Reviewerly/exHarmony huggingface dataset:
Archived Processes Integration for Extension of the Reverse Engineering Approach

Enterprise Information System (EIS) must cover the interoperability criteria inside its “application view” scope. Nevertheless, the urbanization approach, on which we rely to implement this EIS, has to deal with “horizontal fit” and “traversal fit” problems: lack of intra and inter-applicative communications problems. To overcome these deficiencies, we show in this paper our solutions to ensure semantic interoperability between the applications of different EIS, which are involved to exploit collaboratively business processes. For that, we rely on the Semantic Web Services (SWS) to allow enterprises that use multiple software systems for a business management project, to exchange easily business data between these services. In this context, we propose to reuse and extend the reverse engineering approach that serves to describe the SWS. Four phases permit to describe our contribution: extraction of relevant terms, analysis of the filtered terms, construction of Ontology Design Patterns (ODP) and archived processes. We demonstrate that our strategy enhances the semantic interoperability criteria between the enterprises.
IDENTIFICATION OF SPAM POSITIVE NEGATIVE FILTERS IN ONLINE SOCIAL MEDIA NETWORKS

Most existing systems for spam discovery on Twitter plan to distinguish and square clients who post spam tweets. In this paper, we propose a semi-directed spam discovery (S3D) structure for spam identification at tweet-level. The proposed structure comprises of two primary modules: spam recognition module working progressively mode and model refresh module working in cluster mode. The spam discovery module comprises of four lightweight indicators: 1) boycotted space finder to mark tweets containing boycotted URLs; 2) close copy locator to name tweets that are close copies of unquestionably pre named tweets; 3) solid ham identifier to name tweets that are posted by believed clients and that don't contain nasty words; and 4) multi-classifier based finder names the rest of the tweets. The data required by the identification module is refreshed in group mode dependent on the tweets that are marked in the past time window. Analyses on a huge scale informational collection demonstrate that the structure adaptively learns examples of new spam exercises and keep up great exactness for spam recognition in a tweet stream.
Rancang Bangun Sistem Informasi Vaksinasi Pada Balita Menggunakan Metode Waterfall

AbstractIntegrated Service Post or Posyandu is a form of Community-Based Health Efforts (UKBM) carried out by, from and with the community, to empower and provide facilities for the community to obtain health services for mothers, babies and toddlers. However, one of the main problems in the integrated service post is still implementing non-computerized data processing and information dissemination so that the process flow is still recording the attendance list of infants and toddlers, recording immunization data, and recording the weight of infants and toddlers in the form of a health book. Mother and Child (KIA) or recording on paper. This causes several obstacles, namely processing administrative data which takes time, and recording books that are easily lost or damaged or sometimes forgotten books are taken during posyandu activities. Besides that, another problem is the process of searching for data one by one, so it takes a long time. In line with the convenience to the community, the authors propose a Web-based information system for toddlers vaccine data collection at Posyandu as an effort to facilitate information. The toddler vaccine data collection system at the posyandu can help to simplify the process of vaccine data collection and data processing for toddlers at the posyandu and also for posyandu officers to carry out the input process to process data that is good, precise and fast.
Development of Tracking Signal for Non-homogeneous Poisson Process Model

In this new age of rapid industrialization, which is daunting with the rapid speed of technological development, it is important to precisely make accurate prediction in order to stay relevant in the competitive market.
LibGuides: LibGuide Best Practices: Accessibility

TCC Library's best practices, standards, and guidelines to follow when creating and updating LibGuides.
Human susceptibility to subtle adversarial image manipulations with unlimited exposure time

