An Efficient Approach for Uncertain Event Detection in RFID Complex Event Processing

The globalization of manufacturing has increased the risk of counterfeiting as the demand grows, the production flow increases, and the availability expands. The intensifying counterfeit issues causing a worriment to companies and putting lives at risk. Companies have ploughed a large amount of money into defensive measures, but their efforts have not slowed counterfeiters. In such complex manufacturing processes, decision-making and real-time reactions to uncertain situations throughout the production process are one way to exploit the challenges. Detecting uncertain conditions such as counterfeit and missing items in the manufacturing environment requires a specialized set of technologies to deal with a flow of continuously created data. In this paper, we propose an uncertain detection algorithm (UDA), an approach to detect uncertain events such as counterfeit and missing items in the RFID distributed system for a manufacturing environment. The proposed method is based on the hashing and thread pool technique to solve high memory consumption, long processing time and low event throughput in the current detection approaches. The experimental results show that the execution time of the proposed method is averagely reduced 22% in different tests, and our proposed method has better performance in processing time based on RFID event streams.

A Review on Big Data Stream Processing Applications: Contributions, Benefits, and Limitations

The amount of data in our world has been rapidly keep growing from time to time. In the era of big data, the efficient processing and analysis of big data using machine learning algorithm is highly required, especially when the data comes in form of streams. There is no doubt that big data has become an important source of information and knowledge in making decision process. Nevertheless, dealing with this kind of data comes with great difficulties; thus, several techniques have been used in analyzing the data in the form of streams. Many techniques have been proposed and studied to handle big data and give decisions based on off-line batch analysis. Today, we need to make a constructive decision based on online streaming data analysis. Many researchers in recent years proposed some different kind of frameworks for processing the big data streaming. In this work, we explore and present in detail some of the recent achievements in big data streaming in term of contributions, benefits, and limitations. As well as some of recent platforms suitable to be used for big data streaming analytics. Moreover, we also highlight several issues that will be faced in big data stream processing. In conclusion, it is hoped that this study will assist the researchers in choosing the best and suitable framework for big data streaming projects.

Blockchain in Supermarkets: Mitigating the Problem of Organic Waste Generation

This work presents a proposal for a solution to the specific problem of organic waste generated by supermarkets and understood as t merchandise of organic and perishable composition that could not be marketed during its validity period. The goal of this research is to propose a solution based on Blockchain technology in Chile, which would allow an immutable, decentralized, and validated transaction record to be kept. Such a record would enable supermarkets to trace the life cycle of those products that make up organic and perishable merchandise in a transparent, reliable, and scalable way. To this end, the problem is modeled using the Blockchain Hyperledger Fabric platform (an open-source platform started by the Linux Foundation), which is fed with relevant information and data on the status of a representative set of organic merchandise products. At the same time, a qualitative approach is proposed to gather the opinions of executives and logistics operators through semi-structured interviews, and considering a convenience sample. With a sample of 6 executives, it is understood how the proposal is perceived and its applicability in supermarkets and distributors. The data show that both obtaining information and making decisions about it are achieved in a distributed and collaborative way, allowing for reliable and agile traceability, thereby mitigating the low quality of the information provided by the actors that make up the supply chain. This service is perceived as desirable by both customers and operators. The model enables not only horizontal communications between suppliers, distributors, and consumers, but also vertical ones, and thus, ultimately, makes the company's income statement more efficient.

