Autonomous Approach Research Articles

Autonomous Attack Mitigation Through Firewall Reconfiguration

ABSTRACTPacket filtering firewalls represent a main defense line against cyber attacks that target computer networks daily. However, the traditional manual approaches for their configuration are no longer applicable to next‐generation networks, which have become much more complex after the introduction of virtualization paradigms. Some automatic strategies have been investigated in the literature to change that old‐fashioned configuration approach, but they are not fully autonomous and still require several human interventions. In order to overcome these limitations, this paper proposes an autonomous approach for firewall reconfiguration where all steps are automated, from the derivation of the security requirements coming from the logs of IDSs to the deployment of the automatically computed configurations. A core component of this process is React‐VEREFOO, which models the firewall reconfiguration problem as a Maximum Satisfiability Modulo Theories problem, allowing the combination of full automation, formal verification, and optimization in a single technique. An implementation of this proposal has undergone experimental validation to show its effectiveness and performance.

A Coverage Control-Based Idle Vehicle Rebalancing Approach for Autonomous Mobility-on-Demand Systems

A Coverage Control-Based Idle Vehicle Rebalancing Approach for Autonomous Mobility-on-Demand Systems

ICT and the impact of digital textbooks in foreign language teaching and learning

Abstract As part of the effort to find more effective methods for teaching foreign languages, this study focuses on the role and impact of digital books within the Albanian educational context, with particular emphasis on pedagogical practices related to the teaching of foreign languages in general and French language in particular. Using a mixed methodological approach, both quantitative and qualitative, including surveys of teachers, interviews, and analysis of data collected during classroom observations, this study assesses how the use of digital technologies, including digital books, influences student engagement and their achievements in language learning. The data analysis shows a positive correlation between the use of digital books and the improvement of students’ performance in various language skills. The integration of digital technologies not only increases engagement and performance but also contributes to the development of a more personalized and autonomous approach to teaching.

Retraction Note: A new EEG software that supports emotion recognition by using an autonomous approach

Retraction Note: A new EEG software that supports emotion recognition by using an autonomous approach

How to assess the res judicata effects of international arbitral awards: giving concreteness to an autonomous approach

Abstract The article seeks to demonstrate the inadequacy of the ‘conflict-of-laws’ approach to determine the res judicata effects of international arbitral awards. Demystifying the erroneous assumption that the rules on the scope of res judicata are per se a matter of public policy, the authors defend an ‘autonomous’ approach, which dispenses with domestic law and confers broad preclusive effects on awards, with a view to avoiding the re-litigation of a dispute that is, in essence, the same as one already decided by a prior award. The legal bases for such an approach are party autonomy and the inherent powers of arbitrators. Building on the ILA Recommendations and recording the evolution of the conception of res judicata in certain civil law jurisdictions, the article proposes the elements of an arbitration-specific notion of the subject matter scope of the res judicata of awards with respect to issues of substantive law, addressing the situation of the res judicata of an award relied upon in further arbitral proceedings. The authors urge soft-law-making bodies and arbitral institutions to tackle arbitral res judicata proactively and contribute to the development of rules to give secure guidance to arbitrators and courts in determining its proper contours.

Strategies for hollow cathode command in short electrodynamics tether-based deorbit devices

Heaterless hollow cathodes are a promising solution for reaching a good cathodic contact in electrodynamic tether systems. However, these cathodes cannot keep the plasma discharge if the electric current IC is below a threshold Imin. If the tether is short, and to save power and expellant, an appropriate command strategy is needed to ensure that the cathode is triggered in conditions satisfying IC>Imin. This study investigates four command strategies, including two fully autonomous approaches (one based on satellite position and the other on local plasma density) and two strategies involving ground segment access, differing in command generation location (ground segment and on-board computer). The success rates of the four strategies were studied though numerical simulation and for the specific scenario of the E.T.PACK demonstration mission. The results demonstrate that, as compared to continuous operation of the hollow cathode, the proposed strategies reduce propellant wastage by a factor between 5 and 10. The strategy requiring ground segment access and with the onboard computer making the command decision has the highest success rate (94%). Among the autonomous configurations, the strategy based solely on local plasma density achieves the highest success rate (69%). Additionally, the impact of sensor accuracy on success rates was assessed via Monte Carlo analysis. The command strategy relying on plasma density measurement is the most sensitivity strategy, with a 10% decrease in success rate due to sensor uncertainty.

