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This chapter examines the interplay between human resource management theory and human resource management practice. It advocates that effective human resource management practice and theory are intrinsically intertwined, and this indispensable link ought to be central to the pedagogy of management research methods. Through greater embeddedness of the institutional and societal context in research method teaching, students can develop as scholars who understand their roles as facilitators of dialogue between researchers and a significant part of their audience (practitioners). The chapter conceptualizes this perspective as a collaborative model in human resource management research, which then must hold centrality in the teaching of research methods in our university and college classrooms. [ABSTRACT FROM AUTHOR]

The purpose of this nonrandomized study was to compare several attributes of hereditary cancer risk assessment using a collaborative model of service delivery. Arm 1 included patients seen in‐person by a board‐certified genetic counselor (CGC), Arm 2 included high‐complexity triaged patients from distant sites who received telegenetics with a CGC, and Arm 3 included low‐complexity triaged patients from distant sites who had in‐person risk assessment with a locally placed genetic counselor extender (GCE). A total of 152 patients consented and 98 had complete data available for analysis (35 in Arm 1, 33 in Arm 2, and 30 in Arm 3). The three groups were comparable in age, ethnicity, education, employment, and cancer status. There was no significant difference in median wait time or distance traveled to receive care across all three arms. However, if patients in Arms 2 and 3 had to access the CGC in‐person, they would have had to travel significantly further (p < 0.0001). The time spent in a session was significantly longer in Arm 3 with a GCE than with a CGC in‐person or by telegenetics (p < 0.01). There was no difference in the number of essential elements covered in the appointment, change in cancer worry, or appointment satisfaction across all three arms, although the sample size was small. Employing a collaborative model of service delivery with GCEs and telegenetics is feasible, satisfactory to patients and reduces the distance patients travel to access hereditary cancer genetic services. [ABSTRACT FROM AUTHOR]

This study inquires into how students in primary education teaching degrees professionally view teaching–learning situations during their practicum period at schools. In particular, it examines how they professionally view aspects of the teaching models they observe in the classroom. Using a qualitative approach, we analyse the narratives written by the teachers-in-training in which they had to identify and describe teaching–learning situations related to the mathematics competence and the linguistic communication competence. To focus the gaze of the teachers-in-training on the teaching models, they were given several guiding questions. The results show that in their narratives, the students highlight relevant aspects of the teaching models in the situations they observed. However, the changes they proposed show that they focus on the mathematics or language content without taking changes in the aspects of the teaching models observed into account. Therefore, they do not observe changes aimed at including new strategies or resources which resemble the characteristics of a more collaborative model common to learning communities. The main conclusions suggest taking decisions in pre-service teacher training towards a more collaborative model based on participative, dialogic and active collaboration in the educational process. [ABSTRACT FROM AUTHOR]

This chapter examines the interplay between human resource management theory and human resource management practice. It advocates that effective human resource management practice and theory are intrinsically intertwined, and this indispensable link ought to be central to the pedagogy of management research methods. Through greater embeddedness of the institutional and societal context in research method teaching, students can develop as scholars who understand their roles as facilitators of dialogue between researchers and a significant part of their audience (practitioners). The chapter conceptualizes this perspective as a collaborative model in human resource management research, which then must hold centrality in the teaching of research methods in our university and college classrooms.

The optimization efforts of promotion, prevention, provision of preventive treatment, and infection control are strategies to overcome tuberculosis in Indonesia for the period 2020-2024. This research aims to analyze the effects of a collaborative model between health workers and family support to improve the success of treating patients with pulmonary tuberculosis. This is an experimental research using a one-group pre and post-test design. The study samples were TB patients treated at Bainamu and Bontosunggu Public Health Centers, Jeneponto Regency, from June 2021 to January 2022. Using McNemar's statistical test, the data analysis was conducted to determine the differences in patient behavior on factors that support and inhibit the recovery. The results showed that the collaborative model has an impact on eating, healthy living, and spiritual behaviors that support the recovery and cure rate of TB patients. [ABSTRACT FROM AUTHOR]

The present quantitative, non-experimental comparative study, delves into the long-term effects of the collaborative practicum model (specifically the "academy-class" model) on novice teachers. The research aims to discern disparities in the professional self-efficacy of novice educators who underwent training within the collaborative practicum model as opposed to those who adhered to the conventional teaching model. This comparative analysis is based on three variables: perception of the teaching profession, professional self-efficacy, and socio-economic security. Furthermore, the study examines whether the collaborative model contributes to cultivating more favorable attitudes toward the teaching profession and a greater inclination to continue teaching for an extended period exceeding three years. The study encompasses a cohort of 436 Bachelor of Education (B.Ed.) graduates from 22 Israeli Higher Education Institutions who completed their degrees within the past five years. The research findings underscore a higher level of teaching efficacy, socio-economic security, and a more positive outlook among those who participated in the collaborative practicum and expressed their intent to persist in the teaching profession. These outcomes underscore the vital role of the collaborative practicum model, hinting at its potential to positively influence the retention rate within the teaching profession. Furthermore, it underscores the crucial connection between comprehensive and meaningful training within a collaborative practicum framework and the sustainable professional growth of educators. This robust training approach can potentially secure the continued presence of dedicated and enthusiastic educators in the field over the long term. [ABSTRACT FROM AUTHOR]

