Reviewerly/exHarmony huggingface dataset:
Industrial Training Report: Perpustakaan Politeknik Mukah / Nurul Athirah Syazwani Mohd Annuar

Industrial training is one of the compulsory study requirements in order to fulfil the needs of students to hold a degree. To fulfil the industrial training requirement, the students, must undergo at least five (5) month training at the organization that has been approved by the faculty. Related to the. course that have been taken, students should involve in information management in library management. So, I have decided to undergo my industrial training at Perpustakaan Politeknik Mukah, Sarawak (PMU). Started from August 2019 and ended on December 2019 are the period of my internship. I have reporting to undergo internship on 1st August 2019 and end my internship on 31st December 2019. This report is basically consist of four ( 4) chapter which are introduction of the organization, organization information, industrial training activities and industrial training reflection.
A Symmetric Searchable Encryption Identification of Data on Probabilistic Trapdoors

Accessible Encryption (SE) permits a client in accordance with transfer records in accordance with the astronaut and according to seem thru it of a faraway manner while defending the security concerning each the statistics yet the inquiries. Right now, entrust a generative then the simple in imitation of actualize Symmetric Searchable Encryption Scheme because instance (SSE) [4]. This tale takes the some round concerning correspondence namely O(n) instances on calculations upstairs n range regarding records . We likewise presented some other variety of Search Pattern Privacy, as gives a percentage about safety on the spillage structure trapdoor [4]. We also advocate the modifications over our graph because of batch inquire who can not reap the versatile vagary recipe. The current method offers the appropriateness whilst maintaining stuff about the data classified, that is viable so much such has advise bother among the full altar about the demand bill and wants in accordance with remove watchwords now the archives are eke out away. We likewise recommend adjustments in our format so the graph execute be utilized upon the potent enemies at the fee on various rounds of transmission yet the inclination space. [6]We may eke exhibit our layout on a range of commercial enterprise datasets. Likewise correct now, utilizes the Hash tying strategy instead than band over encryption action because file for consideration majority which makes such splendid for light poise applications. We are the first in imitation of suggest pardon trapdoors between Symmetric Searchable Encryption because the tussock search.
A lightweight two-layer blockchain mechanism for reliable crossing-domain communication in smart cities

The smart city is an emerging notion that is leveraging the Internet of Things (IoT) technique to achieve more comfortable, smart and controllable cities. The communications crossing domains between smart cities is indispensable to enhance collaborations. However, crossing-domain communications are more vulnerable since there are in different domains. Moreover, there are huge different devices with different computation capabilities, from sensors to the cloud servers. In this paper, we propose a lightweight two-layer blockchain mechanism for reliable crossing-domain communication in smart cities. Our mechanism provides a reliable communication mechanism for data sharing and communication between smart cities. We defined a two-layer blockchain structure for the communications inner and between smart cities to achieve reliable communications. We present a new block structure for the lightweight IoT devices. Moreover, we present a reputation-based multi-weight consensus protocol in order to achieve efficient communication while resistant to the nodes collusion attack for the proposed blockchain system. We also conduct a secure analysis to demonstrate the security of the proposed scheme. Finally, performance evaluation shows that our scheme is efficient and practical.
Time Series Analysis for Predicting Covid-19 Infection using Facebook Prophet Model

The international fright due to the occurrence of COVID has created an emergency act in the field of healthcare, bio medical and drug discovery process. However, finding the feasible solution to introduce a drug is a time-consuming process due to pre-clinical and post-clinical testing process. Prediction and estimation of COVID-19 can help the medical practitioners and government authorities to take preventable measure against the outcomes of COVID-19. From December 2019 to April 2020, 2844712 cases of COVID-19 have been informed, which includes 201315 deaths according to European Centre for Disease Prevention and Control. This drastic condition should be treated not only physicians and other health care providers. There are two types of time series forecasting techniques. The first technique time-domain approach models the forthcoming values as a function of previous and current values. The groundwork of this approach is the time series regression of current values of a time series on its own past values. The assessments of the model are applied for forecasting process. The second technique known as Frequency domain models are based on the interpretations of time using sines and cosines functions. These interpretations are known as Fourier representations. Overall, the technique utilizes regressions on sines and cosines function, to model the behavior of the data. The proposed work used Facebook Prophet model
RRFT: A Rank-Based Resource Aware Fault Tolerant Strategy for Cloud Platforms.