Autonomous Agent-Based Adaptation of Energy-Optimized Production Schedules Using Extensive-Form Games

Modular cyber-physical production systems are an important paradigm of Industry 4.0 to react flexibly to changes. The flexibility of those systems is further increased with skill-based engineering and can be used to adapt to customer requirements or to adapt manufacturing to disturbances in supply chains. Further potential for application of these systems can be found in the topic of electrical energy supply, which is also characterized by fluctuations. The relevance of energy-optimized production schedules for manufacturing systems in general becomes more important with the increased use of renewable energies. Nevertheless, it is often difficult to adapt when short-term energy price updates or unforeseen events occur. To address these challenges with an autonomous approach, this contribution focuses on extensive-form games to adapt energy-optimized production schedules in an agent-based manner. The paper presents agent-based modeling to transform and monitor energy-optimized production schedules into game trees to respond to changing energy prices and disturbances in production. The game is setup with a scheduler agent and energy agents who are considered players. The implementation of the mechanism is presented in two use cases, realizing decision making for an energy price update in a simulation example and for unforeseen events in a real-world demonstrator.

Tracking the south polar seasonal cap retreat of Mars using computer vision

We use daily polar stereographic mosaics obtained from the Mars Color Imager (MARCI) to autonomously track the recession of the South Polar Seasonal Cap (SPSC) during Mars Years (MYs) 28 to 31, for the period Ls = 178° to Ls = 345°. Utilizing an autonomous approach, we can delineate the SPSC by fitting an ellipse, offering the advantage of faster processing. This allows us to monitor the recession with enhanced time sampling capabilities. The SPSC recession is divided into two segments separated by a discontinuity at Ls = ∼260°, corresponding to the detachment of the Mountains of Mitchel seasonal cap from the main cap. Before the discontinuity, the SPSC exhibits variability where the recession rate can vary by up to 50%, with the fastest recession occurring at Ls = ∼235° and the slowest at Ls = ∼191°. After the discontinuity, the SPSC experiences reduced variability and undergoes a monotonic deceleration until Ls = ∼319°, gradually approaching the stable latitude of ∼86° S, consistent with the South Polar Residual Cap. MY 28 demonstrates distinct behavior in response to the Global Dust Storm (GDS), as the SPSC was smaller and sublimated faster before the onset of the storm compared to the 4-year average. Plain language summaryThe seasonal frost layer on Mars, consisting of frozen carbon dioxide, covers vast expanses during the winters. This frost forms as carbon dioxide gas in the atmosphere turns into solid ice on the surface, impacting the overall atmospheric pressure. We studied daily images captured by the Mars Color Imager (MARCI) to automatically monitor the retreat of the South Polar Seasonal Cap (SPSC) as it returns to the atmosphere during the spring over a span of four years. To achieve this, we employed a computer programming language called Python and a publicly available software library designed for visual analysis. By fitting an ellipse that most closely resembles the contour of the cap, we were able to trace and measure the change in the SPSC over time and determine how quickly SPSC shrinks. We discovered that our strategy produces results that are similar to those of previously published approaches, but in a lot less time and with increasing accuracy. We noticed new patterns in the seasonal retreat of the seasonal cap, including brief periods of faster shrinking, likely influenced by wind patterns. Additionally, we investigated a prominent icy area known as the Mountains of Mitchel seasonal cap and found that its behavior differed before and after a significant global dust storm in Mars Year 28.

Smart Circuit Design Machine Learning-Driven Optimization for Enhanced Performance in Electronics and Computer Engineering.

