The issuing and verification of higher education certificates, including all higher education doc... more The issuing and verification of higher education certificates, including all higher education documents, still functions in a costly and inappropriately bureaucratic manner. Blockchain technology provides a more secure and consistent way to revolutionize the widely used generalized mechanisms and system concepts. In this paper, the most necessary requirements are examined regarding a blockchain-based higher education system, based on the most well-known research papers. Moreover, the opportunities of working on an education system by maintaining a decentralized structure organization are recommended as well. This paper recommends the most suitable blockchain scaling solution for the architecture of an education system which uses the most state-of-the-art EVM (Ethereum virtual machine) compatible approach to implement the higher education system with all the predefined requirements. It is proven that the explained smart contract-based higher education system, which uses zkEVM (zero-k...
Data science techniques have increasing importance in medical data analysis, including detecting ... more Data science techniques have increasing importance in medical data analysis, including detecting and predicting the probability of contracting a disease. A large amount of medical data is generated close to the patients in the form of a stream, such as data from sensors and medical devices. The distribution of these kinds of data may change from time to time; adaptive Machine Learning (ML) consists of a continuous training process responding to the distribution’s change. Adaptive ML models require high computational resources, which can be provided by cloud computing. In this work, a classification model is proposed to utilize the advantages of cloud computing, edge computing, and adaptive ML. It aims to precisely and efficiently classify EEG signal data, thereby detecting the seizures of epileptic patients using Adaptive Random Forest (ARF). It includes a global adaptive classifier in the cloud master node and a local light classifier in each edge node. In this model, the delayed l...
Data stream mining techniques have recently received increasing research interest, especially in ... more Data stream mining techniques have recently received increasing research interest, especially in medical data classification. An unbalanced representation of the classification’s targets in these data is a common challenge because classification techniques are biased toward the major class. Many methods have attempted to address this problem but have been exaggeratedly biased toward the minor class. In this work, we propose a method for balancing the presence of the minor class within the current window of the data stream while preserving the data’s original majority as much as possible. The proposed method utilized similarity analysis for selecting specific instances from the previous window. This group of minor-class was then added to the current window’s instances. Implementing the proposed method using the Siena dataset showed promising results compared to the Skew ensemble method and some other research methods.
With the appearance of large-scale systems, the size of the generated logs increased rapidly. Alm... more With the appearance of large-scale systems, the size of the generated logs increased rapidly. Almost every software produces such files. Log files contain runtime information of the software, as well as indicate noteworthy events or suspicious behaviors like errors. To understand and monitor the operation of the system, log files are a valuable source of information, which can be used to predict upcoming anomalies. In recent years numerous techniques have been proposed for this purpose. There are supervised models like SVM or decision trees and also unsupervised ones like Isolation Forest, Log Clustering, or PCA. There are also methods that use deep learning, like Autoencoder, CNN, LSTM, or Transformer. Many of the above-mentioned methods take advantage of template miners, that extract the event types from the unstructured data. In our paper, we propose a method that uses these templates to predict upcoming anomalies. We use 80% of our data for training, and 20% for tests. First, we use half of the train data and sort the templates that have an occurrence that is followed by an error to create a list of candidate templates. In the second step, we use the other half, to check how often the ten upcoming lines after a candidate template actually contain an anomaly. If a given percentage is reached, we consider the template as an indicator for upcoming anomalies. We conduct various experiments to verify the capability of our method like measuring the precision, recall, f-score accuracy, and speed on various data sources. The proposed method slightly falls behind SVM and CNN with an average of 88.06% precision, 90.43% recall, and 89.11% f-score, however, it has better accuracy with 98.19%. In addition, our algorithm is two times faster than SVM and three and a half times faster than CNN.