The study reported here aimed to explore the Intervention strategies for challenges experienced when supporting learners from child-headed households in secondary schools. Children from child-headed households experience challenges that hinder them from receiving good quality education. It is, therefore important for schools to ensure that classroom teachers are aware of the challenges experienced by children from child-headed households. This study followed the qualitative approach to explore the intervention strategies to be used. The study consisted of 20 participants (principals, Departmental Heads, teachers and School Governing Bodies) in South Africa. Data were collected through individual semi-structured interviews and document analysis. The Bottom-up theory was the underpinning theory for our study. Thematical analysis was followed. This study revealed that children from child-headed households were not identified at schools. Therefore, schools do not have a database of children from child-headed households hence a lack of intervention strategies. We recommended that schools should establish the School Based Support Team and follow the collaborative model as an intervention to support children from child-headed households. The study provided insights on intervention strategies that schools and the Department of Education should implement for challenges experienced by schools when supporting learners from child-headed households.

This practice report describes the design and implementation of a collaborative and virtual exchange-embedded virtual summer school for postgraduate students in the context of European Higher Education. The two-week virtual summer school on multilingualism and intercultural learning brought together 40 students who collaborated to produce ten video-case studies based on their experiences as educators in a number of contexts. We share our experiences as organisers and participants and offer insights from the process of planning for, engaging with and evaluating the summer school. Our paper draws on quantitative and qualitative data, i.e., a pre and post summer school survey, a qualitative evaluation questionnaire and a student-participant account to evidence how the experience benefitted participants. Our findings demonstrate the effectiveness of the model presented here: participants’ understanding of key concepts of interculturality and multilingualism increased, there was a statistically significant difference in perceived development of digital competences and presentation skills, and more than 90% of the participants were very satisfied with their training and international collaboration opportunities. In conclusion, we propose a set of learning points that may support others to design and deliver a collaborative model of summer school and the use of ‘skills passports’ in alternative to accreditation.

Universities often struggle to translate theory of inclusive education into real life practices for pre-service teachers (PSTs). In this paper, we evaluate an innovative partnership between Australian secondary school teachers and university academics where they co-designed and co-taught an undergraduate inclusive education course. We used a quasi-experimental design where 67 PSTs were grouped into two cohorts: one co-taught by the team of school educators and university academics (n = 26), the other taught only by university academics (n = 41). PSTs' scores on teaching efficacy, attitudes and intentions to teach in inclusive classrooms were measured at the pre and posttest stages of the course. Our evaluation showed significant increases in positive attitudes and teaching efficacy skills, decreased concerns and higher intentions to teach in inclusive classrooms among PSTs who were co-taught. The co-teaching team also identified various professional and personal benefits of collaborating. We further discuss the implications of our research for teacher education programmes and the usefulness of including school educators' expertise in bridging the gaps between theory and practice. [ABSTRACT FROM AUTHOR]

To evaluate the impact of a collaborative screening campaign on the prevalence of pre-diabetes and diabetes among the screened population. A Longitudinal, multicentre study was developed. The Finnish Diabetes Risk Score (FINDRISC) was applied to the eligible population in the participating community pharmacies. Individuals with a FINDRISC score ≥ 15, were eligible to measure their glycated haemoglobin (HbA1c) level at the community pharmacy. If HbA1c≥ 5.7%, participants were referred to a general practitioner (GP) appointment for potential diagnosis of Diabetes. Out of 909 screened subjects, 405 (44.6%) presented a FINDRISC score ≥ 15. Among the latter, 94 (23.4%) had HbA1c levels that made them eligible for GP referral, of which 35 (37.2%) completed the scheduled appointments. 24 participants were diagnosed with pre-diabetes, and 11 with diabetes. The prevalence was estimated at 2.5% (CI95% 1.6–3.8%) and 7.8% (CI95% 6.2–9.8%) for diabetes and pre-diabetes, respectively. This collaborative model has proved to be effective in the early detection of diabetes and pre-diabetes. Joint initiatives between health professionals can play a pivotal role in the prevention and diagnosis of diabetes, which may lead to a reduction on the burden to health system and society. • 45% of subjects screened had a high risk of developing diabetes in 10 years. • Over half of the subjects referred to the physician did not attend the appointment. • The prevalence of undiagnosed diabetes was estimated at 2.5% (CI95% 1.6–3.8%). [ABSTRACT FROM AUTHOR]

