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Dehbi, Y., L. Klingbeil, and L. Plümer. "UAV MISSION PLANNING FOR AUTOMATIC EXPLORATION AND SEMANTIC MAPPING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2020 (August 6, 2020): 521–26. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2020-521-2020.
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Abstract. Unmanned Aerial Vehicles (UAVs) are used for the inspection of areas which are otherwise difficult to access. Autonomous monitoring and navigation requires a background knowledge on the surroundings of the vehicle. Most mission planing systems assume collision-free pre-defined paths and do not tolerate a GPS signal outage. Our approach makes weaker assumptions. This paper introduces a mission planing platform allowing for the integration of environmental prior knowledge such as 3D building and terrain models. This prior knowledge is integrated to pre-compute an octomap for collision detection. The semantically rich building models are used to specify semantic user queries such as roof or facade inspection. A reasoning process paves the way for semantic mission planing of hidden and a-priori unknown objects. Subsequent scene interpretation is performed by an incremental parsing process.
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Papoušek, Jan, Radek Pelánek, and Vít Stanislav. "Adaptive Geography Practice Data Set." Journal of Learning Analytics 3, no. 2 (September 17, 2016): 317–21. http://dx.doi.org/10.18608/jla.2016.32.17.
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We present a data set on student learning of geography facts in an open online system slepemapy.cz. The data set has simple format with intuitive interpretation. At the same time it offers rich possibilities for modelling and analysis, for example of prior knowledge, forgetting or response times.
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Schreiber, Jacob M., and William S. Noble. "Finding the optimal Bayesian network given a constraint graph." PeerJ Computer Science 3 (July 3, 2017): e122. http://dx.doi.org/10.7717/peerj-cs.122.
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Despite recent algorithmic improvements, learning the optimal structure of a Bayesian network from data is typically infeasible past a few dozen variables. Fortunately, domain knowledge can frequently be exploited to achieve dramatic computational savings, and in many cases domain knowledge can even make structure learning tractable. Several methods have previously been described for representing this type of structural prior knowledge, including global orderings, super-structures, and constraint rules. While super-structures and constraint rules are flexible in terms of what prior knowledge they can encode, they achieve savings in memory and computational time simply by avoiding considering invalid graphs. We introduce the concept of a “constraint graph” as an intuitive method for incorporating rich prior knowledge into the structure learning task. We describe how this graph can be used to reduce the memory cost and computational time required to find the optimal graph subject to the encoded constraints, beyond merely eliminating invalid graphs. In particular, we show that a constraint graph can break the structure learning task into independent subproblems even in the presence of cyclic prior knowledge. These subproblems are well suited to being solved in parallel on a single machine or distributed across many machines without excessive communication cost.
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Lee, Maria. "Winner of the SLS Annual Conference Best Paper Prize 2016: Knowledge and landscape in wind energy planning." Legal Studies 37, no. 1 (March 2017): 3–24. http://dx.doi.org/10.1111/lest.12156.
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‘Landscape’ is relatively underexplored in legal scholarship, notwithstanding its occasional centrality to legal analysis, and the ways in which law contributes to the shaping of landscape. Wind energy is an especially rich area for the exploration of landscape; not only do wind farms consistently raise concerns about landscape, but the existence of climate change complicates the response to local concern. Most of the legal literature on ‘knowledge’ focuses on the ways in which different ‘expert’ knowledges find their way into, and then shape, legal processes and decisions. This article is more concerned with the ways in which the planning system receives different knowledge claims, and validates some of them. The discussion turns around four tentative categories of knowledge claim: expert or technical knowledge claims; lay (or sometimes local) knowledge claims; prior institutional knowledge claims; and professional planning knowledge claims. Knowledge of the complex, dynamic and plural idea of landscape in any particular case is constructed in the process of decision making and reason giving through a careful layering of these different sorts of knowledge claim.
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Hsu, Chia-Yu, Wenwen Li, and Sizhe Wang. "Knowledge-Driven GeoAI: Integrating Spatial Knowledge into Multi-Scale Deep Learning for Mars Crater Detection." Remote Sensing 13, no. 11 (May 28, 2021): 2116. http://dx.doi.org/10.3390/rs13112116.
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This paper introduces a new GeoAI solution to support automated mapping of global craters on the Mars surface. Traditional crater detection algorithms suffer from the limitation of working only in a semiautomated or multi-stage manner, and most were developed to handle a specific dataset in a small subarea of Mars’ surface, hindering their transferability for global crater detection. As an alternative, we propose a GeoAI solution based on deep learning to tackle this problem effectively. Three innovative features are integrated into our object detection pipeline: (1) a feature pyramid network is leveraged to generate feature maps with rich semantics across multiple object scales; (2) prior geospatial knowledge based on the Hough transform is integrated to enable more accurate localization of potential craters; and (3) a scale-aware classifier is adopted to increase the prediction accuracy of both large and small crater instances. The results show that the proposed strategies bring a significant increase in crater detection performance than the popular Faster R-CNN model. The integration of geospatial domain knowledge into the data-driven analytics moves GeoAI research up to the next level to enable knowledge-driven GeoAI. This research can be applied to a wide variety of object detection and image analysis tasks.
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Peterson, A. Townsend, Adolfo G. Navarro-Sigüenza, and Enrique Martínez-Meyer. "Digital Accessible Knowledge and well-inventoried sites for birds in Mexico: baseline sites for measuring faunistic change." PeerJ 4 (September 7, 2016): e2362. http://dx.doi.org/10.7717/peerj.2362.
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BackgroundFaunal change is a basic and fundamental element in ecology, biogeography, and conservation biology, yet vanishingly few detailed studies have documented such changes rigorously over decadal time scales. This study responds to that gap in knowledge, providing a detailed analysis of Digital Accessible Knowledge of the birds of Mexico, designed to marshal DAK to identify sites that were sampled and inventoried rigorously prior to the beginning of major global climate change (1980).MethodsWe accumulated DAK records for Mexican birds from all relevant online biodiversity data portals. After extensive cleaning steps, we calculated completeness indices for each 0.05° pixel across the country; we also detected ‘hotspots’ of sampling, and calculated completeness indices for these broader areas as well. Sites were designated as well-sampled if they had completeness indices above 80% and >200 associated DAK records.ResultsWe identified 100 individual pixels and 20 broader ‘hotspots’ of sampling that were demonstrably well-inventoried prior to 1980. These sites are catalogued and documented to promote and enable resurvey efforts that can document events of avifaunal change (and non-change) across the country on decadal time scales.ConclusionsDevelopment of repeated surveys for many sites across Mexico, and particularly for sites for which historical surveys document their avifaunas prior to major climate change processes, would pay rich rewards in information about distributional dynamics of Mexican birds.
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Tsai, Chen-Tse, and Dan Roth. "Concept Grounding to Multiple Knowledge Bases via Indirect Supervision." Transactions of the Association for Computational Linguistics 4 (December 2016): 141–54. http://dx.doi.org/10.1162/tacl_a_00089.
