Automatic Actions Research Articles

Explainable semantic wireless anomaly characterization for digital twins

The shift towards software-centric network infrastructures is driven by the increasing need for networks to be responsive, flexible, and scalable in the face of an expanding set of connected devices. The digital twin (DT) approach, mirroring physical entities in a digital format, has emerged as a key enabler of network reliability and availability. Incorporating artificial intelligence (AI) into DTs enhances the resilience of networks by providing in-depth analysis and increasingly automated mitigation strategies against operational disruptions. In this work, we propose a new AI-based information extraction module that is part of the DT Monitoring component able to process RSS data, extract and characterize abrupt anomalies. The output of this component is used to maintain an anomaly history in the Link Abstraction within the DT and subsequently inform possible automatic mitigation actions. We design the AI-based information extraction module to identify and characterize three types of RSS based anomalies. Our extensive performance analysis on 10 versions of the “You Only Look Once” architecture reveals that YOLOv8n produces a good tradeoff between performance and computational complexity. We show that our approach performs on par with the state of the art for anomaly detection, while enabling anomaly characterization by location, duration, and severity. By employing two SotA explainability algorithms, we also provide insights into the important regions of the input that trigger the selected model’s classification and characterization decisions.

World Modeling for Autonomous Wheel Loaders

This paper presents a method for learning world models for wheel loaders performing automatic loading actions on a pile of soil. Data-driven models were learned to output the resulting pile state, loaded mass, time, and work for a single loading cycle given inputs that include a heightmap of the initial pile shape and action parameters for an automatic bucket-filling controller. Long-horizon planning of sequential loading in a dynamically changing environment is thus enabled as repeated model inference. The models, consisting of deep neural networks, were trained on data from a 3D multibody dynamics simulation of over 10,000 random loading actions in gravel piles of different shapes. The accuracy and inference time for predicting the loading performance and the resulting pile state were, on average, 95% in 1.2 ms and 97% in 4.5 ms, respectively. Long-horizon predictions were found feasible over 40 sequential loading actions.

Modelling and energy efficiency analysis of the microwave continuous processing of limestone

Our numerical investigation is oriented to the energy efficiency of the microwave heating technology for the continuous production of cement clinker.Simulations of the novel microwave continuous processing of limestone are performed using the COMSOL Multiphysics® Software. The proposed mathematical model couples Maxwell’s equations with energy and chemical equations, and numerical verification and validation are undertaken to support the accuracy of the results in this work. This approach includes automatic control actions regulating energy input and cavity impedance. A rectangular waveguide, operating as a single-mode cavity at 2.45 GHz, is utilized. The impact of various fill ratios of material on the system’s efficiency unveiled an intriguing heat transfer mechanism responsible for optimal operational conditions, with microwave efficiency up to 75% and thermal efficiency exceeding 90%. Notably, the potential of microwave technology in reducing greenhouse gas emissions is contingent upon establishing clean energy sources for electricity production.

Smart Farming Platform Using IoT and UAVs

With the advancement of communication technology, many innovative applications have developed in agriculture as a result of the integration of the Internet of Things (IoT) with unmanned aerial vehicles (UAVs), leading to the modernization of agriculture. This study seeks to propose an effective and low-cost platform for comprehensive monitoring of environmental parameters using IoT and drones.The preparation of this paper was based on a platform that was tested in a realistic environment on a farm near the city of Al-Median in Tunisia, where the platform was built to suit the realistic environment of a farm in Baghdad, through the use of sensors above and below the ground, which meets the experimental work and standards for automated and real-time monitoring. For environmental standards, the unified theory of acceptance and use of technology model was used, which is a model based on four basic elements: 1) expected performance, 2) expected effort, 3) social impact, and 4) facilitating conditions for obtaining results. The unique integration of IoT sensors with drones has shown impressive experimental results, indicating the possibility of performing both automatic and manual actions by humans. These smart moves contribute significantly to promoting precision agriculture, leading to a significant increase in agricultural production and conservation of natural resources.