Due to laws, regulations, privacy, etc., between 70-90 percent of providers do not share medical data, forming a "data island". It is essential to collaborate across multiple institutions without sharing patient data. Most existing methods adopt distributed learning and centralized federal architecture to solve this problem, but there are problems of resource heterogeneity and data heterogeneity in the practical application process. This paper proposes a collaborative deep learning modelling method based on the blockchain network. The training process uses encryption parameters to replace the original remote source data transmission to protect privacy. Hyperledger Fabric blockchain is adopted to realize that the parties are not restricted by the third-party authoritative verification end. To a certain extent, the distrust and single point of failure caused by the centralized system are avoided. The aggregation algorithm uses the FedProx algorithm to solve the problem of device heterogeneity and data heterogeneity. The experiments show that the maximum improvement of segmentation accuracy in the collaborative training mode proposed in this paper is 11.179% compared to local training. In the sequential training mode, the average accuracy improvement is greater than 7%. In the parallel training mode, the average accuracy improvement is greater than 8%. The experimental results show that the model proposed in this paper can solve the current problem of centralized modelling of multicenter data. In particular, it provides ideas to solve privacy protection and break "data silos", and protects all data. [ABSTRACT FROM AUTHOR]

The present study examined the role of hedges in a referential communication task. Pairs of participants received an identical set of cards, each card displaying a geometric configuration (a "tangram"). One participant, the director, instructed their partner, the matcher, to reproduce a series of predetermined tangram sequences using their own cards. Directors sometimes included a hedge in their description of a tangram (e.g., "looks kinda like an eagle"), and more so the first time than on subsequent mentions. The present study tested the hypothesis that, by revealing their uncertainty regarding the adequacy of their description in conveying the intended meaning, directors signal a possible difficulty in establishing reference. This in turn prompts their addressee to display, rather than merely assert, their understanding (by presenting a description for the tangram the matcher believes the director is referring to, for the director to evaluate). Analyses of matchers' responses to descriptions that directors had hedged or not confirmed the hypothesis. This finding supports the view that conversational partners work together to reach the mutual belief that they have coordinated what the speaker means and what their addressee takes them to mean. Conversational partners expend more joint effort when they deem the risk of misunderstanding to be high than when it is perceived to be low. [ABSTRACT FROM AUTHOR]

The present quantitative, non-experimental comparative study, delves into the long-term effects of the collaborative practicum model (specifically the “academy-class” model) on novice teachers. The research aims to discern disparities in the professional self-efficacy of novice educators who underwent training within the collaborative practicum model as opposed to those who adhered to the conventional teaching model. This comparative analysis is based on three variables: perception of the teaching profession, professional self-efficacy, and socio-economic security. Furthermore, the study examines whether the collaborative model contributes to cultivating more favorable attitudes toward the teaching profession and a greater inclination to continue teaching for an extended period exceeding three years. The study encompasses a cohort of 436 Bachelor of Education (B.Ed.) graduates from 22 Israeli Higher Education Institutions who completed their degrees within the past five years. The research findings underscore a higher level of teaching efficacy, socio-economic security, and a more positive outlook among those who participated in the collaborative practicum and expressed their intent to persist in the teaching profession. These outcomes underscore the vital role of the collaborative practicum model, hinting at its potential to positively influence the retention rate within the teaching profession. Furthermore, it underscores the crucial connection between comprehensive and meaningful training within a collaborative practicum framework and the sustainable professional growth of educators. This robust training approach can potentially secure the continued presence of dedicated and enthusiastic educators in the field over the long term.

Observing the teaching-learning situations that arise in educational contexts is a key part of teacher education. The present study examined how forty-three future teachers constructed their professional knowledge. Adopting a qualitative approach, we discuss the teaching situations observed by pre-service teachers in the subjects of reading/writing and mathematics. Based on the results, educational interactions were far from representing collaborative and dialogic teaching/learning models. In addition, the students’ narratives lacked critical professional reflection. Thus, they did not provide support to possible alterations to the instructional design they observed, and thus failed to make them more participatory. To conclude, we believe that pre-service teacher training should promote a more collaborative model, in which critical and reflective skills are developed based on active student participation. The present study showed that teacher training needs to be redesigned: skills and strategies proper to professional teaching competence need to be emphasised so that pre-service teachers acquire them more significantly during their training.

Innovation of individual learning programs for children with special needs (ABK) in the middle of Emergency Remote Teaching (ERT) is necessary, especially for children with special needs. The purpose of this study was to describe the implementation of individual distance learning programs for children with special needs in inclusive schools during the pandemic. This is qualitative research with a phenomenological approach. Data collection techniques using observation sheets, interviews, and documentation. Data validation is done by source triangulation and technique triangulation. While the data analysis techniques use the theory of Miles and Huberman. The results of this study show the design of distance learning programs made by teachers to learn ABK in inclusive schools, namely with a collaborative model: (flexibility), (modification), (support), and with a 5M strategy. This collaboration Model and design help teachers and students in implementing the learning of children with special needs during the pandemic. In line with that, it is necessary to modify the individual learning program for children with special needs in inclusive schools the learning model that allows it to be used for learning for children with special needs during the pandemic is the 5M learning model.

