Logical Analysis Of Data Research Articles

What Are Statistics: It's Time to set the Record Straight

The authors present their views on the modern concept of statistics as a science, academic discipline, and field of activity. The urgency of the matter lies in an adequate understanding of the essence of statistics, which determines the direction for its further evolution as a tool for increasing the effectiveness of the national socio-economic processes.According to the authors, by the beginning of the XXI century, de jure statistics are associated with activities, measured by data, that reflect a set of phenomena of diverse nature while ignoring de facto activities, associated with an assessment of the quantitative side of mass phenomena as part of information support of cognitive processes and management decision-making in the socio-economic sphere. In the former case, theoretical, methodological, and practical statistical activities are based on the consideration of the statistical population as such, for which is being developed an appropriate toolkit based primarily on the unity of methods of mathematical logic, mathematical statistics, and big data analysis. In the latter case, theoretical, methodological, and practical activities are interpreted within the framework of the concept implying that the quantitative side of mass social phenomena is the object of cognition and management as an objective reality. It is based on the following categories: a statistical indicator, a system of indicators, and an information model.Analysis of the main features of the two types of activity show that their convergence is impossible. The paper concludes that while maintaining the independent status of the first type of activity, called Statistics, it would be appropriate to acknowledge the independent status of the second type of activity, called Socio-economic statistics, as a type of occupation, a specialty and area of training in higher education, a scientific specialty – a branch of science, incorporating it in the professional standard «Statistician», the educational standard of higher education «Socio-economic statistics», and the standard of the scientific specialty «Socio-economic statistics».

CAVIDS: Real time intrusion detection system for connected autonomous vehicles using logical analysis of data

In recent times, Connected Autonomous Vehicles (CAVs) have been gaining popularity and have already begun to revolutionize the transport industry, mainly using real-time monitoring, analysis and exchange of data through In-Vehicle Networks (IVN). IVN can also interact with the outside world and roadside infrastructures. With all these advantages, certain weaknesses also creep in as the communication in IVN lacks proper security measures like encryption and authentication. These lack of security measures in IVN makes CAVs vulnerable to cyber-attacks which may have fatal consequences. The de facto standard for communication in IVN is known as Controller Area Network or CAN, and it also lacks said security features to protect CAN from outside threats like cyber attacks, sensor tempering etc. Real-time intrusion detection with minimal computational resources is the need of the hour to prevent disruptive consequences and fatal crashes. In this paper, we propose an intrusion detection method based on Logical Analysis of Data (LAD) which facilitates real-time intrusion detection in CAVs using minimal computational resources. LAD is a two-class classification technique which uses the concept of a partially defined Boolean function (pdBf) to extract different rules for classification from a historical dataset consisting of CAN messages only. Usually, the normal behaviour of any CAV is specified using the extracted rules, and any unusual behaviour that does not conform with the extracted rules is treated as an anomaly. In this paper, we have extracted rules for anomaly and treated all behaviours that do not conform with these extracted rules as normal. The proposed method outperforms many state-of-the-art approaches, which include other rule-based methods, machine learning and deep learning methods both in terms of F1 score and detection time. For CarChallenge 2020 dataset, the proposed method requires lesser than 47.5 μs for detection using a Raspberry Pi device, which is far less than the average time difference between two consecutive CAN messages and thus can be considered as real-time intrusion detection. Real-time intrusion detection is important in preventing any untoward incidents in CAVs.

Intrusion Detection System for IoT Using Logical Analysis of Data and Information Gain Ratio

The rapidly increasing use of the internet has led to an increase in new devices and technologies; however, attack and security violations have grown exponentially as well. In order to detect and prevent attacks, an Intrusion Detection System (IDS) is proposed using Logical Analysis of Data (LAD). Logical Analysis of Data is a data analysis technique that classifies data as either normal or an attack based on patterns. A pattern generation approach is discussed using the concept of Boolean functions. The IDS model is trained and tested using the Bot-IoT dataset. The model achieves an accuracy of 99.98%, and is able to detect new attacks with good precision and recall.