Proposition for LMS Integration for Share, Exchange, and Spread of Online Lectures under Covid-19 Environment

There has been a movement to share and spread online lectures through OCW and MOOC systems. This movement would have been spread widely and adopted widely if those courses could be easily exchangeable with other platforms or services. If this function is available, learning activities, resources, learning outcomes can be accessed between different platforms and services. With this function, the credit exchange between different platforms or services will be easier. It also facilitates course sharing and circulation. Because the LMS is the basic platform for online classes, providing sharable and reusable learning activities, resources, and learning outcomes across the different LMSs is very demanding for online education. Analyzing LMS use in Korean universities, Moodle, Canvas, and domestic LMSs are founded to be the significant three kinds that are widely used in Korea. In this paper, a method of integrating Moodle, Canvas, and domestic LMS services is proposed. A central Moodle server is installed as the main LMS server, and the method to connect or complement with a central Moodle server is proposed for each different kind of LMS. LMS users can easily access a different kind of LMS as a form of imported course, tightly connected service, or log in as SSO. This proposition can be applied to various service fields such as KMOOC, KOCW, credit exchange, lecture exchange between universities, regional unification of online educational centers as a practical problem-solver.

Monitoring Rice Crop and Paddy Field Condition Using UAV RGB Imagery

An effective crop management practice is very important to the sustenance of crop production. With the emergence of Industrial Revolution 4.0 (IR 4.0), precision farming has become the key element in modern agriculture to help farmers in maintaining the sustainability of crop production. Unmanned aerial vehicle (UAV) also known as drone was widely used in agriculture as one of the potential technologies to collect the data and monitor the crop condition. Managing and monitoring the paddy field especially at the bigger scale is one of the biggest challenges for farmers. Traditionally, the paddy field and crop condition are only monitored and observed manually by the farmers which may sometimes lead to inaccurate observation of the plot due the large area. Therefore, this study proposes the application of unmanned aerial vehicles and RGB imagery for monitoring rice crop development and paddy field condition. The integration of UAV with RGB digital camera were used to collect the data in the paddy field. Result shows that the early monitoring of rice crops is important to identify the crop condition. Therefore, with the use of aerial imagery analysis from UAV, it can help to improve rice crop management and eventually is expected to increase rice crop production.

An Improved Flower Pollination Algorithm for Global and Local Optimization

Meta-heuristic algorithms have emerged as a powerful optimization tool for handling non-smooth complex optimization problems and also to address engineering and medical issues. However, the traditional methods face difficulty in tackling the multimodal non-linear optimization problems within the vast search space. In this paper, the Flower Pollination Algorithm has been improved using Dynamic switch probability to enhance the balance between exploitation and exploration for increasing its search ability, and the swap operator is used to diversify the population, which will increase the exploitation in getting the optimum solution. The performance of the improved algorithm has investigated on benchmark mathematical functions, and the results have been compared with the Standard Flower pollination Algorithm (SFPA), Genetic Algorithm, Bat Algorithm, Simulated annealing, Firefly Algorithm and Modified flower pollination algorithm. The ranking of the algorithms proves that our proposed algorithm IFPDSO has outperformed the above-discussed nature-inspired heuristic algorithms.

Neural Collaborative with Sentence BERT for News Recommender System

The number of news produced every day is as much as 3 million per day, making readers have many choices in choosing news according to each reader's topic and category preferences. The recommendation system can make it easier for users to choose the news to read. The method that can be used in providing recommendations from the same user is collaborative filtering. Neural collaborative filtering is usually being used for recommendation systems by combining collaborative filtering with neural networks. However, this method has the disadvantage of recommending the similarity of news content such as news titles and content to users. This research wants to develop neural collaborative filtering using sentences BERT. Sentence BERT is applied to news titles and news contents that are converted into sentence embedding. The results of this sentence embedding are used in neural collaboration with item id, user id, and news category. We use a Microsoft news dataset of 50,000 users and 51,282 news, with 5,475,542 interactions between users and news. The evaluation carried out in this study uses precision, recall, and ROC curves to predict news clicks by the user. Another evaluation uses a hit ratio with the leave one out method. The evaluation results obtained a precision value of 99.14%, recall of 92.48%, f1-score of 95.69%, and ROC score of 98%. Evaluation measurement using the hit ratio@10 produces a hit ratio of 74% at fiftieth epochs for neural collaborative with sentence BERT which is better than neural collaborative filtering (NCF) and NCF with news category.

