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Engaged scholarship, a movement that has been growing steadily since 1995, offers a new way of bridging gaps between the university and civil society. Numerous papers and reports have been published since Boyer’s foundational discourse in 1996. Yet, beyond a growing interest in orienting universities’ missions, we observed a lack a formal definition and conceptualization of this movement. Based on a scoping review of the literature over the past 20 years, the objective of this article is to propose a conceptualization of engaged scholarship. More specifically, we define its values, principles, and processes. We conclude with a discussion of the implications of this new posture for faculty and students, as well as for the university as an institution. [ABSTRACT FROM AUTHOR]

An extended car-following model is proposed in this paper to analyze the impacts of V2V (vehicle to vehicle) communication on the micro driving behavior at the un-signalized intersection. A four-leg un-signalized intersection with twelve streams (left-turn, through movement, and right turn from each leg) is used. The effect of the guidance strategy on the reduction of the rate of stops and total delay is explored by comparing the proposed model and the traditional FVD car-following model. The numerical results illustrate that potential conflicts between vehicles can be predicted and some stops can be avoided by decelerating in advance. The driving comfort and traffic efficiency can be improved accordingly. More benefits could be obtained under the long communication range, low to medium traffic density, and simple traffic pattern conditions. [ABSTRACT FROM AUTHOR]

This paper reports the results of a lab experiment designed to study the role of observability for peer effects in the setting of a simple production task. In our experiment, participants in the role of workers engage in a team real-effort task. We vary whether they can observe, or be observed by, one of their co-workers. In contrast to earlier findings from the field, we find no evidence that low-productivity workers perform better when they are observed by high-productivity co-workers. Instead, our results imply that peer effects in our experiment are heterogeneous, with some workers reciprocating a high-productivity co-worker but others taking the opportunity to free ride. [ABSTRACT FROM AUTHOR]

The existence of a shared classification system is essential to knowledge production, transfer, and sharing. Studies of knowledge classification, however, rarely consider the fact that knowledge categories exist within hierarchical information systems designed to facilitate knowledge search and discovery. This neglect is problematic whenever information about categorical membership is itself used to evaluate the quality of the items that the category contains. The main objective of this paper is to show that the effects of category membership depend on the position that a category occupies in the hierarchical knowledge classification system of Wikipedia—an open knowledge production and sharing platform taking the form of a freely accessible on-line encyclopedia. Using data on all English-language Wikipedia articles, we examine how the position that a category occupies in the classification hierarchy affects the attention that articles in that category attract from Wikipedia editors, and their evaluation of quality of the Wikipedia articles. Specifically, we show that Wikipedia articles assigned to coarse-grained categories (i. e., categories that occupy higher positions in the hierarchical knowledge classification system) garner more attention from Wikipedia editors (i. e., attract a higher volume of text editing activity), but receive lower evaluations (i. e., they are considered to be of lower quality). The negative relation between attention and quality implied by this result is consistent with current theories of social categorization, but it also goes beyond available results by showing that the effects of categorization on evaluation depend on the position that a category occupies in a hierarchical knowledge classification system. [ABSTRACT FROM AUTHOR]

With the continuing growth of multi-media learning resources, it is important to offer methods helping learners to explore and acquire relevant learning information effectively. As services that organize multi-media learning materials together to support programming learning, the digital case-based learning system is needed. In order to create a case-oriented e-learning system, this paper concentrates on the digital case study of multi-media resources and learning processes with an integrated framework. An integration of multi-media resources, testing and learning strategies recommendation as the learning unit is proposed in the digital case-based learning framework. The learning mechanism of learning guidance, multi-media materials learning and testing feedback is supported in our project. An improved personalized genetic algorithm which incorporates preference information and usage degree into the crossover and mutation process is proposed to assemble the personalized test sheet for each learner. A learning strategies recommendation solution is proposed to recommend learning strategies for learners to help them to learn. The experiments are conducted to prove that the proposed approaches are capable of constructing personalized sheets and the effectiveness of the framework. [ABSTRACT FROM AUTHOR]

