Process Logs Research Articles

PERANCANGAN APLIKASI EVALUASI HASIL BELAJAR SISWA (STUDI KASUS : TAMAN KANAK-KANAK CENDIKIA JAMBI)

Schools are one of the organizational means in providing services to the community in the field of education. One of these components describes the stages of building an Application for Evaluation of Kindergarten Learning Outcomes to facilitate the performance of teachers, especially parents of Kindergarten students, in processing value data quickly, precisely and efficiently, as well as making it easier for students and parents of students to check grades. . As for some of the input requirements needed, namely teacher data at Cendikia Kindergarten, student data at Cendikia Kindergarten, maple data at Cendikia Kindergarten. Some of the process requirements needed in the application that was built were admin login process, teacher data processing, student data processing, value data process, subject process. The making of this application uses the PHP script language.

Provably throttling SQLI using an enciphering query and secure matching

Web applications, which dominate the internet, act as communication media between customers and service providers. Web applications are an internet innovation that provide customer services such as e-banking, e-commerce and e-booking. Developing web applications has become increasingly complicated because of security threats and service issues that involve valuable information. Attack methods such as structured query language (SQL) injection insert malicious code within user input data requests to gain unauthorised access, and then the attacker targets a database to manipulate information. In this paper, we propose a prevention method against SQL injection attacks through cryptography and searchable encryption. The proposed method uses a cryptography technique to encrypt all database information, where each piece of user information is encrypted with a separate key. The rest of the database information is ciphered with secret keys, and a searchable encryption technique is used for other database operations to preserve privacy. The login process compares the ciphered username from the database and user entry to authenticate the user. The proposed method is implemented on the PHP and MySQL databases, which are open-source applications. The results show efficient prevention of SQL injection, and the database remains protected against SQL injection attacks

IMPLEMENTASI CLOUD STORAGE MENGGUNAKAN OWNCOULD DI SD NEGERI LEMPUYANGAN 1 YOGYAKARTA

Cloud storage is a cloud-based data storage technology that makes it easy for us to do storage without the need for physical storage. At SD Negeri Lempuyangan 1 Yogyakarta the storage media still uses physical storage media such as flash drives and one of the cloud storages, namely Google Drive. Limitations of physical storage media such as physically damaged, bad sectors, exposed to viruses, lost and subscription prices or additional storage capacity on Google Drive are quite expensive. The way to overcome damage or obstacles in data storage, researchers use owncoud as a cloud computing technology with cloud storage services with several advantages, including being able to access data anytime and anywhere, the capacity is not limited (depending on hosting), can share data easily, quickly , and accurate, and most importantly free of charge. By having their own server, data security can be monitored in real time and data management and users can be carried out more freely. The results of the blackbox test conclude that the processes that run from the login process to the occurrence of business processes are in accordance with the design made. All forms and features run according to their functions.

Discovering Structural Errors From Business Process Event Logs

Process mining aims at discovering behavioral knowledge of business processes from their event logs, which has received an increasing attention in the era of cloud computing and big data. Surprisingly, to date, discovering structural errors (e.g., deadlocks and lack of synchronization) from event logs has not been considered in state-of-the-art process mining techniques. Moreover, existing process discovery approaches cannot be directly applied to event logs of processes with structural errors due to erroneous event occurrences caused by unsynchronized activities. To address this problem, we first preprocess the event log to obtain two separate event logs that are used to discover deadlocks and lack of synchronization, respectively. Erroneous event occurrences caused by unsynchronized activities are discarded in the two processed event logs, from which our error mining algorithms can discover all process fragments involving structural errors, without the need to obtain the overall process first. We implement our approach in a ProM plugin and evaluate it on event logs of real-life business processes, the results of which demonstrate that our approach can effectively and efficiently discover deadlocks and lack of synchronization if event logs contain sufficient event sequences.

Business process recommendation method based on cost constraints

ABSTRACT Business process recommendation can be used to simplify the working procedures of enterprises, avoid unnecessary expenses, and promote the development of enterprises. In the process of process recommendation, there are a lot of activities that are similar in structure and difficult to choose. Here, a process recommendation method based on cost constraints is proposed to solve the problem of difficult to distinguish similar processes. First, the business process is transformed into a labelled Petri net, and the execution probability of each transition is calculated according to the business process log. Then, the matrix used to represent Petri nets is constructed according to the adjacent relationship between transitions, and the matrix is made into the same dimension, and the similarity between matrices is calculated by biggest–smallest approach degree, and the set of Petri nets with similar structure is established. Finally, a cost constraint-based process recommendation method is proposed to find lower service cost items in similar process sets. In the experimental part, the feasibility of the method is compared and verified.