Action sequence learning, habits, and automaticity in obsessive-compulsive disorder

This study investigates the goal/habit imbalance theory of compulsion in obsessive-compulsive disorder (OCD), which postulates enhanced habit formation, increased automaticity, and impaired goal/habit arbitration. It directly tests these hypotheses using newly developed behavioral tasks. First, OCD patients and healthy participants were trained daily for a month using a smartphone app to perform chunked action sequences. Despite similar procedural learning and attainment of habitual performance (measured by an objective automaticity criterion) by both groups, OCD patients self-reported higher subjective habitual tendencies via a recently developed questionnaire. Subsequently, in a re-evaluation task assessing choices between established automatic and novel goal-directed actions, both groups were sensitive to re-evaluation based on monetary feedback. However, OCD patients, especially those with higher compulsive symptoms and habitual tendencies, showed a clear preference for trained/habitual sequences when choices were based on physical effort, possibly due to their higher attributed intrinsic value. These patients also used the habit-training app more extensively and reported symptom relief post-study. The tendency to attribute higher intrinsic value to familiar actions may be a potential mechanism leading to compulsions and an important addition to the goal/habit imbalance hypothesis in OCD. We also highlight the potential of smartphone app training as a habit reversal therapeutic tool.

Action sequence learning, habits, and automaticity in obsessive-compulsive disorder

This study investigates the goal/habit imbalance theory of compulsion in obsessive-compulsive disorder (OCD), which postulates enhanced habit formation, increased automaticity, and impaired goal/habit arbitration. It directly tests these hypotheses using newly developed behavioral tasks. First, OCD patients and healthy participants were trained daily for a month using a smartphone app to perform chunked action sequences. Despite similar procedural learning and attainment of habitual performance (measured by an objective automaticity criterion) by both groups, OCD patients self-reported higher subjective habitual tendencies via a recently developed questionnaire. Subsequently, in a re-evaluation task assessing choices between established automatic and novel goal-directed actions, both groups were sensitive to re-evaluation based on monetary feedback. However, OCD patients, especially those with higher compulsive symptoms and habitual tendencies, showed a clear preference for trained/habitual sequences when choices were based on physical effort, possibly due to their higher attributed intrinsic value. These patients also used the habit-training app more extensively and reported symptom relief post-study. The tendency to attribute higher intrinsic value to familiar actions may be a potential mechanism leading to compulsions and an important addition to the goal/habit imbalance hypothesis in OCD. We also highlight the potential of smartphone app training as a habit reversal therapeutic tool.

Modulation of intracortical circuits in primary motor cortex during automatic action tendencies.

Humans display automatic action tendencies toward emotional stimuli, showing faster automatic behavior (i.e., approaching a positive stimulus and avoiding a negative stimulus) than regulated behavior (i.e., avoiding a positive stimulus and approaching a negative stimulus). Previous studies have shown that the primary motor cortex is involved in the processing of automatic actions, with higher motor evoked potential amplitudes during automatic behavior elicited by single-pulse transcranial magnetic stimulation. However, it is unknown how intracortical circuits are involved with automatic action tendencies. Here, we measured short-interval intracortical inhibition and intracortical facilitation within the primary motor cortex by using paired-pulse transcranial magnetic stimulation protocols during a manikin task, which has been widely used to explore approaching and avoiding behavior. Results showed that intracortical facilitation was stronger during automatic behavior than during regulated behavior. Moreover, there was a significant negative correlation between reaction times and intracortical facilitation effect during automatic behavior: individuals with short reaction times had stronger faciliatory activity, as shown by higher intracortical facilitation. By contrast, no significant difference was found for short-interval intracortical inhibition between automatic behavior and regulated behavior. The results indicated that the intracortical facilitation circuit, mediated by excitatory glutamatergic neurons, in the primary motor cortex, plays an important role in mediating automatic action tendencies. This finding further supports the link between emotional perception and the action system.

Agents for automatic control of sensors using Multi-Agent Systems and Ontologies: A scalable IoT architecture

Research efforts focused on Smart Building (SB) development have concentrated on the automation of resources within intelligent environments towards an enhanced experience for occupants. The process converts everyday manual activities into automatic actions, such as turning on lights upon entering a room, activating air conditioning on hot days, and switching off a television when there are no viewers. This article addresses a case study on context-aware monitoring conducted at the Laboratory of Distributed Systems and Concurrent Programming (LaSDPC) of the University of São Paulo. The focus is on a Fog layer of the IoT operating closer to sensors and reducing communication delays. Concepts of context-aware systems, multi-agent systems, ontology, and MQTT protocol were considered for the implementation of the intelligent system on-site. The programming languages used were Java and Python, leveraging libraries and frameworks dedicated to such technologies. A scalable system that does not compromise computational resource use and maintains responsiveness with low data exchange latency is proposed and tests checked the intelligent behavior of the laboratory under specific conditions using temperature, luminosity, and presence of a person as parameters.