This paper aimed to investigate the role of parental education in child disability in China. We used nationally representative data from China’s National Sample Survey on Disability, iterated twice, in 1987 and 2006, with data of 764,718 children aged 0–14 years. Logit models were used for statistical analysis. Results showed that the prevalence of child disability was significantly associated with each parent’s education. Maternal education was more important than paternal education in child disability in both surveys. The analysis of marginal effect indicated a one-year increase in maternal and paternal schooling led to an average decrease of 0.121% and 0.091% in the probability of child disability in 1987, and 19 years later, these figures had dwindled to 0.091% and 0.072%, respectively. [ABSTRACT FROM AUTHOR]

Introduction: Inconsistency in the time trade-off (TTO) task in EQ-5D-5L occurs when a respondent gives a higher value to a logically worse health state, the occurrence of inconsistency compromises the quality of the data. It is not yet clear which factors are associated with individual level inconsistency. Relating inconsistency to the characteristics of the respondent, interviewer, and the interview process could be helpful in understanding the causes of inconsistency. The objective of this paper is to discover the factors associated with individual level inconsistencies. Methods: Twenty interviewers interviewed 1,296 respondents and each respondent valued 10 health states using the EQ-VT platform in 5 cities in China. At the respondent level, inconsistency was identified in terms of severity and quantity and related to the respondent’s background characteristics, the time and iterations spent on the wheelchair example task, and the formal TTO tasks, using multilevel multinomial regression analyses. Interviewers’ impact on inconsistencies was analyzed using single level multinomial regression analyses. Results: In the full dataset, slight inconsistency was more related to the interview process (Time spent on TTO task: RRR = 1.246 with 95%CI: 1.076,1.441; time spent on Wheelchair example: RRR = 0.815 with 95%CI:0.699,0.952) while severe inconsistency was more related to respondent’s gender (Gender: RRR = 2.347 with 95%CI:1.429,3.855). One Interviewer (Interviewer 7: RRR = 7.335 with 95%CI:1.908,28.195) and interviewer’s experience (Sequence: RRR = 0.511 with 95%CI:0.385,0.678) in general showed strong influence over inconsistency in the TTO task. Conclusion: In conclusion, logical inconsistency in the valuation of EQ-5D-5L health states is associated not only with respondents’ characteristics but also with interviewers’ performance and the interview process. The role of interviewers and the importance of interviewer training may be more crucial than hitherto believed. This finding could be generalizable to other interviewer-administered health-state valuation study. [ABSTRACT FROM AUTHOR]

This paper explores how information flow properties of a network affect the formation of categories shared between individuals, who are communicating through that network. Our work is based on the established multi-agent model of the emergence of linguistic categories grounded in external environment. We study how network information propagation efficiency and the direction of information flow affect categorization by performing simulations with idealized network topologies optimizing certain network centrality measures. We measure dynamic social adaptation when either network topology or environment is subject to change during the experiment, and the system has to adapt to new conditions. We find that both decentralized network topology efficient in information propagation and the presence of central authority (information flow from the center to peripheries) are beneficial for the formation of global agreement between agents. Systems with central authority cope well with network topology change, but are less robust in the case of environment change. These findings help to understand which network properties affect processes of social adaptation. They are important to inform the debate on the advantages and disadvantages of centralized systems. [ABSTRACT FROM AUTHOR]

Traffic congestion continues to be a persistent problem throughout the world. As vehicle-to-vehicle communication develops, there is an opportunity of using cooperation among close proximity vehicles to tackle the congestion problem. The intuition is that if vehicles could cooperate opportunistically when they come close enough to each other, they could, in effect, spread themselves out among alternative routes so that vehicles do not all jam up on the same roads. Our previous work proposed a decentralized multiagent based vehicular congestion management algorithm entitled Congestion Avoidance and Route Allocation using Virtual Agent Negotiation (CARAVAN), wherein the vehicles acting as intelligent agents perform cooperative route allocation using inter-vehicular communication. This paper focuses on evaluating the practical applicability of this approach by testing its robustness and performance (in terms of travel time reduction), across variations in: (a) environmental parameters such as road network topology and configuration; (b) algorithmic parameters such as vehicle agent preferences and route cost/preference multipliers; and (c) agent-related parameters such as equipped/non-equipped vehicles and compliant/non-compliant agents. Overall, the results demonstrate the adaptability and robustness of the decentralized cooperative vehicles approach to providing global travel time reduction using simple local coordination strategies. [ABSTRACT FROM AUTHOR]