Artificial neural networks (ANNs) have revolutionized multiple fields, and were initially inspired by models of biological neural networks (BNNs). A growing body of work finds similarities between behaviors and representations in ANNs and BNNs. However, despite these similarities and shared foundations, ANNs exhibit surprising properties and failures that are generally believed not to exist in biological networks. One of the most dramatic of these failures is a susceptibility to adversarial perturbations, where a nearly imperceptible perturbation added to an input can cause an ANN to behave in a dramatically different fashion, for instance mislabeling an initially correctly identified school bus as an ostrich. Is human vision also susceptible to adversarial perturbations? Past work has shown that when images with adversarial perturbations of intermediate magnitude (±32 of 256 intensity levels) are shown to humans for a short time (∼70 ms), human object classification judgments are perturbed in the same direction as ANN’s. Other work has found that humans can identify adversarial examples with large magnitude perturbations and extended exposure times. Here, we find that human susceptibility to adversarial examples extends beyond these settings. We display images with small magnitude perturbations (between ±2 and ±32 out of 256 intensity levels) for an unlimited exposure time. We find that even at the smallest ±2 magnitude, these images perturb human judgments of object class in the same direction as an ANN trained to classify images. These results demonstrate that humans exhibit a peculiarity that was once assumed to be specific to machines, and are suggestive that perception in ANNs and BNNs is more similar than commonly believed.
Sensitive Samples Revisited: Detecting Neural Network Attacks Using Constraint Solvers

Neural Networks are used today in numerous security-and safety-relevant domains and are, as such, a popular target of attacks that subvert their classification capabilities, by manipulating the network parameters.Prior work has introduced sensitive samples-inputs highly sensitive to parameter changes-to detect such manipulations, and proposed a gradient ascent-based approach to compute them.In this paper we offer an alternative, using symbolic constraint solvers.We model the network and a formal specification of a sensitive sample in the language of the solver and ask for a solution.This approach supports a rich class of queries, corresponding, for instance, to the presence of certain types of attacks.Unlike earlier techniques, our approach does not depend on convex search domains, or on the suitability of a starting point for the search.We address the performance limitations of constraint solvers by partitioning the search space for the solver, and exploring the partitions according to a balanced schedule that still retains completeness of the search.We demonstrate the impact of the use of solvers in terms of functionality and search efficiency, using a case study for the detection of Trojan attacks on Neural Networks.
Instalasi dan Evaluasi Sistem Peringatan Dini Banjir Berbasis Sensor Ultrasonik HC-SR04 dan Internet of Things (IoT)

Research on installation and evaluation of early warning systems based on the HC-SR04 ultrasonic sensor and the Internet of Things (IoT) has been successfully carried out. This study aimed to install and evaluate a flood early warning system based on the ultrasonic sensor HC-SR04 and IoT at a flood monitoring post, Boyong River Yogyakarta. Installation of a flood early warning system based on the HC-SR04 and IoT ultrasonic sensor was carried out by preparing tools and materials, connecting WiFi, and installing the system. Evaluation accuracy of the system was carried out by varying the distance between the sensor and the water surface, namely 5 cm - 200 cm with 5 cm intervals, while testing of precision and success were done by 10 times repeating for each distance variation. The results showed that flood early warning system based on ultrasonic sensor HC-SR04 and IoT has been successfully installed at the flood monitoring post, Boyong River Yogyakarta. In addition, the flood early warning system based on ultrasonic sensor HC-SR04 and IoT was successfully evaluated with an accuracy value 99.95%, repeatability precision 98.75%, reproducibility 99.27%, and success 89.50%.
IMPROVING THE STUDENTS’ VOCABULARY MASTERY BY USING TREE DIAGRAM ON THE EIGHTH GRADE STUDENTS’ OF SMP DWIJENDRA IN THE ACADEMIC YEAR 2019/2020

