Well-defined Tasks Research Articles

The hype of artificial intelligence in neonatology: How close are we to real AI?

Background. Artificial intelligence (AI) use in medicine has rapidly evolved, offering significant advancements in patient monitoring and diagnosis. However, the application of AI in neonatology presents unique challenges compared to adult medicine. While adult patients can often be monitored and treated autonomously through AI-powered tools like telemedicine, newborns, particularly preterm infants, are entirely dependent on continuous human care. In neonatology, human interaction—especially the tactile and empathetic care provided by nurses and doctors—remains critical, and AI systems are not equipped to replace this essential element of care. Concerns that AI might displace medical personnel in neonatal settings are unfounded, as human intervention remains irreplaceable. Discussion. Current AI technologies, often referred to as AI, are more accurately described as advanced machine learning algorithms and sophisticated software rather than true general artificial intelligence (AGI). These systems can efficiently perform well-defined tasks, such as monitoring vital signs and adjusting mechanical ventilators. However, they lack the broader understanding and adaptability necessary for independent clinical decision-making. They cannot interpret complex clinical contexts or address multifactorial medical decisions, which are still the domain of human expertise. Despite the limitations, AI holds significant potential in neonatology. It can assist in optimizing treatment protocols, such as adjusting mechanical ventilation in real-time or personalizing antibiotic treatments based on microbiological data. Furthermore, clinical decision support systems (CDSS) powered by AI can provide clinicians with evidence-based recommendations, thereby enhancing decision-making processes and improving patient outcomes. Conclusion. While promising, AI in neonatology remains a supplementary tool rather than a replacement for human judgment. Its role will likely expand, but direct human care and expertise will continue to be essential in neonatal medicine, mitigating concerns about job displacement among healthcare professionals.

Advancing continual lifelong learning in neural information retrieval: Definition, dataset, framework, and empirical evaluation

Continual learning refers to the capability of a machine learning model to learn and adapt to new information, without compromising its performance on previously learned tasks. Although several studies have investigated continual learning methods for neural information retrieval (NIR) tasks, a well-defined task definition is still lacking, and it is unclear how typical learning strategies perform in this context. To address this challenge, a systematic task definition of continual NIR is presented, along with a multiple-topic dataset that simulates continuous information retrieval. A comprehensive continual neural information retrieval framework consisting of typical retrieval models and continual learning strategies is then proposed. Empirical evaluations illustrate that the proposed framework can successfully prevent catastrophic forgetting in neural information retrieval and enhance performance on previously learned tasks. The results also indicate that embedding-based retrieval models experience a decline in their continual learning performance as the topic shift distance and dataset volume of new tasks increase. In contrast, pretraining-based models do not show any such correlation. Adopting suitable learning strategies can mitigate the effects of topic shift and data augmentation in continual neural information retrieval.

Remote sensing object detection with feature-associated convolutional neural networks

Neural networks have become integral to remote sensing data processing. Among neural networks, convolutional neural networks (CNNs) in deep learning offer numerous advanced algorithms for object detection in remote sensing imagery, which is pivotal in military and civilian contexts. CNNs excel in extracting features from training samples. However, traditional CNN models often lack specific signal assumptions tailored to remote sensing data at the feature level. In this paper, we propose a novel approach aimed at effectively representing and correlating information within CNNs for remote sensing object detection. We introduce object tokens and incorporate global information features in embedding layers, facilitating the comprehensive utilization of features across multiple hierarchical levels. Consideration of feature maps from images as two-dimensional signals, matrix image signal processing is employed to correlate features for diverse representations within the CNN framework. Moreover, hierarchical feature signals are effectively represented and associated during end-to-end network training. Experiments on various datasets demonstrate that the CNN model incorporating feature representation and association outperforms CNN models lacking these elements in object detection from remote sensing images. Additionally, integrating image signal processing enhances efficiency in end-to-end network training. Various signal processing approaches increase the process ability of the network, and the methodology could be transferred to other specific and well-defined task.