Biological data have gained wider recognition during the last few years, although managing and pr... more Biological data have gained wider recognition during the last few years, although managing and processing these data in an efficient way remains a challenge in many areas. Increasingly, more DNA sequence databases can be accessed; however, most algorithms on these sequences are performed outside of the database with different bioinformatics software. In this article, we propose a novel approach for the comparative analysis of sequences, thereby defining heuristic pairwise alignment inside the database environment. This method takes advantage of the benefits provided by the database management system and presents a way to exploit similarities in data sets to quicken the alignment algorithm. We work with the column-oriented MonetDB, and we further discuss the key benefits of this database system in relation to our proposed heuristic approach.
Currently, as an effect of the COVID-19 pandemic, bioinformatics, genomics, and biological comput... more Currently, as an effect of the COVID-19 pandemic, bioinformatics, genomics, and biological computations are gaining increased attention. Genomes of viruses can be represented by character strings based on their nucleobases. Document similarity metrics can be applied to these strings to measure their similarities. Clustering algorithms can be applied to the results of their document similarities to cluster them. P systems or membrane systems are computation models inspired by the flow of information in the membrane cells. These can be used for various purposes, one of them being data clustering. This paper studies a novel and versatile clustering method for genomes and the utilization of such membrane clustering models using document similarity metrics, which is not yet a well-studied use of membrane clustering models.
Numerical simulations of physical systems are found in many industries, as they currently play a ... more Numerical simulations of physical systems are found in many industries, as they currently play a crucial role in product development. There are many numerical methods for solving differential equations that describe the underlying physics behind the mathematical models in the simulation, among which, the finite element method (FEM) is one of the most commonly used. Although in many applications the FEM seems to provide an acceptable solution to the problem, there are still many complex real-life processes that can be challenging to simulate numerically due to their complexity and large size. Recently, there has been a shift in research towards efficiently applying quantum algorithms in finite element analysis (FEA), as the potential and speedup that they could offer have been shown, but little to no effort has been made towards the applicability and cost efficiency of these algorithms in real-world quantum devices. In this paper, we propose a cost-efficient method for applying quant...
2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), 2016
RDF is a flexible data representation model. Due to its flexibility and simplicity it has become ... more RDF is a flexible data representation model. Due to its flexibility and simplicity it has become a popular framework, hence the amount of RDF data is increasing fast. Querying massive amount of data is a serious challenge in general and it is true for RDF data as well. In this paper we investigating how to evaluate SPARQL queries on a distributed system. We propose a novel method for evaluating SPARQL queries using the GraphX graph analytical tool. GraphX is built on the top of Spark that is an in-memory data processing system for distributive computation. With this tool we managed to utilize the graph-like structure of the RDF statements which are in form of subject-predicate-object.
The Augmented Reality applications have received great attention in the recent years. However, th... more The Augmented Reality applications have received great attention in the recent years. However, there is still a lack of formal description of such systems currently. In this paper, we propose a new formal model for context-aware semantic Augmented Reality systems. The model can be divided into two parts: a set-theory function based method allows the formalization of an Augmented Reality system while an integrated time-space-motion logic provides the description of the behavior of the system. The suggested model enables the characterization of an Augmented Reality system with mathematical precision. In addition, logical inferences can be performed by means of the logic part of the formal description. The practical applicability of the proposed model is shown through use cases.
Proceedings of the 9th International Conference on Applied Informatics, Volume 2, 2015
In this paper we consider three different properties of social networks, namely the number of the... more In this paper we consider three different properties of social networks, namely the number of the different triangles, the sizes of the strongly connected components (SCC) and Linerank, which is offered as a substitute for betweenness and can be efficiently calculated on graphs with more than 10 9 nodes [7]. We implemented the computation of these measures in three different ways: sequentially, using the widely spread MapReduce model-Hadoop in our case-and its newly emerging rival Giraph, which is based on the Pregel system [11]. MapReduce and Pregel use distributed computation enabling high parallelization of the calculations. The algorithms calculating the above measures can be grouped into three different families. In the case of the triangles for a node only its close neighbourhood should be taken into consideration. In the case of the SCC's at least in the sequential approach the graph should be traversed in a depth search manner, which can be prohibitively expensive for the distributive systems. Finally, Linerank can be computed by using an iterative matrix-vector multiplication. We ran our implementations on several generated and real-world networks of different sizes firstly in order to assess the degree of the benefit of parallelization offered by the last two technologies for the algorithm families. Secondly, to see for which magnitude of the size of the networks it is more beneficial to choose one the aforementioned models instead of the sequential approach.
