Search Results (222)

Traditional identification approaches have often excluded many students from underrepresented backgrounds from gifted and talented service programs. This study introduces an innovative identification method based on the Bull’s Eye Model for Affective Development—Expansion (BEM-e), which focuses on identifying giftedness and talents through targeted positive psychological traits. This method is integrated within an affective curriculum designed to create authentic learning environments that align with students’ interests and strengths, fostering deeper engagement, motivation, and self-efficacy. Grounded in the positive psychology theory, the curriculum includes engaging, activity-oriented modules with comprehensive dynamic assessments. These assessments allow teachers to identify a broad range of talents and abilities, promoting equity and a holistic identification process, which contributes to a more equitable and comprehensive education system. Full article

In view of professional learning and practical training in traditional electronic information education of application-oriented universities, this paper constructs electronic information–innovation practice teaching reform (EI-IPTR).In this scheme, by an integrating artificial intelligence (AI)-enabled curriculum with a multi-mode integrated platform and open-style module, big data-based comprehensive education resources are optimally configured. We jointly perform the multi-mode construction of innovative practice teaching, professional education stage design, and teaching management improvement, respectively. Subsequently, new practice teaching mechanisms with information technology and its implementation and management methods are established to achieve better teaching effects. It first strengthens learning and intra-group competition to promote students’ innovation in competitions. Then, the AI technique, i.e., attention mechanism-aided long short-term memory (LSTM), is used to model individual students’ abilities. Thus, it accurately evaluates them for teachers to efficiently manage their teaching process in accordance with their aptitude. The teaching reform practice verifies that the AI-enabled big data optimization of teaching reform has a better effect by the above multi-mode innovation. It exhibits an obvious improvement in the quantity and quality of students’ professional knowledge, personal ability, teamwork, and innovative practice. It is also in accordance with the independent completion of practical course teaching in the analysis of big education data. In addition, it realizes high-quality practical teaching by combining multi-mode, multi-level, and open discipline foundations together with efficient, professional skills. Full article

On schematic metro maps, high-quality label placement is helpful to passengers performing route planning and orientation tasks. It has been reported that the artificial neural network (ANN) has the potential to place labels with learned labeling knowledge. However, the previous ANN-based method only considered the effects of station points and their connected edges. Indeed, unconnected but surrounding features (points, edges, and labels) also significantly affect the quality of label placement. To address this, we have proposed an improved method. The relations between label positions and both connected and surrounding features are first modeled based on labeling natural intelligence (i.e., the experience, knowledge, and rules of labeling established by cartographers). Then, ANN is employed to learn such relations. Quantitative evaluations show that our method reaches lower percentages of label–point overlap (0.00%), label–edge overlap (4.12%), and label–label overlap (20.58%) compared to the benchmark (4.17%, 14.29%, and 35.11%, respectively). On the other hand, our method effectively avoids ambiguous labels and ensures labels from the same line are placed on the same side. Qualitative evaluations show that approximately 75% of users prefer our results. This novel method has the potential to advance the automated generation of schematic metro maps. Full article

Explorations of the current attitudes and practices of dietitians regarding the gut microbiota in health are scarce. In this online survey, we assessed the attitudes and practices of dietitians across Europe concerning gut microbiome parameters and the manipulation of the gut microbiota. Pre-graduate dietetic students and other professionals were also invited to participate. The potential interest and preferences of the participants for future educational initiatives about the gut microbiota and the educational resources used were further explored. A total of 179 full responses were recorded (dietitians, n = 155), mainly from the southern and western regions. Most of the participants (>90.0%) believed that probiotics and prebiotics have a place in nutritional practice and that fermented foods with live microbial cultures should be a part of food-based dietary guidelines. A strong belief in the beneficial roles of probiotics and prebiotics in some health situations was also reported among the participants. Most of the dietitians recognised the importance of gut microbiota manipulation and advised the use of probiotics and prebiotics in dietary practice, and they felt quite confident applying the relevant information in their daily practice. Nevertheless, misconceptions were identified, and further guideline-oriented education is necessary. The interest in future e-learning initiatives was high among the participants, and the sources of knowledge, educative formats, and potential areas for further educational efforts were indicated. Full article