The aim of this research was to know the process of improve the students vocabulary mastery throught tree diagram strategy for eighth grade students of SMP Dwijendra Denpasar in academic year 2019/2020. The subject was the students of VIII B in the academic year 2019/2020. The subjects of this research were consisted of 30 students of second grade. The instruments that was used in collecting the data was test, interview and observation.The method used in this research was Classroom Action Research (CAR). Based on the result of this research showed that there was a development on the students’ vocabulary mastery, it was be seen from the mean score of pretest were 61,5 the mean of posttest cycle 1 was 73,8 and the mean score of posttest cycle 2 was 83,5. In addition there were 3 students (10%) who passed Minimum Mastery Criterion (KKM) in the pretest. Meanwhile, in cycle 1, there were 12 students (40%) who passed Minimum Mastery Criterion (KKM) and it gained which was in the posttest cycle 2 there were 27 students (90%) who passed  Minimum Mastery Criterion. So, the criteria of success were achieved. Furthermore, the result of observation and interview showed that the students were motivated in the teaching-learning process during the implementation of Tree Diagram Strategy.
항공관광 관련 전공 대학생들의 직업이미지와 직업가치관이 취업준비행동에 미치는 영향

The global economy has never faced a challenge like the recent outbreak of COVID-19. As a result, most service-related industries, including domestic and international airlines and hotels, have stopped hiring new employees, increasing the Employment stress level of university students studying fields related to the hospitality and airline industry. This study aims to analyze the correlates of both job image and occupational values among university students who major in the disciplines relevant to aviation tourism and examine their impact on the students’ employment preparation behavior. The results of this study is that the job image has a positive and statistically significant influence on occupational values and that the job image does not have any statistically significant association with employment preparation behavior, either preliminary or full-scale preparation behavior. The analysis also shows that occupational values have a significant positive impact not only on the preliminary preparation behavior but also on the active employment preparation behavior. Based on the analysis of the survey, this paper provides the foundation for strengthening the employment preparation behavior by emphasizing a clear understanding of the job features, appropriate occupational image, and proper work values.
Technological Alternative to Tax Compensation (Icms Credits): Case Study of the Feasibility of Using Dlt in the Brazilian Electronic Invoice System

Due to the importance of using the tax compensation institute, this study advocates the creation of a tax credit clearinghouse and analyzes the applicability of blockchain and DLT to the Brazilian Electronic Invoice System. Firstly it explains the technology and its different types. Next point, make a presentation regarding compensation in tax law. Following it analyzes the legal and technical characteristics of the current system identifying the opportunities and challenges arising from an eventual change of technology. And then it concludes that there are several advantages such as the curtailing of tax evasion, the optimization of tax substitution mechanisms with transparency and immediate applicability for the entire production chain and the possibility of establishing a reliable market for tax credit. In this paper, we posit the adoption of Hyperledger Composer Fabric or R3 Corda as encrypted platform able to create a secure environment for the storage and analysis of information by using DLT, as an effective solution to address the privacy and security concerns of the stakeholders. The first option is better to provide value chain assessments and the second fiscal secrecy, in order to allow the safe activity of a clearinghouse for tax credits.
Next-Generation IoT Use Cases across Industry Verticals Using Machine Learning Algorithms