ABSTRACT Nowadays, companies have to handle large amounts of data every day (e.g. employee data, ... more ABSTRACT Nowadays, companies have to handle large amounts of data every day (e.g. employee data, reports, statements). These data are usually unstructured or semi-structured; they do not have a consistent format, since they come from various sources. As a solution, the Semantic Web provides a common data format, which helps integrate the heterogeneous data. In the industry, in order to be efficient and to remain competitive, it is crucial to have quick, easy, and reliable access to your data from anywhere. In this paper, we introduce a client-server system, which enables browsing of semantic data with smartphones and tablets running Android operating system. The users can access their data using predefined views. These views determine which parts of the data a user can see, and how to present them in the client application. Each user can have different access privileges to different views, so each employee can have a level of access suitable to his status.
Proceedings of the 9th International Conference on Applied Informatics, Volume 1, 2015
A new research direction has emerged as the investigation of On-line Social Networks. Twitter is ... more A new research direction has emerged as the investigation of On-line Social Networks. Twitter is one of the most well-known social networks. Analysis of the Twitter is easier than other social networks because it provides the opportunity for collecting and downloading of a certain percentage of the messages without any restrictions. There are several researches on topics as detecting news and events, human behaviors, analyzing and mining of opinions. The on-line messages are available only through a continuous stream. To store the messages from the stream effectively and efficiently is a serious challenge against software system design and architecture. Every day about 10 GBs data are generated by this way and storing of this volume of data is not an easy task. In this paper we present a technique and architecture for collecting and storing the messages of the Twitter, and we present a prototype where data can be accessed for further analysis. Our system makes use specific techniques and methods of Oracle environment. Our software architecture approach is in contrast to previous solutions in which the systems use MSSQL or MySQL DBMS. We demonstrate that indexing and Job scheduler of the Oracle provide advantages to retrieve and handle large amounts of data.
In the present day, virtually every application software generates large amounts of log entries d... more In the present day, virtually every application software generates large amounts of log entries during its work. The log files that are made from these entries are a collection of information about what happened while the program was running. This report can be used for multiple purposes such as performance monitoring, maintaining security, or improving business decision making. Log entries are usually generated in a disorganized manner. Using template miners, the different ‘event types’ can be distinguished (each log entry is an event), and the set of all entries is split into disjointed subsets according to the event types. These events consist of two parts. The first is the constant part, which is the same for all occurrences of the same event type. The second is the parameter part, which can be different for each occurrence. Since software mass-produces log files, in our previous paper, we introduced an algorithm that uses the templates mined from the data to create a dictionary...
Creating a widely excepted model on the measure of intelligence became inevitable due to the exis... more Creating a widely excepted model on the measure of intelligence became inevitable due to the existence of an abundance of different intelligent systems. Measuring intelligence would provide feedback for the developers and ultimately lead us to create better artificial systems. In the present paper, we show a solution where learning as a process is examined, aiming to detect pre-written solutions and separate them from the knowledge acquired by the system. In our approach, we examine image recognition software by executing different transformations on objects and detect if the software was resilient to it. A system with the required intelligence is supposed to become resilient to the transformation after experiencing it several times. The method is successfully tested on a simple neural network, which is not able to learn most of the transformations examined. The method can be applied to any image recognition software to test its abstraction capabilities.