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We consider the problem of disambiguating concept mentions appearing in documents and grounding them in multiple knowledge bases, where each knowledge base addresses some aspects of the domain. This problem poses a few additional challenges beyond those addressed in the popular Wikification problem. Key among them is that most knowledge bases do not contain the rich textual and structural information Wikipedia does; consequently, the main supervision signal used to train Wikification rankers does not exist anymore. In this work we develop an algorithmic approach that, by carefully examining the relations between various related knowledge bases, generates an indirect supervision signal it uses to train a ranking model that accurately chooses knowledge base entries for a given mention; moreover, it also induces prior knowledge that can be used to support a global coherent mapping of all the concepts in a given document to the knowledge bases. Using the biomedical domain as our application, we show that our indirectly supervised ranking model outperforms other unsupervised baselines and that the quality of this indirect supervision scheme is very close to a supervised model. We also show that considering multiple knowledge bases together has an advantage over grounding concepts to each knowledge base individually.
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Calderon, Maria Apolonia, Daniel E. Chand, and Daniel P. Hawes. "Final Lines of Defense: Explaining Policy Advocacy by Immigrant-Serving Organizations." Nonprofit Policy Forum 12, no. 2 (March 8, 2021): 285–310. http://dx.doi.org/10.1515/npf-2020-0023.
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Abstract Nonprofit scholars have developed a rich literature on nonprofit advocacy. While the literature is rich, however, gaps remain in our collective knowledge, especially regarding specific sectors of nonprofit human service organizations. Here, we apply existing theory on advocacy by human service organizations to an important subset of the nonprofit community, that being immigrant-serving organizations (ISOs). Most prior research on nonprofit advocacy has not focused on politically polarized issues, such as contemporary immigration policy. Using a nationwide survey of ISOs, we find that unlike other types of human service organizations, the majority of ISOs do engage in at least some forms of policy advocacy. However, those that report using the H-election status on their Form 990s are significantly more likely to engage in advocacy and do so to a wide variety of policymakers, including legislators, chief executives, and even local law enforcement agencies. H-election groups are also more likely to perceive their advocacy activities as effective. These findings add to the evolving knowledge on when and how human service groups seek policy change for marginalized groups.
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Zhang, Zhenyu, Xiaobo Shu, Bowen Yu, Tingwen Liu, Jiapeng Zhao, Quangang Li, and Li Guo. "Distilling Knowledge from Well-Informed Soft Labels for Neural Relation Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9620–27. http://dx.doi.org/10.1609/aaai.v34i05.6509.
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Extracting relations from plain text is an important task with wide application. Most existing methods formulate it as a supervised problem and utilize one-hot hard labels as the sole target in training, neglecting the rich semantic information among relations. In this paper, we aim to explore the supervision with soft labels in relation extraction, which makes it possible to integrate prior knowledge. Specifically, a bipartite graph is first devised to discover type constraints between entities and relations based on the entire corpus. Then, we combine such type constraints with neural networks to achieve a knowledgeable model. Furthermore, this model is regarded as teacher to generate well-informed soft labels and guide the optimization of a student network via knowledge distillation. Besides, a multi-aspect attention mechanism is introduced to help student mine latent information from text. In this way, the enhanced student inherits the dark knowledge (e.g., type constraints and relevance among relations) from teacher, and directly serves the testing scenarios without any extra constraints. We conduct extensive experiments on the TACRED and SemEval datasets, the experimental results justify the effectiveness of our approach.
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Broadhead, Geoffrey D., and Allen W. Burton. "The Legacy of Early Adapted Physical Activity Research." Adapted Physical Activity Quarterly 13, no. 2 (April 1996): 116–26. http://dx.doi.org/10.1123/apaq.13.2.116.
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In this opinion paper, we pose the question whether the current generation of scholars have taken advantage of the rich legacy of early adapted physical activity (APA) research. We believe that this legacy often has been ignored, even though it holds many treasures waiting to be rediscovered. We begin with a brief description of the knowledge base in APA prior to 1980, then evaluate the present recognition of past research contributions. Finally, we recommend how students, professionals, and researchers might be encouraged to take advantage of the vast body of literature in APA and related fields.
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Liu, Yan, Bin Guo, Daqing Zhang, Djamal Zeghlache, Jingmin Chen, Sizhe Zhang, Dan Zhou, Xinlei Shi, and Zhiwen Yu. "MetaStore: A Task-adaptative Meta-learning Model for Optimal Store Placement with Multi-city Knowledge Transfer." ACM Transactions on Intelligent Systems and Technology 12, no. 3 (April 22, 2021): 1–23. http://dx.doi.org/10.1145/3447271.
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Optimal store placement aims to identify the optimal location for a new brick-and-mortar store that can maximize its sale by analyzing and mining users’ preferences from large-scale urban data. In recent years, the expansion of chain enterprises in new cities brings some challenges because of two aspects: (1) data scarcity in new cities, so most existing models tend to not work (i.e., overfitting), because the superior performance of these works is conditioned on large-scale training samples; (2) data distribution discrepancy among different cities, so knowledge learned from other cities cannot be utilized directly in new cities. In this article, we propose a task-adaptative model-agnostic meta-learning framework, namely, MetaStore, to tackle these two challenges and improve the prediction performance in new cities with insufficient data for optimal store placement, by transferring prior knowledge learned from multiple data-rich cities. Specifically, we develop a task-adaptative meta-learning algorithm to learn city-specific prior initializations from multiple cities, which is capable of handling the multimodal data distribution and accelerating the adaptation in new cities compared to other methods. In addition, we design an effective learning strategy for MetaStore to promote faster convergence and optimization by sampling high-quality data for each training batch in view of noisy data in practical applications. The extensive experimental results demonstrate that our proposed method leads to state-of-the-art performance compared with various baselines.
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Bentley, Nokome. "Data and time poverty in fisheries estimation: potential approaches and solutions." ICES Journal of Marine Science 72, no. 1 (March 5, 2014): 186–93. http://dx.doi.org/10.1093/icesjms/fsu023.
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Abstract The increasingly sophisticated methods developed for stock assessment are not always suited to data-poor fisheries. Data-poor fisheries are often low in value, so the researcher time available for their assessment is also small. The dual constraints of reduced data and reduced time make stock assessments for low-value stocks particularly challenging. Prior probability distributions are useful for transferring knowledge from data-rich to data-poor fisheries. When data are limited, it is important to make the most of what few data is available. However, fully understanding potential biases in data are just as important in the data-poor context as it is in data-rich fisheries. A key aspect of stock assessment is peer review. Providing a comprehensive, yet concise, set of diagnostics is crucial to a stock assessment where time is limited. Against the standards by which data-rich stock assessments are judged, stock assessments for data-poor stocks are likely to be found deficient. A key challenge is to maintain a balance between the opposing risks of inappropriate management “action” due to assessment inaccuracy, and inappropriate management “inaction” due to assessment uncertainty.
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Poggiolini, Ilaria, Daniela Saverioni, and Piero Parchi. "Prion Protein Misfolding, Strains, and Neurotoxicity: An Update from Studies on Mammalian Prions." International Journal of Cell Biology 2013 (2013): 1–24. http://dx.doi.org/10.1155/2013/910314.