A New Hybrid Supervisory Control System for Cabinet-Type Firebox Furnaces

In this paper, an intelligent hybrid Industrial Control System (ICS) and a Supervisory Control System (SCS) are proposed to improve the efficiency, safety, availability, and control capabilities of industrial furnaces. The main components of ICS are process control systems and advanced control systems that consist of overheating protection and load control. New soft sensors are designed as a combination of Laguerre filters and an artificial neural network to estimate the surface temperature of the furnace’s tubes, which allows the protection system to adjust fuel flow rate via overriding commands. Model-based fault detection systems are developed to detect faults in the combustion system and fouling in the furnace tubes and prepare features for the supervisory system. The supervisory control system is responsible for interfering between different components, evaluating the situation, and decision making based on the unit status and process conditions. An intuitionistic fuzzy inference system is employed as the core of the supervisory controller to tolerate disturbance and faults by switching the control modes. Test studies using experimental data of the furnace indicate the capability of the proposed monitoring and control system to operate in various loading situations and recover the system from abnormal conditions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In petrochemical industries, several reports have been issued about the load reduction of fired-heater furnaces imposed by combustion system faults and emergency shutdowns to carry out un-planned repairs due to fouling and wax-formation in tubes. Different activities such as detecting abnormal conditions, identifying faults, and enforcing corrective action can be performed by operators through manual actions. This paper is focused on designing a new supervisory control system (SCS) to be able to recover the fired heater furnace from abnormal conditions and keep running the plant. The SCS evaluates the condition of the unit by acquiring information from main variables, sensors, actuators, operating status of components and utilities, and operator commands. By identifying the root cause of faults, SCS makes decision on recognizing hazard degree, raising alarms, and applying automatic corrective actions.

Hierarchical Decision-Making Mechanism of the Frontal Lobe

Actions performed by humans require various levels of volitional control, ranging from highly practiced, automatic actions to those done in unfamiliar environments which necessitate deliberate controls. The multiple cortical motor areas in the frontal lobe regulate actions that vary in the levels of automaticity vs. intentionality. Further more, a part of the medial prefrontal cortex was found to contribute to the selection of the behavioral tactics, an internal protocol of how to decide what to do, which is a more abstract level of decision making than the selection of action per se. The frontal lobe as a whole functions as a hierarchically organized neural network that regulate both automatic and intentional actions.

Kompiuteryje atliekamų pasikartojančių veiksmų automatizavimo sistemos kūrimas

As technology advances and time saves time, more and more tasks are being automated, reducing the need for manual work. The process of automation is beneficial in computer-based work as well. This article analyzes automation systems for mouse clicks, their benefits, and their applications in various fields. Project problem. The automated click systems do not provide flexible, reliable, and convenient algorithmic functions for the use of complex marketing systems, mobile games, or a broader market segment. The objective of this work is to create a system that simulates mouse clicks, swipes, and text input, allowing for repetitive actions, creating and saving action execution algorithms. The theoretical part discusses similar automated systems, their advantages and disadvantages, text and image recognition algorithms, and popular libraries. The compliance of the systems with the ISO/IEC 9126 standard is analyzed. The programming language chosen for development is C++, Qt framework, the Tesseract library is used for text recognition, while the OpenCV library is used for graphic objects. The practical part presents the project of the created automated action system, the development process, the system itself, and the system's application possibilities. The system includes 10 control tools: text, color, and image recognition systems, mouse click, text input, and swipe simulation systems, automated action deletion, algorithm execution/stop, settings selection, menu collapse/expand, and system shutdown. The system allows for the creation of mouse click, swipe, and text input algorithms, defining conditions, creating action instructions, and sharing instructions on a forum. The system recognizes texts, colors, and images. Keywords: automating click operations, automatic actions system, automated optical character recognition, automatic clicker.

Review: Fundamentals, limitations and pitfalls on the development and application of precision nutrition techniques for precision livestock farming.

Precision livestock farming (PLF) concerns the management of livestock using the principles and technologies of process engineering. Precision nutrition (PN) is part of the PLF approach and involves the use of feeding techniques that allow the proper amount of feed with the suitable composition to be supplied in a timely manner to individual animals or groups of animals. Automatic data collection, data processing, and control actions are required activities for PN applications. Despite the benefits that PN offers to producers, few systems have been successfully implemented so far. Besides the economical and logistical challenges, there are conceptual limitations and pitfalls that threaten the widespread adoption of PN. Developers have to avoid the temptation of looking for the application of available sensors and instead concentrate on identifying the most appropriate and relevant information needed for the optimal functioning of PN applications. Efficient PN applications are obtained by controlling the nutrient requirement variations occurring between animals and over time. The utilization of feedback control algorithms for the automatic determination of optimal nutrient supply is not recommended. Mathematical models are the preferred data processing method for PN, but these models have to be designed to operate in real time using up-to-date information. These models are therefore structurally different than traditional nutrition or growth models. Combining knowledge- and data-driven models using machine learning and deep learning algorithms will enhance our ability to use real-time farm data, thus opening up new opportunities for PN. To facilitate the implementation of PN in farms, different experts and stakeholders should be involved in the development of the fully integrated and automatic PLF system. Precision livestock farming and PN should not be seen as just being a question of technology, but a successful marriage between knowledge and technology.

