What Determines Employment of Part-Time Faculty in Higher Education Institutions?

This study uses a cross-section national sample of four-year colleges and universities in the United States to examine the variation of part-time faculty employment. Results of this study suggest that higher educational institutions actively design and adopt contingent work arrangements to save on labor costs and to manage their resource dependence with constituencies. Institutions that pay high salaries to their full-time faculty members, have limited resource slack, and are located in major urban areas tend to employ a high proportion of part-time faculty. Furthermore, institutions that have small student enrollment and large proportion of part-time students are found to rely more heavily on part-time faculty employment. Private institutions, on average, have higher levels of part-time faculty than their public counterparts; however, this result does not hold for doctoral and research institutions. Finally, institutions that rely more on tuition and fees revenue tend to employ more part-time faculty. Such a relationship is significantly moderated by institutional quality, suggesting that different institutions may adopt different strategies to attract students and secure their tuition revenues

The 1st International Workshop on Context-Aware Recommendation Systems with Big Data Analytics (CARS-BDA)

Motivation and Goals. With the explosive growth of online service platforms, increasing number of people and enterprises are doing everything online. In order for organizations, governments, and individuals to understand their users, and promote their products or services, it is necessary for them to analyse big data and recommend the media or online services in real time. Effective recommendation of items of interest to consumers has become critical for enterprises in domains such as retail, e-commerce, and online media. Driven by the business successes, academic research in this field has also been active for many years. Though many scientific breakthroughs have been achieved, there are still tremendous challenges in developing effective and scalable recommendation systems for real-world industrial applications. Existing solutions focus on recommending items based on pre-set contexts, such as time, location, weather etc. The big data sizes and complex contextual information add further challenges to the deployment of advanced recommender systems. This workshop aims to bring together researchers with wide-ranging backgrounds to identify important research questions, to exchange ideas from different research disciplines, and, more generally, to facilitate discussion and innovation in the area of context-aware recommender systems and big data analytics. In a broad sense, the objective of such a workshop is to present results of the research undertaken in the area of data driven context-aware recommender systems, as a fishow and tellfi occasion. To some extent, the workshop is an exercise in showcasing research activities and findings, rather than in and not of fiworkshoppingfi or holding group discussions on research. This orientation, and the large number of presentations which are being made, means that tight timelines have to be followed. An intensive series of presentations is made, the downside of which is that the time available for group discussion is limited

An analysis of querying behaviors between domain knowledgeable users and novice users

Users with different amount of domain or topic knowledge tend to find different results, when searching on the same topic. We were interested in finding out how domain or topic knowledge is associated with users’ querying behaviors, which would result in different results. In this study, thirty-five students participated in a user experiment, conducted searches from genomics data set that was used by TREC Genomics track 2004. The participants’ background domain knowledge and the knowledge of each individual topics was assessed, before they performed the search tasks. Their querying behaviors were recorded. The results demonstrate that domain knowledge level is significantly associated with the number of search terms in queries: the more knowledgeable, the more search terms. Further analyses are planned to be conducted.ye

Spatial residual blocks combined parallel network for hyperspectral image classification.

In hyperspectral image (HSI) classification, there are challenges of the spatial variation in spectral features and the lack of labeled samples. In this paper, a novel spatial residual blocks combined parallel network (SRPNet) is proposed for HSI classification. Firstly, the spatial residual blocks extract spatial features from rich spatial contexts information, which can be used to deal with the spatial variation of spectral signatures. Especially, the skip connection in spatial residual blocks is conducive to the backpropagation of gradients and mitigates the declining-accuracy phenomenon in the deep network. Secondly, the parallel structure is employed to extract spectral features. Spectral feature learning on parallel branches contains fewer independent connection weighs through parameter sharing. Thus, fewer parameters of the network require a lesser number of training samples. Furthermore, the feature fusion is conducted on the multi-scale features from different layers in the spectral feature learning part. Extensive experiments of three representative HSI data sets illustrate the effectiveness of the proposed network

Potential Odor Intensity Grid Based UAV Path Planning Algorithm with Particle Swarm Optimization Approach