The COVID-19 pandemic and the racial justice uprisings of 2020–2022 created an altered and challenging landscape for teaching public health. Challenging and direct experiences with these public health issues and their reverberations shaped how some faculty and many students participated in both online and in-person classrooms. In this project, we conducted a narrative analysis of oral history interviews with eight faculty members at a public university in New York City to understand how they reacted to these events and reconsidered their public health teaching during this period. We map what propelled faculty along paths of change and where these paths led. We learn that participating faculty shifted in varied ways toward more intentional and sometimes more antiracist teaching practices. Two experiences were foundational to these shifts: (1) faculty attunement to student realities during this time, and (2) faculty reflection on their own social positionings (i.e., race, gender identity, sexual orientation, class, age, immigration status, etc.) and their development of critical consciousness. These findings provide insights into how faculty conceptualize, support, and change their teaching approaches during periods of upheaval, particularly in the context of limited institutional support for faculty development. Finally, we discuss key issues for institutions seeking to formalize and enhance shifts like those described. Full article

Climbing gyms aim to continuously improve their offerings and make the best use of their infrastructure to provide a unique experience for their clients, the climbers. One approach to achieve this goal is to track and analyze climbing sessions from the beginning of the ascent until the climber’s descent. Detecting the climber’s descent is crucial because it indicates when the ascent has ended. This paper discusses an approach that preserves climber privacy (e.g., not using cameras) while considering the convenience of climbers and the costs to the gyms. To this aim, a hardware prototype has been developed to collect data using accelerometer sensors attached to a piece of climbing equipment mounted on the wall, called a quickdraw, which connects the climbing rope to the bolt anchors. The sensors are configured to be energy-efficient, making them practical in terms of expenses and time required for replacement when used in large quantities in a climbing gym. This paper describes the hardware specifications, studies data measured by the sensors in ultra-low power mode, detects sensors’ orientation patterns during descent on different routes, and develops a supervised approach to identify lowering. Additionally, the study emphasizes the benefits of multidisciplinary feature engineering, combining domain-specific knowledge with machine learning to enhance performance and simplify implementation. Full article

Object-oriented programming (OOP) has long been a dominant paradigm in software development, but it is not without its challenges. One major issue is the problem of tight coupling between objects, which can hinder flexibility and make it difficult to modify or extend code. Additionally, the complexity of managing inheritance hierarchies can lead to rigid and fragile designs, making it hard to maintain and evolve the software over time. This paper introduces a software development pattern that seeks to offer a renewed approach to writing code in object-oriented (OO) environments. Addressing some of the limitations of the traditional approach, the Puzzle Pattern focuses on extreme modularity, favoring writing code exclusively in building blocks that do not possess a state (e.g., Java interfaces that support concrete methods definitions in interfaces starting from version 8). Concrete classes are subsequently assembled through the implementation of those interfaces, reducing coupling and introducing a new level of flexibility and adaptability in software construction. The highlighted pattern offers significant benefits in software development, promoting extreme modularity through interface-based coding, enhancing adaptability via multiple inheritance, and upholding the SOLID principles, though it may pose challenges such as complexity and a learning curve for teams. Full article

The arising of the Cyber–Physical Systems’ vision and concepts drives technological evolution toward a new architectural design for the infrastructure of an environment referred to as a Smart Environment. This perspective alters the way systems within Smart City landscapes are conceived, designed, and ultimately realized. Modular architecture, resource-sharing techniques, and precise deployment approaches (such as microservices-oriented or reliant on the FaaS paradigm) serve as the cornerstones of a Smart City cognizant of multiple Cyber–Physical Systems composing it. This paper presents a framework integrating Digital Decisioning, encompassing the automated combination of human-derived knowledge and data-derived knowledge (e.g., business rules and machine learning), to enhance decision-making processes and application definition within the Smart City context. Full article