In this paper, we present a novel implementation of an ecosystem simulation. In our previous work... more In this paper, we present a novel implementation of an ecosystem simulation. In our previous work, we implemented a 3D environment based on a predator–prey model, but we found that in most cases, regardless of the choice of starting parameters, the simulation quickly led to extinctions. We wanted to achieve system stabilization, long-term operation, and better simulation of reality by incorporating genetic evolution. Therefore we applied the predator–prey model with an evolutional approach. Using the Unity game engine we created and managed a closed 3D ecosystem environment defined by an artificial or real uploaded map. We present some demonstrative runs while gathering data, observing interesting events (such as extinction, sustainability, and behavior of swarms), and analyzing possible effects on the initial parameters of the system. We found that incorporating genetic evolution into the simulation slightly stabilized the system, thus reducing the likelihood of extinction of diffe...
Presently, almost every computer software produces many log messages based on events and activiti... more Presently, almost every computer software produces many log messages based on events and activities during the usage of the software. These files contain valuable runtime information that can be used in a variety of applications such as anomaly detection, error prediction, template mining, and so on. Usually, the generated log messages are raw, which means they have an unstructured format. This indicates that these messages have to be parsed before data mining models can be applied. After parsing, template miners can be applied on the data to retrieve the events occurring in the log file. These events are made from two parts, the template, which is the fixed part and is the same for all instances of the same event type, and the parameter part, which varies for all the instances. To decrease the size of the log messages, we use the mined templates to build a dictionary for the events, and only store the dictionary, the event ID, and the parameter list. We use six template miners to a...
Social media platforms are increasingly being used to communicate information, something which ha... more Social media platforms are increasingly being used to communicate information, something which has only intensified during the pandemic. News portals and governments are also increasing attention to digital communications, announcements and response or reaction monitoring. Twitter, as one of the largest social networking sites, which has become even more important in the communication of information during the pandemic, provides space for a lot of different opinions and news, with many discussions as well. In this paper, we look at the sentiments of people and we use tweets to determine how people have related to COVID-19 over a given period of time. These sentiment analyses are augmented with information extraction and named entity recognition to get an even more comprehensive picture. The sentiment analysis is based on the ’Bidirectional encoder representations from transformers’ (BERT) model, which is the basic measurement model for the comparisons. We consider BERT as the baseli...
Before intruding into a system attackers need to collect information about the target machine. Po... more Before intruding into a system attackers need to collect information about the target machine. Port scanning is one of the most popular techniques for that purpose, it enables to discover services that may be exploited. In this paper we propose an accurate port scan detection method that can detect port scanning attacks earlier with higher reliability than the widely used Snort-based approaches. Our method is profile-based, meaning that it does not only set a threshold on the connection attempts in a given time interval, like most of the current methods, but builds an IP profile of four features that enables a more fine-grained detection. We use the Budapest node of the FIWARE Lab community cloud as a natural honeypot to identify malicious activities in it.
Studia Universitatis Babeș-Bolyai Informatica, 2021
With the development of sequencing technologies, more and more amounts of sequence data are avail... more With the development of sequencing technologies, more and more amounts of sequence data are available. This poses additional challenges, such as processing them is usually a complex and time-consuming computational task. During the construction of phylogenetic trees, the relationship between the sequences is examined, and an attempt is made to represent the evolutionary relationship. There are several algorithms for this problem, but with the development of computer science, the question arises as to whether new technologies can be exploited in these areas of computational biology. In the following publication, we investigate whether the reinforced learning model of machine learning can generate accurate phylogenetic trees based on the distance matrix.
Intelligent Information and Database Systems, 2019
There can be a data boom in the near future, due to cheaper methods make possible for everyone to... more There can be a data boom in the near future, due to cheaper methods make possible for everyone to keep their own DNA on their own device or on a central medical cloud. With the development of sequencing methods, we are able to get the sequences of more and more species. However the size of the human genome is about 3 GB for each person. And for other species it can be more. The need is growing for the efficient compression of these data and general compressors can not reach a satisfying result. These are not aware of the special structure of these data. There are already some algorithms tried to reach smaller and smaller rates. In this paper, we would like to present our new method to accomplish this task.
