Requirements Identification for a Blockchain-Based Traceability Model for Animal-Based Medicines

In this paper, the traceability of heparin medicine will be studied. Currently, the registration of traceability data is conducted in a decentralized manner. With blockchain implementation, the traceability systems that use this data could become semi-automated, increasing the quality, security, and confidence of the information generated in the supply chain. This paper presents the essential requirements and activities wherein information must be collected within the heparin drug supply chain, focusing on the animal raw materials production link and its requirements. Blockchain technology is proposed to increase traceability and reliability in relation to the current situation. It also fulfills all the requirements identified if used as part of a traceability system. These requirements are: the existence of a consensus mechanism; anonymity; protocol, efficiency, and consumption; immutability; ownership and management; and approval time. We conclude the paper by presenting the mapping of requirements and entities and critical activities for adopting blockchain technology to support the traceability of raw materials from animals used in heparin production.

Data Mining of Job Requirements in Online Job Advertisements Using Machine Learning and SDCA Logistic Regression

There are currently many job portals offering job positions in the form of job advertisements. In this article, we are proposing an approach to mine data from job advertisements on job portals. Mainly, it would concern job requirements mining from individual job advertisements. Our proposed system consists of a data mining module, a machine learning module, and a postprocessing module. The machine learning module is based on the SDCA logistic regression. The postprocessing module includes several approaches to increase the success rate of the job requirements identification. The proposed system was verified on 20 most searched IT job positions from the selected job portal. In total, 9971 job advertisements were analyzed. Our system’s verification is finding all job requirements in 80% of analyzed advertisements. The detected job requirements were also compared with the Open Skills database. Based on this database and the extension of IT job positions with other typical job skills, we created a list of the most frequent job skills in selected IT job positions. The main contribution is the development of a universal system to detect job requirements in job advertisements. The proposed approach can be used not only for IT positions, but also for various job positions. The presented data mining module can also be used for various job portals.

Certain issues on methodical support of forensic examinations of images on the dorsum of the hands

With the proliferation of video surveillance systems and an increase in their quality, it is becoming increasingly important to identify suspects using these video recordings. Given the importance of wearing masks, due to quarantine requirements, identification by facial images is not always possible. The next element of an appearance, which is usually not covered by clothing, is the hands, namely their dorsum. At the same time, there are certain gaps in the methodological support of forensic examination of hand images. The article deals with certain problematic issues of scientific and methodological support as to forensic examination of images on the dorsum of the hands. Modern scientific developments in this area are indicated, namely guidelines: "Personal identification by photographic images of hands", subsection 2.1.3: "Examination of traces of the dorsum of the hand" taken from the textbook: "Forensic examinations in the procedural law of Ukraine" and a methodological manual which is now being developed at the Ukrainian Research Institute of Special Equipment and Forensic Science of the Security Service of Ukraine. The Expert specializations were analyzed for their compliance with the type of research under consideration. Based on the results of the analysis, it is concluded that the research of images on the dorsum of the hands now corresponds to several expert specializations at the same time. An opinion is also expressed about the imperfection of the modern system of expert specializations in Ukraine.

DEVELOPMENT OF RECOMMENDATIONS FOR THE QUANTITATIVE INDICATORS TYPE SELECTION OF ARMAMENT AND MILITARY EQUIPMENT STANDARDIZATION AND COMMONALITY

The national standard of Ukraine DSTU V–P 15.111: 2019 “System of development and launching into manufacture of аrmament and military equipment. Standardization and commonality of armament and military equipment. Rules of work” which was put into effect on the 1st of January 2021 requires analysis of innovations and identification of problematic issues regarding its application, which is definitely relevant. The problematic issue remained in the national standard inherited from the repealed interstate standards is the admittance of calculation of quantitative indicators of standardization and commonality level by several types, but there are no recommendations pertaining to indicator type selection. The purpose of this paper is to develop recommendations for the quantitative indicators type selection of armament and military equipment standardization and commonality. The article analyzes the calculation procedure and types of quantitative indicators of standardization and commonality established by the national standard and identifies the main factors influencing the indicators of different types. During further analysis, the types of indicators which have one–factor influence, personal factors of influence along with others, as well as a certain correlation are defined. Possibilities of increasing the values of quantitative indicators by types and analysis of the interaction of indicators that have a certain correlation are considered. Based on the results of the analysis, the preferred type of quantitative indicator for assessing the level of standardization and commonality is defined. It can be recommended for the requirements identification in technical development requirements (statement of requirement) for the development of armament and military equipment, as well as areas for further research on this issue.

Requirements identification for distributed agile team communication using high level carotene

Communication plays an important role to deliver the correct information. However, the communication became challenging especially for agile software teams, which are in geographical distributed. The problem arise when there are exchanging information using unstructured communication platform, misunderstanding on the information communicated and lack of documentation. The aim of this study is to propose a text classification technique for requirements identification in text messages. In this study, we adopted the cascade and cluster classification concept of Carotene that relies on the hash tag function. It classifies the text messages into requirements types instead of job title. This technique called as high-level carotene (HLC) technique that embedded into the tool to identify the functional requirement and non-functional requirements. The result shows that most of criterias evaluated have achieved more than 85% of effectiveness in identifying both of requirement in text messaging by using this technique.

Automated Ontology-Based Security Requirements Identification for the Vehicular Domain

Many electronic and electrical systems are now incorporated with modern vehicles to control functional safety. Lack of security protection mechanisms in vehicular design may lead to different ways of executing malicious attacks against the vehicular network. These attacks may have various types of negative consequences, such as safe vehicle operation. This work presents an ontology-based framework as a new automated approach to verify and validate security requirements against security issues in the vehicular domain. The system also applies a set of logical rules to identify a set of security requirements as a category of necessary security requirements that could be proposed to be integrated within the vehicle design to address a specific security issue.

A Systematic Literature Review on Using Machine Learning Algorithms for Software Requirements Identification on Stack Overflow

Context. The improvements made in the last couple of decades in the requirements engineering (RE) processes and methods have witnessed a rapid rise in effectively using diverse machine learning (ML) techniques to resolve several multifaceted RE issues. One such challenging issue is the effective identification and classification of the software requirements on Stack Overflow (SO) for building quality systems. The appropriateness of ML-based techniques to tackle this issue has revealed quite substantial results, much effective than those produced by the usual available natural language processing (NLP) techniques. Nonetheless, a complete, systematic, and detailed comprehension of these ML based techniques is considerably scarce. Objective. To identify or recognize and classify the kinds of ML algorithms used for software requirements identification primarily on SO. Method. This paper reports a systematic literature review (SLR) collecting empirical evidence published up to May 2020. Results. This SLR study found 2,484 published papers related to RE and SO. The data extraction process of the SLR showed that (1) Latent Dirichlet Allocation (LDA) topic modeling is among the widely used ML algorithm in the selected studies and (2) precision and recall are amongst the most commonly utilized evaluation methods for measuring the performance of these ML algorithms. Conclusion. Our SLR study revealed that while ML algorithms have phenomenal capabilities of identifying the software requirements on SO, they still are confronted with various open problems/issues that will eventually limit their practical applications and performances. Our SLR study calls for the need of close collaboration venture between the RE and ML communities/researchers to handle the open issues confronted in the development of some real world machine learning-based quality systems.