Artificial Intelligence and Expert Systems

Artificial Intelligence (AI) is a specialized field of computer science focused on creating machines that can emulate human intelligence and behaviour. The term "Artificial Intelligence" was first introduced by John McCarthy in 1956 at the Massachusetts Institute of Technology (MIT) in the USA. AI encompasses a variety of applications including game playing, expert systems, natural language processing, neural networks, and robotics. As of now, no computer systems have achieved true artificial general intelligence, which would enable them to perform any intellectual task that a human can. However, significant progress has been made in specific domains, particularly in game playing. Modern computer chess programs, for instance, have surpassed human players in terms of skill and performance. In the early 1980s, expert systems were heralded as the future of AI and computing. These systems are designed to mimic the decision-making abilities of human experts in specialized fields such as medicine and engineering. Despite their potential, expert systems have not fully met the high expectations set for them. They tend to be costly to develop and maintain, and their utility is often limited to specific, well-defined tasks. Currently, neural networks represent one of the most dynamic and rapidly advancing areas of AI. These algorithms, inspired by the structure of the human brain, have demonstrated success in various applications, including voice recognition and natural language processing. When it comes to programming AI applications, LISP (List Processing) and Prelog (Programming in Logic) are two of the most widely used languages due to their suitability and flexibility for AI-related tasks.

EXPLORING EFFECTIVE METHODS TO BOOST VIRTUAL WORKERS' MORALE FOR IMPROVED PROJECT PERFORMANCE

Background: The continuous advancement of information technology has transformed how businesses and organizations carry out their day-to-day activities. Many people are choosing to work remotely because they are handling a significant number of human interactions through various virtual communication platforms. Remote work facilitates business growth and improves customer service, yet it presents its challenges, necessitating an investigation into virtual team morale to guarantee project success. Method: This study employed content analysis of readily available secondary data to examine the investigated phenomenon. Results: The study determined that the key elements for enhancing project success in virtual teams are technology adoption, an environment free from distractions, effective leadership, trust, communication, a well-defined task, active team engagement, and motivation. The study also found that morale strongly influences engagement and productivity in virtual teams. Therefore, when morale is high, virtual teams achieve their optimal performance. Conclusion: The research concluded that selecting the suitable technology for communication, assigning virtual team members with distinct roles and responsibilities, fostering a culture of accountability and trust within virtual teams, promoting efficient team collaboration, and motivating virtual team members are the most impactful tactics for enhancing employee engagement and performance in virtual teams.

Адаптація як складова процесу онбордингу персоналу

The purpose of the article is to define the differences between the concepts of «onboarding» and «adaptation», the tasks and methods of onboarding, as well as to define the concept of onboarding as a process that ensures the effective adaptation of new employees. Nowadays, modern business is impossible without human resources. The labor market, acquaintances, interviews – all this facilitates the rapid search for new employees who can take a certain place in the organization. But can a new employee quickly adapt to a new environment and start performing tasks without financial losses for the organization during the adaptation period of the employee himself/herself? Experience shows that some employees who are on probation or have just started working cannot agree to the conditions and resign only because of difficulties in the process of adapting to the new environment. That is why both small organizations and large companies should pay attention to the importance and role of onboarding in retaining new employees for a long period of time. Some HR specialists and part of the scientific community equate onboarding with adaptation. We have identified the differences between these concepts, because, in our opinion, adaptation is a component of the staff onboarding process. The processes associated with onboarding are preboarding, offboarding, and crossboarding. The period when after the final interview and before the «newcomer» begins to perform functional duties is called preboarding. If the onboarding process still fails at the enterprise, the HR team prepares for the offboarding (dismissal) process. And if new opportunities open up for the «newcomer», then the process of crossboarding is irreversible, which ensures the successful adaptation of the employee in a new position, but within the same enterprise. Well-defined tasks and methods for implementing onboarding make it possible to reduce staff turnover among «newcomers» to enterprises. And its essence substantiates the expediency and effectiveness of onboarding in the process of personnel management in general.

Expertise in Non-Well-Defined Task Domains: The Case of Reading

ABSTRACT In this article, we discuss expertise by considering the activity of reading. Cognitive scientists have traditionally conceptualised reading as a single, well-defined task, namely the decoding of letter sequences into meaningful sequences of speech sounds. This definition captures a core feature of the reading activity at the computational level, but it is an overly narrow model of how reading behaviour occurs in the real world. We propose a more expansive model of expertise. In our view, expertise in general is best conceptualised as a distributed process that takes place within a cultural-cognitive ecosystem. Our model allows for a more inclusive view of expertise in reading. We argue that reading is better understood as a manifold task domain that admits multiple reasonable criteria for evaluating performance. We draw on ethnographic data to show how our model allows for a wider appreciation of what expertise in reading amounts to in educational contexts.

Investigation of Learning Experiences of Primary School Students with Mathematics Learning Disability with Authentic Activities

This study aimed to examine the primary school children learning experiences with mathematics learning difficulties in authentic contexts. Eight primary school students with mathematics difficulties participated in the research as multiple case studies. Research data were obtained with three different data collection methods. These were determined as semi-structured interviews (before and after observation), in-class observation and document review. At the end, as the result of the data collection process, which lasted for a total of five months, the students’ opinions showed that; Mathematics teaching supported by authentic activities brought along features such as familiar problems, well-defined tasks, cognitive support, authentic assessment and reflection. In addition, it was seen from the student data who participated in all of the authentic tasks that; Students who participated in authentic tasks with their peers, teachers, or parents had higher completion rates.