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Prion diseases, also known as transmissible spongiform encephalopathies (TSEs), are a group of fatal neurodegenerative disorders affecting humans and other mammalian species. The central event in TSE pathogenesis is the conformational conversion of the cellular prion protein,PrPC, into the aggregate,β-sheet rich, amyloidogenic form,PrPSc. Increasing evidence indicates that distinctPrPScconformers, forming distinct ordered aggregates, can encipher the phenotypic TSE variants related to prion strains. Prion strains are TSE isolates that, after inoculation into syngenic hosts, cause disease with distinct characteristics, such as incubation period, pattern ofPrPScdistribution, and regional severity of histopathological changes in the brain. In analogy with other amyloid forming proteins,PrPSctoxicity is thought to derive from the existence of various intermediate structures prior to the amyloid fiber formation and/or their specific interaction with membranes. The latter appears particularly relevant for the pathogenesis of TSEs associated with GPI-anchoredPrPSc, which involves major cellular membrane distortions in neurons. In this review, we update the current knowledge on the molecular mechanisms underlying three fundamental aspects of the basic biology of prions such as the putative mechanism of prion protein conversion to the pathogenic formPrPScand its propagation, the molecular basis of prion strains, and the mechanism of induced neurotoxicity byPrPScaggregates.
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Tsai, HsingYi, Deborah Compeau, and Darren Meister. "Voluntary Use of Information Technology: An Analysis and Synthesis of the Literature." Journal of Information Technology 32, no. 2 (June 2017): 147–62. http://dx.doi.org/10.1057/jit.2016.6.
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Voluntariness is recognized as an important influence on individual and collective technology acceptance. We conducted a comprehensive review of this literature and identified a rich set of voluntariness concepts and methods of operationalization. However, while considerable empirical evidence is reported in the literature, our review also revealed inconsistent results concerning the relationship between voluntariness and other concepts. Against that backdrop, we synthesized the literature into three types of voluntariness - perceived, intended and realizable voluntariness (RVOL), and showed how prior literature had not adequately accounted for RVOL. Moreover, we examined the multiple mechanisms that influence voluntariness and created a model to describe how to advance new knowledge about the important relationships among the three types of voluntariness and between voluntariness and user behavior. We argue that these concepts and relationships may help advance our knowledge of how a new technology is used individually and collectively in organizations.
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Streit-Ciećkiewicz, Dominika, Konrad Futyma, Paweł Miotła, Magdalena Emilia Grzybowska, and Tomasz Rechberger. "Platelet-Rich Plasma as Adjuvant Therapy for Recurrent Vesicovaginal Fistula: A Prospective Case Series." Journal of Clinical Medicine 8, no. 12 (December 2, 2019): 2122. http://dx.doi.org/10.3390/jcm8122122.
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Vesicovaginal fistula (VVF) is the nonphysiological communication between the bladder and vagina, and surgical closure is the gold treatment standard. Despite that successful closure occurs in around 85% of patients after the first repair, recurrence remains a highly distressing complication for patients and surgeons. The aim of our study was to evaluate the efficacy of a platelet-rich plasma (PRP) injection as a supportive treatment in the surgical repair of recurrent VVF. Between January 2018 and July 2019, 16 patients with recurrent VVF were injected with PRP in a tertiary gynecological department. Subsequently, a surgical Latzko procedure for VVF closure was scheduled 6–8 weeks after the PRP injection allowing proper neovascularization and remodeling of surrounding tissues. Patients were considered cured if no leakage was observed after surgery and negative dye test results were indicated at follow-up. All patients who were examined therein remained dry. To the best of our knowledge, this is the first study aiming to assess PRP injections as a supporting treatment prior to surgical procedure for recurrent VVF. Preliminary results are encouraging, and we incorporated this method in our clinical practice. Further reports on a larger group will follow.
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Li, Xuguang, Andrew Cox, and Zefeng Wang. "How do social network sites support product users’ knowledge construction? A study of LinkedIn." Online Information Review 42, no. 3 (June 11, 2018): 304–23. http://dx.doi.org/10.1108/oir-04-2017-0133.
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Purpose Social network sites are emerging as a popular communication tool for knowledge sharing and construction. LinkedIn, which concentrates on professional networking, is reported to generate great informational benefits to its users. The purpose of this paper is to explore product users’ knowledge construction in solving technical problems on LinkedIn, which was chosen as a case example. Design/methodology/approach Discussion threads with rich knowledge elements were selected from an interest group about solving technical problems with laptops. Adopting a qualitative content analysis method, selected threads were analysed with a prior analysis framework built in the context of traditional IT company sponsored peer user support forums. Findings The analysis revealed that the iterative and progressive knowledge construction process and associated trial-and-error strategy used on LinkedIn are similar to those found in peer support forums. However, LinkedIn members are more engaged in knowledge construction episodes. Meanwhile, the sub-category “proposing a new idea” accounts for a larger portion of discussions reflecting the high-level of expertise. One-to-one direct interaction is quite salient. Therefore, LinkedIn can support knowledge construction in a more efficient way due to the character of its social capital, including trust, sense of belonging, norms of cooperation, visible identity, knowledge articulation skills, one-to-one direct interaction and suitable strength of ties. Originality/value This research is novel in empirically revealing how LinkedIn attributes and its social capital attributes interact with each other and together facilitate an efficient knowledge construction process.
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Xie, Gangcai, Chengliang Dong, Yinfei Kong, Jiang Zhong, Mingyao Li, and Kai Wang. "Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features." Genes 10, no. 3 (March 21, 2019): 240. http://dx.doi.org/10.3390/genes10030240.
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Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design of clinical trials. Both clinical features and molecular data can be used for this purpose, for instance, to predict the survival of patients censored at specific time points. Multi-omics data, including genome-wide gene expression, methylation, protein expression, copy number alteration, and somatic mutation data, are becoming increasingly common in cancer studies. To harness the rich information in multi-omics data, we developed GDP (Group lass regularized Deep learning for cancer Prognosis), a computational tool for survival prediction using both clinical and multi-omics data. GDP integrated a deep learning framework and Cox proportional hazard model (CPH) together, and applied group lasso regularization to incorporate gene-level group prior knowledge into the model training process. We evaluated its performance in both simulated and real data from The Cancer Genome Atlas (TCGA) project. In simulated data, our results supported the importance of group prior information in the regularization of the model. Compared to the standard lasso regularization, we showed that group lasso achieved higher prediction accuracy when the group prior knowledge was provided. We also found that GDP performed better than CPH for complex survival data. Furthermore, analysis on real data demonstrated that GDP performed favorably against other methods in several cancers with large-scale omics data sets, such as glioblastoma multiforme, kidney renal clear cell carcinoma, and bladder urothelial carcinoma. In summary, we demonstrated that GDP is a powerful tool for prognosis of patients with cancer, especially when large-scale molecular features are available.
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Wijekumar, Kausalai (Kay), Bonnie J. F. Meyer, Puiwa Lei, Weiyi Cheng, Xuejun Ji, and R. M. Joshi. "Evidence of an Intelligent Tutoring System as a Mindtool to Promote Strategic Memory of Expository Texts and Comprehension With Children in Grades 4 and 5." Journal of Educational Computing Research 55, no. 7 (April 5, 2017): 1022–48. http://dx.doi.org/10.1177/0735633117696909.