An Embedded Microcontroller-Based Access Authentication System of Wireless Sensor Network

ABSTRACT In order to improve the communication reliability of wireless sensor networks, a wireless sensor network access authentication system based on embedded microcontroller is designed. Design the overall structure and hardware configuration of wireless sensor network access authentication system, and collect the information of sensor nodes; Creatively based on the duapii protocol, the node trust is calculated according to the node behavior, and the dynamic access authentication of wireless sensor network users is realized through the authentication and login process. According to the system test results, the designed system can realize the access authentication of wireless sensor networks. When the wireless sensor network is attacked by different types of attacks, the communication delay is less than 100 ms, which improves the reliability of network communication.

서사 텍스트를 사회해석 기제로 활용하는 소통 중심 다문화 교수·학습 방안 탐색

This study designed the ‘discourse synthesis’ activity using narrative text as a ‘social interpretation mechanism’ as a prototype based on communication-centered multicultural teaching and learning components. The first step in the process of using the narrative text as a ‘social interpretation mechanism’ is to recognize the tacit references and prejudices of society by interpreting the interrelationship between characters in the text and society in the text world. The second step was to recognize the establishment of one's own values by interpreting the interrelationships of society at the time of creation. The third step was to search for a desirable community by interpreting the interrelationship between the reader and the society with regard to the text acceptance. After in-depth reading of the narrative text, the learner writes a ‘process log’ and a ‘critical essay’, imagining different levels of readers and forming internal communication with them. In this case, the first reader is ‘the self as audience’ corresponding to the learner himself, and the second reader is the real existence ‘audience addressed’ All objects existing in the classroom directly conducting the class. Correspondingly, the last reader refers to a large number of unspecified readers who are thought to be able to read the learner’s text as ‘audience invoked’ including fictional beings. Through this, learners will be able to find a desirable community in a multicultural society.

Establishing log reduction values for wastewater treatment processes from ambient influent and effluent pathogen monitoring data

In developing wastewater treatment regulations and guidelines for potable reuse, the reduction of pathogens is of critical importance. In draft regulations, the state of California has proposed overall treatment process log reduction values (LRVs) of 20, 14, and 15 respectively for viruses, Giardia cysts, and Cryptosporidium oocysts, for direct potable reuse. In meeting the log reduction requirements, agencies have sought to obtain credit for LRVs achieved consistently through secondary biological and tertiary wastewater treatment, in addition to the LRVs achieved through advanced water treatment. The purpose of this paper is to illustrate how to determine pathogen LRVs, using ambient wastewater treatment pathogen monitoring data and the rank-paired covariant method of analysis.

SporTran: A code to estimate transport coefficients from the cepstral analysis of (multivariate) current time series

SporTran is a Python utility designed to estimate generic transport coefficients in extended systems, based on the Green-Kubo theory of linear response and the recently introduced cepstral analysis of the current time series generated by molecular dynamics simulations. SporTran can be applied to univariate as well as multivariate time series. Cepstral analysis requires minimum discretion from the user, in that it weakly depends on two parameters, one of which is automatically estimated by a statistical model-selection criterion that univocally determines the resulting accuracy. In order to facilitate the optimal refinement of these parameters, SporTran features an easy-to-use graphical user interface. A command-line interface and a Python API, easy to embed in complex data-analysis workflows, are also provided. Program summaryProgram Title:SporTranCPC Library link to program files:https://doi.org/10.17632/hm48f8kgj9.1Licensing provisions: GPLv3Programming language: PythonNature of problem: Given an M-variate time series, Jj(t), j=0,…M−1, typically describing a number of currents resulting from a molecular-dynamics simulation, SporTran estimates the transport coefficient κ=1/(Λ−1)00, where Λij=∫0∞〈Jj(t)Jk(0)〉dt is the matrix of the Onsager linear-response coefficients, and 〈⋅〉 indicates an equilibrium average over initial conditions.Solution method:i)It is first observed that the Onsager transport coefficients are the zero-frequency values of the cross power spectra of the currents under scrutiny: Λij=12Sij(ω=0), where Sij(ω)=∫−∞∞〈Ji(t)Jj(0)〉eiωtdt.ii)We next define the (cross) periodogram as the product of pairs of Fourier transforms of the current time series: Skij=ϵNJ˜kiJ˜kj⁎, where ϵ is the time step of the time series, N the number of their terms, and J˜kj=∑n=0N−1Jnje2πiknN their discrete Fourier transforms, and Jnj=Jj(ϵn).iii)As the current time series are realisations of a Gaussian process, in the long-time limit and for k≠k′ the Skij are uncorrelated complex Wishart random matrices (a matrix generalisation of the χ2 distribution) whose expectation, according to the Wiener-Khintchine theorem, is the cross power spectrum we are after. It follows that (Sk−1)00 is proportional to a set of uncorrelated χ2 deviates;iv)A consistent estimator for log⁡(κ)=−log⁡((Λ−1)00) is finally obtained by applying a low-pass filter to the process log⁡((Sk−1)00). The theoretical background of the methodology implemented in SporTran is thoroughly presented in Refs. [1-3].