Dynamics of Judges' Considerations in The Determination Of Marriage Dispensation at The Enrekang Religion Court

The main study in this thesis is the dynamics of judges' considerations in determining the dispensation of marriage at the Enrekang Religious Court. Ideally, marriages are carried out in accordance with existing regulations, but there are still opportunities provided by the state for prospective brides and families/guardians of prospective brides who still wish to enter into underage marriages. Data at the Religious Court of Enrekang every year there has been an increase in marriage dispensation cases being handled in the last 4 years. In 2017, 35 cases were received, in 2018, 36 cases were received, but in 2019 there was a spike in cases as many as 42 cases, this was as a result of the enactment of Law no. 16 of 2019 and in 2020 as of July 10 2020 there have been 70 cases that have been submitted.
 This type of research is field researchqualitative research methods through normative juridical and sociological juridical approaches.The paradigm of this research is constructivist. Data sourceprimary is the determination of marriage dispensation andthe testimony of the judges, while secondary data comes from scientific books, accredited journals, theses, dissertations and documents related to the object of research. The research instruments are observation guidelines, interview guidelines and documentation tools.Data collection techniques areobservation, in-depth interviews and documentation. Data processing techniquesby describing it in the form of sentences that are orderly, coherent, logical, not overlapping and effectivewhile data analysis includes:data checking, classification, verification, data analysis, conclusion. As for tthe technique of testing the validity of the data is using a triangulation technique.
 The results of the study showed that (1) the discrepancy between the applicant's expectations and the request for a marriage dispensation was not granted in accordance with the Marriage Dispensation Stipulation of the Enrekang Religious Court, indicating that there was a social conflict contained in the stipulation. And the applicant still marries her child with her future husband but is not registered at the Office of Religious Affairs or married in an unregistered manner which will have a social impact because there is no recognition from the state, problems in managing population administration and others. (2) The factors considered by the judge in determining the dispensation for marriage at the Enrekang Religious Court are psychological, health, educational and economic factors. (3). The dynamics in determining the marriage dispensation at the Enrekang Religious Court can be seen in the determination of the marriage dispensation, differ from one case to another. Because sometimes judges differ in giving legal considerations to a case. If there is a difference of opinion in an assembly, a vote is made on the case by following the majority vote after going through deliberation. After the entry into force of PERMA No. 5 of 2019 which regulates the settlement of marriage dispensation cases is a single judge, this will reduce the complexity in examining and determining marriage dispensation. This provision has been enforced at the Enrekang Religious Court since the date of its stipulation. If there is a difference of opinion in an assembly, voting will be carried out on the case by following the majority vote after going through deliberation. After the entry into force of PERMA No. 5 of 2019 which regulates the settlement of marriage dispensation cases is a single judge, this will reduce the complexity in examining and determining marriage dispensation. This provision has been enforced at the Enrekang Religious Court since the date of its stipulation. If there is a difference of opinion in an assembly, voting will be carried out on the case by following the majority vote after going through deliberation. After the entry into force of PERMA No. 5 of 2019 which regulates the settlement of marriage dispensation cases is a single judge, this will reduce the complexity in examining and determining marriage dispensation. This provision has been enforced at the Enrekang Religious Court since the date of its stipulation.

Paired Patterns in Logical Analysis of Data for Decision Support in Recognition

Logical analysis of data (LAD), an approach to data analysis based on Boolean functions, combinatorics, and optimization, can be considered one of the methods of interpretable machine learning. A feature of LAD is that, among many patterns, different types of patterns can be identified, for example, prime, strong, spanned, and maximum. This paper proposes a decision-support approach to recognition by sharing different types of patterns to improve the quality of recognition in terms of accuracy, interpretability, and validity. An algorithm was developed to search for pairs of strong patterns (prime and spanned) with the same coverage as the training sample, having the smallest (for the prime pattern) and the largest (for the spanned pattern) number of conditions. The proposed approach leads to a decrease in the number of unrecognized observations (compared with the use of spanned patterns only) by 1.5–2 times (experimental results), to some reduction in recognition errors (compared with the use of prime patterns only) of approximately 1% (depending on the dataset) and makes it possible to assess in more detail the level of confidence of the recognition result due to a refined decision-making scheme that uses the information about the number and type of patterns covering the observation.