Relationship between Korean Informatics Curriculum and Textbook Learning Element Considering Compound Word

With the development of information and communication technology, countries around the world have strengthened their computer science curriculums. Korea also revised the informatics curriculum(The name of a subject related to computer science in Korea is informatics.) in 2015 with a focus on computer science. The purpose of this study was to automatically extract and analyze whether textbooks reflected the learning elements of the informatics curriculum in South Korea. Considering the forms of terms of the learning elements mainly comprised of compound words and the characteristics of Korean language, which makes natural language processing difficult due to various transformations, this study pre-processed textbook texts and the learning elements and derived their reflection status and frequencies. The terms used in the textbooks were automatically extracted by using the indexes in the textbooks and the part-of-speech compositions of the indexes. Moreover, this study analyzed the relevance between the terms by deriving confidence of other terms for each learning element used in the textbooks. As a result of the analysis, this study revealed that the textbooks did not reflect some learning elements in the forms presented in the curriculum, suggesting that the textbooks need to explain the concepts of the learning elements by using the forms presented in the curriculum at least once. This study is meaningful in that terms were automatically extracted and analyzed in Korean textbooks based on the words suggested by the curriculum. Also, the method can be applied equally to textbooks of other subjects.

Feature-reduction Fuzzy c-means Clustering for Basketball Players Positioning

One of the best-known clustering methods is the fuzzy c-means clustering algorithm, besides k-means and hierarchical clustering. Since FCM treats all data features as equally important, it may obtain a poor clustering result. To solve the problem, feature selection with feature weighting is needed. Besides feature selection by assigning feature weights, there is also feature selection by assigning feature weights and eliminating the unrelated feature(s). THE Feature-reduction FCM (FRFCM) clustering algorithm can improve the FCM clustering result by weighting the features and discarding the unrelated feature(s) during the clustering process. Basketball is one of the famous sports, both international and national. There are five players in basketball, each with a different position. A player can generally be in guard, forward, or center position. Those three general positions need different characteristics of players’ physical conditions. In this paper, FRFCM is used to select the related physical feature(s) for basketball players, consisting of height, weight, age, and body mass index. to determine the basketball players’ position. The result shows that FRFCM can be applied to determine the basketball players’ position, where the most related physical feature is the player’s height. FRFCM gets one incorrect player’s position, so the error rate is 0.0435. As a comparison, FCM gets five incorrect player’s positions, with an error rate of 0.2174. This method can help the coach decide the basketball new player’s position.

A Portable Device of Air Pollution Measurement Due to Highway Exhaust Emissions Using LabVIEW Programming

A multisensory gas device integrated with myRIO module to measure air pollution has been established. This device is programmed using the LabVIEW programming language and can measure CO2, CO, NOX, and HC pollution on roads due to motor vehicle exhaust emissions. The device and the display system are made separately using wireless network communication to make this tool portable. Exhaust Gas Analyzer (EGA) was chosen for device calibration, obtaining 3.62% on the average error after performing 30 tests. The tests for measuring CO, CO2, NOX, and HC gas levels were conducted in several locations in Padang City and performed in the morning, afternoon, and evening. The result showed that the system properly measured CO2, CO, NOX and HC pollution in parks and highways in real-time in parts per million (ppm). It also displayed varied gas measurement results in terms of time and test location with a range of CO gas values at 0.034 – 0.15 ppm, CO2 151.3 – 815.2 ppm, NOX 0.0001 – 0.004 ppm, and HC 0.04 – 0.65 ppm. In addition, the system could perform well in providing warnings by automatically activating the air indicator alert at several measurement places when the gas content on one of the gas elements and compounds at a particular location has exceeded the threshold for the clean air category. Thus, this device can be used as initial research to build a real-time air pollution measurement system using the Internet of Things (IoT).