In the realm of Electronics and Computer engineering, achieving optimal performance of circuits amidst escalating complexity poses significant challenges. Traditional manual optimization techniques are often inadequate to navigate the intricacies of modern electronic systems. This paper advocates for the adoption of machine learning-driven optimization as a transformative approach to smart circuit design. By leveraging machine learning algorithms, engineers can systematically explore the expansive design space, discern complex relationships between circuit parameters and performance metrics, and ultimately enhance the efficiency and effectiveness of electronic circuits. This paper comprehensively reviews the application of machine learning techniques in circuit design optimization. Supervised learning algorithms such as neural networks, support vector machines, and decision trees enable the modeling of intricate interdependencies within electronic circuits. Unsupervised learning techniques, including clustering and dimensionality reduction, facilitate efficient exploration of the design landscape by identifying patterns and correlations. Additionally, reinforcement learning algorithms offer an autonomous approach to circuit optimization through iterative learning and refinement. Real-world applications of machine learning-driven optimization in electronics and computer engineering span various domains, including power-efficient integrated circuits, signal processing algorithm optimization, and layout optimization for enhanced performance and reliability. Moreover, machine learning techniques play a crucial role in mitigating variability in semiconductor manufacturing processes, ensuring robustness and reliability of electronic systems in the face of uncertainties. Despite the promising potential of machine learning in circuit design optimization, challenges such as dataset acquisition, model interpretability, and scalability to complex circuits persist. Addressing these challenges requires innovative research endeavors, including the development of hybrid optimization techniques and novel hardware architectures. DOI: https://doi.org/10.52783/tjjpt.v45.i02.6339

Academic Motivation and Quality of Life of Polish Medical Students.

The skills and attitudes of medical staff affect the quality of the healthcare system, hence the study of academic motivation and quality of life of medical students. The study involved 203 students of the Jagiellonian University Medical College. Academic motivation was assessed using the Academic Motivation Scale and quality of life using the World Health Organization Quality of Life-BREF questionnaire. Academic Motivation Scale is based on the Self-Determination Theory, which distinguishes several dimensions of motivation arranged along self-determination continuum from amotivation, through extrinsic, controllable motivation, to intrinsic, autonomous motivation. For our students, the main reason for taking up studies was identified regulation, it means that they perceive studying as something important for them, giving more opportunities in the future. Next was intrinsic motivations to know, where gaining knowledge is a value in itself. The third was external regulation, which indicate that the choice of studies was regulated by the dictates of the environment or the desire to obtain a reward. Female students showed a more intrinsically motivational profile than male students. Motivation became less autonomous as the years of study progressed. Most students rated their quality of life as good or very good. There was weak correlation between students' good quality of life and more self-determined academic motivation. Our students are mainly intrinsically motivated, most of them positively assess the quality of life. A more autonomous approach to learning coexisted with a positive assessment of quality of life.

Stigmatization Through Positioning and Interpretative Repertoires: The Case of Honor Crimes in the Arab World

ABSTRACT In the context of “honor” crimes in the Arab world, victims are subjected to stigmatization for deviating from societal norms by perpetrators seeking to assuage their culpability. Understudied is stigma as constructed by the Arab world’s general public to establish different positions in relation to these cruel acts. Taking a critical discursive psychological approach, this research analyzed comments posted on a YouTube video reporting the killing of the Saudi university student Nada Al-Qahtani. The study identified three positions through which the different individuals practiced stigmatization: 1) alignment with the perpetrator; 2) alignment with the victim; and 3) a partial alignment position involving avoidance to commit oneself to the crime. These positions were framed through an array of conflictual psychological, cultural, and religious interpretive repertoires. The study concluded that there is a shift in the discourse of HCs in the Arab world, where certain individuals position themselves and influence the positioning of others toward a more individualistic, self-centered, and autonomous approach for problem solving. It also suggested that social media platforms are a venue through which muted voices condemning such crimes should be promoted.