Internet of Things is an interconnection of objects or things through the internet and enables exchange of data where every object is provided a unique identifier and the data are transferred through the network. All the devices can be connected to a network and controlled at our fingertips using web-based applications or mobile applications. This technology fosters the growth of the internet and reduces human intervention, thereby making life very easy. Due to the transformation of agriculture to smart agriculture, cities to smart cities, payments to smart payments, IoT plays a major role in all the fields prevalent in today’s society. It is also predicted that in the coming years, the number of devices and applications that are connected will exponentially increase. With the rapid growth and a busy world, it is necessary that this kind of technology would be able to drive us and control everything at our own comfort. It has greater advantages like optimization, decision-making, and improved business strategies for satisfying the customers. Industrial IoT is a subset of IoT, which takes the concept of IoT, is applied with business scenarios, and leads to increased efficiency and greater savings. The market is highly benefited with the capability to monitor the machines remotely and monitor environmental conditions. It is not limited to specific domains; it plays a pivotal role in the field of health care by enabling patients and medical practitioners to communicate regularly in terms of the treatment, medications, and so on. This chapter is focused on the industrial use cases, which are elaborated below: Machine diagnosis systems detect faults in the machines, and also monitor the operating conditions. The performance of the machine is analyzed by comparing it with normal operating conditions. Inventory management system is used to monitor the inventor and maintain inventory level using RFID tags. Monitor the environmental conditions inside the industry by identifying toxic gases using various gas sensors, based on which hazardous zones can be identified. Hence, remedial measures can be taken by providing proper ventilation. Monitor the noise level in industry using noise monitoring stations. Noise maps are generated based on collected data. Devices in the form of wearables make remote monitoring of patients in the healthcare sector easy.
THE STUDENTS’MASTERY OF VOCABULARY ANDGRAMMAR TOWARDWRITING DESCRIPTIVE TEXT(A CORRELATION STUDY AT SEVENTH GRADE OFSMPIT ALMADINAH TANJUNGPINANG IN THEACADEMIC YEAR OF 2017/2018)

This research discussed the students’ mastery of vocabulary and grammartoward writing descriptive text. The goal of this research was to find out whether ornot there is a correlation between the students’ mastery of vocabulary and grammartoward writing descriptive text. The populations of the research were 64 students and32 students were being the sample of the research. All was from the seventh grade ofSMPIT Al Madinah Tanjungpinang in the academic year of 2017/2018. The samplewas taken by simple random sampling. This research was used correlation study bycollecting the data from the multiple choices and essay test. The researcher usedSPSS version 22.00 to calculate the data. The result demonstrated that the data of thisresearch was normal, homogeny and linear. There was positive correlation of eachvariable. Based on the data analysis computed by using application, it was obtainedthat the number of r is 0.539. It showed there was moderately correlation between thestudents’ mastery of vocabulary and grammar toward writing descriptive text. Fromthe T-test, the result showed the value of tcount > ttable, it means Ha was accepted.Therefore, there was a significant correlation between the students' mastery ofvocabulary, and grammar toward writing descriptive text at the seventh grade ofSMPIT Al Madinah Tanjungpinang in the academic year of 2017/2018.
Modeling ways of developing students' creativity as the main path to their professional development

One of the important tasks of the modern system of psychology higher education is not only the development of the student’s professional potential, but also the comprehensive development of his personality. This is due to the fact that the unilateral development of professional skills and the rich theoretical base that a psychology student masters throughout the entire course of study can become practically unrealized in the future work process. The purpose of the article is to systematize the process of increasing the creativity of psychology students with the help of mechanisms and elements of the functional model of IDEF0. Using the IDEF0 functional model, we have clearly demonstrated all the functional levels and structural elements of the process of enhancing the creativity of psychology students.
Assessment Problems during Face-to-face and Distance Learning

Much investigation and changes are needed to refine assessment systems. The purpose of the presented research was to investigate if students were satisfied with classroom or distant assessment and provide some recommendations on online assessments for both teachers and students. It was conducted at Shota Meskhia State Teaching University of Zugdidi (Georgia) in order to get some insight into students’ perspectives towards their assessment. Students' assessment is not a new phenomenon, universities have been evaluating students for nearly a hundred years for different purposes however the main function is to inform students about their achievements. Due to the COVID-19 emergency, higher education institutions have had to transit from the traditional form of teaching to the online teaching and learning format, which appeared to be a big challenge for the majority of them, both in terms of resources and practical experience.
Extracting data from a large dataset of song titles and reviews towards malay songs / Nur Hajar Amirah Abd Aziz @ Abd Manaf, Nur Atikah Wan Mohamad Kamil and Nurul Afiqah Ramli