Anomaly Detection for Service-Oriented Business Processes Using Conformance Analysis

A significant challenge for organisations is the timely identification of the abnormalities or deviations in their process executions. Abnormalities are generally due to missing vital aspects of a process or possession of unwanted behaviour in the process execution. Conformance analysis techniques examine the synchronisation between the recorded logs and the learned process models, but the exploitation of event logs for abnormality detection is a relatively under-explored area in process mining. In this paper, we proposed a novel technique for the identification of abnormalities in business process execution through the extension of available conformance analysis techniques. Non-traditional conformance analysis techniques are used to find correlations and discrepancies between simulated and observed behaviour in process logs. Initially, the raw event log is filtered into two variants, successful and failed, based upon the outcome of the instances. Successfully executed instances refer to an ideal conduct of process and are utilised to discover an optimal process model. Later, the process model is used as a behavioural benchmark to classify the abnormality in the failed instances. Abnormal behaviour is compiled grounded on three dimensions of conformance, control flow-based alignment, trace-level alignment and event-level alignment. For early predictions, we introduced the notion of conformance lifeline presenting the impact of varying fitness scores during process execution. We applied the proposed methodology to a real-world event log and presented several process-specific improvement measures in the discussion section.

Geographical Distribution of Three Forest Invasive Beetle Species in Romania.

Simple SummaryBiological invasions in the forests of Europe are becoming more frequent, and bark- and wood-boring insects are increasingly important invasive forest pests. Global trade intensification facilitates the introduction and spread of these species across new areas, while climate change and humans contribute to their establishment and spread. If such species enter new territories, the application of appropriate management measures requires—among other things—a better knowledge of their distribution. Using traps with different attractants, we attempted to establish the current distribution in Romania of three of the beetle species that are predicted to be important forest pests: Ips duplicatus, Xylosandrus germanus and Neoclytus acuminatus.Ips duplicatus (Sahlberg, 1836), Xylosandrus germanus (Blandford, 1894) and Neoclytus acuminatus (Fabricius, 1775) are invasive species reported in Romania, but their current distribution is poorly known. The research aim was to provide new information on this issue. A survey was conducted over the period 2015–2017 in 82 locations, using flight-interception traps and bottle traps, baited with different attractants. Data obtained in our other unpublished studies were also taken into account. A total of 35,136 I. duplicatus beetles were collected in 30 survey locations. The highest captures were in the log yards of some factories processing logs of Norway spruce (Picea abies (L.) H. Karst.). Considering all known records so far, most of these are in the eastern part of Romania, where an outbreak took place during the years 2005–2014, mainly in spruce stands growing outside their natural range. During the survey, 4259 specimens of X. germanus were collected in 35 locations, but in our other studies the species was found in 13 additional places. It was collected at altitudes of 18–1200 m, and the largest catches were from beech stands, growing at 450–950 m. N. acuminatus was found in only six locations, in the western and southern parts of the country, at low altitudes, in tree stands composed of Fraxinus excelsior L., Quercus spp. and other broadleaf species, as well as in broadleaf log yards. The results suggest that I. duplicatus is established in most parts of the Norway spruce’s range, X. germanus is still spreading in the country, with some areas having quite high populations, while N. acuminatus is present only in the warmest regions of the country.

