Boundary-and-object collaborative learning network for camouflaged object detection

BackgroundAdequate implementation of work-related stress management interventions can reduce or prevent work-related stress and sick leave in organizations. We developed a multifaceted integral stress-prevention strategy for organizations from several sectors that includes a digital platform and collaborative learning network. The digital platform contains a stepwise protocol to implement work-related stress-management interventions. It includes stress screeners, interventions and intervention providers to facilitate access to and the selection of matching work-related stress-management interventions. The collaborative learning network, including stakeholders from various organizations, plans meetings focussing on an exchange of experiences and good practices among organizations for the implementation of stress prevention measures. This paper describes the design of an integral stress-prevention strategy, Stress Prevention@Work, and the protocol for the evaluation of: 1) the effects of the strategy on perceived stress and work-related outcomes, and 2) the barriers and facilitators for implementation of the strategy.MethodsThe effectiveness of Stress Prevention@Work will be evaluated in a cluster controlled trial, in a large healthcare organization in the Netherlands, at six and 12 months. An independent researcher will match teams on working conditions and size and allocate the teams to the intervention or control group. Teams in the intervention group will be offered Stress Prevention@Work. For each intervention team, one employee is responsible for applying the strategy within his/her team using the digital platform and visiting the collaborative learning network. Using a waiting list design, the control group will be given access to the strategy after 12 months. The primary outcome is the employees’ perceived stress measured by the stress subscale of the Depression, Anxiety, and Stress Scale (DASS-21). Secondary outcome measures are job demands, job resources and the number of preventive stress measures implemented at the team level. Alongside the trial, a process evaluation, including barriers and facilitators of the implementation of Stress Prevention@Work, will be conducted in one healthcare organisation.DiscussionIf Stress Prevention@Work is found to be effective in one healthcare organisation, further implementation on a broader scale might lead to increased productivity and decreased stress and sick leave in other organizations. Results are expected in 2018.Trial registrationNTR5527. Registered 7 Dec 2015.

This study proposes an easy-to-use model for firms to create their own values. It explores the importance of learning activities, collaborative networks, knowledge value creation and their interrelationships. This paper also identifies the interactive process between capital accumulation and value creation. It explains how utilizing flexible human capital and integrated social capital through collaborative relationship networks, learning activities can accumulate intellectual capital and bring important knowledge value to an enterprise above its financial book value. A firm with the model will create its competitive values, by identifying the learning activities for critical knowledge and utilizing its relationship networks to enhance the learning effectiveness.

This paper proposes a facial attributes learning algorithm with deep convolutional neural networks (CNN). Instead of jointly predicting all the facial attributes (40 attributes in our case) with a shared CNN feature extraction hierarchy, we cluster the facial attributes into groups and the CNN only shares features within each group in later feature extraction stages to jointly predicts the attributes in each group respectively. This paper also proposes a simple yet effective attribute clustering algorithm, based on the observation that some attributes are more collaborated (their prediction accuracy improve more when jointly learned) than others, and the proposed deep network is referred to as the collaborative learning network. Contrary to the previous state-of-the-art facial attribute recognition methods which require pre-training on external datasets, the proposed collaborative learning network is trained for attribute recognition from scratch without external data while achieving the best attribute recognition accuracy on the challenging CelebA dataset and the second best on the LFW dataset.

Collaborative learning can effectively improve the learning efficiency of learners rather individual learning. Activity theory is a theoretical framework for analyzing human practices as developmental processes with both individual and social levels interlinked at the same time. Learn is a activity, in this paper, we analyze the composition of collaborative network learning activity including goals, subjects, objects , tools and rules. finally ,We design a multi-agent system for collaborative network learning , structure and function of every type agent are discussed.

This work presents a report on research carried out in the field or networked collaborative learning. ln particular, we present a theory/model-based approach applied to a distance education course that is developed and caught in a virtual learning environment. In this educational practice, our objective has been twofold: first, to improve distance teaching and learning, and second, to facilitate social interaction among students and between tutor and students via the Web. To that end, our research approach has been based on the following actions: First, we analysed the goals, needs. expectations and preferences of our students. based on a previous pilot experience on distance collaborative learning, in order to understand what is actually happening in networked learning when collaboration becomes an integrated part or the whole learning process. Second, we proceeded to the design, development and implementation of a new pedagogical practice, called Virtual Study Group, to encourage and enhance learning through collaborative construction of knowledge and reflective interaction which contribute to a deeper understanding or the course contents. The paper focuses mainly on the latter. Finally, we provide a critical analysis and evaluation or the outcomes of this experience and of the many issues arising from applying this collaborative pedagogical practice to a virtual learning environment.