Detection of cyber attacks on smart grids

The paper analyzes observations using a logic-based numerical methodology in Python. The Logical Analysis of Data (LAD) specializes in selecting a minimal number of features and finding unique patterns within it to distinguish ‘positive’ from ‘negative’ observations. The Python implementation of the classification model is further improved by introducing adaptations to pattern generation techniques. Finally, a case study of the Power Attack Systems Dataset used to improvise Smart Grid technology is performed to explore real-life applications of the classification model and analyze its performance against commonly used techniques.

Condition-based monitoring of the rail wheel using logical analysis of data and ant colony optimization

PurposeIn this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical analysis of data (LAD) and ant colony optimization (ACO) algorithms in extracting patterns of high impact loads and normal loads from historical railway records. In addition, the patterns are employed in establishing a classification model used for classifying unseen observations. A case study representing real-world impact load data is presented to illustrate the impact of the proposed approach in improving railway services.Design/methodology/approachApplication of artificial intelligence and machine learning approaches becomes an essential tool in improving the performance of railway transportation systems. By using these approaches, the knowledge extracted from historical data can be employed in railway assets monitoring to maintain the assets in a reliable state and to improve the service provided by the railway network.FindingsResults achieved by the proposed approach provide a prognostic system used for monitoring the conditions surrounding rail wheels. Incorporating this prognostic system in surveilling the rail wheels indeed results in better railway services as trips with no-delay or no-failure can be realized. A comparative study is conducted to evaluate the performance of the proposed approach versus other classification algorithms. In addition to the highly interpretable results obtained by the generated patterns, the comparative study demonstrates that the proposed approach provides classification accuracy higher than other common machine learning classification algorithms.Originality/valueThe methodology followed in this research employs ACO algorithm as an artificial intelligent technique and LDA as a machine learning algorithm in analyzing wheel impact load alarm-collected datasets. This new methodology provided a promising classification model to predict future alarm and a prognostic system to guide the system while avoiding this alarm.

ONLINE SHOPPING PERCEIVED RISKS DURING THE COVID-19 PANDEMIC: ON THE STUDENT’S CASE FOR THE APPAREL AND FOOTWEAR

The purpose of the article is to develop and test a methodological approach to assess the impact of the online shopping perceived risks during the COVID-19 pandemic: on the student’s case for apparel and footwear. The study used a set of general and special methods of cognition: logical generalization, qualimetric comparison, taxonomic and index analysis, graphical visualization, scientific abstraction, and systematization of data. Online shopping is now a widespread and popular sales channel of an extremely large range of consumer goods in almost all regions and countries of the world, especially in developing countries. The online sales segment in the Ukrainian apparel and footwear market has high growth rates and development prospects. The purpose of the article is to determine the perceived risks while shopping Apparel and Footwear (AF) online during a COVID-19 pandemic. To collect primary information, 634 students of Ukrainian and foreign universities were surveyed. Information processing was carried out by applying methods of mathematical statistics. The main conclusions of the study relate to both the theoretical foundations of consumer behavior and their practical aspects. Firstly, the majority of respondents, regardless of the difference in their socio-demographic characteristics, positively perceive online shopping as a protective measure against infection with the COVID-19 virus. Secondly, as for consumer sentiment in the post-quarantine period, we can expect further growth of Internet trade in AF, as almost a third of respondents are going to buy more than before quarantine, and their number is 2-3 times higher than the number of those who have the intention to buy less. Thirdly, the ranking of perceived risks while shopping AF online allowed us to identify the most important ones, namely the risks associated with the product itself (risks of improper quality of goods, and financial ones) and the risk caused by the online shopping process - the risk of unsuccessful return of goods to the seller. This study provides practical confirmation for understanding the risks of online trade-in AF. The results of the study will contribute to the adoption of scientifically based decisions by the management of online stores and future research of online shopping processes by scientists.