Bimodal Keystroke Dynamics-Based Authentication for Mobile Application Using Anagram

Currently, most of the smartphones recognize uses based on static biometrics, such as face and fingerprint. However, those traits were vulnerable against spoofing attack. For overcoming this problem, dynamic biometrics like the keystroke and gaze are introduced since it is more resistant against spoofing attack. This research focuses on keystroke dynamics for strengthening the user recognition system against spoofing attacks. For recognizing a user, the user keystrokes feature used in the login process is compared with keystroke features stored in the keystroke features database. For evaluating the accuracy of the proposed system, words generated based on the Indonesian anagram are used. Furthermore, for conducting the experiment, 34 participants were asked to type a set of words using the smartphone keyboard. Then, each user’s keystroke is recorded. The keystroke dynamic feature consists of latency and digraph which are extracted from the record. According to the experiment result, the error of the proposed method is decreased by 23.075% of EER with FAR and FRR are decreased by 16.381% and 10.41% respectively, compared with Kim’s method. It means that the proposed method is successful increase the biometrics performance by reducing the error rates

An Enhanced Security Model for Simple Reformation-Based Password Scheme using PHP

Lack of security has become a major concern, given the prevalence of attackers, hackers, crackers, scammers and spammers. A key area in security research and practice is authentication, the determination of whether a user should be allowed to access a given system or resource. Existing authentication processes are usually accomplished by user ID and password, with the authentication schemes Pair-based Authentication scheme based. Textual passwords are the most common method used for authentication. But textual passwords are vulnerable to eves dropping, dictionary attacks, social engineering and shoulder surfing. Graphical passwords are introduced as alternative techniques to textual passwords. Most of the graphical schemes are vulnerable to shoulder surfing. To address this problem, text can be combined with images or colors to generate session passwords for authentication. Session passwords can be used only once and every time a new password is generated. In this research paper, two different techniques are proposed to generate session passwords using text and colors which are resistant and more secured. These methods are suitable for Personal Digital Assistants. These methods are suitable for Personal Digital Assistants. This scheme can authenticate the user by session passwords that are used only once. It is no longer useful if the session is terminated. For every login process, users have to feed the input with different passwords. It provides better security against dictionary and brute force attacks as password changes for every session. The proposed authentication schemes use text, colors and images for generating session passwords.

PENERAPAN METODE SINGLE EXPONENTIAL SMOOTHING DALAM PENGEMBANGAN APLIKASI PREDIKSI JUMLAH SISWA BARU

The vocational Forecasting process is an important thing for the Forecasting process to determine new students to obtain data and information on the number of prospective new students who will register. Darussalam Islamic Vocational School does not yet have a Forecasting application so predicting the number of students is still conventional which causes school to be unable to plan for future needs. Therefore the Forecasting applications are needed. The purpose of this study is to produce an application to make it easier for schools to predict the number of new students in the future. This research method uses the Single Exponential Smoothing method, while the development method uses the Waterfall. method. In this study, Testing. will be carried out using a Black-Box involving users, namely the principal and of school study program. The test consists of 4 aspects of Testing, namely the login process, the import process, the process of adding data, the process of Forecasting the number of new students. With the Forecasting application. the number of new students that have been MAD can predict the number of students who will register in the following year based on previous data.

Semi-Supervised Discovery of DNN-Based Outcome Predictors from Scarcely-Labeled Process Logs

AbstractPredicting the final outcome of an ongoing process instance is a key problem in many real-life contexts. This problem has been addressed mainly by discovering a prediction model by using traditional machine learning methods and, more recently, deep learning methods, exploiting the supervision coming from outcome-class labels associated with historical log traces. However, a supervised learning strategy is unsuitable for important application scenarios where the outcome labels are known only for a small fraction of log traces. In order to address these challenging scenarios, a semi-supervised learning approach is proposed here, which leverages a multi-target DNN model supporting both outcome prediction and the additional auxiliary task of next-activity prediction. The latter task helps the DNN model avoid spurious trace embeddings and overfitting behaviors. In extensive experimentation, this approach is shown to outperform both fully-supervised and semi-supervised discovery methods using similar DNN architectures across different real-life datasets and label-scarce settings.