Machine Learning Generation of Dynamic Protein Conformational Ensembles.

Machine learning has achieved remarkable success across a broad range of scientific and engineering disciplines, particularly its use for predicting native protein structures from sequence information alone. However, biomolecules are inherently dynamic, and there is a pressing need for accurate predictions of dynamic structural ensembles across multiple functional levels. These problems range from the relatively well-defined task of predicting conformational dynamics around the native state of a protein, which traditional molecular dynamics (MD) simulations are particularly adept at handling, to generating large-scale conformational transitions connecting distinct functional states of structured proteins or numerous marginally stable states within the dynamic ensembles of intrinsically disordered proteins. Machine learning has been increasingly applied to learn low-dimensional representations of protein conformational spaces, which can then be used to drive additional MD sampling or directly generate novel conformations. These methods promise to greatly reduce the computational cost of generating dynamic protein ensembles, compared to traditional MD simulations. In this review, we examine recent progress in machine learning approaches towards generative modeling of dynamic protein ensembles and emphasize the crucial importance of integrating advances in machine learning, structural data, and physical principles to achieve these ambitious goals.

An IoT Toolchain Architecture for Planning, Running and Managing a Complete Condition Monitoring Scenario

Condition Monitoring (CM) is an extremely critical application of the Internet of Things (IoT) within Industry 4.0 and Smart City scenarios, especially following the recent energy crisis. CM aims to monitor the status of a physical appliance over time and in real time in order to react promptly when anomalies are detected, as well as perform predictive maintenance tasks. Current deployments suffer from both interoperability and management issues within their engineering process at all phases– from their design to their deployment, to their management–, often requiring human intervention. Furthermore, the fragmentation of the IoT landscape and the heterogeneity of IoT solutions hinder a seamless onboarding process of legacy devices and systems. In this paper, we tackle these problems by first proposing an architecture for CM based on both abstraction layers and toolchains, i.e., automated pipelines of engineering tools aimed at supporting the engineering process. In particular, we introduce four different toolchains, each of them dedicated to a well-defined task (e.g., energy monitoring). This orthogonal separation of concerns aims to simplify both the understanding of a complex ecosystem and the accomplishment of independent tasks. We then illustrate our implementation of a complete CM system that follows said architecture as a real Structural Health Monitoring (SHM) pilot of the Arrowhead Tools project, by describing in detail every single tool that we developed. We finally show how our pilot achieves the main objectives of the project: the reduction of engineering costs, the integration of legacy systems, and the interoperability with IoT frameworks.

Self-Supervised Language Learning From Raw Audio: Lessons From the Zero Resource Speech Challenge

Recent progress in self-supervised or unsupervised machine learning has opened the possibility of building a full speech processing system from raw audio without using any textual representations or expert labels such as phonemes, dictionaries or parse trees. The contribution of the Zero Resource Speech Challenge series since 2015 has been to break down this long-term objective into four well-defined tasks -- Acoustic Unit Discovery, Spoken Term Discovery, Discrete Resynthesis, and Spoken Language Modeling -- and introduce associated metrics and benchmarks enabling model comparison and cumulative progress. We present an overview of the six editions of this challenge series since 2015, discuss the lessons learned, and outline the areas which need more work or give puzzling results.

Implications of data-driven product design: From information age towards intelligence age

Data-driven design (D3), a new design paradigm benefited from advanced data analytics and computational intelligence, has gradually promoted the research of data-driven product design (DDPD) ever since 2000 s. In today’s Intelligence Age, some theoretical and practical studies have tried to achieve the advanced intelligence capabilities in DDPD. However, to the authors' best knowledge, there is still a lack of a holistic summary of DDPD with chronological concern in the intelligence age. To bridge the gap, this research undertakes a literature review of DDPD publications from 2000 to date (19/09/2022), of which 172 relevant papers are discussed via bibliometric analysis and state-of-the-art analysis. The results shown that DDPD has vitality in the Intelligence Age by combining the cutting-edge digital technologies, such as AI, additive manufacturing, digital twin, and so on. Moreover, current DDPD studies could outperform the classical design methods on the well-defined tasks, but it still cannot master the creative/innovative design tasks which require the cognitive capability. This survey further highlights several future research potentials including cognitive intelligence-enabled design, end-to-end design integration, advanced design knowledge support, design for additive manufacturing, and sustainable smart product-service systems. It is hoped that this work can be regarded as a reference to understand the roadmap of DDPD and offer insights for the design practitioners to complete relevant tasks in today’s intelligence age.