Sprawstwo nauczycieli edukacji wczesnoszkolnej w pracy z podręcznikiem szkolnym

Introduction: Schoolbooks are the basic didactic tool in early childhood education classes. As evidenced by numerous research reports, their content is significantly diversified in terms of quality. However, empirical research providing insight into teachers’ experiences of working with school textbooks is lacking. Research Aim: This study examines how teachers influence the use of a school textbook. It looks at their views on daily work with textbooks, the advantages and disadvantages they perceive, and their responses to these observations. Method: Six focus group interviews with 37 early childhood education teachers were conducted. The collected material was subsequently subjected to meaning-oriented content analysis. Results: On the basis of the collected data, it was established that teachers construe textbooks as: predestined fate, a stabilizing factor in the didactic process, a tool for everyday work, a subject- matter expert and a surety of their responsibility for the learning process. The work of educators with the school textbook is characterized by conformity, limited flexibility, adaptation to the range of children’s aptitudes, rationalization and, in rare cases, resistance. Conclusions: With respect to working with school textbooks, the agency of teachers manifests itself only as a superficial response to the identified shortcomings, without an autonomous approach to the content as well as tasks and instructions in school textbooks. Such a conclusion may offer grounds for a pedagogical discussion on the limitations and needs of training early education teachers in terms of enhancing their agency while working with a school textbook.

Written Corrective Feedback in EFL Context: Contextual and Individual Factors Influencing Students’ Responses

This study explores the written corrective feedback (WCF) behaviors of English as a foreign language (EFL) students in an EFL context and the individual and contextual factors that shape these behaviors. Ten students from the English language and translation department were interviewed and their WCF behaviors were explored. The participants were aged between 19 and 23 years, and had studied at least two writing courses taught by different instructors at a Saudi University. The findings revealed that most students had positive affective attitudes toward WCF. Most of them appreciated receiving both positive and negative comments, as the former were encouraging, while the latter helped improve their writing skills. Students’ behavioral responses to WCF varied; while some students were eager to read the feedback and correct their errors, others could correct them only when a second draft was requested. Additionally, most students relied on instructors for error correction, while only a few attempted more autonomous approaches to correction using textbooks or electronic resources. This variation in behavior could be attributed to the students’ different levels of motivation to improve their writing skills and their different individual goals for the writing course. Moreover, students’ responses to WCF were influenced by contextual factors such as the type of WCF received, the instructor’s professionalism and relationship with the students, number of assignments, and time constraints.

Architects from Different Fields of Activities in Lithuania and their Specific Mindsets

Abstract The paper focuses on the heterogeneity of the Lithuanian architects’ community across different fields of practice, including building design, building design and other projects, urban design, interior design, and other underrepresented design or non-design activities. It is based on a sociological survey involving 450 practising architects in Lithuania. The study identifies the distinct attitudes of each activity group towards architecture, architectural practice, and professional paths, generalises the profiles of these activity groups, and detects shared similarities or discrepancies among them. Architects involved in building design and those working on building design and other projects tend to share similar attitudes, forming the foundational worldview of the architectural community due to their quantitative prevalence. Interior designers and architects engaged in underrepresented design or non-design activities demonstrate relatively autonomous approaches. The latter group may either stand-alone or cluster with urban designers. The divergences in attitudes may lead to miscommunication within the professional community.

An Introduction to Radical Biocracy: A relational, autonomous approach to decision-making towards emergent and symbiotic design

Abstract Any system health work must look at decision-making because decisions propagate throughout a system, shaping system dynamics. Usually, human decision-making is conducted from an individualist, objectivist perspective. What happens when we use an approach based on the radical relationality of Radical Participatory Design and Relational Design? This is the fourth paper in a series of papers which introduced Radical Participatory Design in the first two papers and Relational Design in the third paper. In this fourth paper, we explore the decision-making dynamics in Radical Participatory Design and Relational Design projects. We use the term political ecology to speak about the power dynamics within any ecological system – a geographical population, a community, an ecosystem, and so forth. We analyze the political ecologies of individualist decision-making models. Then we explore how to embody a relational ontology within decision-filled human ecosystems and how a relational way of being changes decision-making. Referring to biology, we discuss ingredients for relational decision-making – relationality, emergent design principles, and autonomy. Those ingredients can lead to emergent and symbiotic design. Emergent design refers to design that emerges from consistently following a few basic principles. Symbiotic design occurs over time when deeply, relationally embedded entities retain autonomy and indirectly evolve to create a design that would not have occurred through an intentional design process. We then introduce Radical Biocracy as a type of decision-making model where decisions are not deliberated by groups or team members but emerge from the relationally autonomous choices and actions of individuals.

Autonomous nanomanufacturing of lead-free metal halide perovskite nanocrystals using a self-driving fluidic lab.