The definition of vehicle viewpoint annotations is ambiguous due to human subjective judgment, which makes the cross-domain vehicle re-identification methods unable to learn the viewpoint invariance features during source domain pre-training. This will further lead to cross-view misalignment in downstream target domain tasks. To solve the above challenges, this paper presents a dual-level viewpoint-learning framework that contains an angle invariance pre-training method and a meta-orientation adaptation learning strategy. The dual-level viewpoint-annotation proposal is first designed to concretely redefine the vehicle viewpoint from two aspects (i.e., angle-level and orientation-level). An angle invariance pre-training method is then proposed to preserve identity similarity and difference across the cross-view; this consists of a part-level pyramidal network and an angle bias metric loss. Under the supervision of angle bias metric loss, the part-level pyramidal network, as the backbone, learns the subtle differences of vehicles from different angle-level viewpoints. Finally, a meta-orientation adaptation learning strategy is designed to extend the generalization ability of the re-identification model to the unseen orientation-level viewpoints. Simultaneously, the proposed meta-learning strategy enforces meta-orientation training and meta-orientation testing according to the orientation-level viewpoints in the target domain. Extensive experiments on public vehicle re-identification datasets demonstrate that the proposed method combines the redefined dual-level viewpoint-information and significantly outperforms other state-of-the-art methods in alleviating viewpoint variations. Full article

Face recognition has been well studied under visible light and infrared (IR) in both intra-spectral and cross-spectral cases. However, how to fuse different light bands for face recognition, i.e., hyperspectral face recognition, is still an open research problem, which has the advantages of richer information retention and all-weather functionality over single-band face recognition. Thus, in this research, we revisit the hyperspectral recognition problem and provide a deep learning-based approach. A new fusion model (named HyperFace) is proposed to address this problem. The proposed model features a pre-fusion scheme, a Siamese encoder with bi-scope residual dense learning, a feedback-style decoder, and a recognition-oriented composite loss function. Experiments demonstrate that our method yields a much higher recognition rate than face recognition using only visible light or IR data. Moreover, our fusion model is shown to be superior to other general-purpose image fusion methods that are either traditional or deep learning-based, including state-of-the-art methods, in terms of both image quality and recognition performance. Full article

Human activity recognition (HAR) technology enables continuous behavior monitoring, which is particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR (i.e., DeepConvLSTM and ConvTransformer), were employed for classification. The results demonstrate the ear sensor’s efficacy, with deep learning models achieving a 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear’s efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital sign monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes. Full article

The article suggests a Value Proposition (VP) framework that enables analysis of the beneficial impact of Artificial Intelligence (AI) resources and capabilities on specific VP activities. To develop such a framework, we examined existing business and management research publications to identify and extract assertions that could be used as a source of actionable insights for early-stage growth-oriented companies. The extracted assertions were assembled into a corpus of texts that was subjected to topic modelling analysis—a machine learning approach to natural language processing that is used to identify latent themes in large corpora of text documents. The topic modelling resulted in the identification of seven topics. Each topic is defined by a set of most frequent words co-occurring in a distinctive subset of texts that could be interpreted in terms of activities constituting the core elements of the VP framework. We then examined each activity in terms of its potential to be enhanced by employing AI resources and capabilities. The interpretation of the topic modelling results led to the identification of seven topics: (1) Value created; (2) Stakeholder value propositions; (3) Foreign market entry; (4) Customer base; (5) Continuous improvement; (6) Cross-border operations; and (7) Company image. The uniqueness of the adopted topic modelling approach consists in the quality of the assertions and the interpretation of the seven topics as an activity framework, i.e., in its capacity to generate actionable insights for practitioners. The additional analysis suggests that there is a potential for AI to enhance the emerging four core elements of the VP framework: Value created, Stakeholder value propositions, Foreign market entry, and Customer base. More importantly, we found that the greatest number of assertions related to activities that could be enhanced by AI are part of the Customer base topic, i.e., the topic that is most directly related to the growth potential of the companies. In addition, the VP framework suggests that a company’s customer base growth is continuously enhanced through a positive loop enabled by activities focused on the Continuous improvement of the activities and the amount of Value created, the alignment of Stakeholder value propositions, and companies’ Foreign market entry. Thus, the multiple-stakeholder perspective on VP development and foreign market entry appears as a factor that helps in understanding the beneficial impact of AI on the enhancement of the VP of early-stage growth-oriented companies. Full article