Quantum computational advantage with a programmable photonic processor

A quantum computer attains computational advantage when outperforming the best classical computers running the best-known algorithms on well-defined tasks. No photonic machine offering programmability over all its quantum gates has demonstrated quantum computational advantage: previous machines1,2 were largely restricted to static gate sequences. Earlier photonic demonstrations were also vulnerable to spoofing3, in which classical heuristics produce samples, without direct simulation, lying closer to the ideal distribution than do samples from the quantum hardware. Here we report quantum computational advantage using Borealis, a photonic processor offering dynamic programmability on all gates implemented. We carry out Gaussian boson sampling4 (GBS) on 216 squeezed modes entangled with three-dimensional connectivity5, using a time-multiplexed and photon-number-resolving architecture. On average, it would take more than 9,000 years for the best available algorithms and supercomputers to produce, using exact methods, a single sample from the programmed distribution, whereas Borealis requires only 36 μs. This runtime advantage is over 50 million times as extreme as that reported from earlier photonic machines. Ours constitutes a very large GBS experiment, registering events with up to 219 photons and a mean photon number of 125. This work is a critical milestone on the path to a practical quantum computer, validating key technological features of photonics as a platform for this goal.

Human hand motion prediction based on feature grouping and deep learning: Pipe skid maintenance example

Human-robot collaboration has gained popularity in various construction applications. The key to a successful human-robot collaboration at the same construction workplace is the delicate algorithm for predicting human motions to strengthen the robot's situational awareness, i.e., robot-human awareness. Most existing approaches have focused on predicting human motions based on repetitive patterns of human behaviors in well-defined task contexts, such as specific object picking tasks, for a relatively short period of time. These methods can hardly capture the “pattern inconsistency” of human actions, i.e., the differences across people in terms of motion features and even for the same person at different time points of the task. This paper proposes an analytical pipeline that segments and clusters the human inconsistent behaviors into different pattern groups and builds separate human motion pattern prediction models correspondingly. The proposed method, Human Motion Feature Grouping and Prediction (HMFGP), quantifies the spatiotemporal relationship between gaze focus and hand movement trajectories, segments the raw data based on the detected gaze-hand relationship pattern changes, and clusters the matched gaze-hand data segments into several pattern groups based on the pattern similarity of the gaze-hand relationships. Then a time series Deep Learning method is used to predict hand motions based on gaze focus trajectories for each of the pattern groups. The gaze and hand motion data of a human subject experiment (n = 120) for pipe skid maintenance was used to test the prediction performance of HMFGP. The result shows that HMFGP can significantly improve the accuracy of human hand motion prediction and help quantity different patterns of human motions for specific analyses.

Experiences of French medical students during their clerkship in adolescent psychiatry: a qualitative study.

The epidemiology of adolescent psychiatric disorders and the relational complexity of their management make exposure to adolescent psychiatry essential during medical school. However, some clinical particularities can complicate the students' learning experiences. Our work aimed to explore the experience of being a medical student during clerkships in adolescent psychiatry. Following the Interpretative Phenomenological Analysis qualitative approach, 20 semi-structured face-to-face interviews were conducted with medical students at the end of their clerkship in adolescent psychiatry. Three super-ordered themes emerged to describe their experience: in-depth self-exploration calling on emotions, thoughts and experiences; changes in the view of adolescent mental health; better understanding of the role and meaning of adolescent psychiatric care and how to approach it. Identification between students and patients could result from time-related factors (the end of adolescent brain remodeling, long, demanding studies, and financial and material dependence). In addition, the predominant use of non-analytical clinical reasoning processes-less valued in the rest of the graduate curriculum-poses a challenge for students. Indeed, for a student to find his or her place in adolescent psychiatry requires the student to reinvent him or herself, because the codes are different (no gown, less well-defined tasks, etc.). Finally, the excess prevalence of mental disorders among medical students requires increased vigilance on the part of tutors. For all these reasons, close, attentive tutoring seems essential to support students, while these clerkships afford a real opportunity for students to broaden their interpersonal skills.