Demurrage pattern analysis using logical analysis of data: A case study of the Ulsan Port Authority

Maritime logistics, which accounts for around 80% of international trade around the world, has been a driving force for economic growth. Increases in maritime traffic, however, lead to increased congestion in berths and terminals. This congestion in turn negatively affects the total ship turnaround time and leads to decreased efficiency in port operations and a higher demurrage rate, which refers to the number of vessels in queue for more than a fixed time period waiting to load/unload out of the total number of vessels entering a port. The demurrage rate directly affects a port’s operating profits; thus, this rate needs to stay as low as possible. In this study, we focus on developing a methodology to address the demurrage rate of a port. To this end, we first collect two sets of vessel data (2016 annual data for training and 2019 annual data for validating) for ships entering and leaving the Port of Ulsan in the Republic of Korea and integrate these datasets with berth data and weather data. We tailor the logical analysis of data (LAD) technique to derive the patterns from the training data that mitigate or aggravate the demurrage rate. We use these patterns to predict the demurrage rate for the validating set of data. The overall binary classification results demonstrate the proposed LAD technique’s competitive performance, compared with other state-of-the-art machine learning methods. We then analyze the patterns to derive policy suggestions that can lower the demurrage rate at the Port of Ulsan. Our computational experiments find that the availability of tugs or pilots and port arrival times mainly affect the demurrage rate at the Port of Ulsan. Finally, our study showcases new possibilities for using patterns of demurrage and non-demurrage vessels obtained by LAD to help policymakers and port operators address the growing demurrage problem.

Formation of Fuzzy Patterns in Logical Analysis of Data Using a Multi-Criteria Genetic Algorithm

The formation of patterns is one of the main stages in logical data analysis. Fuzzy approaches to pattern generation in logical analysis of data allow the pattern to cover not only objects of the target class, but also a certain proportion of objects of the opposite class. In this case, pattern search is an optimization problem with the maximum coverage of the target class as an objective function, and some allowed coverage of the opposite class as a constraint. We propose a more flexible and symmetric optimization model which does not impose a strict restriction on the pattern coverage of the opposite class observations. Instead, our model converts such a restriction (purity restriction) into an additional criterion. Both, coverage of the target class and the opposite class are two objective functions of the optimization problem. The search for a balance of these criteria is the essence of the proposed optimization method. We propose a modified evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to solve this problem. The new algorithm uses pattern formation as an approximation of the Pareto set and considers the solution’s representation in logical analysis of data and the informativeness of patterns. We have tested our approach on two applied medical problems of classification under conditions of sample asymmetry: one class significantly dominated the other. The classification results were comparable and, in some cases, better than the results of commonly used machine learning algorithms in terms of accuracy, without losing the interpretability.

Filsafat Ilmu sebagai Landasan Pengembangan Ilmu Pendidikan

The philosophy of science clarify the existence of science to the other science that requires knowledge as a medium of thinking and means of scientific communication. These are language, logic, mathematics, statistics, and other data analysis techniques. Specifications and independency science faced by the increasing number problems that can not be answered by science, therefore, philosophy appears as the answer. Philosophy gives an explanation or an answer onthat problems substantial and radical, while the science continues to develop it self- in its limit , while still radically criticized, process or the interaction basically is a field of philosophy of science. Philosophy of science therefore can be put as an attempt to bridge the gap between the philosophy and science, so that science does not despise the philosophy, and the philosophy does not see science as a superficial understanding of nature . Reality is “what is naturally “ or existence , while the appearance is that “artificially real“. Also how the relationship to both with subject /human. Epistemology is considered synonymous with the theory of knowledge. At the present time theory of knowledge can not be ignored. Epistemology of education science related to know how science education obtaining gain processing, what is the procedure to get the true scientific knowledge. Axiologiy related to what is the advantages of science education, what ethical relationship with science and its application science education in daily life . Key words : the philosophy of science, education, ontology, epistemology, axiology.

Logical analysis of data for ranking green technologies

Selection of the best green technology for a given application is a multi-criterion decision analysis problem. The tradeoffs that need to be made among conflicting attributes of the alternatives are inherently subjective and require the use of interactive decision analysis tools. In this work, a machine-learning based methodology is developed for ranking green technologies based on multiple criteria. First, an expert generates a training data set by ranking a subset of the alternative technologies using his/her tacit knowledge and preferences. Then, a machine learning procedure known as logical analysis of data (LAD) is used to detect patterns in these rankings. These patterns are approximations of the mental rules used by the expert to compare and rank the competing alternatives; after validation, they can be used to rank other technologies in the same class as those in the training data. This novel methodology is illustrated here for the case of ranking alternative energy storage technologies for stationary applications. This technique provides a rapid means of eliciting expert knowledge for ranking alternatives based on multiple criteria.

