Automation of Psychological Selection Procedures for Personnel to Specific Activities

The article presents a universal method for determining the professional suitability (PS) of a candidate and an algorithm for forming a psychological profile for a specific profession based on determining the psychological potential of personality. The developed method is based on the use of automated support systems. Based on the obtained value of the integral indicator, a decision is made on the PS group of this candidate. This method adapts to the requirements of the profession to candidates, taking into account changes in the conditions of activity by adjusting the typical psychological profile of the personality. The developed method for determining a candidate’s PS has been brought to practical implementation in the form of an Automated Psychodiagnostic Complex (APDC) “Psychodiagnostics.” APDC has been tested on the example of the psychological selection procedures of personnel for military service in units with law enforcement functions. APDС allows to reduce the time and labor costs for conducting psychodiagnostic studies, increases the reliability of tests due to a higher degree of standardization of the testing procedure, increases the accuracy of assessing psychological characteristics, and reduces the likelihood of errors in the processing of test results. APDС can be used for recruiting in various sectors of the economy, education, and military sphere.

Design and Realisation of Scalable Business Process Management Systems for Deployment in the Cloud

Business Process Management Systems ( BPMSs ) provide automated support for the execution of business processes in modern organisations. With the emergence of cloud computing, BPMS deployment considerations are shifting from traditional on-premise models to the Software-as-a-Service ( SaaS ) paradigm, aiming at delivering Business Process Automation as a Service. However, scaling up a traditional BPMS to cope with simultaneous demand from multiple organisations in the cloud is challenging, since its underlying system architecture has been designed to serve a single organisation with a single process engine. Moreover, the complexity in addressing both the dynamic execution environment and the elasticity requirements of users impose further challenges to deploying a traditional BPMS in the cloud. A typical SaaS often deploys multiple instances of its core applications and distributes workload to these application instances via load balancing. But, for stateful and often long-running process instances, standard stateless load balancing strategies are inadequate. In this article, we propose a conceptual design of BPMS capable of addressing dynamically varying demands of end users in the cloud, and present a prototypical implementation using an open source traditional BPMS platform. Both the design and system realisation offer focused strategies on achieving scalability and demonstrates the system capabilities for supporting both upscaling, to address large volumes of user demand or workload, and downscaling, to release underutilised computing resources, in a cloud environment.

An Ontology Based Approach for Regulatory Compliance of EU Reg. No 995/2010 in Greece

Illegal logging has always been considered as a major environmental and social global concern, as it is directly associated with deforestation and climate change. Nowadays, EU Regulation No 995/2010 has been successfully enforced to impede the placement of illegally produced timber within the EU market and therefore to efficiently enhance sustainable forest management and restore ecosystem balance. However, EU 995 regulatory compliance and enforcement itself is quite complex, since it requires long-term conformity, on a common basis for various heterogeneous groups and communities of stakeholders, in a global, even beyond EU, rule regulation framework. To make things worse, such a framework must be applied to the entire supply distribution chain and a wide variety of wood products, ranging from paper pulp to solid wood and flooring. Hence, in such complex and multivariate information environments, an ontological approach can more efficiently support regulatory compliance and knowledge management, due to its openness and richness of semantics for representing, analyzing, interpreting and managing such kind of information. In this paper, a rule-based regulatory compliance ontology is proposed, which fully captures EU Regulation No 995/2010 concepts and compliance rules and guidelines, as well as Greek legislations governing wood trade. The proposed ontology can be the basis for a computerized system providing automated support for illegal wood trade and monitoring EU regulation information provision and audit information storage and analysis.

Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data

In the definition of fruitful strategies to contrast the worldwide diffusion of SARS-CoV-2, maximum efforts must be devoted to the early detection of dangerous variants. An effective help to this end is granted by the analysis of deep sequencing data of viral samples, which are typically discarded after the creation of consensus sequences. Indeed, only with deep sequencing data it is possible to identify intra-host low-frequency mutations, which are a direct footprint of mutational processes that may eventually lead to the origination of functionally advantageous variants. Accordingly, a timely and statistically robust identification of such mutations might inform political decision-making with significant anticipation with respect to standard analyses based on consensus sequences. To support our claim, we here present the largest study to date of SARS-CoV-2 deep sequencing data, which involves 220,788 high quality samples, collected over 20 months from 137 distinct studies. Importantly, we show that a relevant number of spike and nucleocapsid mutations of interest associated to the most circulating variants, including Beta, Delta and Omicron, might have been intercepted several months in advance, possibly leading to different public-health decisions. In addition, we show that a refined genomic surveillance system involving high- and low-frequency mutations might allow one to pinpoint possibly dangerous emerging mutation patterns, providing a data-driven automated support to epidemiologists and virologists.

Improving the Requirements Engineering Process Through Automated Support: An Industrial Case Study

Although Distributed Software Development (DSD) has been a growing trend in the software industry, performing requirements management in such conditions implies overcoming new limitations resulting from geographic separation. SIDIA is a Research and Development (R&D) Institute, located in Brazil, responsible for producing improvements on the Android Platform for Samsung Products in all Latin America. As we work in collaboration stakeholders provided by Mobile Network Operators (MNO) from Latin countries, it is common that software requirements be provided by external stakeholders. As such, it is difficult to manage these requirements due to the coordination of many different stakeholders in a distributed setting. In order to minimize the risks, we developed a tool to assist our requirements management and development process. This experience paper explores the experience in designing and deploying a software approach that facilitates (I) Distributed Software Development, (II) minimizes requirements error rate, (III) teams and task allocations and (IV) requirements managements. We also report three lessons learned from adopting automated support in the DDS environment.