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Reading and comprehending content area texts require learners to effectively select and encode with hierarchically strategic memory structures in order to combine new information with prior knowledge. Unfortunately, evidence from state and national tests shows that children fail to successfully navigate the reading comprehension challenges they face. Schools have struggled to find approaches that can help children succeed in this important task. Typical instruction in classrooms across the country has focused on procedural application of strategies or content-focused approaches that encourage rich discussions. Both approaches have achieved success but have limitations-related transparency and specificity of scaffolds and guidance for the teacher and learner in today’s diverse and complex classroom settings. The text structure strategy combines content and strategy to provide pragmatic, transparent, and scaffolded instruction addressing these challenges. A web-based intelligent tutoring system for the text structure strategy, named ITSS, was designed and developed to provide consistent and high-quality instruction to learners in Grades 4 and 5 about how to read, select main ideas, encode strategic memory structures, make inferences, and monitor comprehension during reading. In this article, we synthesize results from two recent large-scale randomized controlled studies to showcase how the ITSS supports selection and encoding of students’ strategic memory structures and how prior knowledge affects the memory structures. We provide greater depth of information about such processing than examined and reported in extant literature about overall increases in reading comprehension resulting from students using ITSS.
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Tscholl, Michael, Jason Morphew, and Robb Lindgren. "Inferences on enacted understanding: using immersive technologies to assess intuitive physical science knowledge." Information and Learning Sciences 122, no. 7/8 (July 16, 2021): 503–24. http://dx.doi.org/10.1108/ils-12-2020-0260.
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Purpose This study aims to advance the proposal to use immersive virtual learning environments to stimulate and reveal deep-seated knowledge about science, giving instructors and researchers unique possibilities for assessing and identifying intuitive physical science knowledge. Aside from the ability to present rich and dynamic stimuli, these environments afford bodily enactment of people’s understanding, which draws less from declarative knowledge stores and more from everyday experiences with the physical world. Design/methodology/approach The authors ground their proposal in a critical review of the impact of stimulus and task characteristics of traditional physics inventories. Using a grounded theory approach, the authors present classifications and interpretations of observed bodily enactments of physics understandings in a study where participants enacted their understanding of force and motion of space in an immersive, interactive mixed reality (MR) environment. Findings The authors find that instances of these action categories can be interpreted as relating to underlying knowledge, often identified by other studies. The authors thus replicate a number of prior findings, which provide evidence to establish validation for using MR simulation as a tool for identifying people’s physical intuitions. Research limitations/implications This study targeted only a few specific physical science scenarios. Further, while a number of key insights about student knowledge came from the analysis, many of the observations are mere leads in need of further investigation and interpretation rather than core findings. Originality/value Immersive digital learning environments are primarily used for instruction. The authors propose to use and design them for assessment as well. This paper should prompt more research and development in this direction.
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Hu, Kejin. "Become Competent within One Day in Generating Boxplots and Violin Plots for a Novice without Prior R Experience." Methods and Protocols 3, no. 4 (September 23, 2020): 64. http://dx.doi.org/10.3390/mps3040064.
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The boxplot is a powerful visualization tool of sampled continuous data sets because of its rich information delivered, compact size, and effective visual expression. The advantage of boxplots is not widely appreciated. Many top journals suggest that boxplots should be used in place of bar charts, but have been wrongly replaced by bar charts. One technical barrier to the usage of boxplots in reporting quantitative data is that bench scientists are not competent in generating boxplots, and are afraid of R, a programming tool. This tutorial provides an effective training material in that even a novice without prior R experience can become competent, within one day, in generating professional boxplots. The available R scripts for boxplots are very limited in scope and are aimed at specialists, and the bench scientists have difficulty in following these scripts. This tutorial provides extensive step-by-step R scripts and instructions, as well as 29 illustrations for customizing every detail of the boxplot structures. Basic R commands and concepts are introduced for users without prior R experiences, which can be skipped by audiences with R knowledge. Violin plots are the enhanced version of boxplots, and therefore, this tutorial also provides a brief introduction and usage of the R package vioplot with one additional illustration. While the protocol is prepared for the newbies and trainees it will be a handy tool for infrequent users, and may benefit the experienced users as well since it provides scripts for customizing every detail of boxplots.
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Boulet, Sandrine, Moreno Ursino, Peter Thall, Bruno Landi, Céline Lepère, Simon Pernot, Anita Burgun, et al. "Integration of elicited expert information via a power prior in Bayesian variable selection: Application to colon cancer data." Statistical Methods in Medical Research 29, no. 2 (April 9, 2019): 541–67. http://dx.doi.org/10.1177/0962280219841082.
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Background Building tools to support personalized medicine needs to model medical decision-making. For this purpose, both expert and real world data provide a rich source of information. Currently, machine learning techniques are developing to select relevant variables for decision-making. Rather than using data-driven analysis alone, eliciting prior information from physicians related to their medical decision-making processes can be useful in variable selection. Our framework is electronic health records data on repeated dose adjustment of Irinotecan for the treatment of metastatic colorectal cancer. We propose a method that incorporates elicited expert weights associated with variables involved in dose reduction decisions into the Stochastic Search Variable Selection (SSVS), a Bayesian variable selection method, by using a power prior. Methods Clinician experts were first asked to provide numerical clinical relevance weights to express their beliefs about the importance of each variable in their medical decision making. Then, we modeled the link between repeated dose reduction, patient characteristics, and toxicities by assuming a logistic mixed-effects model. Simulated data were generated based on the elicited weights and combined with the observed dose reduction data via a power prior. We compared the Bayesian power prior-based SSVS performance to the usual SSVS in our case study, including a sensitivity analysis using the power prior parameter. Results The selected variables differ when using only expert knowledge, only the usual SSVS, or combining both. Our method enables one to select rare variables that may be missed using only the observed data and to discard variables that appear to be relevant based on the data but not relevant from the expert perspective. Conclusion We introduce an innovative Bayesian variable selection method that adaptively combines elicited expert information and real world data. The method selects a set of variables relevant to model medical decision process.
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Wilson, David, and Norelle L. Daly. "Nuclear Magnetic Resonance seq (NMRseq): A New Approach to Peptide Sequence Tags." Toxins 10, no. 11 (October 28, 2018): 437. http://dx.doi.org/10.3390/toxins10110437.
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Structural analysis of peptides with nuclear magnetic resonance (NMR) spectroscopy generally relies on knowledge of the primary sequence to enable assignment of the resonances prior to determination of the three-dimensional structure. Resonance assignment without knowledge of the sequence is complicated by redundancy in amino acid type, making complete de novo sequencing using NMR spectroscopy unlikely to be feasible. Despite this redundancy, we show here that NMR spectroscopy can be used to identify short sequence tags that can be used to elucidate full-length peptide sequences via database searching. In the current study, we have used this approach to identify conotoxins from the venom of the cone snail Conus geographus and determined the three-dimensional structure of a member of the I3 superfamily. This approach is most likely to be useful for the characterization of disulfide-rich peptides, such as those that were chosen for this study, as they generally have well-defined structures, which enhances the quality of the NMR spectra. In contrast to other sequencing methods, the lack of sample manipulation, such as protease digestion, allows for subsequent bioassays to be carried out using the native sample used for sequence identification.
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Ewais, Ahmed, and Olga De Troyer. "Usability Evaluation of an Adaptive 3D Virtual Learning Environment." International Journal of Virtual and Personal Learning Environments 4, no. 1 (January 2013): 16–31. http://dx.doi.org/10.4018/jvple.2013010102.
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Using 3D virtual environments for educational purposes is becoming attractive because of their rich presentation and interaction capabilities. Furthermore, dynamically adapting the 3D virtual environment to the personal preferences, prior knowledge, skills and competence, learning goals, and the personal or (social) context in which the learning takes place becomes interesting, as there is a bulk of research demonstrating that individualized instruction is superior to the uniform approach of more traditional and one-size-fits-all teaching approaches. However, although such adaptive 3D Virtual Learning Environments (3D VLE) seem to be promising, this needs to be evaluated in practice. Usability of adaptive 3D VLE could be a problem since the user interface could become relatively complex. In this paper, the authors describe an experiment performed to validate the issues of usability and acceptability of an adaptive 3D VLE. This pilot evaluation reveals some important recommendations and improvements.
