scholarly journals Identifying the Author Group of Malwares through Graph Embedding and Human-in-the-Loop Classification

2021 ◽  
Vol 11 (14) ◽  
pp. 6640
Author(s):  
Dong-Kyu Chae ◽  
Sung-Jun Park ◽  
Eujeanne Kim ◽  
Jiwon Hong ◽  
Sang-Wook Kim
Keyword(s):  
Private Information ◽  
Final Decision ◽  
K Nearest Neighbors ◽  
Human In The Loop ◽  
Research Areas ◽  
Classification Framework ◽  
Malware Propagation ◽  
Traditional Research ◽  
Author Group

Malware are developed for various types of malicious attacks, e.g., to gain access to a user’s private information or control of the computer system. The identification and classification of malware has been extensively studied in academic societies and many companies. Beyond the traditional research areas in this field, including malware detection, malware propagation analysis, and malware family clustering, this paper focuses on identifying the “author group” of a given malware as a means of effective detection and prevention of further malware threats, along with providing evidence for proper legal action. Our framework consists of a malware-feature bipartite graph construction, malware embedding based on DeepWalk, and classification of the target malware based on the k-nearest neighbors (KNN) classification. However, our KNN classifier often faced ambiguous cases, where it should say “I don’t know” rather than attempting to predict something with a high risk of misclassification. Therefore, our framework allows human experts to intervene in the process of classification for the final decision. We also developed a graphical user interface that provides the points of ambiguity for helping human experts to effectively determine the author group of the target malware. We demonstrated the effectiveness of our human-in-the-loop classification framework via extensive experiments using real-world malware data.

scholarly journals Interconnecting Product and Manufacturing Domains: A Literature Review

2020 ◽  
Author(s):  
Paraskeva Wlazlak ◽  
Ann-Louise Andersen ◽  
Dag Raudberget
Keyword(s):  
Conference Proceedings ◽  
Literature Review ◽  
Research Area ◽  
Process Modelling ◽  
Process Models ◽  
Future Research ◽  
Research Areas ◽  
Classification Framework ◽  
The Creation

Research on product-process modelling has been significant over the last decade. In this paper, we present a literature review of 13 papers published in journals and conference proceedings between 2012–2019. The purpose of this paper is to review and classify the literature on integrated product-process modelling utilizing ontologies. Specifically, the objectives of the paper are; (1) to develop a classification framework that is based on the existing research on integrated product-process modelling; (2) to use the classification framework to synthesize what is known in this research area (qualitative issues that have been raised that are useful for both researchers and practitioners); (3) to use the classification framework to propose future avenues in this research area. The classification framework consists of three major categories; namely, (1) integrated product-process model’s application; (2) approaches to modelling; and (3) practical challenges for implementation of integrated product-process models. The classification of the published literature and the analysis provides insights for practitioners and researchers on the creation and accumulation of knowledge in the product-process modelling area and interconnecting of product and manufacturing domains. This paper is intended to highlight the importance of integrated product-process models utilizing ontologies and identify areas for future research areas.

A Study of the Cloud Computing Adoption Issues and Challenges

2020 ◽  
Vol 13 (3) ◽  
pp. 313-318 ◽  
Author(s):  
Dhanapal Angamuthu ◽  
Nithyanandam Pandian
Keyword(s):  
Cloud Computing ◽  
High Performance ◽  
Cloud Service ◽  
Ease Of Use ◽  
Modern Trend ◽  
Security Issues ◽  
Research Areas ◽  
Data Location ◽  
Broad Classification

<P>Background: The cloud computing is the modern trend in high-performance computing. Cloud computing becomes very popular due to its characteristic of available anywhere, elasticity, ease of use, cost-effectiveness, etc. Though the cloud grants various benefits, it has associated issues and challenges to prevent the organizations to adopt the cloud. </P><P> Objective: The objective of this paper is to cover the several perspectives of Cloud Computing. This includes a basic definition of cloud, classification of the cloud based on Delivery and Deployment Model. The broad classification of the issues and challenges faced by the organization to adopt the cloud computing model are explored. Examples for the broad classification are Data Related issues in the cloud, Service availability related issues in cloud, etc. The detailed sub-classifications of each of the issues and challenges discussed. The example sub-classification of the Data Related issues in cloud shall be further classified into Data Security issues, Data Integrity issue, Data location issue, Multitenancy issues, etc. This paper also covers the typical problem of vendor lock-in issue. This article analyzed and described the various possible unique insider attacks in the cloud environment. </P><P> Results: The guideline and recommendations for the different issues and challenges are discussed. The most importantly the potential research areas in the cloud domain are explored. </P><P> Conclusion: This paper discussed the details on cloud computing, classifications and the several issues and challenges faced in adopting the cloud. The guideline and recommendations for issues and challenges are covered. The potential research areas in the cloud domain are captured. This helps the researchers, academicians and industries to focus and address the current challenges faced by the customers.</P>

scholarly journals 174 A comparison of machine learning algorithms in the classification of beef steers finished in feedlot

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 126-127
Author(s):  
Lucas S Lopes ◽  
Christine F Baes ◽  
Dan Tulpan ◽  
Luis Artur Loyola Chardulo ◽  
Otavio Machado Neto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Final Decision ◽  
Relevant Parameter ◽  
Good Prediction ◽  
Quality Traits ◽  
C4.5 Decision Tree