Free will, moral responsibility and automatisms

Abstract Some determinist approaches to free will opine that the human brain is subordinate to physical laws not fully under our control. This results in a weakening of the concept of the personal autonomy and moral responsibility of humans. Were we to acknowledge this assumption, we might consider automatic machines unable to influence the thoughts and intentions from which our actions take root. The key issue lies in the fact that an individual does not consciously engage in particular actions (automatisms), which challenges the concept of free will in an individual’s complex behaviour. Despite this issue, not all automatisms that lack conscious will can be viewed as lacking free will. The paper examines whether classical philosophical concepts may weaken the strict determinist approach, which seeks to deny that individuals have free will due to the existence of automatic actions.

Unconscious Intelligence in the Skilled Control of Expert Action

What occurs in the mind of an expert who is performing at their very best? In this paper, I survey the history of debate concerning this question. I suggest that expertise is neither solely a mastery of the automatic nor solely a mastery of intelligence in skilled action control. Experts are also capable of performing automatic actions intelligently. Following this, I argue that unconscious-thought theory (UTT) is a powerful tool in coming to understand the role of executive, intelligent action control in the fluidly automatic performance of expertise. Relying on a body of empirical evidence concerning the cognitive structures and perceptual strategies employed by experts, I show that the realm of skilled action is an ideal environment within which the powers of unconscious cognitive processing can have positive effects on action. I conclude that experts rely upon unconscious thinking in the production of intelligently automatic skilled actions.

Optimal Lane Change Path Planning Based on the NSGA-II and TOPSIS Algorithms

Among so many autonomous driving technologies, autonomous lane changing is an important application scenario, which has been gaining increasing amounts of attention from both industry and academic communities because it can effectively reduce traffic congestion and improve road safety. However, most of the existing researchers transform the multi-objective optimization problem of lane changing trajectory into a single objective problem, but how to determine the weight of the objective function is relatively fuzzy. Therefore, an optimization method based on the combination of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which provides a new idea for solving the multi-objective optimization problem of lane change trajectory algorithm, is proposed in this paper. Firstly, considering the constraints of lane changing and combining with the collision detection algorithm, the feasible lane changing trajectory cluster is obtained based on the quintic polynomial. In order to ensure the comfort, stability and high efficiency of the lane changing process, the NSGA-II Algorithm is used to optimize the longitudinal displacement and time of lane changing. The continuous ordered weighted averaging (COWA) operator is introduced to calculate the weights of three objective optimization functions. Finally, the TOPSIS Algorithm is applied to obtain the optimal lane change trajectory. The simulations are conducted, and the results demonstrate that the proposed method can generate a satisfactory trajectory for automatic lane changing actions.

A software framework for virtual testing of a production control system

Production planning poses major challenges due to high product diversity, high fluctuations in demand and strong dynamic fluctuations in delivery quantities and times. This makes automation of production planning increasingly necessary to enable optimized sequence planning based on current production data. Production control systems (PRS) offer the possibility of automatic corrective actions in the event of deviations from the specified target dates. Production, PRS and data acquisition are considered as a single control loop and are the subject of various development and research topics. However, automatic interventions in planning and control can lead to serious disruptions if there are errors in the software of the PRS. Therefore, testing and validation of PRS is a crucial factor in development, but is hardly possible in real production environments due to the availability of physical manufacturing facilities and the associated risks. Therefore, in the scope of the paper, a concept for a software framework is proposed to provide a test environment for a PRS. A virtual production environment, which can be combined with the PRS and parameterized in form of a discrete-event simulation, offers the possibility to test the PRS. It is also possible to couple the simulation with a reference calculation to show the behavior of the production environment. The entire framework is designed to enable the identification of critical machines and times as well as, in further steps, the detection of software errors in the PRS.