The applications that are deployed in the cloud to provide services to the users encompass a large number of interconnected dependent cloud components. Multiple identical components are scheduled to run concurrently in order to handle unexpected failures and provide uninterrupted service to the end user, which introduces resource overhead problem for the cloud service provider. Furthermore such resource-intensive fault tolerant strategies bring extra monetary overhead to the cloud service provider and eventually to the cloud users. In order to address these issues, a novel fault tolerant strategy based on the significance level of each component is developed. The communication topology among the application components, their historical performance, failure rate, failure impact on other components, dependencies among them, etc., are used to rank those application components to further decide on the importance of one component over others. Based on the rank, a Markov Decision Process (MDP) model is presented to determine the number of replicas that varies from one component to another. A rigorous performance evaluation is carried out using some of the most common practically useful metrics such as, recovery time upon a fault, average number of components needed, number of parallel components successfully executed, etc., to quote a few, with similar component ranking and fault tolerant strategies. Simulation results demonstrate that the proposed algorithm reduces the required number of virtual and physical machines by approximately 10% and 4.2%, respectively, compared to other similar algorithms.
PELAKSANAAN PROYEK REHABILITASI JARINGAN IRIGASI ( DAK ) DAERAH IRIGASI CIBUTUL

Rehabilitation of Cibutul Irrigation Area, located in Sukahaji Village, Sukahaji District, Majalengka Regency is a package of work for the Rehabilitation of Irrigation Networks / DAK. It is one of the efforts to restore the function of the irrigation because the malfunction or damage of one of the irrigation structures will affect the performance of the existing irrigation system, resulting in decreased irrigation efficiency and effectiveness.Implementation of rehabilitation work for Cibutul Irrigation Area with scattered work points. The work carried out included making gabions, repairing sabodam wings and repairing linging pairs in the main and secondary channels.
Unsupervised Foreground Extraction via Deep Region Competition

We present Deep Region Competition (DRC), an algorithm designed to extract foreground objects from images in a fully unsupervised manner. Foreground extraction can be viewed as a special case of generic image segmentation that focuses on identifying and disentangling objects from the background. In this work, we rethink the foreground extraction by reconciling energy-based prior with generative image modeling in the form of Mixture of Experts (MoE), where we further introduce the learned pixel re-assignment as the essential inductive bias to capture the regularities of background regions. With this modeling, the foreground-background partition can be naturally found through Expectation-Maximization (EM). We show that the proposed method effectively exploits the interaction between the mixture components during the partitioning process, which closely connects to region competition, a seminal approach for generic image segmentation. Experiments demonstrate that DRC exhibits more competitive performances on complex real-world data and challenging multi-object scenes compared with prior methods. Moreover, we show empirically that DRC can potentially generalize to novel foreground objects even from categories unseen during training.
Protected Entry Design for Data Encryption and Decryption Using Big Data in Cloud

Due to the difficulty and capacity, cryptography to a cloud is to be one of the most effective attributes for big data storage and access. However, verifying the access legitimacy of a user and securely updating cryptography in the cloud based on a new access policy selected by the data owner are also critical challenges to make cloud-based big data storage practical and active. In this paper,we propose a secure and access control based on NTRU cryptosystem for big data. Our process allows the cloud server to efficiently update the cryptography when a new access policy is identified by the data owner, who is also able to authenticate the update to counter against cheating behaviours of the protected entry design for big data in cloud. It can jointly permit the data owner and eligible users to effectively verify the legitimacy of a user for accessing the data, and a user to validate the information provided by different users for proper plaintext recovery.
Merging Two Cultures: Deep and Statistical Learning

Merging the two cultures of deep and statistical learning provides insights into structured high-dimensional data. Traditional statistical modeling is still a dominant strategy for structured tabular data. Deep learning can be viewed through the lens of generalized linear models (GLMs) with composite link functions. Sufficient dimensionality reduction (SDR) and sparsity performs nonlinear feature engineering. We show that prediction, interpolation and uncertainty quantification can be achieved using probabilistic methods at the output layer of the model. Thus a general framework for machine learning arises that first generates nonlinear features (a.k.a factors) via sparse regularization and stochastic gradient optimisation and second uses a stochastic output layer for predictive uncertainty. Rather than using shallow additive architectures as in many statistical models, deep learning uses layers of semi affine input transformations to provide a predictive rule. Applying these layers of transformations leads to a set of attributes (a.k.a features) to which predictive statistical methods can be applied. Thus we achieve the best of both worlds: scalability and fast predictive rule construction together with uncertainty quantification. Sparse regularisation with un-supervised or supervised learning finds the features. We clarify the duality between shallow and wide models such as PCA, PPR, RRR and deep but skinny architectures such as autoencoders, MLPs, CNN, and LSTM. The connection with data transformations is of practical importance for finding good network architectures. By incorporating probabilistic components at the output level we allow for predictive uncertainty. For interpolation we use deep Gaussian process and ReLU trees for classification. We provide applications to regression, classification and interpolation. Finally, we conclude with directions for future research.
Contribution of Writing Interest toward Indonesian Language Learning of Students in Vocational High Schools