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KANGMYUNGHEE, Koo, Jina, Park, Hyojin, Kim, MinJeong, and Kim, Miyoung. "Investigating the Relationships among Prior knowledge, Cognitive Load, Flow, and Achievement." Journal of Research in Curriculum Instruction 13, no. 2 (June 2009): 369–91. http://dx.doi.org/10.24231/rici.2009.13.2.369.
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Li, Liang, Weirui Ye, Mingsheng Long, Yateng Tang, Jin Xu, and Jianmin Wang. "Simultaneous Learning of Pivots and Representations for Cross-Domain Sentiment Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 8220–27. http://dx.doi.org/10.1609/aaai.v34i05.6336.
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Cross-domain sentiment classification aims to leverage useful knowledge from a source domain to mitigate the supervision sparsity in a target domain. A series of approaches depend on the pivot features that behave similarly for polarity prediction in both domains. However, the engineering of such pivot features remains cumbersome and prevents us from learning the disentangled and transferable representations from rich semantic and syntactic information. Towards learning the pivots and representations simultaneously, we propose a new Transferable Pivot Transformer (TPT). Our model consists of two networks: a Pivot Selector that learns to detect transferable n-gram pivots from contexts, and a Transferable Transformer that learns to generate domain-invariant representations by modeling the correlation between pivot and non-pivot words. The Pivot Selector and Transferable Transformer are jointly optimized through end-to-end back-propagation. We experiment with real tasks of cross-domain sentiment classification over 20 domain pairs where our model outperforms prior arts.
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Ang, Nicole P., and Ken T. Trotman. "The Utilization of Quantitative and Qualitative Information in Groups' Capital Investment Decisions." Behavioral Research in Accounting 27, no. 1 (September 1, 2014): 1–24. http://dx.doi.org/10.2308/bria-50917.
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ABSTRACT Organizations frequently use interactive groups to make strategic decisions, aiming to capitalize on individual members' unique knowledge. However, research shows that groups focus on information that members have in common, not unique information, resulting in suboptimal outcomes. Given that accounting systems can present information in various forms, we experimentally examine whether quantitative information results in greater information sharing and use than qualitative information. We take advantage of a rich dataset created by videoing groups making a capital investment decision. Consistent with prior research, we find that groups prefer common to unique information, regardless of whether it is quantitative or qualitative. However, individuals use quantitative information more than qualitative information before group interaction, and make more references to it during discussion. Added insights from the videos include identifying what determines greater use of quantitative cues, the importance of the numbers attached to cues, and how successful groups use quantitative cues. Data Availability: Please contact the authors.
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Rosenbaum, David, A. M. Elizabeth More, and Peter Steane. "Applying Grounded Theory to Investigating Change Management in the Nonprofit Sector." SAGE Open 6, no. 4 (October 2016): 215824401667920. http://dx.doi.org/10.1177/2158244016679209.
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Grounded theory is well supported as a qualitative research method that historically responded to the epistemological challenges of defining knowledge and determining how it has been acquired. While its historical and unique methodological underpinnings remain consistent, its ongoing application and methods of execution continue to expand its use. The consideration of using grounded theory by researchers embodies the need to explore the methodology and thereafter seek to develop the method that reflects the researcher’s skills, the research setting, and the research aims. This article sets out a particular method of applying it to the study of change management using a rich single case study in the nonprofit sector. Key findings are that nonprofit specific change management models may need to incorporate a focus on formal reflection for change agents and change recipients, development of trust and confidence in the organization prior to the actual change, focusing on the individual experience of change, and recognizing the sequencing of events from a planning perspective.
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Scott, Bernard, and Abhinav Bansal. "Learning about learning: a cybernetic model of skill acquisition." Kybernetes 43, no. 9/10 (November 3, 2014): 1399–411. http://dx.doi.org/10.1108/k-07-2014-0157.
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Purpose – The purpose of this paper is to explain some phenomena observed in the acquisition of motor skills: the loss of conscious access to knowledge of the structure of a skill and the awareness that an error has been made prior to the receipt of knowledge of results. Although there are rich descriptive accounts of skill acquisition in the literature, there are no satisfactory explanatory models of the cognitive processes involved. The paper provides such a model. Design/methodology/approach – In the 1970s, the first author implemented a computer program model of the cognitive processes involved in learning and skill acquisition, based on a series of empirical investigations. Recently, with assistance from the second author, the model has been reviewed, updated and re-implemented. The paper sets this work in the broader context of a theory of learning and teaching, conversation theory. Findings – The model provides a constructivist account of skill acquisition and associated phenomena. The model provides theoretical foundations for conversation theory. Practical implications – The model adds to the understanding of motor skill acquisition and to the understanding of processes of learning and teaching in general. Originality/value – The model and its interpretation are an original contribution to the skills acquisition literature.
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Gerçek, Cem. "PROSPECTIVE TEACHERS’ COGNITIVE STRUCTURES CONCERNING PROTEIN SYNTHESIS AND THEIR DEGREE OF UNDERSTANDING." Journal of Baltic Science Education 17, no. 1 (February 20, 2018): 19–30. http://dx.doi.org/10.33225/jbse/18.17.19.
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The purpose of education is to actualise meaningful learning. Therefore, researching the issues on how students process information and how they configure it is important for meaningful learning. The issue of protein synthesis contains a number of abstract topics and concepts. Hence, it is important in biology teaching to be informed of students’ cognitive structures concerning protein synthesis. This research aims to analyse prospective teachers’ cognitive structures about protein synthesis and their degree of understanding the subject. The research group was composed of 17 volunteering prospective teachers who had been chosen through purposeful sampling. The data were collected via semi-structured interviews. Flow maps and content analysis were used in analysing the data. The results demonstrated that prospective teachers had too many misconceptions about protein synthesis and that their knowledge extent and rich connection are inadequate. The prospective teachers’ degree of understanding protein synthesis was divided into three categories. The results obtained in this research suggested that teachers should be careful in teaching the subject of protein synthesis. Students’ prior knowledge and their misconceptions should be determined and content or contexts to facilitate them to learn an abstract subject such as protein synthesis should be presented. Keywords: cognitive structure, degree of understanding, flow map, meaningful learning, protein synthesis
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Song, Shiran, Jianhua Liu, Yuan Liu, Guoqiang Feng, Hui Han, Yuan Yao, and Mingyi Du. "Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery." Sensors 20, no. 2 (January 10, 2020): 397. http://dx.doi.org/10.3390/s20020397.