„Kaip įvertinti, ar aš geresnis, ar tu geresnis“: bibliotekininkų apdovanojimų tradicijos

‘How to Judge which one of us is the Best’: Librarian Award Traditions Based on the theoretical approach to invented traditions, the article looks at the award traditions created by the staff of the Ukmergė Vladas Šlaitas Public Library. Awards can be seen as a means to establish the power of the governing group; on the other hand, positive initiatives can be found within a team, where the prevailing tradition of electing the best members of the community strengthens the professional identity. This raises the problematic question of the research: were the award traditions which were introduced during the Soviet years only automatic actions, or did they acquire distinct interpretations and become important? Key words: librarians, traditions, awards, professional communities, organisations.

The role of creativity in expertise and skilled action

Perhaps a part of what makes expertise so inspiring to the curious researcher is the possibility of appropriating the structural components of skilled action to draw a roadmap towards their achievement that anyone might be able to follow. Accordingly, the purpose of this essay is to shed light upon the role that creativity plays in the production and environment of skilled action to that foregoing end. In doing so, I suggest that the lessons to be learned from recent empirical research on creativity has much to offer to the cognitive science of skill and expertise. Experts are able to bring their intelligence to bear in controlling fast and seemingly automatic actions by utilizing a form of control often called ‘intelligent automaticity.’ In this spirit, I argue that the environment of intelligently automatic action control curates a similarly ideal environment for the processes of creativity. Moreover, insofar as creativity is ideally operative within the environment of expert action control, I argue further that creativity functions as one representative form of ‘intelligence’ embedded within otherwise fluid, and automatic expert actions. Creativity is able to do so even without conscious representation through the powers of incubated cognition.

Social avoidance behavior modulates motivational responses to social reward-threat conflict signals: A preliminary fMRI study.

Social avoidance behavior (SAB) produces impairment in multiple domains and contributes to the development and maintenanceof several psychiatric disorders. Social behaviors such as SABare influenced by approach-avoidance (AA) motivational responses to affective facial expressions. Notably, affective facial expressions communicate varying degrees of social reward signals (happiness), social threat signals (anger), or social reward-threat conflict signals (co-occurring happiness and anger). SAB is associated with dysregulated modulation of automatic approach-avoidance (AA) motivational responses exclusively to social reward-threat conflict signals. However, no neuroimaging research has characterized SAB-related modulation of automatic and subjective AA motivational responses to social reward-threat conflict signals. We recruited 30 adults reporting clinical, moderate, or minimal SAB based on questionnaire cutoff scores. SAB groups were matched on age range and gender. During fMRI scanning, participants completed implicit and subjective approach-avoidance tasks (AATs), which involved moreincidental or moreexplicit evaluation of facial expressions that parametrically varied in social reward signals (e.g., 50%Happy), social threat signals (e.g., 50%Angry), or social reward-threat conflict signals (e.g., 50%Happy + 50%Angry). In the implicit AAT, SAB was associated with slower automatic avoidance actions and weaker amygdala-pgACC connectivity exclusively as a function of social reward-threat conflict signals. In the subjective AAT, SAB was associated with smaller increases in approach ratings, smaller decreases in avoidance ratings, and weaker dlPFC-pgACC connectivity exclusively in response to social reward-threat conflict signals. Thus, SAB is associated with dysregulated modulation of automatic and subjective AA motivational sensitivity to social reward-threat conflict signals, which may be facilitated by overlapping neural systems.

Cyberattack Defense With Cyber-Physical Alert and Control Logic in Industrial Controllers

Power system substations have intelligent electronic devices (IEDs) that collect data and control other devices. As the bridge between the physical and cyber parts of the power system, IEDs capture some key system behaviors. Since adversaries can modify the system’s behavior, physical and cyber data can be used to infer characteristics about the adversary. In this article, we present alert and control logic for hardware-based power system defense using the physical data and communication status in substation IEDs for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cyber threat detection</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cyber-physical contingency detection and response</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">physical contingency identification and response</i> . The proposed alert and control logic routines are implemented in an industrial real-time automation controller using IEC 61131-3 in the resilient energy systems lab testbed. The goal is to help operators identify adversaries and protect the power grid in a cyber-physical environment. The effectiveness and accuracy of logic schemes are validated under different adversarial scenarios. Comparing the proposed schemes with an intrusion detection system, Snort, our results also suggest the benefits of using cyber and physical data to identify threats. The results also suggest the use of such hardware-based schemes with software algorithms in a next-generation cyber-physical energy management system, which can implement automatic control actions to protect power grids and its physical equipment against cyber threats.