With a renewed global scientific and technological revolution and industrial reform, the digital economy, with data resources as the key element, has rapidly developed. This study proposes a data-driven measurement and evaluation method to promote the coordinated development of the digital economy and logistics industry. An evaluation index system is constructed, which comprehensively considers the index dimensions that reflect the development level of the digital economy and logistics industry. A Z-score standardisation method is applied to data processing, to carry out dimensionless standardisation processing of the original index data. A collaborative degree model is constructed to evaluate the collaborative development level of the digital economy and logistics industry composite system. We demonstrate the implementation process of these models using data from Anhui province from 2013 to 2020. The results verify the feasibility of the research method and emphasise that the development level of the composite system of the digital economy and logistics industry in Anhui province shows a fluctuating growth trend, with variations between the types and degrees of collaboration policy; suggestions are made accordingly. This study provides theoretical and methodological support for the coordinated development of the regional digital economy and logistics industry. [ABSTRACT FROM AUTHOR]

Supplemental Digital Content is Available in the Text. Introduction.: A clinical collaborative learning experience (CCLE) is a viable clinical education model in physical therapy (PT). The purposes of this study were to 1) examine factors perceived to influence a successful CCLE, and 2) examine processes that make it successful. Review of Literature.: Factors contributing to a successful CCLE, including teaching strategies, student characteristics, and environment, have been reported. The processes that occur during a CCLE that influence its success have not been studied. Subjects.: Twenty-nine students and 18 clinical instructors (CIs) involved in CCLEs. Methods.: Using a grounded theory approach, phase 1 examined perceived factors contributing to efficacy of the CCLE through surveys and focus groups. Phase 2 examined the processes involved in team building, leading to an effective CCLE through journals and focus groups. Results.: Student and clinic factors were perceived to affect the success of the CCLE. The evolution of an effective team that depended on effective communication was deemed the cornerstone of a successful CCLE. The student strategies of self-reflection, idea sharing, and active observation along with clinic-driven strategies of role assignment and a positive learning environment fostered team development. All students in the CCLE were successful as measured by the Clinical Performance Instrument (full-time experiences) and the integrated clinical experience evaluation supporting the trustworthiness of the qualitative findings. Discussion and Conclusion.: These findings expand the considerations for an effective CCLE and provide a basis for understanding the processes involved in a successful CCLE. This process is focused on developing an effective student–CI team. Preparation and training of students and CIs in the importance of team development and strategies to achieve this are warranted. The CCLE may address the shortage of clinical placements in PT and enhance the learning that occurs in PT clinical education. [ABSTRACT FROM AUTHOR]

Background: Neurocritical care (NCC) and neuropalliative care (NPC) clinicians provide care in specialized intensive care units (ICU). There is a paucity of data regarding the impact of NCC and NPC collaboration in smaller, community-focused settings. Objective: To determine the clinical impact of introducing a NCC/NPC collaborative model in a mixed ICU community-based teaching hospital. Design: Retrospective pre/post cohort study. Subjects: Patients ≥18 years of age admitted to the ICU who received neurology and palliative care consultations between September 1, 2015 and August 31, 2017 at a 300 bed community-focused hospital were included. Intervention: The addition of a NCC/NPC collaborative model took place in September of 2016. The time periods before (9/1/2015 to 8/31/2016) and after (9/1/2016 to 8/31/2017) the addition were compared. Results: A total of 274 admissions (pre: 130, post: 144) were included. There were significantly more NCC consultations provided in the post-period (44.6% vs 57.6%; P =.03). NPC consultation increased (55.4% vs 66.7%; P =.056) Median LOS was significantly shorter after implementation of the collaborative model (11 vs 8 days; P =.01). Median ICU LOS was also shorter by 1 ICU-day in the post-period, though this was not statistically significant (P =.23). Mortality rates were similar (P =.95). Conclusions: Our findings suggest NCC/NPC collaboration in a community-focused teaching hospital was associated with more NCC consultations, as well as shorter LOS without increasing mortality. These data highlight the importance of supporting collaborative models of care in community settings. Further research is warranted. [ABSTRACT FROM AUTHOR]

Cellular Bucket Brigades (CBBs) can absorb the impact of velocity variability on throughput, which makes them perform better than traditional Bucket Brigades under random worker velocities. However, the throughput of CBBs may decrease because of blocking and/or halting at discrete workstations. Among the various solutions, collaboration has emerged as an interesting candidate because it can substantially reduce the impact of blocking and halting. To improve the throughput of the existing collaborative model, two types of collaboration models are proposed from different perspectives. One of the problems addressed is that the existing collaborative model performs unsatisfactorily to achieve higher throughput in some cases due to its design, and another is that it has complex worker operation principles and many unproductive movements. A comparison of the proposed and existing models shows that the proposed models achieve outstanding improvements in throughput figures. [ABSTRACT FROM AUTHOR]