The issuing and verification of higher education certificates, including all higher education doc... more The issuing and verification of higher education certificates, including all higher education documents, still functions in a costly and inappropriately bureaucratic manner. Blockchain technology provides a more secure and consistent way to revolutionize the widely used generalized mechanisms and system concepts. In this paper, the most necessary requirements are examined regarding a blockchain-based higher education system, based on the most well-known research papers. Moreover, the opportunities of working on an education system by maintaining a decentralized structure organization are recommended as well. This paper recommends the most suitable blockchain scaling solution for the architecture of an education system which uses the most state-of-the-art EVM (Ethereum virtual machine) compatible approach to implement the higher education system with all the predefined requirements. It is proven that the explained smart contract-based higher education system, which uses zkEVM (zero-k...
Data science techniques have increasing importance in medical data analysis, including detecting ... more Data science techniques have increasing importance in medical data analysis, including detecting and predicting the probability of contracting a disease. A large amount of medical data is generated close to the patients in the form of a stream, such as data from sensors and medical devices. The distribution of these kinds of data may change from time to time; adaptive Machine Learning (ML) consists of a continuous training process responding to the distribution’s change. Adaptive ML models require high computational resources, which can be provided by cloud computing. In this work, a classification model is proposed to utilize the advantages of cloud computing, edge computing, and adaptive ML. It aims to precisely and efficiently classify EEG signal data, thereby detecting the seizures of epileptic patients using Adaptive Random Forest (ARF). It includes a global adaptive classifier in the cloud master node and a local light classifier in each edge node. In this model, the delayed l...
Data stream mining techniques have recently received increasing research interest, especially in ... more Data stream mining techniques have recently received increasing research interest, especially in medical data classification. An unbalanced representation of the classification’s targets in these data is a common challenge because classification techniques are biased toward the major class. Many methods have attempted to address this problem but have been exaggeratedly biased toward the minor class. In this work, we propose a method for balancing the presence of the minor class within the current window of the data stream while preserving the data’s original majority as much as possible. The proposed method utilized similarity analysis for selecting specific instances from the previous window. This group of minor-class was then added to the current window’s instances. Implementing the proposed method using the Siena dataset showed promising results compared to the Skew ensemble method and some other research methods.
With the appearance of large-scale systems, the size of the generated logs increased rapidly. Alm... more With the appearance of large-scale systems, the size of the generated logs increased rapidly. Almost every software produces such files. Log files contain runtime information of the software, as well as indicate noteworthy events or suspicious behaviors like errors. To understand and monitor the operation of the system, log files are a valuable source of information, which can be used to predict upcoming anomalies. In recent years numerous techniques have been proposed for this purpose. There are supervised models like SVM or decision trees and also unsupervised ones like Isolation Forest, Log Clustering, or PCA. There are also methods that use deep learning, like Autoencoder, CNN, LSTM, or Transformer. Many of the above-mentioned methods take advantage of template miners, that extract the event types from the unstructured data. In our paper, we propose a method that uses these templates to predict upcoming anomalies. We use 80% of our data for training, and 20% for tests. First, we use half of the train data and sort the templates that have an occurrence that is followed by an error to create a list of candidate templates. In the second step, we use the other half, to check how often the ten upcoming lines after a candidate template actually contain an anomaly. If a given percentage is reached, we consider the template as an indicator for upcoming anomalies. We conduct various experiments to verify the capability of our method like measuring the precision, recall, f-score accuracy, and speed on various data sources. The proposed method slightly falls behind SVM and CNN with an average of 88.06% precision, 90.43% recall, and 89.11% f-score, however, it has better accuracy with 98.19%. In addition, our algorithm is two times faster than SVM and three and a half times faster than CNN.