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High spatial resolution remote sensing image (HSRRSI) data provide rich texture, geometric structure, and spatial distribution information for surface water bodies. The rich detail information provides better representation of the internal components of each object category and better reflects the relationships between adjacent objects. In this context, recognition methods such as geographic object-based image analysis (GEOBIA) have improved significantly. However, these methods focus mainly on bottom-up classifications from visual features to semantic categories, but ignore top-down feedback which can optimize recognition results. In recent years, deep learning has been applied in the field of remote sensing measurements because of its powerful feature extraction ability. A special convolutional neural network (CNN) based region proposal generation and object detection integrated framework has greatly improved the performance of object detection for HSRRSI, which provides a new method for water body recognition based on remote sensing data. This study uses the excellent “self-learning ability” of deep learning to construct a modified structure of the Mask R-CNN method which integrates bottom-up and top-down processes for water recognition. Compared with traditional methods, our method is completely data-driven without prior knowledge, and it can be regarded as a novel technical procedure for water body recognition in practical engineering application. Experimental results indicate that the method produces accurate recognition results for multi-source and multi-temporal water bodies, and can effectively avoid confusion with shadows and other ground features.
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Turner, Michelle. "Beyond the iron triangle: reflections of an early career academic." International Journal of Managing Projects in Business 9, no. 4 (September 5, 2016): 892–902. http://dx.doi.org/10.1108/ijmpb-01-2016-0005.
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Purpose The Rethinking Project Management (RPM) research agenda has been influential in multiple domains. These include industry, education and research. In response to the call for papers for this special issue, the purpose of this paper is to consider RPM with a particular focus on the human side of project management. Design/methodology/approach Prior to joining academia, the author worked as a project manager for 15 years. This provided an opportunity for the author to consider the influence of RPM from three viewpoints: project practitioner; project educator; and researcher in project management. Findings Resources originating from project management bodies of knowledge and professional associations relating to the human side of project management are limited. This serves to emphasize the importance of the RPM-inspired research and its influence on the teaching and education of project professionals. The RPM agenda has also served to endorse a research agenda which is wide ranging and one that seeks to better understand and support the human element of project management. Originality/value RPM has encouraged researchers to consider project management beyond classical project management and the iron triangle of time, cost and quality. In doing so, there has emerged a rich and diverse body of knowledge which underpins the human element of project management and positively impacts the skills development of project professionals and the practice of project management.
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Tang, Pingjie, Meng Jiang, Bryan (Ning) Xia, Jed W. Pitera, Jeffrey Welser, and Nitesh V. Chawla. "Multi-Label Patent Categorization with Non-Local Attention-Based Graph Convolutional Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9024–31. http://dx.doi.org/10.1609/aaai.v34i05.6435.
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Patent categorization, which is to assign multiple International Patent Classification (IPC) codes to a patent document, relies heavily on expert efforts, as it requires substantial domain knowledge. When formulated as a multi-label text classification (MTC) problem, it draws two challenges to existing models: one is to learn effective document representations from text content; the other is to model the cross-section behavior of label set. In this work, we propose a label attention model based on graph convolutional network. It jointly learns the document-word associations and word-word co-occurrences to generate rich semantic embeddings of documents. It employs a non-local attention mechanism to learn label representations in the same space of document representations for multi-label classification. On a large CIRCA patent database, we evaluate the performance of our model and as many as seven competitive baselines. We find that our model outperforms all those prior state of the art by a large margin and achieves high performance on P@k and nDCG@k.
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Wahyu, Kamirsyah, Dwi Ratnasari, Sofyan Mahfudy, and Desventri Etmy. "Mathematics Teachers and Digital Technology: A Quest for Teachers’ Professional Development in Indonesia." JRAMathEdu (Journal of Research and Advances in Mathematics Education) 1, no. 1 (January 29, 2019): 31–44. http://dx.doi.org/10.23917/jramathedu.v1i1.7547.
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This article aims to explore a possible criterion of digital technology mathematics teachers’ professional development[1]. The criterion was canvassed through qualitative exploratory study which involve a hybrid model of DigiTech TPD, online published articles of related TPD, and theoretical perspective which relate to digital technology in mathematics education. Related frameworks (Drijverset al, 2010; Trocki & Hollebrands, 2018) and content analysis were utilized to analyze the first two data. Theoretical perspectives of digital technology in mathematics education were accounted to reflect prior data and explore the criterion. We found that the current TPD[2]has not developed the knowledge of task design and supported teachers' roles in orchestrating technology-rich mathematics teaching as seen in the low level of tasks and teachers' orchestration in the classroom. Related articles on TPD in Indonesia show that the programs have not touched decisive factors of successfully implementing digital technology. An alternative criterion for DigiTech TPD is explored which includes three aspects namely theoretical approach, model and content. It could be alternative point of departure for designing and conducting DigiTech TPD in Indonesia.
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Wahyu, Kamirsyah, Dwi Ratnasari, Sofyan Mahfudy, and Desventri Etmy. "Mathematics Teachers and Digital Technology: A Quest for Teachers’ Professional Development in Indonesia." JRAMathEdu (Journal of Research and Advances in Mathematics Education) 4, no. 1 (January 29, 2019): 31–44. http://dx.doi.org/10.23917/jramathedu.v4i1.7547.
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This article aims to explore a possible criterion of digital technology mathematics teachers’ professional development[1]. The criterion was canvassed through qualitative exploratory study which involve a hybrid model of DigiTech TPD, online published articles of related TPD, and theoretical perspective which relate to digital technology in mathematics education. Related frameworks (Drijverset al, 2010; Trocki Hollebrands, 2018) and content analysis were utilized to analyze the first two data. Theoretical perspectives of digital technology in mathematics education were accounted to reflect prior data and explore the criterion. We found that the current TPD[2]has not developed the knowledge of task design and supported teachers' roles in orchestrating technology-rich mathematics teaching as seen in the low level of tasks and teachers' orchestration in the classroom. Related articles on TPD in Indonesia show that the programs have not touched decisive factors of successfully implementing digital technology. An alternative criterion for DigiTech TPD is explored which includes three aspects namely theoretical approach, model and content. It could be alternative point of departure for designing and conducting DigiTech TPD in Indonesia.
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Todorovich, John R., Daniel K. Drost, F. Stephen Bridges, and Christopher K. Wirth. "A Doctoral Degree in Physical Education and Health: A Next Generation Perspective." Kinesiology Review 4, no. 4 (November 2015): 403–8. http://dx.doi.org/10.1123/kr.2015-0042.
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Disciplinary isolation has facilitated health education, public health, and physical education professionals to sometimes pursue common goals without the benefit of interdisciplinary collaboration and perspectives. Recognizing the potential benefits of interdisciplinary collaboration efforts to solve complex problems, faculty members at the University of West Florida developed an innovative doctoral program combining the disciplines of physical education, health education, and health promotion. Beginning with the salient common ground of issues related to engagement in physical activity, the program is designed to explore, compare, and contrast best practices in research and practice from each discipline. Benefits include synergistic solutions to common problems, graduates who transcend traditional professional silos to be more impactful, and the creation of innovative research endeavors. Graduates also find that they meet contemporary workforce needs outside of academia and are more marketable as faculty in kinesiology and health-related departments because of their rich, multidisciplinary knowledge base. Challenges to program implementation include prior student socialization from traditional studies in their disciplines and faculty working to move beyond their professional comfort zones to collaboratively mentor students in the program.
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Lera, Maria-Jose. "Colonización y colonialidad: un estudio de Alcalá de Guadaíra." Anduli, no. 20 (2021): 137–58. http://dx.doi.org/10.12795/anduli.2021.i20.08.