Lead-based metal halide perovskite (MHP) nanocrystals (NCs) have emerged as a promising class of semiconducting nanomaterials for a wide range of optoelectronic and photoelectronic applications. However, the intrinsic lead toxicity of MHP NCs has significantly hampered their large-scale device applications. Copper-base MHP NCs with composition-tunable optical properties have emerged as a prominent lead-free MHP NC candidate. However, comprehensive synthesis space exploration, development, and synthesis science studies of copper-based MHP NCs have been limited by the manual nature of flask-based synthesis and characterization methods. In this study, we present an autonomous approach for the development of lead-free MHP NCs via seamless integration of a modular microfluidic platform with machine learning-assisted NC synthesis modeling and experiment selection to establish a self-driving fluidic lab for accelerated NC synthesis science studies. For the first time, a successful and reproducible in-flow synthesis of Cs3Cu2I5 NCs is presented. Autonomous experimentation is then employed for rapid in-flow synthesis science studies of Cs3Cu2I5 NCs. The autonomously generated experimental NC synthesis dataset is then utilized for fast-tracked synthetic route optimization of high-performing Cs3Cu2I5 NCs.

An integrated UWB-Vision framework for autonomous approach and landing of UAVs in GPS-Denied environments

An integrated UWB-Vision framework for autonomous approach and landing of UAVs in GPS-Denied environments

Vision Intelligence for Smart Sheep Farming: Applying Ensemble Learning to Detect Sheep Breeds

The ability to automatically recognize sheep breeds holds significant value for the sheep industry. Sheep farmers often require breed identification to assess the commercial worth of their flocks. However, many farmers specifically the novice one encounter difficulties in accurately identifying sheep breeds without experts in the field. Therefore, there is a need for autonomous approaches that can effectively and precisely replicate the breed identification skills of a sheep breed expert while functioning within a farm environment, thus providing considerable benefits the industry-specific to the novice farmers in the industry. To achieve this objective, we suggest utilizing a model based on convolutional neural networks (CNNs) which can rapidly and efficiently identify the type of sheep based on their facial features. This approach offers a cost-effective solution. To conduct our experiment, we utilized a dataset consisting of 1680 facial images which represented four distinct sheep breeds. This paper proposes an ensemble method that combines Xception, VGG16, InceptionV3, InceptionResNetV2, and DenseNet121 models. During the transfer learning using this pre-trained model, we applied several optimizers and loss functions and chose the best combinations out of them. This classification model has the potential to aid sheep farmers in precisely and efficiently distinguishing between various breeds, enabling more precise assessments of sector-specific classification for different businesses.

The Aesthetics of Disharmony: Harnessing Sounds and Images for Dynamic Soundscapes Generation

This work presents an autonomous approach that explores the dynamic generation of relaxing soundscapes for games and artistic installations. Differently from past works, this system can generate music and images simultaneously, preserving human intent and coherency. We present our algorithm for the generation of audiovisual instances and also a system based on this approach, verifying the quality of the outcomes it can produce in light of current approaches for the generation of images and music. We also instigate the discussion around the new paradigm in arts, where the creative process is delegated to autonomous systems, with limited human participation. Our user study (N=74) shows that our approach overcomes current deep learning models in terms of quality, being recognized as human production, as if the outcome were being generated out of an endless musical improvisation performance.

Fully Automatic Approach for Smoke Tracking Based on Deep Image Quality Enhancement and Adaptive Level Set Model

In recent decades, the need for advanced systems with good precision, low cost, and high-time response for wildfires and smoke detection and monitoring has become an absolute necessity. In this paper, we propose a novel, fast, and autonomous approach for denoising and tracking smoke in video sequences captured from a camera in motion. The proposed method is based mainly on two stages: the first one is a reconstruction and denoising path with a novel lightweight convolutional autoencoder architecture. The second stage is a specific scheme designated for smoke tracking, and it consists of the following: first, the foreground frames are extracted with the HSV color model and textural features of smoke; second, possible false detections of smoke regions are eliminated with image processing technique and last smoke contours detection is performed with an adaptive nonlinear level set. The obtained experimental results exposed in this paper show the potential of the proposed approach and prove its efficiency in smoke video denoising and tracking with a minimized number of false negative regions and good detection rates.