Target tracking is an important task in computer vision. Now many tracking algorithms have achieved great results. However, several challenges still hinder the development of tracking algorithms, such as abrupt motion, occlusion and so on. In order to use the feature information of the target more effectively and improve the accuracy and robustness of target tracking, a novel model is designed which is different from the previous discriminative component and generative component, and a novel discriminative-generative collaborative appearance model is presented to combine the two components in this paper. First, for the discriminative component, Locality-Constrained Sparse Coding Algorithm is proposed. In this algorithm, the objective function of the local feature of the target spatial information is determined by fusing the pyramid maximum pool and local feature histogram method. The objective function has three important parameters, which are solved by different optimization strategies. Second, for the generative component, the Histogram of Locality-Constrained Feature Algorithm is proposed. In this algorithm, the locality constraint is served to describe the spatial information of the target as a generative appearance model. Each image patch can be approximated by a linear combination of a local coordinate system formed by a dictionary whose elements are cluster centers that contain the most representative model of the target. Third, this paper designs a collaborative target tracking framework based on semi-supervised learning algorithm with locality constraint coding. The framework can quickly and robustly determine the feature information of the tracking region. The proposed algorithm is evaluated on the comprehensive test platform. The experimental results show that our method is more robust and efficient, and the precision and success rate of our algorithm are improved by 5.4% and 4.7%, respectively.

Since 2009, a community-based, collaborative exemplar in Richmond, Virginia, involving 8 satellite clinics of the state health department, has been implemented within public housing units to focus on prevention and access to care. These satellite centers, known a resource center (RCs), are staffed by public health nurses, community health workers, and nurse practitioners working in complementary roles. The RCs have provided education to 2609 clients on family planning, safe sex practices, safe car seat use, and screening for sexually transmitted infections and have made 9591 patient referrals to health, social, and community services. [ABSTRACT FROM AUTHOR]

Supervising teachers, as a basic component of an educational plan, has positive effects on teachers’ classroom behaviors. Following the tenets of Sociocultural theory of mind, this qualitative study delvd into the perception of EFL teachers regarding their worldviews of what supervision is and what it must be like. For this purpose, 25 participants (10 supervisors,10 in-service teachers, and 5 pre-service teachers) were recruited and interviewed. Their responses to a semi-structured interview were recorded by mobile phone, transcribed, and then translated into English for ease of interpretation. To analyze the data, thematic analysis was used. The emerged themes showed that the participants preferred supervision with feedback which is constructive, timely, with effective solution in challenging situations, effective in identifying tecaher’s strengths and weaknesses, suggestive and non-judgemental. They also believed that supervision must be with no imposition, with prior notice, with a checklist, in a relaxed atmosphere, with a competent supervisor and without supervisor presence. Criticizing the running model of supervision, they believed that the feedback offered is judgemental and inappropriate, offers no solution to the problems, and has no purpose for improvement. Finally, some valuable implications for TTC holders, supervisors, and on-thejob mentors, and also several recommendations for further research are presented. [ABSTRACT FROM AUTHOR]

Abstract Background General practitioners (GPs) play an important role in the physical care of patients with severe mental illness, so our aim was to analyse the relationships between GPs’ sociodemographic status and worked-related variables and their perceptions about mental illness. Methods A descriptive, cross-sectional study was conducted in the Clinical Management Unit of Mental Health (CMU-MH) of the Regional Hospital of Malaga (Spain). The eligible population comprised all GPs working in the 13 primary care centres (PCCs) in the hospital’s catchment area during the study period. GPs were interviewed to collect data on their attitudes to and knowledge of mental illness, psychiatry and the local mental health team, as well as their sociodemographic status, professional qualifications and experience. Bivariate analysis was carried out. Results 145 GPs answered the questionnaire (77%). ANOVA revealed that most of the PCCs with the best relationship with their mental health team and best attitude to mental illness were in the Central Community Mental Health Unit, which operated a collaborative model of care. Conclusions These results indicated that GPs who worked more closely with their specialist mental health team had a better perception of their relationship with the mental health centre and less stigmatisation in regard to mental illness.

Currently, the discriminative correlation filter‐based trackers have achieved higher tracking accuracy. However, visual tracking still faces challenges in terms of heavy occlusion, scale variation and so on. In this study, the authors intend to solve heavy occlusion by introducing collaborative model into classifier‐box. Firstly, they introduce complex colour features into correlation filter tracker to improve the effect of the tracker. Secondly, they introduce a multi‐scale method into their tracker to ease the scale problem. Thirdly, in order to solve the heavy occlusion in the tracking process, they adopt the locally weighted distance and classifier‐box. Their algorithm achieves distance precision rates of 81.7 and 77.4% on OTB2013 dataset and OTB2015 dataset, respectively. Their contribution focuses on solving heavy occlusion by using colour features, locally weighted distance and classifier‐box. The experimental results on OTB2013 and OTB2015 datasets demonstrate their algorithm to perform better than state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

We propose a novel collaborative discriminative model based on extreme learning machine (ELM) for object tracking in this paper. In order to represent the object more precisely, we first propose a new collaborative discriminative representation model, which includes both a global discriminative sub-model and a local discriminative sub-model. Different from traditional local representation models, in particular, our local sub-model integrates several classifiers which have structural relations to improve the expression. The global discriminative model represents the appearance comprehensively while the local discriminative sub-model can effectively address occlusions and assist the update. Second, to have better combination of these sub-models, we propose a novel collaboration strategy based on the Kullback-Leibler (KL) distance. The novel strategy can determine the weights of the sub-models adaptively by measuring their KL distances reciprocally. Third, we introduce ELM into tracking and adopt it to build both the global and the local discriminative sub-models simultaneously. Since ELM has a good generalization performance and is robust to the imbalance of the training samples, it is suitable to be used for tracking. Experimental results demonstrate that our method can achieve comparable performance to many state-of-the-art tracking approaches. [ABSTRACT FROM AUTHOR]