Abstract The aim of this project is to compare some of the state-of-the-art machine learning algorithms on the classification of steers finished in feedlots based on performance, carcass and meat quality traits. The precise classification of animals allows for fast, real-time decision making in animal food industry, such as culling or retention of herd animals. Beef production presents high variability in its numerous carcass and beef quality traits. Machine learning algorithms and software provide an opportunity to evaluate the interactions between traits to better classify animals. Four different treatment levels of wet distiller’s grain were applied to 97 Angus-Nellore animals and used as features for the classification problem. The C4.5 decision tree, Naïve Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP) Artificial Neural Network algorithms were used to predict and classify the animals based on recorded traits measurements, which include initial and final weights, sheer force and meat color. The top performing classifier was the C4.5 decision tree algorithm with a classification accuracy of 96.90%, while the RF, the MLP and NB classifiers had accuracies of 55.67%, 39.17% and 29.89% respectively. We observed that the final decision tree model constructed with C4.5 selected only the dry matter intake (DMI) feature as a differentiator. When DMI was removed, no other feature or combination of features was sufficiently strong to provide good prediction accuracies for any of the classifiers. We plan to investigate in a follow-up study on a significantly larger sample size, the reasons behind DMI being a more relevant parameter than the other measurements.

Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods

2017 ◽  
Vol 100 (2) ◽  
pp. 345-350 ◽  
Author(s):  
Ana M Jiménez-Carvelo ◽  
Antonio González-Casado ◽  
Estefanía Pérez-Castaño ◽  
Luis Cuadros-Rodríguez
Keyword(s):  
Olive Oil ◽  
Vegetable Oils ◽  
Edible Oils ◽  
Support Vector ◽  
Classification Methods ◽  
Multivariate Classification ◽  
Chromatographic Fingerprint ◽  
K Nearest Neighbors ◽  
Pls Discriminant Analysis

Abstract A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phaseLC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis tookonly 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil wereused: one input-class, two input-class, and pseudo two input-class.

Decision support or autonomous artificial intelligence? The case of wrong blood in tube errors

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Christopher-John L. Farrell
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Computer Simulation ◽  
Clinical Chemistry ◽  
Human Interaction ◽  
Final Decision ◽  
P Values ◽  
Laboratory Staff

Abstract Objectives Artificial intelligence (AI) models are increasingly being developed for clinical chemistry applications, however, it is not understood whether human interaction with the models, which may occur once they are implemented, improves or worsens their performance. This study examined the effect of human supervision on an artificial neural network trained to identify wrong blood in tube (WBIT) errors. Methods De-identified patient data for current and previous (within seven days) electrolytes, urea and creatinine (EUC) results were used in the computer simulation of WBIT errors at a rate of 50%. Laboratory staff volunteers reviewed the AI model’s predictions, and the EUC results on which they were based, before making a final decision regarding the presence or absence of a WBIT error. The performance of this approach was compared to the performance of the AI model operating without human supervision. Results Laboratory staff supervised the classification of 510 sets of EUC results. This workflow identified WBIT errors with an accuracy of 81.2%, sensitivity of 73.7% and specificity of 88.6%. However, the AI model classifying these samples autonomously was superior on all metrics (p-values<0.05), including accuracy (92.5%), sensitivity (90.6%) and specificity (94.5%). Conclusions Human interaction with AI models can significantly alter their performance. For computationally complex tasks such as WBIT error identification, best performance may be achieved by autonomously functioning AI models.

Which way records management research?

2020 ◽  
Vol 39 (1) ◽  
pp. 29-45
Author(s):  
Omwoyo Bosire Onyancha
Keyword(s):  
Information Management ◽  
Health Sector ◽  
Records Management ◽  
Medical Computing ◽  
Research Areas ◽  
The Social ◽  
Computer Based ◽  
Scopus Database ◽  
Broad Subject

This paper evaluates the keywords and subject areas in records management (RM) publications, as indexed in the Scopus database, with a view to mapping RM research from 1971 to 2018 so as to determine the direction of research in the field. A total of 4 762 documents were obtained from the Scopus database using the term records management and searching within the title, abstract and keywords fields. The data was analysed using VOSviewer software. The findings reveal that interest in RM research has grown as the volume of publications has continued to increase. Whereas there was no dominant area of research in the 1980s, as far as RM research is concerned, the main focus in the 2010s was the management of electronic health records, thereby signalling a shift in RM research from being just an information management exercise to being used for the management of records in the medical and health sector. Other popular research areas in the 2010s were health care, electronic medical record/s, information management, medical computing, information systems, and electronic document exchange. A classification of the RM publications according to Scopus’s broad subject fields revealed that RM research is mainly conducted in computer science, engineering, medicine, and the social sciences. The study predicts a slow growth in the number of RM publications in the next ten years (2019-2028), greater focus on RM in the health sector, and continued dominance of computer-based systems and electronic records as topics of RM research.

Application of Problem Situations for the Development of Learning Skills in the Study of Natural Sciences

2021 ◽  
Vol 9 (4) ◽  
Author(s):  
Margarita Slavova ◽  
Angel Slavchev
Keyword(s):  
Health Education ◽  
Personal Development ◽  
Logical Structure ◽  
Natural Sciences ◽  
Final Decision ◽  
Problem Situation ◽  
Learning Skills ◽  
Problem Situations ◽  
The Individual

The problem situation is one of the ways to form general learning skills in the study of natural sciences. It provides an opportunity to apply the individual approach, choosing a path for making a final decision and full personal development of the student. This article reviews the nature of the problem situation and learning skills, presents a classification of species and offers an example of use in teaching biology and health education - 7th grade to develop the skill of comparison. The article aims to guide teachers in the logical structure for creating a problem situation and the requirements for the content of individual elements. An option for linking it with a specific educational content is also shown.

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