In this paper, the perceptive user, who could identify the high-quality objects in their initial lifespan, is presented. By tracking the ratings given to the rewarded objects, we present a method to identify the user perceptibility, which is defined as the capability that a user can identify these objects at their early lifespan. Moreover, we investigate the behavior patterns of the perceptive users from three dimensions: User activity, correlation characteristics of user rating series and user reputation. The experimental results for the empirical networks indicate that high perceptibility users show significantly different behavior patterns with the others: Having larger degree, stronger correlation of rating series and higher reputation. Furthermore, in view of the hysteresis in finding the rewarded objects, we present a general framework for identifying the high perceptibility users based on user behavior patterns. The experimental results show that this work is helpful for deeply understanding the collective behavior patterns for online users. [ABSTRACT FROM AUTHOR]

Introduction: This research paper aims to assess factors reported by parents associated with the successful transition of children with complex additional support requirements that have undergone a transition between school environments from 8 European Union member states. Methods: Quantitative data were collected from 306 parents within education systems from 8 EU member states (Bulgaria, Cyprus, Greece, Ireland, the Netherlands, Romania, Spain and the UK). The data were derived from an online questionnaire and consisted of 41 questions. Information was collected on: parental involvement in their child’s transition, child involvement in transition, child autonomy, school ethos, professionals’ involvement in transition and integrated working, such as, joint assessment, cooperation and coordination between agencies. Survey questions that were designed on a Likert-scale were included in the Principal Components Analysis (PCA), additional survey questions, along with the results from the PCA, were used to build a logistic regression model. Results: Four principal components were identified accounting for 48.86% of the variability in the data. Principal component 1 (PC1), ‘child inclusive ethos,’ contains 16.17% of the variation. Principal component 2 (PC2), which represents child autonomy and involvement, is responsible for 8.52% of the total variation. Principal component 3 (PC3) contains questions relating to parental involvement and contributed to 12.26% of the overall variation. Principal component 4 (PC4), which involves transition planning and coordination, contributed to 11.91% of the overall variation. Finally, the principal components were included in a logistic regression to evaluate the relationship between inclusion and a successful transition, as well as whether other factors that may have influenced transition. All four principal components were significantly associated with a successful transition, with PC1 being having the most effect (OR: 4.04, CI: 2.43–7.18, p<0.0001). Discussion: To support a child with complex additional support requirements through transition from special school to mainstream, governments and professionals need to ensure children with additional support requirements and their parents are at the centre of all decisions that affect them. It is important that professionals recognise the educational, psychological, social and cultural contexts of a child with additional support requirements and their families which will provide a holistic approach and remove barriers for learning. [ABSTRACT FROM AUTHOR]

A popular objective criterion for partitioning a set of actors into core and periphery subsets is the maximization of the correlation between an ideal and observed structure associated with intra-core and intra-periphery ties. The resulting optimization problem has commonly been tackled using heuristic procedures such as relocation algorithms, genetic algorithms, and simulated annealing. In this paper, we present a computationally efficient simulated annealing algorithm for maximum correlation core/periphery partitioning of binary networks. The algorithm is evaluated using simulated networks consisting of up to 2000 actors and spanning a variety of densities for the intra-core, intra-periphery, and inter-core-periphery components of the network. Core/periphery analyses of problem solving, trust, and information sharing networks for the frontline employees and managers of a consumer packaged goods manufacturer are provided to illustrate the use of the model. [ABSTRACT FROM AUTHOR]

People reliably encode information more effectively when it is related in some way to the self—a phenomenon known as the self-reference effect. This effect has been recognized in psychological research for almost 40 years, and its scope as a tool for investigating the self-concept is still expanding. The self-reference effect has been used within a broad range of psychological research, from cultural to neuroscientific, cognitive to clinical. Traditionally, the self-reference effect has been investigated in a laboratory context, which limits its applicability in non-laboratory samples. This paper introduces an online version of the self-referential encoding paradigm that yields reliable effects in an easy-to-administer procedure. Across four studies (total N = 658), this new online tool reliably replicated the traditional self-reference effect: in all studies self-referentially encoded words were recalled significantly more than semantically encoded words (d = 0.63). Moreover, the effect sizes obtained with this online tool are similar to those obtained in laboratory samples, and are robust to experimental variations in encoding time (Studies 1 and 2) and recall procedure (Studies 3 and 4), and persist independent of primacy and recency effects (all studies). [ABSTRACT FROM AUTHOR]