PurposeSelf-directed learning (SDL) has been advocated for effective training of final-year health professions students. COVID-19 challenges conventional teaching, learning, and assessment in the clinical environment. This study aimed to identify and explore enablers and barriers to SDL among final-year health professions students training during the COVID-19 pandemic.MethodsAdopting the SWOT (strengths, weaknesses, opportunities and threats) framework, this study explored the clinical learning and training experiences of final-year health professions students during the pandemic. A survey was conducted via online platforms. Data from 155 respondents were thematically analyzed.ResultsPersonal attributes such as reflection, self-determination, motivation, resilience, and positive learning behaviors and skills were identified as SDL enablers. Collaborative learning networks and online learning platforms facilitate learning needs and goals. Fear and anxiety, untrusted learning sites, uncertainty about graduation, financial issues and challenges in the learning environments were the major themes related to barriers to SDL.ConclusionThe importance of SDL as a skill for uncertain times warrants further investigation in the training of future healthcare professionals. Inclusive planning and engagement with final-year health professions students to address identified stressors, as well as the creation of shared platforms where students are part of the decision-making processes for clinical learning and training are recommended. Responsive curricula that optimize unpredictable disruptions in clinical training are needed to equip students to diagnose their own learning needs and implement appropriate learning strategies.

Professional development (PD) for teachers in social sciences is crucial for enhancing pedagogical skills, updating content knowledge, and fostering innovative teaching practices in an ever-evolving educational landscape. This research paper explores the multifaceted dimensions of PD and teacher education in social sciences, drawing on a synthesis of existing literature, empirical studies, and contemporary practices. The study examines key components such as content-specific PD, the role of technology, collaborative learning networks, and barriers to effective implementation. Utilizing a systematic literature review methodology, data from 35 scholarly sources, including journal articles, reports, and professional guidelines, were analyzed. Findings reveal that effective PD programs in social sciences lead to improved teacher efficacy, student engagement, and learning outcomes, with quantitative data indicating an average 15-20% increase in teacher confidence post-intervention. Tables and figures illustrate trends in PD participation rates and impact metrics. The discussion highlights implications for policy, recommending integrated, ongoing PD models tailored to social sciences educators. This paper contributes to the discourse on teacher education by proposing a framework for sustainable PD that aligns with global standards, such as those from the National Council for the Social Studies (NCSS). Keywords: Teacher Professional Development, Social Sciences Education, Pedagogical Innovation, Teacher Efficacy, Content-Specific Training.

Moving object detection and tracking have various applications, including surveillance, anomaly detection, vehicle navigation, etc. The literature on object detection and tracking is rich enough, and there exist several essential survey papers. However, the research on camouflage object detection and tracking is limited due to the complexity of the problem. Existing work on this problem has been done based on either biological characteristics of the camouflaged objects or computer vision techniques. In this paper, we review the existing camouflaged object detection and tracking techniques using computer vision algorithms from the theoretical point of view. This paper also addresses several issues of interest as well as future research direction in this area. We hope this paper will help the reader to learn the recent advances in camouflaged object detection and tracking.

A systematic review of image-level camouflaged object detection with deep learning

This chapter is a checklist of what organisational decision-makers need to do now to prepare future leaders to be effective in industry 4.0. Regarding structure, decision-makers need to remodel their organisation using ecosytem theory by eliminating operant conditioning and cultivating internal and external collaborative networks. The organisational structure needs to reinforce the leadership system. Regarding connections, departments need to be wired together to embrace the coming diversity of multiple stakeholders and human/machine resources. Data science, collaborative networks, and data infrastructure needs to be at the core of the organisation. Regarding mindset, a shift is required from horizontal to vertical development where leaders need to abandon dependent models and learn how to be responsive and sensemaking connectors. Self-directed and technology-based learning is set to overtake more traditional methods. Traditional roles such as LD advisors are set to change to become network enablers who support and stimulate self-directed leadership learning through learning management systems and networks. The chapter proposes a practical two-speed approach to change which focuses on eradicating conditioning structures, creating swarms and collaborative networks, merging divisions, and managing resources.

