International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

Distributed systems are the fundamental building blocks of today’s internet and cloud data centers. Applications running on these systems are divided into multiple tasks, which are executed in parallel across different nodes. A key aspect of these systems is task allocation, or load balancing, which involves distributing tasks among the compute nodes. The performance of the application and the utilization of system resources heavily depend on the task allocation algorithm. In this paper, we address the task allocation problem in distributed systems where each node is connected to several neighbouring nodes, forming a graph-like structure. We propose a distributed task allocation algorithm inspired by the hierarchical interactions within wasp insect colonies. The key features of our algorithm include its reliance on simple probabilistic rules and a decentralized approach, which eliminates the need for a central coordinator. This makes the algorithm highly robust to failures and scalable. Through simulations, we compare our algorithm with another simple distributed approach and demonstrate its superiority, particularly in cases involving large data transfers. The probabilistic nature of our algorithm leads to fewer task transfers, resulting in more efficient task allocation.

OCR technology is widely used in various applications, including document digitization, data extraction, and document management systems. The OCR technology has seen significant advancements in recent years, especially with the integration of machine learning and artificial intelligence techniques. These advancements have led to substantial improvements in accuracy, particularly for standard fonts and clear document images. However, challenges still exist, especially when dealing with low-quality images, noisy images, handwritten text, or documents with unusual fonts. Some documents like ID cards, driving licenses, etc. use some security features like deliberate errors, OVI (Optical Variable Ink), Rainbow print, Guilloche pattern, fine line, and microprint to protect the documents from being counterfeited. These security elements also generate noise on the image to perform the OCR. In this paper, we present a way to enhance the OCR accuracy for ID-1 documents with security features through machine learning-driven image optimization. Albanian driving license images on the personalization process are used as a dataset to train the model. During the training process, the model of the ID-1 card is presented with a dataset containing input features (such as images, texts, or numerical data) along with corresponding labels or outcomes. After training, the model and the implemented algorithm to optimize the image for the OCR process are implemented in real-life application.

In this paper we will explain a solution to identify the users which poetically are trying to cheat the computer-based exam. Nowadays, the online exams are very useful to identify the people with right knowledge to do a job or to certificate them based on their knowledge. One of the major concerns for the organization of computer-based exams is the potential for cheating in collaboration with employee responsible of monitoring or due to the lack of human invigilation. The proposed solution will analyze in real-time the behavior of student and will alert the monitoring employee for suspicious activity of any exam.

The use of ID-1 cards is in an extremely large increase nowadays starting from identity cards, bank cards, driving licenses, membership cards, etc. In some of the uses, the security of the card and the data that the card holds are very important. Some of the security elements are defined in the production of the card and some during the personalization of the data on the card. Laser card engraving is a high-security feature that is applied during the process of personalizing the data on the card. In this article, we will present h ow to implement the digital signature in the data personalization using laser engraving and QR barcodes to minimize the costs of cards and to ensure the data on cards are not modified.

Application developers mostly must choose between two main categories of the database: SQL (Structured Query Language) and NoSQL (Not Only SQL) to build their solutions. SQL databases, also known as relational databases, have been in use for a very long time and despite their age, SQL databases remain extremely popular with developers. Six of the top 10 results on DB-Engines’ list of most popular DBMS are relational DBs (September 2021). In the meantime, NoSQL databases or non-relational databases, have gained popularity year after year. One of the most widely used NoSQL databases is MongoDB, ranked 5th on DB-Engines’ list. In this article, we will explore what are the differences between SQL and NoSQL, and which database to use for application development.