Melioration learning is an empirically well-grounded model of reinforcement learning. By means of computer simulations, this paper derives predictions for several repeatedly played two-person games from this model. The results indicate a likely convergence to a pure Nash equilibrium of the game. If no pure equilibrium exists, the relative frequencies of choice may approach the predictions of the mixed Nash equilibrium. Yet in some games, no stable state is reached. [ABSTRACT FROM AUTHOR]

In the existing centralized location services system structure, the server is easily attracted and be the communication bottleneck. It caused the disclosure of users’ location. For this, we presented a new distributed collaborative recommendation strategy that is based on the distributed system. In this strategy, each node establishes profiles of their own location information. When requests for location services appear, the user can obtain the corresponding location services according to the recommendation of the neighboring users’ location information profiles. If no suitable recommended location service results are obtained, then the user can send a service request to the server according to the construction of a k-anonymous data set with a centroid position of the neighbors. In this strategy, we designed a new model of distributed collaborative recommendation location service based on the users’ location information profiles and used generalization and encryption to ensure the safety of the user’s location information privacy. Finally, we used the real location data set to make theoretical and experimental analysis. And the results show that the strategy proposed in this paper is capable of reducing the frequency of access to the location server, providing better location services and protecting better the user’s location privacy. [ABSTRACT FROM AUTHOR]

Product aspect recognition is a key task in fine-grained opinion mining. Current methods primarily focus on the extraction of aspects from the product reviews. However, it is also important to cluster synonymous extracted aspects into the same category. In this paper, we focus on the problem of product aspect clustering. The primary challenge is to properly cluster and generalize aspects that have similar meanings but different representations. To address this problem, we learn two types of background knowledge for each extracted aspect based on two types of effective aspect relations: relevant aspect relations and irrelevant aspect relations, which describe two different types of relationships between two aspects. Based on these two types of relationships, we can assign many relevant and irrelevant aspects into two different sets as the background knowledge to describe each product aspect. To obtain abundant background knowledge for each product aspect, we can enrich the available information with background knowledge from the Web. Then, we design a hierarchical clustering algorithm to cluster these aspects into different groups, in which aspect similarity is computed using the relevant and irrelevant aspect sets for each product aspect. Experimental results obtained in both camera and mobile phone domains demonstrate that the proposed product aspect clustering method based on two types of background knowledge performs better than the baseline approach without the use of background knowledge. Moreover, the experimental results also indicate that expanding the available background knowledge using the Web is feasible. [ABSTRACT FROM AUTHOR]

Cooperation amongst resource users holds the key to overcoming the social dilemma that characterizes community-based common-pool resource management. But is cooperation alone enough to achieve sustainable resource use? The short answer is no. Developing management strategies in a complex social-ecological environment also requires ecological knowledge and approaches to deal with perceived environmental uncertainty. Recent behavioral experimental research indicates variation in the degree to which a group of users can identify a sustainable exploitation level. In this paper, we identify social-ecological micro-foundations that facilitate cooperative sustainable common-pool resource use. We do so by using an agent-based model (ABM) that is informed by behavioral common-pool resource experiments. In these experiments, groups that cooperate do not necessarily manage the resource sustainably, but also over- or underexploit. By reproducing the patterns of the behavioral experiments in a qualitative way, the ABM represents a social-ecological explanation for the experimental observations. We find that the ecological knowledge of each group member cannot sufficiently explain the relationship between cooperation and sustainable resource use. Instead, the development of a sustainable exploitation level depends on the distribution of ecological knowledge among the group members, their influence on each other’s knowledge, and the environmental uncertainty the individuals perceive. The study provides insights about critical social-ecological micro-foundations underpinning collective action and sustainable resource management. These insights may inform policy-making, but also point to future research needs regarding the mechanisms of social learning, the development of shared management strategies and the interplay of social and ecological uncertainty. [ABSTRACT FROM AUTHOR]