Biological data have gained wider recognition during the last few years, although managing and pr... more Biological data have gained wider recognition during the last few years, although managing and processing these data in an efficient way remains a challenge in many areas. Increasingly, more DNA sequence databases can be accessed; however, most algorithms on these sequences are performed outside of the database with different bioinformatics software. In this article, we propose a novel approach for the comparative analysis of sequences, thereby defining heuristic pairwise alignment inside the database environment. This method takes advantage of the benefits provided by the database management system and presents a way to exploit similarities in data sets to quicken the alignment algorithm. We work with the column-oriented MonetDB, and we further discuss the key benefits of this database system in relation to our proposed heuristic approach.
Currently, as an effect of the COVID-19 pandemic, bioinformatics, genomics, and biological comput... more Currently, as an effect of the COVID-19 pandemic, bioinformatics, genomics, and biological computations are gaining increased attention. Genomes of viruses can be represented by character strings based on their nucleobases. Document similarity metrics can be applied to these strings to measure their similarities. Clustering algorithms can be applied to the results of their document similarities to cluster them. P systems or membrane systems are computation models inspired by the flow of information in the membrane cells. These can be used for various purposes, one of them being data clustering. This paper studies a novel and versatile clustering method for genomes and the utilization of such membrane clustering models using document similarity metrics, which is not yet a well-studied use of membrane clustering models.
Numerical simulations of physical systems are found in many industries, as they currently play a ... more Numerical simulations of physical systems are found in many industries, as they currently play a crucial role in product development. There are many numerical methods for solving differential equations that describe the underlying physics behind the mathematical models in the simulation, among which, the finite element method (FEM) is one of the most commonly used. Although in many applications the FEM seems to provide an acceptable solution to the problem, there are still many complex real-life processes that can be challenging to simulate numerically due to their complexity and large size. Recently, there has been a shift in research towards efficiently applying quantum algorithms in finite element analysis (FEA), as the potential and speedup that they could offer have been shown, but little to no effort has been made towards the applicability and cost efficiency of these algorithms in real-world quantum devices. In this paper, we propose a cost-efficient method for applying quant...
2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), 2016
RDF is a flexible data representation model. Due to its flexibility and simplicity it has become ... more RDF is a flexible data representation model. Due to its flexibility and simplicity it has become a popular framework, hence the amount of RDF data is increasing fast. Querying massive amount of data is a serious challenge in general and it is true for RDF data as well. In this paper we investigating how to evaluate SPARQL queries on a distributed system. We propose a novel method for evaluating SPARQL queries using the GraphX graph analytical tool. GraphX is built on the top of Spark that is an in-memory data processing system for distributive computation. With this tool we managed to utilize the graph-like structure of the RDF statements which are in form of subject-predicate-object.
The Augmented Reality applications have received great attention in the recent years. However, th... more The Augmented Reality applications have received great attention in the recent years. However, there is still a lack of formal description of such systems currently. In this paper, we propose a new formal model for context-aware semantic Augmented Reality systems. The model can be divided into two parts: a set-theory function based method allows the formalization of an Augmented Reality system while an integrated time-space-motion logic provides the description of the behavior of the system. The suggested model enables the characterization of an Augmented Reality system with mathematical precision. In addition, logical inferences can be performed by means of the logic part of the formal description. The practical applicability of the proposed model is shown through use cases.
Proceedings of the 9th International Conference on Applied Informatics, Volume 2, 2015
In this paper we consider three different properties of social networks, namely the number of the... more In this paper we consider three different properties of social networks, namely the number of the different triangles, the sizes of the strongly connected components (SCC) and Linerank, which is offered as a substitute for betweenness and can be efficiently calculated on graphs with more than 10 9 nodes [7]. We implemented the computation of these measures in three different ways: sequentially, using the widely spread MapReduce model-Hadoop in our case-and its newly emerging rival Giraph, which is based on the Pregel system [11]. MapReduce and Pregel use distributed computation enabling high parallelization of the calculations. The algorithms calculating the above measures can be grouped into three different families. In the case of the triangles for a node only its close neighbourhood should be taken into consideration. In the case of the SCC's at least in the sequential approach the graph should be traversed in a depth search manner, which can be prohibitively expensive for the distributive systems. Finally, Linerank can be computed by using an iterative matrix-vector multiplication. We ran our implementations on several generated and real-world networks of different sizes firstly in order to assess the degree of the benefit of parallelization offered by the last two technologies for the algorithm families. Secondly, to see for which magnitude of the size of the networks it is more beneficial to choose one the aforementioned models instead of the sequential approach.