Communication is dynamic, social, challenging, and a key quality factor for construction projects. This is especially the case in multinational and inter-organizational infrastructure projects where factors like culture and language differ among the involved actors. As infrastructure projects usually extend over longer periods of time, collaborative relationships need to be established in which the actors can develop, for example, mutual understanding, learning, and efficient working routines. By building on empirical data from contemporary infrastructure projects, we explore how international contractors and a large public client communicate in multinational infrastructure projects (i.e., what the challenges are and what competences are needed). The analysis is based on the linguistic framework of communicative competence, and we contribute to the development of collaborative models in construction project management by suggesting the concept of contextual communicative competence.

MoJo refers to the emerging framework of covering news reporting workflows using smart mobile devices with dedicated software or hardware. However, a fully integrated and dedicated computing environment that can address the individual needs of both professional and citizen journalists is still missing. We introduce MoJo--MATE, (Mobile Journalism Machine AssisTEd reporting) a platform that integrates tools concerning all workflows at all stages of news reporting (content creation, editing, fact-checking, content management and publishing), thus providing interconnection of the newsroom collaborators and the public. We distinguish the client-/terminal-side and the server-side as two discrete components of the platform. The so-called jReporter mobile application includes tools and automations to support journalists in audiovisual capturing and editing along with semantic metadata extraction by making use of the smart devices. This is essential, since, in MOJO, the reporter has full responsibility of correct camera and microphone handling, without the assistance of a professional operator. Dedicated editing tools enhance portability, allowing at the same time the collection of log files with editing history, for authentication purposes. The created content is uploaded from the mobile devices to the server-side repositories, where Graphical User Interfaces are provided for accessing and managing media assets. A database containing information about the registered contributors and their respective types of engagement is maintained, controlling content rights and functionalities depending on the discrete users' roles (e.g. reporter, technical stuff, editor, journalist, and contributor). The metadata created in client-side is utilized for intelligent Multimedia Asset Management, efficient and comprehensible content publishing, including cross-validation and fact-checking aspects. [ABSTRACT FROM AUTHOR]

For considering the connection and mutual influences between ride comfort and handing stability of a vehicle, a collaborative study on the two performances is carried out in the paper. Firstly based on the UniTire model and combined with filtered white noise models for front and real road excitations, 4-DOF plane model for ride comfort and 2-DOF plane model for handling stability, a collaborative model for ride comfort and handing stability is built by adopting state equations, and a collaborative simulation algorithm for them is also proposed. Then, a collaborative simulation on the ride comfort and handing stability of a vehicle under common road grade and speed conditions is conducted. The results show that the ride comfort and handling stability of a vehicle can be simulated simultaneously by using the collaborative model. The handling stability parameters simulated with the linear UniTire model are larger than those simulated with the nonlinear UniTire model, indicating that it is obviously conservative to study vehicle handling stability with the linear UniTire model. [ABSTRACT FROM AUTHOR]

Visual tracking is a challenging task since it involves developing an effective appearance model to deal with numerous factors. In this paper, we propose a robust object tracking algorithm based on a collaborative model with adaptive selection scheme. Specifically, based on the discriminative features selected from the feature selection scheme, we develop a sparse discriminative model (SDM) by introducing a confidence measure strategy. In addition, we present a sparse generative model (SGM) by combining ℓ1 regularization with PCA reconstruction. In contrast to existing hybrid generative discriminative tracking algorithms, we propose a novel adaptive selection scheme based on the Euclidean distance as the joint mechanism, which helps to construct a more reasonable likelihood function for our collaborative model. Experimental results on several challenging image sequences demonstrate that the proposed tracking algorithm leads to a more favorable performance compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The rapid development of information technologies provided unprecedented big data environments for condition monitoring and degradation analyses. However, the available big data sets are often sparse with a limited number of observations per recorded unit. For example, in many healthcare systems, data are collected from a large number of patients, but the available observations from each patient are quite limited. Unfortunately, most of the existing approaches for data-driven degradation modeling may not work well in this scenario as they either pool the information from the population or require rich historical observations in each unit. To address the challenges in “sparse data environments,” this paper proposes a structural degradation modeling framework (SDM). The SDM is inspired by the recommender system, which provides recommendations about specific items for the user. In addition, it is also tailored to the needs of degradation modeling. In particular, the framework takes into consideration: 1) the available data from the unit of interest; 2) the population characteristics; 3) the relationship between the available units; and 4) the precision of the available units. Simulation studies and a case study that involves the Alzheimer’s disease (AD) neuroimaging initiative data set are conducted, which shows satisfactory performance of the proposed method. Note to Practitioners—This paper proposes a framework for modeling and predicting the degradation level and/or condition of units with time. Our framework is particularly useful where many units have missing and/or limited degradation observations. Essentially, our proposed method integrates two important ideas: 1) leveraging the available data from the unit of interest to improve the modeling fitting of the individual unit over the observed time domain and 2) considering the relationship between the available units to extract proper and accurate population characteristics to address the challenge of limited observations. The proposed approach is validated via simulation studies as well as a healthcare case study based on AD. In the future research, we will further explore the extension of the proposed method such as considering more generic degradation models and optimal parameter tunings. [ABSTRACT FROM AUTHOR]