Prevention and detection of FDIA on power-network protection scheme using multiple support set

Usage of ICT tools for monitoring and control of power networks warrants mechanisms for robust security of supervisory control and data acquisition (SCADA) system against false data injection attack (FDIA) and detection of intruder induced manipulation of sensor information. Motivated by the aim of improving network resilience against FDIA, the present work aims at the twin objectives of providing robustness to one of the principle control actions in power networks i.e. protection relaying against FDIA and early detection of FDIA. In this context, a computationally cheap and economically viable schema based on logical analysis of data(LAD) has been proposed. The proposed scheme identifies a minimal set of secured sensors for which the network observability necessary for fault detection and classification is maintained. While preserving the observability, the mapping between sensor information and network state (healthy/faulty) allows for detecting FDIA and providing immunity to the protection scheme, irrespective of the type and extent of FDIA on the unsecured sensors.

An iterative feature selection procedure for a classification problem based on the method of logical analysis of data

An iterative procedure for selecting features for classifying observations is proposed. The main principles of the proposed iterative procedure are ranking and selection of features according to the frequency of their use when constructing logical patterns based on the method of logical analysis of data. The empirical confirmation of the expediency of this procedure is given.

Administrative suspension of operations for violations of industrial safety: theoretical and practical issues

The subject. The main issues of law enforcement activity on the application of administrative punishment in the form of administrative suspension of operations for identified offenses in the field of industrial safety of hazardous production facilities.The purpose of the article is to confirm or disprove hypothesis that Russian legislation allows the resumption of activities after the expiration of the period of its suspension without eliminating violations of industrial safety.The methodology of research is logical analysis of Russian legislation, statistical data and judicial decisions concerning enforcement of administrative suspension of operations in Russia.The main results. There is an ambiguous approach in the scientific literature to fixing the administrative suspension of operations in the system of administrative penalties. The effectiveness of its application is noted by some authors. At the same time, there are adverse consequences associated with the application of administrative suspension of operations for the further production activities of economic entities. Social tension in the collective of enterprises, difficulties of recovery after forced downtime, unclear prospects for further economic activity – this is not a complete list of problems arising in connection with the administrative suspension of activity. When making a court decision, judges often appoint a fine as a penalty and rarely a penalty in the form of suspension of operations. This is due to the complexity of the actual realization of suspension of operations, the special social significance of objects; the lack of a specialist's conclusion about the real danger of an offense. But if violations of industrial safety are detected during the operation of hazardous production facilities, it is initially possible to assume a high probability of serious consequences for the life and health of people, the environmental safety. Administrative suspension of activities is carried out by both judicial and non-judicial control authorities. In authors’ opinion, the application of this type of administrative punishment should be exclusively in the judicial jurisdiction. The law enforcement judicial practice concerning administrative suspension of operations in Russia is not uniform.Conclusions. There is a legal uncertainty in the mechanism of imposing administrative punishment in the form of administrative suspension of operations for violations of industrial safety of hazardous production facilities (Article 9.1 of the Russian Code of Administrative Offences). The uncertainty is manifested in the fact that the economic entity does not always eliminate the detected violations within the legally established period and after the expiration of the period for which the activity was suspended, the company resumes its activities nevertheless. Such opportunity reduces the preventive value of this punishment.

Computational Complexity and ILP Models for Pattern Problems in the Logical Analysis of Data

Logical Analysis of Data is a procedure aimed at identifying relevant features in data sets with both positive and negative samples. The goal is to build Boolean formulas, represented by strings over {0,1,-} called patterns, which can be used to classify new samples as positive or negative. Since a data set can be explained in alternative ways, many computational problems arise related to the choice of a particular set of patterns. In this paper we study the computational complexity of several of these pattern problems (showing that they are, in general, computationally hard) and we propose some integer programming models that appear to be effective. We describe an ILP model for finding the minimum-size set of patterns explaining a given set of samples and another one for the problem of determining whether two sets of patterns are equivalent, i.e., they explain exactly the same samples. We base our first model on a polynomial procedure that computes all patterns compatible with a given set of samples. Computational experiments substantiate the effectiveness of our models on fairly large instances. Finally, we conjecture that the existence of an effective ILP model for finding a minimum-size set of patterns equivalent to a given set of patterns is unlikely, due to the problem being NP-hard and co-NP-hard at the same time.