ABSTRACT Nowadays, companies have to handle large amounts of data every day (e.g. employee data, ... more ABSTRACT Nowadays, companies have to handle large amounts of data every day (e.g. employee data, reports, statements). These data are usually unstructured or semi-structured; they do not have a consistent format, since they come from various sources. As a solution, the Semantic Web provides a common data format, which helps integrate the heterogeneous data. In the industry, in order to be efficient and to remain competitive, it is crucial to have quick, easy, and reliable access to your data from anywhere. In this paper, we introduce a client-server system, which enables browsing of semantic data with smartphones and tablets running Android operating system. The users can access their data using predefined views. These views determine which parts of the data a user can see, and how to present them in the client application. Each user can have different access privileges to different views, so each employee can have a level of access suitable to his status.
Proceedings of the 9th International Conference on Applied Informatics, Volume 1, 2015
A new research direction has emerged as the investigation of On-line Social Networks. Twitter is ... more A new research direction has emerged as the investigation of On-line Social Networks. Twitter is one of the most well-known social networks. Analysis of the Twitter is easier than other social networks because it provides the opportunity for collecting and downloading of a certain percentage of the messages without any restrictions. There are several researches on topics as detecting news and events, human behaviors, analyzing and mining of opinions. The on-line messages are available only through a continuous stream. To store the messages from the stream effectively and efficiently is a serious challenge against software system design and architecture. Every day about 10 GBs data are generated by this way and storing of this volume of data is not an easy task. In this paper we present a technique and architecture for collecting and storing the messages of the Twitter, and we present a prototype where data can be accessed for further analysis. Our system makes use specific techniques and methods of Oracle environment. Our software architecture approach is in contrast to previous solutions in which the systems use MSSQL or MySQL DBMS. We demonstrate that indexing and Job scheduler of the Oracle provide advantages to retrieve and handle large amounts of data.
In the present day, virtually every application software generates large amounts of log entries d... more In the present day, virtually every application software generates large amounts of log entries during its work. The log files that are made from these entries are a collection of information about what happened while the program was running. This report can be used for multiple purposes such as performance monitoring, maintaining security, or improving business decision making. Log entries are usually generated in a disorganized manner. Using template miners, the different ‘event types’ can be distinguished (each log entry is an event), and the set of all entries is split into disjointed subsets according to the event types. These events consist of two parts. The first is the constant part, which is the same for all occurrences of the same event type. The second is the parameter part, which can be different for each occurrence. Since software mass-produces log files, in our previous paper, we introduced an algorithm that uses the templates mined from the data to create a dictionary...
Creating a widely excepted model on the measure of intelligence became inevitable due to the exis... more Creating a widely excepted model on the measure of intelligence became inevitable due to the existence of an abundance of different intelligent systems. Measuring intelligence would provide feedback for the developers and ultimately lead us to create better artificial systems. In the present paper, we show a solution where learning as a process is examined, aiming to detect pre-written solutions and separate them from the knowledge acquired by the system. In our approach, we examine image recognition software by executing different transformations on objects and detect if the software was resilient to it. A system with the required intelligence is supposed to become resilient to the transformation after experiencing it several times. The method is successfully tested on a simple neural network, which is not able to learn most of the transformations examined. The method can be applied to any image recognition software to test its abstraction capabilities.