PAMOJA: A component framework for grammar-aware engineering

PAMOJA is a Java-based component framework for Grammar-Aware Engineering (GAE) in an Integrated Development Environment (IDE). The PAMOJA system is being developed to explore the possibility of Component-Based Software Development (CBSD) in the grammarware field. Our main goal is to develop a coherent set of small GAE components, where each component is dedicated to a single well-defined task. The components should fit into a general-purpose framework like NetBeans or Eclipse and it should be possible to manipulate them inside the IDE just like any other component. This paper describes the PAMOJA architecture supporting this development style. We illustrate its use with the aid of examples, and present a case of composing new components at a higher level from the existing GAE components. For this case, we use a hybrid text/structure editor application as an example. This case study serves as a proof of concept of our approach.

Multi-Robot Task Sequencing & Automatic Path Planning for Cycle Time Optimization: Application for Car Production Line

Industrial robots are programmed to repeat a sequence of well-defined tasks, e.g. deburring, cutting and welding. The production cycle time is directly influenced by the task order as well as the way trajectories linking this tasks are generated. In this letter, we present an optimization approach that minimizes the production time as well as the overall movements duration of the robots. We propose a fast algorithm that generates a sequenced near-optimal solution for Multi-Robotic Task Sequencing Problem. We model the problem in the form of a new min (sum-max) Multiple Generalized Traveling Salesman Problem ‘min(sum-max) MGTSP’ model. Near-optimal solutions are obtained to automatically minimize cycle time in collision-free path. The originality of our method lies in its flexibility and ability to be integrated into industrial processes. In this study, we perform a double optimization in both the task and configuration space. Also, the proposed algorithm is able to automatically plan a collision-free trajectory between two robots’ configurations by generating relevant via points which minimizes movements duration. Comparing to other approaches, experiment reveal positive results in terms of efficiency and demonstrate the ability of this method to be integrated into existing industrial software.

Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis

Artificial intelligence (AI) and machine learning, a subset of AI, are increasingly used in medicine. AI excels at performing well-defined tasks, such as image recognition; for example, classifying skin biopsy lesions, determining diabetic retinopathy severity, and detecting brain tumors. This article provides an overview of the use of AI in medicine and particularly in respiratory medicine, where it is used to evaluate lung cancer images, diagnose fibrotic lung disease, and more recently is being developed to aid the interpretation of pulmonary function tests and the diagnosis of a range of obstructive and restrictive lung diseases. The development and validation of AI algorithms requires large volumes of well-structured data, and the algorithms must work with variable levels of data quality. It is important that clinicians understand how AI can function in the context of heterogeneous conditions such as asthma and chronic obstructive pulmonary disease where diagnostic criteria overlap, how AI use fits into everyday clinical practice, and how issues of patient safety should be addressed. AI has a clear role in providing support for doctors in the clinical workplace, but its relatively recent introduction means that confidence in its use still has to be fully established. Overall, AI is expected to play a key role in aiding clinicians in the diagnosis and management of respiratory diseases in the future, and it will be exciting to see the benefits that arise for patients and doctors from its use in everyday clinical practice.

Variations in cycle-time when using knowledge-based tasks for humans and robots

Operator4.0 was coined in 2016 to create a research arena to understand how the physical, cognitive, and sensorial capabilities of an operator could be enhanced by automation. To create an interaction between operator and robots, there are important factors that needs to be defined. Two important factors are the task and function allocation. Without well-defined tasks it is hard to allocate the tasks between the robot and the human to create resource flexibility. Furthermore, it the tasks are knowledge-based rather than rule-based, the cycle time between operators can differ a lot. Two assumptions are discussed regarding knowledge-based tasks and automation. These are also tested in an experiment. Results show that it is a large variation of the cycle time for both humans (between 1,58 minutes up to 4,40 minutes) and robots (between 1,94 minutes up to 4,49 minutes) when it comes to knowledge-based and machine learning systems.

Challenges to Managing Virtual Teams in Georgian SMEs

The novel coronavirus pandemic has brought about an unprecedented economic and social crisis. The work methods have seen a significant change, and telework has experienced swift growth. The practical application of teleworking needs technology, social and organizational support. Employees who are less tech-savvy require digital training. There is limited data on this topic. Therefore, this research will contribute to a deeper understanding of the issue related to managing virtual teams. An objective of this study is to examine how virtual teams are developed and to identify the challenges to managing virtual reams in selected Georgian SMEs. This study seeks to obtain data that will help to analyze and review issues that arise in the process of managing diverse and geographically dispersed virtual teams. This research is constructed on a brief literature review, survey and focus group discussions among the representatives of SMEs operating in the financial and IT sector in Georgia. According to study findings, companies that pursue successful eleadership consider teleworking as an opportunity. The most interesting finding is that timely availability of necessary information, effective communication and well-defined tasks influence the collaboration between virtual team members and is pivotal whilst managing a virtual team.