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Colonization processes have their continuation in the colonized person, a phenomenon studied by psychiatrists Fanon and Memmi in the 1960s. Currently, this phenomenon is defined as coloniality and is made up of three dimensions: the coloniality of power, knowledge and being. For this study, we wondered if the conquest of Andalusia continued in coloniality, and if so whether it has consequences today. To address these issues, we analyzed the coloniality of power and being and their consequences in the population of Alcalá de Guadaíra using the bibliographic sources that exist in that locality. The results indicate that it is a population with no history prior to the conquest, structured in poor workers and wealthy landowners, with collective trauma, sheltered in family and religion, and without cultural references. The current consequences of this coloniality are observed in the loss of historical heritage, the preservation of a fractured population of rich and poor, and no memory of the trauma experienced. The recovery of historical consciousness is identified as one of the keys to overcoming this coloniality
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Johnstone, Justine. "Towards a creativity research agenda in information ethics." International Review of Information Ethics 7 (September 1, 2007): 316–25. http://dx.doi.org/10.29173/irie34.
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The value for human wellbeing and social development of information and its associated tools and technolo-gies is no longer controversial. While still less well-endowed than other regions, Africa has growing numbers of print and electronic journals, funding programmes, and researcher and practitioner networks concerned with the generation and use of information in multiple domains. Most of this activity focuses on information as a knowledge resource, providing the factual basis for policy and intervention. By contrast more creative applications of information – as the basis for new ideas whether or not they turn out to be factual – has been almost entirely ignored. Being able to generate and develop new ideas is, however, an equally important and arguably a prior capability, but until recently one that has been little understood. Recent advances in cogni-tive science and creativity research are changing this, however, and it is now possible to see how a rich research agenda can be developed concerned with the role of information and information and communica-tion technology (ICT) as creative resources.
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Esterline, Albert, and Sridhar Kota. "A general paradigm for routine design—theory and implementation." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 6, no. 2 (May 1992): 73–93. http://dx.doi.org/10.1017/s0890060400002973.
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The concept of discretization of a design space is used to make initial selection of prior designs using specification matching, and to direct redesign with evaluation and iteration. A general paradigm for routine design has been developed and implemented in a system called IDS (Initial Design Selection.) For a given design domain, certain characteristics are identified that allow all specifications and models (which represent prior designs) in that design domain to be described in terms of their values or intervals that these values may lie in. These characteristics are seen as dimensions of a design space discretized into a finite number of partitions. Each partition is represented by a model, thought of as occupying its center. Each such model is associated with a deep model, which contains sufficient information for the modeled device, process, or system to be realized. Despite the fact that the models inhabiting the space are shallow, the paradigm comprises a relatively rich mathematical structure. This paper describes in detail a computational methodology to implement this domain-independent paradigm. The IDS paradigm presents a convenient and structured framework for acquiring and representing domain knowledge. This paper also briefly discusses an enhanced version of the system, which attempts iterative redesign directed by the particular mismatch between a specification and an otherwise promising model. To date, this methodology has been applied in a variety of design domains, including mechanism design, hydraulic component selection, assembly methods, and non-destructive testing methods.
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Ren, Pengzhen, Yun Xiao, Xiaojun Chang, Po-yao Huang, Zhihui Li, Xiaojiang Chen, and Xin Wang. "A Comprehensive Survey of Neural Architecture Search." ACM Computing Surveys 54, no. 4 (May 2021): 1–34. http://dx.doi.org/10.1145/3447582.
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Deep learning has made substantial breakthroughs in many fields due to its powerful automatic representation capabilities. It has been proven that neural architecture design is crucial to the feature representation of data and the final performance. However, the design of the neural architecture heavily relies on the researchers’ prior knowledge and experience. And due to the limitations of humans’ inherent knowledge, it is difficult for people to jump out of their original thinking paradigm and design an optimal model. Therefore, an intuitive idea would be to reduce human intervention as much as possible and let the algorithm automatically design the neural architecture. Neural Architecture Search ( NAS ) is just such a revolutionary algorithm, and the related research work is complicated and rich. Therefore, a comprehensive and systematic survey on the NAS is essential. Previously related surveys have begun to classify existing work mainly based on the key components of NAS: search space, search strategy, and evaluation strategy. While this classification method is more intuitive, it is difficult for readers to grasp the challenges and the landmark work involved. Therefore, in this survey, we provide a new perspective: beginning with an overview of the characteristics of the earliest NAS algorithms, summarizing the problems in these early NAS algorithms, and then providing solutions for subsequent related research work. In addition, we conduct a detailed and comprehensive analysis, comparison, and summary of these works. Finally, we provide some possible future research directions.
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Ying, Zilu, Chen Xuan, Yikui Zhai, Bing Sun, Jingwen Li, Wenbo Deng, Chaoyun Mai, et al. "TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR." Sensors 20, no. 6 (March 19, 2020): 1724. http://dx.doi.org/10.3390/s20061724.
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Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model’s recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets.
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Wang, Mengmeng, Wanli Zuo, and Ying Wang. "A Novel Adaptive Conditional Probability-Based Predicting Model for User’s Personality Traits." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/472917.
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With the pervasive increase in social media use, the explosion of users’ generated data provides a potentially very rich source of information, which plays an important role in helping online researchers understand user’s behaviors deeply. Since user’s personality traits are the driving force of user’s behaviors, hence, in this paper, along with social network features, we first extract linguistic features, emotional statistical features, and topic features from user’s Facebook status updates, followed by quantifying importance of features via Kendall correlation coefficient. And then, on the basis of weighted features and dynamic updated thresholds of personality traits, we deploy a novel adaptive conditional probability-based predicting model which considers prior knowledge of correlations between user’s personality traits to predict user’s Big Five personality traits. In the experimental work, we explore the existence of correlations between user’s personality traits which provides a better theoretical support for our proposed method. Moreover, on the same Facebook dataset, compared to other methods, our method can achieve anF1-measure of 80.6% when taking into account correlations between user’s personality traits, and there is an impressive improvement of 5.8% over other approaches.
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Shen, Changqing, Jiaqi Xie, Dong Wang, Xingxing Jiang, Juanjuan Shi, and Zhongkui Zhu. "Improved Hierarchical Adaptive Deep Belief Network for Bearing Fault Diagnosis." Applied Sciences 9, no. 16 (August 16, 2019): 3374. http://dx.doi.org/10.3390/app9163374.
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Rotating machinery plays a vital role in modern mechanical systems. Effective state monitoring of a rotary machine is important to guarantee its safe operation and prevent accidents. Traditional bearing fault diagnosis techniques rely on manual feature extraction, which in turn relies on complex signal processing and rich professional experience. The collected bearing signals are invariably complicated and unstable. Deep learning can voluntarily learn representative features without a large amount of prior knowledge, thus becoming a significant breakthrough in mechanical fault diagnosis. A new method for bearing fault diagnosis, called improved hierarchical adaptive deep belief network (DBN), which is optimized by Nesterov momentum (NM), is presented in this research. The frequency spectrum is used as inputs for feature learning. Then, a learning rate adjustment strategy is applied to adaptively select the descending step length during gradient updating, combined with NM. The developed method is validated by bearing vibration signals. In comparison to support vector machine and the conventional DBN, the raised approach exhibits a more satisfactory performance in bearing fault type and degree diagnosis. It can steadily and effectively improve convergence during model training and enhance the generalizability of DBN.
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Massingham, Peter Rex. "Measuring the impact of knowledge loss: a longitudinal study." Journal of Knowledge Management 22, no. 4 (May 14, 2018): 721–58. http://dx.doi.org/10.1108/jkm-08-2016-0338.