Secure E-Learning System Based on ZNP and AES

A secure electronic learning platform has been created to enable teachers and students to log into their accounts to learn efficiently and safely at any place and time. This platform has been proposed due to the urgent need to develop the education system and move it from traditional to interactive e-learning. In this paper, an application implemented that access remotely using a web browser interface and saved on a server depends on a Zero-Knowledge Proof (ZKP) system with an RSA algorithm was employed to solve registration and login challenges and securely transfer passwords. Using adapted AES to encrypt each user's personal information, Exams, and save it in in encrypted form in the database. The simulated results in this paper indicate the existence of a secure e-learning system, where security was achieved by performing the registration and login process without sending the password in its explicit form over an insecure network such as the Internet, in addition to encrypting the necessary information to be stored in an incomprehensible manner in the database, in the case of presence of an attack on the database.

Effects of log temperature, moisture content, and cutting width on energy requirements for processing logs by a chipper–canter

ABSTRACT Four matched groups of 15 black spruce logs were processed with a chipper–canter at temperatures of 20, 0, −10, and −20°C. Each log was transformed at two moisture contents (MCs) (green and air-dried) using two cutting widths (CWs) (12.7 and 25.4 mm). Mean MC for each CW was assessed from a sample of the obtained chips. The results showed that mean power, energy consumption, and specific cutting energy were significantly affected by the temperature and CW, and in a lesser degree by the MC. These parameters were greater when cutting frozen logs. Mean power and energy consumption were higher at the larger CW, as a result of the higher volume of removed wood. However, specific cutting energy increased as CW decreased. This behavior was related to the higher level of fragmentation occurred with the smaller CW. These results are useful to estimate changes in energy requirements occurred within the range of cutting conditions studied here.

Smart Finder Using IoT System

Practical learning in Electronics requires handling and managing many electronic components, tools, and equipment. A study has been done to improve the time-consuming process to locate these parts in an electronic workshop. As a result, Smart Finder was designed to replace the manual search method. This product utilizes the Internet of Things (IoT) as a link between a smartphone and NodeMCU microcontroller. Connectivity between the phone and microcontroller enables users to locate electronic parts from anywhere within the communication coverage. Smart Finder Application on the smartphone is built with MIT App Inventor. After the required login process , users need to select the component/part on the menu in the application. Once the component/part is selected, LED which acts as indicator on the selected component/part storage turns on. These few steps would simplify and expediate the searching process. Based on the result of the questionnaires, more than 95% of respondents agreed that Smart Finder helps in saving time in preparing parts for practical classes. Further improvements on the project would be adding better security measures, application for IOS users, and more useful interfaces or features to improve workshop management in Electronic Program.

Deep Learning for Predictive Business Process Monitoring: Review and Benchmark

Predictive monitoring of business processes is concerned with the prediction of ongoing cases on a business process. Lately, the popularity of deep learning techniques has propitiated an ever-growing set of approaches focused on predictive monitoring based on these techniques. However, the high disparity of process logs and experimental setups used to evaluate these approaches makes it especially difficult to make a fair comparison. Furthermore, it also difficults the selection of the most suitable approach to solve a specific problem. In this paper, we provide both a systematic literature review of approaches that use deep learning to tackle the predictive monitoring tasks. In addition, we performed an exhaustive experimental evaluation of 10 different approaches over 12 publicly available process logs.

Discovering System Dynamics Simulation Models Using Process Mining

Process mining techniques are able to describe and model real processes using historic event data extracted from the information systems of organizations. Later, these insights are used for process improvement. For instance, Discrete Event Simulation (DES) uses process models that are able to mimic real-world events. However, the aggregated performance status of processes over time reveals various hidden relationships between process variables. Coarse-grained process logs are sets of performance variables over steps of time, generated using event data from processes. The coarse-grained process logs describe processes at higher levels. <i>System Dynamics</i> completes process mining by capturing the relationships between various process variables at a higher level of abstraction. In this paper, we propose a new framework for capturing conceptual models of processes using transformed event data. The main idea is to automatically discover the underlying relations as equations. This allows us to generate system dynamics simulations of processes. We employ a variety of statistical and machine learning techniques to discover the hidden relationships between process variables. The framework supports the simulation modeling task in the context of system dynamics simulations. The experiments using real event logs demonstrate that our approach is able to generate valid models and capture the underlying relationships.