TOrPEDO: witnessing model correctness with topological proofs

Model design is not a linear, one-shot process. It proceeds through refinements and revisions. To effectively support developers in generating model refinements and revisions, it is desirable to have some automated support to verify evolvable models. To address this problem, we recently proposed to adopt topological proofs, which are slices of the original model that witness property satisfaction. We implemented , a framework that provides automated support for using topological proofs during model design. Our results showed that topological proofs are significantly smaller than the original models, and that, in most of the cases, they allow the property to be re-verified by relying only on a simple syntactic check. However, our results also show that the procedure that computes topological proofs, which requires extracting unsatisfiable cores of LTL formulae, is computationally expensive. For this reason, currently handles models with a small dimension. With the intent of providing practical and efficient support for flexible model design and wider adoption of our framework, in this paper, we propose an enhanced—re-engineered—version of . The new version of relies on a novel procedure to extract topological proofs, which has so far represented the bottleneck of performances. We implemented our procedure within by considering Partial Kripke Structures (PKSs) and Linear-time Temporal Logic (LTL): two widely used formalisms to express models with uncertain parts and their properties. To extract topological proofs, the new version of converts the LTL formulae into an SMT instance and reuses an existing SMT solver (e.g., Microsoft ) to compute an unsatisfiable core. Then, the unsatisfiable core returned by the SMT solver is automatically processed to generate the topological proof. We evaluated by assessing (i) how does the size of the proofs generated by compares to the size of the models being analyzed; and (ii) how frequently the use of the topological proof returned by avoids re-executing the model checker. Our results show that provides proofs that are smaller ($$\approx $$ ≈ 60%) than their respective initial models effectively supporting designers in creating model revisions. In a significant number of cases ($$\approx $$ ≈ 79%), the topological proofs returned by enable assessing the property satisfaction without re-running the model checker. We evaluated our new version of by assessing (i) how it compares to the previous one; and (ii) how useful it is in supporting the evaluation of alternative design choices of (small) model instances in applied domains. The results show that the new version of is significantly more efficient than the previous one and can compute topological proofs for models with less than 40 states within two hours. The topological proofs and counterexamples provided by are useful to support the development of alternative design choices of (small) model instances in applied domains.

Using visual thinking for computer support in teaching the design of technological equipment

The aim of the work is to substantiate the possibility of using multidisciplinary visualization tools in the educational process based on a logical-semantic scheme (LSS), which acts as a methodological platform for building a system of automated support for engineering decisions in designing technological equipment. An interactive learning environment is examined, which, by actively interacting with students, significantly increases the effectiveness, being able to accommodate and coordinate together more multidisciplinary information. In a visual version, the characteristics of the technologization means are presented, in conditions of computerization having logical and semantic schemes about the studied subject area, and an example of a multidisciplinary presentation of a course for students on designing heater plates for injection molding of plastics is considered. A diagram is presented that illustrates the current state of theoretical knowledge. It allows visualizing the basic concepts, formation, structure and arrangement of the stated knowledge for students. It clearly shows the cause-and-effect relationship. This, on the one hand, makes it easier for a young specialist to understand the knowledge being studied, and on the other hand, it allows having deeper understanding of the design of technological equipment.

Being a Mentor in open source projects

Mentoring is a well-known way to help newcomers to Open Source Software (OSS) projects overcome initial contribution barriers. Through mentoring, newcomers learn to acquire essential technical, social, and organizational skills. Despite the importance of OSS mentors, they are understudied in the literature. Understanding who OSS project mentors are, the challenges they face, and the strategies they use can help OSS projects better support mentors’ work. In this paper, we employ a two-stage study to comprehensively investigate mentors in OSS. First, we identify the characteristics of mentors in the Apache Software Foundation, a large OSS community, using an online survey. We found that less experienced volunteer contributors are less likely to take on the mentorship role. Second, through interviews with OSS mentors (n=18), we identify the challenges that mentors face and how they mitigate them. In total, we identified 25 general mentorship challenges and 7 sub-categories of challenges regarding task recommendation. We also identified 13 strategies to overcome the challenges related to task recommendation. Our results provide insights for OSS communities, formal mentorship programs, and tool builders who design automated support for task assignment and internship.

On Free Description Logics with Definite Descriptions

Definite descriptions are phrases of the form ‘the x such that φ’, used to refer to single entities in a context. They are often more meaningful to users than individual names alone, in particular when modelling or querying data over ontologies. We investigate free description logics with both individual names and definite descriptions as terms of the language, while also accounting for their possible lack of denotation. We focus on the extensions of ALC and, respectively, EL with nominals, the universal role, and definite descriptions. We show that standard reasoning in these extensions is not harder than in the original languages, and we characterise the expressive power of concepts relative to first-order formulas using a suitable notion of bisimulation. Moreover, we lay the foundations for automated support for definite descriptions generation by studying the complexity of deciding the existence of definite descriptions for an individual under an ontology. Finally, we provide a polynomial-time reduction of reasoning in other free description logic languages based on dual-domain semantics to the case of partial interpretations.