From the foods we eat and the houses we construct, to our religious practices and political organization, to who we can marry and the types of games we teach our children, the diversity of cultural practices in the world is astounding. Yet, our ability to visualize and understand this diversity is limited by the ways it has been documented and shared: on a culture-by-culture basis, in locally-told stories or difficult-to-access repositories. In this paper we introduce D-PLACE, the Database of Places, Language, Culture, and Environment. This expandable and open-access database (accessible at ) brings together a dispersed corpus of information on the geography, language, culture, and environment of over 1400 human societies. We aim to enable researchers to investigate the extent to which patterns in cultural diversity are shaped by different forces, including shared history, demographics, migration/diffusion, cultural innovations, and environmental and ecological conditions. We detail how D-PLACE helps to overcome four common barriers to understanding these forces: i) location of relevant cultural data, (ii) linking data from distinct sources using diverse ethnonyms, (iii) variable time and place foci for data, and (iv) spatial and historical dependencies among cultural groups that present challenges for analysis. D-PLACE facilitates the visualisation of relationships among cultural groups and between people and their environments, with results downloadable as tables, on a map, or on a linguistic tree. We also describe how D-PLACE can be used for exploratory, predictive, and evolutionary analyses of cultural diversity by a range of users, from members of the worldwide public interested in contrasting their own cultural practices with those of other societies, to researchers using large-scale computational phylogenetic analyses to study cultural evolution. In summary, we hope that D-PLACE will enable new lines of investigation into the major drivers of cultural change and global patterns of cultural diversity. [ABSTRACT FROM AUTHOR]

Background: Despite growing interest in the value of human-animal interactions (HAI) to human mental and physical health the quality of the evidence on which postulated benefits from animals to human psychological health are based is often unclear. To date there exist no systematic reviews on the effects of HAI in educational settings specifically focussing on the perceived benefits to children of reading to dogs. With rising popularity and implementation of these programmes in schools, it is essential that the evidence base exploring the pedagogic value of these initiatives is well documented. Methods: Using PRISMA guidelines we systematically investigated the literature reporting the pedagogic effects of reading to dogs. Because research in this area is in the early stages of scientific enquiry we adopted broad inclusion criteria, accepting all reports which discussed measurable effects related to the topic that were written in English. Multiple online databases were searched during January-March 2015; grey literature searches were also conducted. The search results which met the inclusion criteria were evaluated, and discussed, in relation to the Oxford Centre for Evidence Based Medicine levels of evidence; 27 papers were classified as Level 5, 13 as Level 4, 7 as Level 2c and 1 as Level 2b. Conclusion: The evidence suggests that reading to a dog may have a beneficial effect on a number of behavioural processes which contribute to a positive effect on the environment in which reading is practiced, leading to improved reading performance. However, the evidence base on which these inferences are made is of low quality. There is a clear need for the use of higher quality research methodologies and the inclusion of appropriate controls in order to draw causal inferences on whether or how reading to dogs may benefit children’s reading practices. The mechanisms for any effect remain a matter of conjecture. [ABSTRACT FROM AUTHOR]

Graphlets are small subgraphs, usually containing up to five vertices, that can be found in a larger graph. Identification of the graphlets that a vertex in an explored graph touches can provide useful information about the local structure of the graph around that vertex. Actually finding all graphlets in a large graph can be time-consuming, however. As the graphlets grow in size, more different graphlets emerge and the time needed to find each graphlet also scales up. If it is not needed to find each instance of each graphlet, but knowing the number of graphlets touching each node of the graph suffices, the problem is less hard. Previous research shows a way to simplify counting the graphlets: instead of looking for the graphlets needed, smaller graphlets are searched, as well as the number of common neighbors of vertices. Solving a system of equations then gives the number of times a vertex is part of each graphlet of the desired size. However, until now, equations only exist to count graphlets with 4 or 5 nodes. In this paper, two new techniques are presented. The first allows to generate the equations needed in an automatic way. This eliminates the tedious work needed to do so manually each time an extra node is added to the graphlets. The technique is independent on the number of nodes in the graphlets and can thus be used to count larger graphlets than previously possible. The second technique gives all graphlets a unique ordering which is easily extended to name graphlets of any size. Both techniques were used to generate equations to count graphlets with 4, 5 and 6 vertices, which extends all previous results. Code can be found at and . [ABSTRACT FROM AUTHOR]