Discriminative correlation filter (DCF) based tracking algorithms have recently shown impressive performance on benchmark datasets. However, amount of recent researches are vulnerable to heavy occlusions, irregular deformations and so on. In this paper, we intend to solve these problems and handle the contradiction between accuracy and real-time in the framework of tracking-by-detection. Firstly, we propose an innovative strategy to combine the template and color-based models instead of a simple linear superposition and rely on the strengths of both to promote the accuracy. Secondly, to enhance the discriminative power of the learned template model, the spatial regularization is introduced in the learning stage to penalize the objective boundary information corresponding to features in the background. Thirdly, we utilize a discriminative multi-scale estimate method to solve the problem of scale variations. Finally, we research strategies to limit the computational complexity of our tracker. Abundant experiments demonstrate that our tracker performs superiorly against several advanced algorithms on both the OTB2013 and OTB2015 datasets while maintaining the high frame rates. [ABSTRACT FROM AUTHOR]

Highlights • A collaborative model is developed to take advantages of both holistic model and part model. • A local kernel feature is designed to encode the geometric information of a target. • A metric based on the local feature is proposed to measure the reliability of a tracking object. The estimation results can be treated to fusion weights to locate the final target position. • Extensive experiments are carried out to demonstrate the effectiveness and robustness of our method with comparisons to state-of-the-art methods. Abstract Partial occlusion is one of a challenging problem in unmanned aerial vehicle (UAV) tracking. In this paper, we propose a novel collaborative model based tracking method which attempts to exploit a holistic model and a part model that tracks an object consistently through the entire video sequence. Specifically, we first develop a robust local kernel feature which learns the data around to encode the geometric information of the object. Next, the target is divided into four parts. And structure support vector machine (SSVM) is employed to integrate with the local feature to a robust visual tracking framework. Furthermore, we adopt a reliable metric to measure the reliability of a patch. Kalman filter is used to fuse the holistic model and part model tracking results smoothly according to the metric. Extensive experimental results demonstrate our tracker achieves comparable performance to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Highlights • A method for visual object tracking is proposed. • We propose a decomposition model in visual tracking. • The decomposition model is utilized in an integrated generative model and discriminative model tracking framework. • Alternating direction method of multipliers algorithm guarantees the convergence of the decomposition model. This paper presents a multi-task based object tracking algorithm via a collaborative model. Under the framework of particle filtering, we develop a multi-task sparse learning based generative and discriminative classifier model. In the generative model, we propose a histogram based subspace learning method which takes advantage of adaptive templates update. In the discriminative model, we introduce an effective method to compute the confidence value which assigns more weights to the foreground than the background. A decomposition model is employed to take the common features and outliers of each particle into consideration. The alternating direction method of multipliers (ADMM) algorithm guarantees the optimization problem can be solved robustly and accurately. Qualitative and quantitative comparisons with nine state-of-the-art methods demonstrate the effectiveness and efficiency of our method in handling various challenges during tracking. [ABSTRACT FROM AUTHOR]

Clinical placements are specifically designed to facilitate authentic learning opportunities and are an integral component of undergraduate nursing programs. However, as academics and clinicians frequently point out, clinical placements are fraught with problems that are long-standing and multidimensional in nature. Collaborative placement models, grounded in a tripartite relationship between students, university staff and clinical partners, and designed to foster students’ sense of belonging, have recently been implemented to address many of the challenges associated with clinical placements. In this study a qualitative descriptive design was undertaken with the aim of exploring 14 third year third year nursing students’ perceptions of a collaborative clinical placement model undertaken in an Australian university. Students participated in audio recorded focus groups following their final clinical placement. Thematic analysis of the interview data resulted in identification of six main themes: Convenience and Camaraderie, Familiarity and Confidence, Welcomed and Wanted, Belongingness and Support, Employment, and The Need for Broader Clinical Experiences. The clinical collaborative model fostered a sense of familiarity for many of the participants and this led to belongingness, acceptance, confidence and meaningful learning experiences. [ABSTRACT FROM AUTHOR]

Object tracking is always a very attractive research topic in computer vision and image processing. In this paper, an innovative method called salient-sparse-collaborative tracker (SSCT) is put forward, which exploits both object saliency and sparse representation. Within the proposed collaborative appearance model, the object salient feature map is built to create a salient-sparse discriminative model (SSDM) and a salient-sparse generative model (SSGM). In the SSDM module, the presented sparse model effectively distinguishes the target region from its background by using the salient feature map that further helps locate the object in complex environment. In the SSGM module, a sparse representation method with salient feature map is designed to improve the effectiveness of the templates and deal with occlusions. The update scheme takes advantage of salient correction, thus the SSCT algorithm can both handle the appearance variation as well as reduce tracking drifts effectively. Plenty of experiments with quantitative and qualitative comparisons on benchmark reveal the SSCT tracker is more competitive than several popular approaches. [ABSTRACT FROM AUTHOR]