Music is the written signs representing instrumental sound that produce beauty of form, harmony and expression of emotion. It is the act or art of singing burst into song. This study focused on analyzing Malay's song titles and reviews. The data was generated from the JOOX music player and Youtube channel. The objectives of this study are to identify the similar words used for song titles and reviews and to determine the relationship between words used for song titles and reviews. There are 250 songs have been collected in various genre with 609 reviews. The data is in a text form and being analyze by extracting word by word for each song's title and reviews. The most common word in Malay song titles and reviews are determine by using tibble function in R programming and presented in cloud form. The relationship among words in song's reviews are measured using Pairwise correlation coefficient. The Malay songs titles are also presented via visualization of a network of bigrams. The analysis of results found that the most common word used for Malay songs titles is ''cinta . While the most common word used for Malay songs reviews is song. The relationship among the words in song reviews are statistically significant with positive relationship between sad and you, sad and all, songs and singer, deep and you, deep and sad, and deep and all. 'cinta' is a common center of node. which is often followed by the song title.
Photovoltaic Yield Nowcasting: For Residential Solar Systems in the Netherlands Using a Machine Learning Approach

An increasing number of photovoltaic (PV) systems are being installed worldwide and residential sector is responsible for a large part of this growth. Small scale PV systems do not have complex measuring devices and their breakdowns are not spotted immediately by the system owners. This might lead to prolonged time without generating power and creating both financial loss and environmental damage. This thesis presents a method of PV yield nowcasting laying foundations for remote monitoring. Early detection of faults is the first step towards eliminating the described issues. In this project four machine learning models for predicting solar yield were developed: ElasticNet, Polynomial Regression, Random Forest, and Extreme Gradient Boosting (XGBoost). The models were created both for daily and hourly data sets, as some inverters can log daily yields only. In both cases, the utilized data set consisted of data for the time between July 1st, 2018 and June 30th, 2019 and corresponding to 1,102 PV systems which is five times more than the largest data set studied in the found literature. The average PV system size in the data set is 4.44 kWp. Utilized inputs next to weather data and previous yields included shading factor describing fraction of direct light unable to reach PV system due to surrounding obstacles. Calculation of shading facor was based on 360⁰ pictures taken at the site. XGBoost algorithm turned out to be the most suitable for the task of PV yield nowcasting obtaining RMSE of 1.48 kWh and MAE of 0.877 kWh for hourly data aggregated to daily values and evaluated on future time steps. Currently used commercial software of Solar Monkey has RMSE equal 2.237 kWh and MAE equal 1.5 kWh. XGBoost model trained on daily data obtained RMSE 1.185 kWh and MAE 0.698 kWh outperforming hourly model most likely due to utilization of Hidden Markov Model for data cleaning. Next to overall performance, per system metrics were calculated for the hourly XGBoost. Mean individual RMSE for previously seen systems is 1.656 kWh while for unseen systems it equals 1.666 kWh. This means the model scales well to previously unseen systems and implies that its parallelized version is not necessary. Also, the model's learning saturates after seeing data corresponding to one year and 278 PV systems. Precalculation of GPOA worsened performance with respect to the model utilizing GHI. Hourly XGBoost has hourly RMSE of 0.281 kWh under clear sky and 0.377 kWh under partly cloudy sky which indicates it is more mistaken for cloudy conditions. This could be caused by low quality of cloud coverage data. The model also has large relative errors for small irradiance values which occur mostly in January and December, as well as just after sunrise and just before sunset. This issue is caused by using squared error as loss function during model training. Despite these shortcomings, the conclusive results recommend industrial implementation of the developed model.
Language Decoding From Visual Neural Activity Evoked by Natural Images

The paper is concerned with the construction and convergence analysis of the simplest C0 virtual element method for the Cahn–Hilliard problem in mixed form. This virtual element method leads to a low requirement on regularity and a treatment of general polygonal elements, including non-convex and degenerate elements. Moreover, by introducing two elliptic operators, we prove the L2-error estimate for concentration ϕ in the semidiscrete scheme. Furthermore, numerical results of the full discrete scheme are presented.