International audienceThis paper proposes a potential odor intensity grid based optimization approach for unmanned aerial vehicle (UAV) path planning with particle swarm optimization (PSO) technique. Odor intensity is created to color the area in the searching space with highest probability where candidate particles may locate. A potential grid construction operator is designed for standard PSO based on different levels of odor intensity. The potential grid construction operator generates two potential location grids with highest odor intensity. Then the middle point will be seen as the final position in current particle dimension. The global optimum solution will be solved as the average. In addition, solution boundaries of searching space in each particle dimension are restricted based on properties of threats in the flying field to avoid prematurity. Objective function is redesigned by taking minimum direction angle to destination into account and a sampling method is introduced. A paired samples -test is made and an index called straight line rate (SLR) is used to evaluate the length of planned path. Experiments are made with other three heuristic evolutionary algorithms. The results demonstrate that the proposed method is capable of generating higher quality paths efficiently for UAV than any other tested optimization techniques

Examining Users’ Knowledge Change in the Task Completion Process

This paper examines the changes of information searchers’ topic knowledge levels in the process of completing information tasks. Multi-session tasks were used in the study, which enables the convenience of eliciting users’ topic knowledge during their process of completing the whole tasks. The study was a 3-session laboratory experiment with 24 participants, each time working on one subtask in an assigned 3-session general task. The general task was either parallel or dependently structured. Questionnaires were administered before and after each session to elicit users’ perceptions of their knowledge levels, task attributes, and other task features, for both the overall task and the sub-tasks. Our results support the assumption that users’ knowledge generally increases after each search session, but there were exceptions in which a “ceiling” effect was shown. We also found that knowledge was correlated with users’ perceptions of task attributes and accomplishment. In addition, task type was found to affect several aspects of knowledge levels and knowledge change. These findings further our understanding of users’ knowledge in information tasks and are thus helpful for information retrieval research and system design

Inferring User Knowledge Level from Eye Movement Patterns

The acquisition of information and the search interaction process is influenced strongly by a person’s use of their knowledge of the domain and the task. In this paper we show that a user’s level of domain knowledge can be inferred from their interactive search behaviors without considering the content of queries or documents. A technique is presented to model a user’s information acquisition process during search using only measurements of eye movement patterns. In a user study (n=40) of search in the domain of genomics, a representation of the participant’s domain knowledge was constructed using self-ratings of knowledge of genomics-related terms (n=409). Cognitive effort features associated with reading eye movement patterns were calculated for each reading instance during the search tasks. The results show correlations between the cognitive effort due to reading and an individual’s level of domain knowledge. We construct exploratory regression models that suggest it is possible to build models that can make predictions of the user’s level of knowledge based on real-time measurements of eye movement patterns during a task session

Insulin-stimulated phosphorylation of protein phosphatase 1 regulatory subunit 12B revealed by HPLC-ESI-MS/MS

BACKGROUND: Protein phosphatase 1 (PP1) is one of the major phosphatases responsible for protein dephosphorylation in eukaryotes. Protein phosphatase 1 regulatory subunit 12B (PPP1R12B), one of the regulatory subunits of PP1, can bind to PP1cδ, one of the catalytic subunits of PP1, and modulate the specificity and activity of PP1cδ against its substrates. Phosphorylation of PPP1R12B on threonine 646 by Rho kinase inhibits the activity of the PP1c-PPP1R12B complex. However, it is not currently known whether PPP1R12B phosphorylation at threonine 646 and other sites is regulated by insulin. We set out to identify phosphorylation sites in PPP1R12B and to quantify the effect of insulin on PPP1R12B phosphorylation by using high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry. RESULTS: 14 PPP1R12B phosphorylation sites were identified, 7 of which were previously unreported. Potential kinases were predicted for these sites. Furthermore, relative quantification of PPP1R12B phosphorylation sites for basal and insulin-treated samples was obtained by using peak area-based label-free mass spectrometry of fragment ions. The results indicate that insulin stimulates the phosphorylation of PPP1R12B significantly at serine 29 (3.02 ± 0.94 fold), serine 504 (11.67 ± 3.33 fold), and serine 645/threonine 646 (2.34 ± 0.58 fold). CONCLUSION: PPP1R12B was identified as a phosphatase subunit that undergoes insulin-stimulated phosphorylation, suggesting that PPP1R12B might play a role in insulin signaling. This study also identified novel targets for future investigation of the regulation of PPP1R12B not only in insulin signaling in cell models, animal models, and in humans, but also in other signaling pathways