In this paper, robust visual tracking scheme is achieved through a novel sparse tracking via collaborative motion and appearance (TCMA). A coarse-to-fine framework with both motion and holistic appearance information is taken into consideration. In coarse search, we employ an optical flow map for the generation of motion particles. A rough estimation of target image patch is obtained using $l_2$ -regularized least square method in coarse search stage. In fine search, a novel smooth term is proposed in the cost function to improve the robustness of the tracker. With this smooth term, the object appearance in the previous frame will also affect the calculation of sparse coefficient in the current frame. It allows the tracker involving temporal information between consecutive frames instead of only considering single frame appearance information as in the conventional sparse coding-based tracking algorithms. In order to reserve the original and latest appearance information simultaneously in the template, a quadratic-function-like weight allocation scheme combining with particle contributed histogrammic correlation is developed in the updating stage. Both qualitative and quantitative studies are conducted on a set of challenging image sequences. The superior performance over other state-of-the-art algorithms is verified through the experiment. [ABSTRACT FROM PUBLISHER]

Low-rank models have been successfully applied to background modeling and achieved promising results on moving object detection. However, the assumption that moving objects are modelled as sparse outliers limits the performance of these models when the sizes of moving objects are relatively large. Meanwhile, inspired by the visual system of human brain which can cognitively perceive the physical size of the object with different sizes of retina imaging, we propose a novel approach, called Collaborative Low-Rank And Sparse Separation (CLASS), for moving object detection. Given the data matrix that accumulates sequential frames from the input video, CLASS detects the moving objects as sparse outliers against the low-rank structure background while pursuing global appearance consistency for both foreground and background. The sparse and the global appearance consistent constraints are complementary but simultaneously competing, and thus CLASS can detect the moving objects with different sizes effectively. The smoothness constraints of object motion are also introduced in CLASS for further improving the robustness to noises. Moreover, we utilize the edge-preserving filtering method to substantially speed up CLASS without much losing its accuracy. The extensive experiments on both public and newly created video sequences suggest that CLASS achieves superior performance and comparable efficiency against other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This paper presents a novel discriminative, generative, and collaborative appearance model for robust object tracking. In contrast to existing methods, we use different appearance manifolds to represent the target in the discriminative and generative appearance models and propose a novel collaborative scheme to combine these two components. In particular: 1) for the discriminative component, we develop a graph regularized discriminant analysis (GRDA) algorithm that can find a projection to more effectively distinguish the target from the background; 2) for the generative component, we introduce a simple yet effective coding method for object representation. The method involves no optimization, and thus better efficiency can be achieved; and 3) for the collaborative model, we apply GRDA again to find a subspace for discriminating the likelihood features (generated from the discriminative and generative appearance models) and use the nearest neighbor criterion to determine the final likelihood. Besides, all the components are online updated so that our tracker can deal with appearance changes effectively. The experimental results over 23 challenging image sequences demonstrate that the proposed algorithm achieves better performance compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The past decade has witnessed significant progress in object detection and tracking in videos. In this paper, we present a collaborative model between a pre-trained object detector and a number of single-object online trackers within the particle filtering framework. For each frame, we construct an association between detections and trackers, and treat each detected image region as a key sample, for online update, if it is associated to a tracker. We present a motion model that incorporates the associated detections with object dynamics. Furthermore, we propose an effective sample selection scheme to update the appearance model of each tracker. We use discriminative and generative appearance models for the likelihood function and data association, respectively. Experimental results show that the proposed scheme generally outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Segmenting organisms or tumors from medical data (e.g., computed tomography volumetric images, ultrasound, or magnetic resonance imaging images/image sequences) is one of the fundamental tasks in medical image analysis and diagnosis, and has received long-term attentions. This paper studies a novel computational framework of interactive segmentation for extracting liver tumors from image sequences, and it is suitable for different types of medical data. The main contributions are twofold. First, we propose a collaborative model to jointly formulate the tumor segmentation from two aspects: 1) region partition and 2) boundary presence. The two terms are complementary but simultaneously competing: the former extracts the tumor based on its appearance/texture information, while the latter searches for the palpable tumor boundary. Moreover, in order to adapt the data variations, we allow the model to be discriminatively trained based on both the seed pixels traced by the Lucas–Kanade algorithm and the scribbles placed by the user. Second, we present an effective inference algorithm that iterates to: 1) solve tumor segmentation using the augmented Lagrangian method and 2) propagate the segmentation across the image sequence by searching for distinctive matches between images. We keep the collaborative model updated during the inference in order to well capture the tumor variations over time. We have verified our system for segmenting liver tumors from a number of clinical data, and have achieved very promising results. The software developed with this paper can be found at http://vision.sysu.edu.cn/projects/med-interactive-seg/ [ABSTRACT FROM PUBLISHER]