This research aims to find out how the contribution of writing interest toward Indonesian language learning of students in vocational high schools in the city of Kefamenanu, East Nusa Tenggara province. This study utilised a quantitative method with a correlational approach. Based on the results of data analysis, data on student writing interest was in averaged 74.8% with good category. Based on the calculation results, there was a correlation of about 0.290 and was included correlation coefficient in the low category. The result of the significance t-test shows that t count score was 3.248. Referring to the results of the study, it can be concluded that there is a positive and significant correlation between the writing interest toward Indonesian language learning of the students of grade X of vocational high schools in Kefamenanu.
Assessment Cost Estimation for Component Based Software: A Tool Based Technique

Assessment cost of the software component contributes a major part in software cost estimation and it is one of the major cost of the software out of- integration cost i.e. the cost of glue codes, assessment costs and tailoring cost. Many researchers have proposed formulas for evaluating assessment and tailoring costs theoretically. Assessment cost is very often considered to be theoretical cost which involves cost of component selection and composition. According to Moguel Goulao et. al assessment cost for overall component can be measured qualitatively and quantitatively both. He has suggested that qualitative measurement is mostly based on views provided by the experts whereas the quantitative measurement is more subjective and repetitive in nature. Various metrics has been suggested by different authors to quantitatively measure the assessment cost for software components. In this work we applying the metrics on case study of UCRS and developed a tool for evaluating the assessment cost which can be used in calculating the overall cost of the software.
Neural Semantic Parsing for Syntax-Aware Code Generation

The task of mapping natural language expressions to logical forms is referred to as semantic parsing. The syntax of logical forms that are based on programming or query languages, such as Python or SQL, is defined by a formal grammar. In this thesis, we present an efficient neural semantic parser that exploits the underlying grammar of logical forms to enforce well-formed expressions. We use an encoder-decoder model for sequence prediction. Syntactically valid programs are guaranteed by means of a bottom-up shift-reduce parser, that keeps track of the set of viable tokens at each decoding step. We show that the proposed model outperforms the standard encoder-decoder model across datasets and is competitive with comparable grammar-guided semantic parsing approaches.
Designing of 3-D Animation Videos in Arduino Assembly Learning with The Multimedia Development Life Cycle Method

Abstract The interest in the Internet of Things (IoT) field is getting more and more popular in the world of technology this recently. This can be seen from the number of researchers, students and students starting to study the Internet of Things (IoT). If viewed globally, then the notion of the Internet of Things (IoT) is that everything in the form of living or inanimate objects in this universe has a code/identity that can be recognized by a system. Many parties have begun to introduce and teach Internet of Things (IoT) technology to Generation Z children. The thought of the importance of Arduino technology learning media is that it can facilitate the learning process for students and students in recognizing and making Arduino devices. Arduino devices consist of CPU, ROM and RAM. Arduino power supply uses a dc voltage between 9-12 volts. In learning Arduino assembly so far, lecturers do not use supporting media other than direct practice and books and power point slide shows. students also have difficulty in absorbing Arduino assembly subject matter because students are faced with new material that has never been obtained before. In the Internet of Things (IoT) course, there is Arduino assembly material. Arduino assembly material cannot be separated from the practicum. Therefore, students need learning media that can package practical activities into a video that can be studied anywhere and anytime. By using learning video media, it is hoped that there will be an increase in student learning outcomes, especially in Arduino assembly materials. Therefore, in this study, a 3-dimensional animation video will be designed in learning Arduino assembly using the Multimedia Development Life Cycle (MDLC) method.
초산모의 모유수유 이행 구조모형

Purpose: This study aimed to develop a structural model to explain breastfeeding behavior of primipara based on information-motivation-behavioral skills. Variables were breastfeeding knowledge, breastfeeding motivation, breastfeeding self-efficacy, and parenting stress.Methods: Participants were first-time mothers who were breastfeeding infants from birth to 6 months. Data were collected from 5 pediatric hospitals and 1 public health center in Jeollanam-do from December 24, 2018 to February 28, 2019. A total of 285 questionnaires were included in the analysis. Data were analyzed using IBM SPSS Statistics ver. 20.0 and AMOS 20.0 programs.Results: Fitness statistics for the hypothetical model were appropriate (χ2=48.30 [degrees of freedom= 14, p<0.001], goodness of fit index=0.96, normed fit index=0.89, comparative fit index=0.92, standardized root mean square residual=0.06). Breastfeeding self-efficacy had the most direct and total impact on breastfeeding behaviors (β=0.38, p=0.01). Breastfeeding self-efficacy was directly and totally influenced by breastfeeding knowledge (β=-0.18, p=0.01), breastfeeding motivation (β=0.26, p=0.01). Parenting stress showed a direct effect on breastfeeding self-efficacy (β=-0.14, p=0.02) and breastfeeding behavior (β=0.20, p=0.01), confirming its modulating effect on both variables.Conclusion: Since breastfeeding self-efficacy was the most important variable for the breastfeeding behavior in first-time mothers, nursing interventions should be developed to promote breastfeeding self-efficacy including accurate breastfeeding information and enhanced breastfeeding motivation. Strategies that could relieve or adjust parenting stress should be considered since parenting stress had a causal relationship with breastfeeding self-efficacy and breastfeeding behavior.
Image Steganography Built on Pixel Value Difference in Spatial Domain using Range Table

