduce turbidity in sample water after treatment and it is recommended to increase the mass variation of straw activated carbon. KEYWORDS : dug well water, manganese, straw activated carbon
Pearson Correlation Coefficient k-Nearest Neighbor Outlier Classification on Real-Time Data Set

Detection and classification of data that do not meet the expected behavior (outliers) plays the major role in wide variety of applications such as military surveillance, intrusion detection in cyber security, fraud detection in on-line transactions. Nowadays, an accurate detection of outliers with high dimension is the major issue. The trade-off between the high-accuracy and low computational time is the major requirement in outlier prediction and classification. The presence of large size diverse features need the reduction mechanism prior to classification approach. To achieve this, the Distance-based Outlier Classification (DOC) is proposed in this paper. The proposed work utilizes the Pearson Correlation Coefficient (PCC) to measure the correlation between the data instances. The minimum instance learning through PCC estimation reduces the dimensionality. The proposed work is split up into two phases namely training and testing.  During the training process, the labeling of most frequent samples isolates them from the infrequent reduce the data size effectively. The testing phase employs the k-Nearest Neighborhood (k-NN) scheme to classify the frequent samples effectively. The dimensionality and the k-value are inversely proportional to each other. In proposed work, the selection of large value of k offers the significant reduction in dimensionality. The combination of PCC-based instance learning and the high value of k reduces the dimensionality and noise respectively. The comparative analysis between the proposed PCC-k-NN with the conventional algorithms such as Decision Tree, Naive Bayes, Instance-Based K-means (IBK), Triangular Boundary-based Classification (TBC) regarding sensitivity, specificity, accuracy, precision, and recall proves its effectiveness in OC. Besides, the experimental validation of proposed PCC-k-NN with the state-of art methods regarding the execution time assures trade-off between the low-time consumption and high-accuracy.
Practical Strategies to Boost Press Conference Success

Nonprofit Communications ReportVolume 18, Issue 3 p. 7-7 Press Conferences Practical Strategies to Boost Press Conference Success Yvette Boysen, Yvette BoysenSearch for more papers by this author Yvette Boysen, Yvette BoysenSearch for more papers by this author First published: 05 February 2020 https://doi.org/10.1002/npcr.31420Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume18, Issue3March 2020Pages 7-7 RelatedInformation
Research and Practice of the New Mode of Rural Primary School Teachers' Education and Teaching under the “Internet +” Environment

The advent of the “Internet plus” era has broken through the time and geographical constraints, and has created a new way for rural primary school mathematics teachers to improve their teaching ability. This paper proposes to make good use of the Internet Education and teaching resources, share the network resources of excellent teachers, develop the network interaction mode, and organize the network teaching and research activities, so as to achieve the goal of comprehensively improving the teaching ability of rural primary school mathematics teachers.
Semantic Knowledge Management for Herbal Medicines Used in Primary Health Care

The use of medicinal plants or herbal medicines for the treatment of common and minor diseases and symptoms has been a part of culture and way of life. Semantic Web technologies enable people to create data stores on the Web, build vocabularies, and write rules for handling data. Ontologies provide a shared and common understanding of a domain that can be communicated across people and application. In this study, we propose the herbal medicines used in primary health care ontology. The ontology was developed by using the Hozo, which is an ontology editor environment. The process of ontology development of this research are 2 phases. Phases 1: Domain analysis for knowledge organization approach and Phase 2: Ontology life cycle for an evolutionary approach. The Concepts of Herbal Medicines classes can be divided into 6 concepts. These include Habit, UseTheRightParts, Taste, HealthProblem, MethodForPreparation, and PartsUsed. The consequently developed ontology will serve to be highly useful for the development of ontology-based recommendation system.