Reconstruction Rating Model of Sovereign Debt by Logical Analysis of Data

Sovereign debt ratings provided by rating agencies measure the solvency of a country, as gauged by a lender or an investor. It is an indication of the risk involved in investment and should be determined correctly and in a well-timed manner. The current system is lacking transparency of rating criteria and mechanism. The present study reconstructs sovereign debt ratings through logical analysis of data (LAD), which is based on the theory of Boolean functions. It organizes groups of countries according to 20 World Bank-defined variables for the period 2012–2015. The Fitch Rating Agency, one of the three big global rating agencies, is used as a case study. An approximate algorithm was crucial in exploring the rating method, in correcting the agency’s errors, and in determining the estimated rating of otherwise unrated countries. The outcome was a decision tree for each year. Each country was assigned a rating. On average, the algorithm reached almost 98% matched ratings in the training set and was verified by 84% in the test set.

The Transition of Economy from Analogue to Digital in the XXI Century by the case of the Republic of Korea

The article demonstrates the impact of digitalization on the political system and economy of the Republic of Korea based on the IT-revolution of the XXI century. Research methods used include comparative, system and logical data analysis, and the investigation of the digitalization trends. Data from Korean National Statistical Office and other official open sources are used for the determination of the main digitalization trends in the Korean economy. An analysis of specific phenomena arising in the economy and society as a whole is carried out under conditions of the digital economy. The evolution of the digitalization of hardware and software in the Republic of Korea is presented. The consequences of the transition of the economy from analogue to digital are revealed in such areas as the structure of the economy and business model, economic integration and liberalization, resource allocation and balanced development of regions, the role of government, and the intellectual property system.

Information resources for the reconstruction of the collective portrait of the headquarters of the 316th rifle division in 1941

The article talks about the author’s search work for staff of the headquarters of the first body of the 316th rifle division in 1941 in the battle of Moscow. The goal: based on the open archives of Russia and Kazakhstan in 2017, to give an analysis of the personal data of staff employees of 1941. Methods: Methodology of comparative-historical research, methodology and technique of socio- logical research, comparative-historical method and analysis of statistical data, etc. Take advantage of interdisciplinary methodology, content analysis, and qualitative document analysis. Based on the study of only recently discovered data for researchers, a number of important conclu- sions that refute the opinions voiced in the twentieth and early 21st centuries were made. The headquar- ters included representatives of 6 nationalities of the country. More than a quarter of the headquarters were non-partisan people. 77.8% of commanders with higher and secondary education worked at the headquarters. 54.2% of staff employees did not have a military education, which is one of the reasons why the division was called militia. The division served as a bridgehead for the offensive operations of the Moscow battle, which drove the enemy hundreds of kilometers from the capital. The practical significance of the study is the possibility of using its results in the field of culture and history. The results of the study can be integrated into educational processes. Tips and recommendations are given to journalists and PR specialists on how to cover topics based on the search for archival materials and its analysis. Materials can be included in history, journalism and public relations textbooks of the twentieth century. Key words: 316 rifle division, Central Archive of the Ministry of Defense of Russia, Central Archive of the Republic of Kazakhstan, journalists, archive works coverage in media.

Efficient feature selection for logical analysis of large-scale multi-class datasets

Feature selection in logical analysis of data (LAD) can be cast into a set covering problem. In this paper, extending the results on feature selection for binary classification using LAD, we present a mathematical model that selects a minimum set of necessary features for multi-class datasets and develop a heuristic algorithm that is both memory and time efficient for this model correspondingly. The utility of the algorithm is illustrated on a small example and the superiority of our work is demonstrated through experiments on 6 real-life multi-class datasets from UCI repository.