A local area configures a socio-economic system in which several institutions interact. As stakeholders hold different values and perhaps conflicting interests, managing local area performance is a dynamic and complex issue. In these inter-institutional settings, performance management may help address such complexity. Traditional performance management approaches, mostly based on static and linear analysis, fail to capture the dynamic complexity of local-area performance, bounding decision-makers’ mindsets to an organizational view of performance. Overcoming such limitations requires methods oriented to grasp a better understanding of the social reality in which their institutions operate. This contribution aims to illustrate how the Dynamic Performance Management (DPM) approach may foster a “performative” use of performance information by decision-makers in inter-institutional settings. To this end, the article highlights the importance of designing conducive learning settings (i.e., action research enhanced by a system dynamics-based interactive learning environment) to support decision-makers make such a cognitive leap. Drawing from empirical evidence on destination governance studies, the article shows that enriching performance management with system dynamics modeling may help decision-makers to reflect on key issues impacting local area development, sparking a discussion on potential actions to balance economic, social, and competitive dimensions of performance. Findings reveal that DPM insight modeling holds explanatory and communicative potential in real forums by providing decision-makers with an understanding of the means-end relationships linking strategic resources to outcomes through value drivers. The use of such performance information can help local area stakeholders to (re)conceptualize the social reality in which their institutions operate. By acting as a “maieutic machine”, DPM fosters a shift from an organizational and static to an inter-organizational and dynamic view of local area performance. Implications of the study include the opportunity to provide training to strengthen the active use of performance information by decision-makers in inter-institutional settings. Full article

The ongoing COVID-19 pandemic has had a significant impact globally, and the understanding of the disease’s clinical features and impacts remains insufficient. An important metric to evaluate the severity of pneumonia in COVID-19 is the CT Involvement Score (CTIS), which is determined by assessing the proportion of infections in the lung field region using computed tomography (CT) images. Interactive augmented reality visualization and quantification of COVID-19 infection from CT allow us to augment the traditional diagnostic techniques and current COVID-19 treatment strategies. Thus, in this paper, we present a system that combines augmented reality (AR) hardware, specifically the Microsoft HoloLens, with deep learning algorithms in a user-oriented pipeline to provide medical staff with an intuitive 3D augmented reality visualization of COVID-19 infections in the lungs. The proposed system includes a graph-based pyramid global context reasoning module to segment COVID-19-infected lung regions, which can then be visualized using the HoloLens AR headset. Through segmentation, we can quantitatively evaluate and intuitively visualize which part of the lung is infected. In addition, by evaluating the infection status in each lobe quantitatively, it is possible to assess the infection severity. We also implemented Spectator View and Sharing a Scene functions into the proposed system, which enable medical staff to present the AR content to a wider audience, e.g., radiologists. By providing a 3D perception of the complexity of COVID-19, the augmented reality visualization generated by the proposed system offers an immersive experience in an interactive and cooperative 3D approach. We expect that this will facilitate a better understanding of CT-guided COVID-19 diagnosis and treatment, as well as improved patient outcomes. Full article

Anorexia nervosa is associated with impaired cognitive flexibility and central coherence, i.e., the ability to provide an overview of complex information. Therefore, the aim of the present study was to evaluate EEG features elicited from patients with anorexia nervosa and healthy controls during mental tasks (valid and invalid Aristotelian syllogisms and paradoxes). Particularly, we examined the combination of the most significant syllogisms with selected features (relative power of the time–frequency domain and wavelet-estimated EEG-specific waves, Higuchi fractal dimension (HFD), and information-oriented approximate entropy (AppEn)). We found that alpha, beta, gamma, theta waves, and AppEn are the most suitable measures, which, when combined with specific syllogisms, form a powerful tool for efficiently classifying healthy subjects and patients with AN. We assessed the performance of triadic combinations of “feature–classifier–syllogism” via machine learning techniques in correctly classifying new subjects in these two groups. The following triads attain the best classifications: (a) “AppEn-invalid-ensemble BT classifier” (accuracy 83.3%), (b) “Higuchi FD-valid-linear discriminant” (accuracy 75%), (c) “alpha amplitude-valid-SVM” (accuracy 83.3%), (d) “alpha RP-paradox-ensemble BT” (accuracy 85%), (e) “beta RP-valid-ensemble” (accuracy 85%), (f) “gamma RP-valid-SVM” (accuracy 85%), and (g) “theta RP-valid-KNN” (accuracy 80%). Our findings suggest that anorexia nervosa has a specific information-processing style across reasoning tasks in the brain as measured via EEG activity. Our findings also contribute to further supporting the view that entropy-oriented, i.e., information-based features (the AppEn measure used in this study) are promising diagnostic tools (biomarkers) in clinical applications related to medical classification problems. Furthermore, the main EEG-specific frequency waves are extremely enhanced and become powerful classification tools when combined with Aristotle’s syllogisms. Full article