This article studies the learning and capability formation of a biotechnology firm by analysing its development path, which is composed of successive product development and innovation processes. In collaborative product development work, network collaboration, as well as the acquisition of new competences and learning, evolves simultaneously and interactively. Searching for and encountering partners with complementary knowledge and resources is important in the emergence of new product-development processes. To improve understanding of this path formation, the article draws on cultural—historical activity theory, science and technology studies and the epistemology of things and effects. These all underline the significance of material artefacts for learning and activity. The epistemology of things and effects addresses the knowledge of how things work in experimental systems and products. Enzymes, proteins and instruments are put to work in a stabilized way as parts of new products. The effects so mastered entail the functional qualities that make products competitive in the marketplace.

Camouflaged object detection (COD), in a broad sense, aims to detect image objects that have high degrees of similarity to the background. COD is more challenging than conventional object detection because of the high degree of “fusion” between a camouflaged object and the background. In this paper, we focused on the accurate detection of camouflaged objects, conducting an in-depth study on COD and addressing the common detection problems of high miss rates and low confidence levels. We proposed a ternary cascade perception-based method for detecting camouflaged objects and constructed a cascade perception network (CPNet). The innovation lies in the proposed ternary cascade perception module (TCPM), which focuses on extracting the relationship information between features and the spatial information of the camouflaged target and the location information of key points. In addition, a cascade aggregation pyramid (CAP) and a joint loss function have been proposed to recognize camouflaged objects accurately. We conducted comprehensive experiments on the COD10K dataset and compared our proposed approach with other seventeen-object detection models. The experimental results showed that CPNet achieves optimal results in terms of six evaluation metrics, including an average precision (AP)50 that reaches 91.41, an AP75 that improves to 73.04, and significantly higher detection accuracy and confidence.

Traditional knowledge distillation uses a two-stage training strategy to transfer knowledge from a high-capacity teacher model to a compact student model, which relies heavily on the pre-trained teacher. Recent online knowledge distillation alleviates this limitation by collaborative learning, mutual learning and online ensembling, following a one-stage end-to-end training fashion. However, collaborative learning and mutual learning fail to construct an online high-capacity teacher, whilst online ensembling ignores the collaboration among branches and its logit summation impedes the further optimisation of the ensemble teacher. In this work, we propose a novel Peer Collaborative Learning method for online knowledge distillation, which integrates online ensembling and network collaboration into a unified framework. Specifically, given a target network, we construct a multi-branch network for training, in which each branch is called a peer. We perform random augmentation multiple times on the inputs to peers and assemble feature representations outputted from peers with an additional classifier as the peer ensemble teacher. This helps to transfer knowledge from a high-capacity teacher to peers, and in turn further optimises the ensemble teacher. Meanwhile, we employ the temporal mean model of each peer as the peer mean teacher to collaboratively transfer knowledge among peers, which helps each peer to learn richer knowledge and facilitates to optimise a more stable model with better generalisation. Extensive experiments on CIFAR-10, CIFAR-100 and ImageNet show that the proposed method significantly improves the generalisation of various backbone networks and outperforms the state-of-the-art methods.

ABSTRACTPurpose: We investigate how the structural conditions of eight different European agricultural innovation systems can facilitate or hinder collaboration and social learning in multidisciplinary innovation networks.Methodology: We have adapted the Innovation System Failure Matrix to investigate the main barriers and enablers eight countries (England, France, Germany, Hungary, Italy, Latvia, The Netherlands and Switzerland).Findings: Results show some of the recent trends the AKS actors in these countries have experienced and how these have affected their potential to act as collaborators in multidisciplinary innovation networks. Lack of funds, combined with horizontal and vertical fragmentation and the lack of proper evaluation criteria for collaborative innovation networks are among the most important threats we found.Practical Implications: This study shows that each national AIS has some unique features. This means that the implementation of policies promoting collaboration and social learning (e.g. the European Innovation Partnerships and Operational Groups) should depend on a critical reflection of the existing structural elements of the AIS in each country and whether there is a need for inclusion of new actors, or whether certain innovations for collective goods should be promoted.Originality: The paper contributes to the ongoing discussion in the scientific literature on the advantages and disadvantages of privatization of extension and advisory services and the shift from thinking in terms of the traditional Agricultural Knowledge System towards a broader Agricultural Innovation System.

Graph-Mamba Collaborative Learning Network for Camouflaged Object Detection