In this paper, we present a novel implementation of an ecosystem simulation. In our previous work... more In this paper, we present a novel implementation of an ecosystem simulation. In our previous work, we implemented a 3D environment based on a predator–prey model, but we found that in most cases, regardless of the choice of starting parameters, the simulation quickly led to extinctions. We wanted to achieve system stabilization, long-term operation, and better simulation of reality by incorporating genetic evolution. Therefore we applied the predator–prey model with an evolutional approach. Using the Unity game engine we created and managed a closed 3D ecosystem environment defined by an artificial or real uploaded map. We present some demonstrative runs while gathering data, observing interesting events (such as extinction, sustainability, and behavior of swarms), and analyzing possible effects on the initial parameters of the system. We found that incorporating genetic evolution into the simulation slightly stabilized the system, thus reducing the likelihood of extinction of diffe...
Presently, almost every computer software produces many log messages based on events and activiti... more Presently, almost every computer software produces many log messages based on events and activities during the usage of the software. These files contain valuable runtime information that can be used in a variety of applications such as anomaly detection, error prediction, template mining, and so on. Usually, the generated log messages are raw, which means they have an unstructured format. This indicates that these messages have to be parsed before data mining models can be applied. After parsing, template miners can be applied on the data to retrieve the events occurring in the log file. These events are made from two parts, the template, which is the fixed part and is the same for all instances of the same event type, and the parameter part, which varies for all the instances. To decrease the size of the log messages, we use the mined templates to build a dictionary for the events, and only store the dictionary, the event ID, and the parameter list. We use six template miners to a...
Social media platforms are increasingly being used to communicate information, something which ha... more Social media platforms are increasingly being used to communicate information, something which has only intensified during the pandemic. News portals and governments are also increasing attention to digital communications, announcements and response or reaction monitoring. Twitter, as one of the largest social networking sites, which has become even more important in the communication of information during the pandemic, provides space for a lot of different opinions and news, with many discussions as well. In this paper, we look at the sentiments of people and we use tweets to determine how people have related to COVID-19 over a given period of time. These sentiment analyses are augmented with information extraction and named entity recognition to get an even more comprehensive picture. The sentiment analysis is based on the ’Bidirectional encoder representations from transformers’ (BERT) model, which is the basic measurement model for the comparisons. We consider BERT as the baseli...
Before intruding into a system attackers need to collect information about the target machine. Po... more Before intruding into a system attackers need to collect information about the target machine. Port scanning is one of the most popular techniques for that purpose, it enables to discover services that may be exploited. In this paper we propose an accurate port scan detection method that can detect port scanning attacks earlier with higher reliability than the widely used Snort-based approaches. Our method is profile-based, meaning that it does not only set a threshold on the connection attempts in a given time interval, like most of the current methods, but builds an IP profile of four features that enables a more fine-grained detection. We use the Budapest node of the FIWARE Lab community cloud as a natural honeypot to identify malicious activities in it.
Studia Universitatis Babeș-Bolyai Informatica, 2021
With the development of sequencing technologies, more and more amounts of sequence data are avail... more With the development of sequencing technologies, more and more amounts of sequence data are available. This poses additional challenges, such as processing them is usually a complex and time-consuming computational task. During the construction of phylogenetic trees, the relationship between the sequences is examined, and an attempt is made to represent the evolutionary relationship. There are several algorithms for this problem, but with the development of computer science, the question arises as to whether new technologies can be exploited in these areas of computational biology. In the following publication, we investigate whether the reinforced learning model of machine learning can generate accurate phylogenetic trees based on the distance matrix.
Intelligent Information and Database Systems, 2019
There can be a data boom in the near future, due to cheaper methods make possible for everyone to... more There can be a data boom in the near future, due to cheaper methods make possible for everyone to keep their own DNA on their own device or on a central medical cloud. With the development of sequencing methods, we are able to get the sequences of more and more species. However the size of the human genome is about 3 GB for each person. And for other species it can be more. The need is growing for the efficient compression of these data and general compressors can not reach a satisfying result. These are not aware of the special structure of these data. There are already some algorithms tried to reach smaller and smaller rates. In this paper, we would like to present our new method to accomplish this task.
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Papers by Attila Kiss