With the increase of digital transactions, providing security for the network and its devices is a big challenge today. Sensitive information that has more value in the network is to be protected from unauthorized users in accessing it. Various algorithms are being developed to provide security services for the data. In this paper security is provided by using pixel value differencing technique. Valuable data is embedded in each of the component of color like red, blue and green of a pixel in a color image. In addition to that in providing additional security pixel value has been updated conditionally, that would be more complex to trace the incremented or decremented bits in a pixel of the stegoimage. From this experiment the proposed method provides better visual quality of stego image. This proposed algorithm is suitable for small business applications where small size data is important and threats are more common.
Controlling and Monitoring of Hospital via IoT

Digital Hospital is a concept contributing to enhancing personal productivity, facilitating hospital operations, improving the process quality and ensuring patient safety by integrating cutting-edge technologies such as medical devices, smart information systems, facility control and automatic conveyor systems, location-based services, sensors and digital communication tools into health processes. Many times, due to inattention of hospital staff, or inattentiveness of relatives it may happen that medication is not observed properly and it may lead to heart attack or other life threatening situations. Thus, this research study presents a proposed system which includes a combination of sensor technology and Internet of Things (IoT).Using this system one can control electricity equipment and monitor level of the medication from a remote place and monitor the entire process.
ALGORITMA K-MEANS CLUSTERING DALAM PENYEBARAN PENYAKIT DIARE DI KOTA JAMBI:

Jambi Health Office is the agency responsible for health in its work area. The system used previously uses manual calculations with Microsoft Excel rock. So as to form an effective and efficient assessment process, the system is developed by developing software that uses Web programming language using PHP MySQL database. The purpose of the system thesis report is to process diarrhea data so as to facilitate Health Officers in processing data more effectively and efficiently, assisting the process of grouping the spread of diarrheal diseases in the City of Jambi, facilitating officers in making reports of diarrhea distribution and as a means to find out the percentage of the spread diarrhea recorded by the health of the city of Jambi. The main element in forming a system consists of input, process and output. The input needed from this system is diarrhea data, diarrhea distribution criteria data. With a process that includes the process of diarrhea data processing, the process of grouping the spread of diarrhea criterion data and the process of grouping the spread of diarrhea diarrhea data using the K-means clustering method and the report data processing process. Which will produce outputs from Puskesmas data reports, diarrhea distribution data grouping reports, graph reports. From this research, it is expected to produce a draft of the K-means clustering algorithm that can facilitate the distribution of diarrhea data at the Jambi Health Office in the distribution of reports and report generation.
LibGuides: ESU Institutional Repository: ESU Institutional Repository

This guide highlights East Stroudsburg University's Institutional Repository (IR), how to use it, and how to submit your scholarly or creative works to the IR.
Solar Tracking System Using Arduino

Nowadays, renewable energy or another nickname is 'Green Energy' is a rapidly developing energy in our country. There are many types of renewable energy available, but in our country, a solar system is an option because of the Malaysia position located on the equatorial line. Besides, this system is an efficient system for producing solar energy from sunlight. However, the widely used solar panels in Malaysia are static. Since the suns position changes over time, the efficiency of the solar panel to get enough sunlight to produce minima energy. Therefore, the project is produced to improve the existing solar panel system. The resulting project allows solar panels to move according to a place that has a high rate of light for energy production. To move solar panels on two different axes, two servo motors are used, and they are also connected to Arduino. To detect the sun's position, sensor photoresistor is used. The primary purpose of this project is to generate a ‘Solar Tracking System using Arduino’. This system will help move parallel solar panels to sunlight for maximum power generation. The system is also capable of collecting voltage generation data generated by solar panels at a given time. Outcomes of this project, students can observe the volatility caused by solar panels at a given time and create solutions for energy efficiency problems in solar panels.