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Purpose Knowledge loss caused by employee exit has become a significant corporate risk. This paper aims to explore how to measure the impact of knowledge loss. The paper is based on empirical evidence from a five-year longitudinal study. Design/methodology/approach This paper is based on a longitudinal change project for a large Australian Research Council Linkage Project grant in the period 2008-2013. The method was a single case study using a critical realism paradigm. The project was a transformational change programme which aimed to help make the partner organization a learning organization to minimize the impact of knowledge loss. The partner organization was a large Australian Government Department, which faced the threat of knowledge loss caused by its ageing workforce. The sample was 118 respondents, mainly engineering and technical workers. A total of 150 respondents were invited to participate in the study which involved an annual survey and attendance at regular training workshops and related activities, with a participation rate of 79 per cent. Findings The results found that knowledge loss has most negative impact in terms of organizational problems including low productivity (morale), strategic misalignment of the workforce (capability gaps), resource cuts (stakeholders unhappy with performance), decreased work quantity and quality (inexperienced employees), work outputs not being used (customers mistrust), longer time to competence (learning cost) and slow task completion (increased search cycle time). The second most significant impact was increased sense of risk associated with work activities and declining capacity to manage the risk. The third main impact was decreased organizational knowledge base: knowledge loss creates knowledge deficit which is unlikely to be filled over time, as shown by the knowledge accounts of surviving employees which remained stable overall. The two remaining measurement constructs – psychological contract and learning organizational capacity – improved, which suggests that the negative impact of knowledge loss may be addressed with appropriate knowledge management. Research limitations/implications The research is based on a single case study in a public sector organization. While the longitudinal nature of the study and the rich data collected offsets this issue, it also presents good opportunities for researchers and practitioners to test the ideas presented in this paper in other industry contexts. The complexity and range of the constructs, concepts and scale items is acknowledged. Tables have been used wherever possible to help the reader access the findings. Practical implications Knowledge loss is perhaps the greatest corporate risk facing organizations today. This paper provides a method to measure the impact of knowledge loss. Managers may use this to assess the significance of the risk and use this as a business case to take action to minimize the impact of knowledge loss. Originality/value Prior research has found knowledge loss has caused decreased psychological contract, lost organizational memory, inefficiency and ineffectiveness and declining capability; however, these concepts are discussed in broad terms only. This paper addresses the need for measurement concepts which helps us understand the nature of the impact of knowledge loss. Five knowledge loss concepts are developed: knowledge resources, psychological contract, learning organization capacity, risk management and organizational problems. The results are based on a large-scale longitudinal study providing empirical evidence of change over a three-year period, situated within the context of a research intervention, i.e. knowledge management programme.
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Gaikwad, Avinash S., Jinghua Hu, David G. Chapple, and Moira K. O’Bryan. "The functions of CAP superfamily proteins in mammalian fertility and disease." Human Reproduction Update 26, no. 5 (May 7, 2020): 689–723. http://dx.doi.org/10.1093/humupd/dmaa016.
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Abstract BACKGROUND Members of the cysteine-rich secretory proteins (CRISPS), antigen 5 (Ag5) and pathogenesis-related 1 (Pr-1) (CAP) superfamily of proteins are found across the bacterial, fungal, plant and animal kingdoms. Although many CAP superfamily proteins remain poorly characterized, over the past decade evidence has accumulated, which provides insights into the functional roles of these proteins in various processes, including fertilization, immune defence and subversion, pathogen virulence, venom toxicology and cancer biology. OBJECTIVE AND RATIONALE The aim of this article is to summarize the current state of knowledge on CAP superfamily proteins in mammalian fertility, organismal homeostasis and disease pathogenesis. SEARCH METHODS The scientific literature search was undertaken via PubMed database on all articles published prior to November 2019. Search terms were based on following keywords: ‘CAP superfamily’, ‘CRISP’, ‘Cysteine-rich secretory proteins’, ‘Antigen 5’, ‘Pathogenesis-related 1’, ‘male fertility’, ‘CAP and CTL domain containing’, ‘CRISPLD1’, ‘CRISPLD2’, ‘bacterial SCP’, ‘ion channel regulator’, ‘CatSper’, ‘PI15’, ‘PI16’, ‘CLEC’, ‘PRY proteins’, ‘ASP proteins’, ‘spermatogenesis’, ‘epididymal maturation’, ‘capacitation’ and ‘snake CRISP’. In addition to that, reference lists of primary and review article were reviewed for additional relevant publications. OUTCOMES In this review, we discuss the breadth of knowledge on CAP superfamily proteins with regards to their protein structure, biological functions and emerging significance in reproduction, health and disease. We discuss the evolution of CAP superfamily proteins from their otherwise unembellished prokaryotic predecessors into the multi-domain and neofunctionalized members found in eukaryotic organisms today. At least in part because of the rapid evolution of these proteins, many inconsistencies in nomenclature exist within the literature. As such, and in part through the use of a maximum likelihood phylogenetic analysis of the vertebrate CRISP subfamily, we have attempted to clarify this confusion, thus allowing for a comparison of orthologous protein function between species. This framework also allows the prediction of functional relevance between species based on sequence and structural conservation. WIDER IMPLICATIONS This review generates a picture of critical roles for CAP proteins in ion channel regulation, sterol and lipid binding and protease inhibition, and as ligands involved in the induction of multiple cellular processes.
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Kidala, Diana, Ted Greiner, and Mehari Gebre-Medhin. "Five-year follow-up of a food-based vitamin A intervention in Tanzania." Public Health Nutrition 3, no. 4 (December 2000): 425–31. http://dx.doi.org/10.1017/s1368980000000495.
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AbstractObjectiveTo evaluate the long-term effects of a horticultural and nutrition education intervention in rural Tanzania.DesignA quasi-experimental post-test design was used.SettingThe research was carried out in 10 villages in Singida region, Tanzania.SubjectsMothers and their children aged 6–71 months (n = 236) from an experimental (Ilongero) and control (Ihanja) area were interviewed regarding knowledge and practices related to vitamin A nutrition. Intake of vitamin A-rich foods by the children during the 7 days prior to the interview was recorded. Stools were examined for helminths and serum samples were analysed for retinol and C-reactive protein (CRP) (n = 146) for the children aged 12–71 months.ResultsKnowledge and practices were more favourable to vitamin A intake in the experimental area than in the control area, and an increased frequency of intake of green leaves was associated with higher serum retinol values. The experimental area had lower mean serum retinol levels (13.7 μg dl−1, n = 75) than the control area (19.3 μg dl−1, n = 71). One likely confounder was the higher helminth infestation in the experimental area (n = 75, 79%) than in the control area (n = 71, 49%) (P > 0.001). Children with helminths (n = 94) had a lower mean serum retinol level than those without (n = 52) (12.3 ± 5 vs. 24 ± 10 μg dl−1; P = 0.001).ConclusionsFood-based vitamin A programmes can make sustainable improvements in knowledge and dietary practices but these may not necessarily be reflected in increases in serum retinol. Programme implementation and evaluation should take confounders into consideration as, in this case, helminth infestation.
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Tirado, Rafael Guzmán. "Translation of Words with a Cultural Component (Based on the Spanish Translation of the Novel by Eugene Vodolazkin “The Aviator”)." Russian Journal of Linguistics 23, no. 2 (December 15, 2019): 473–86. http://dx.doi.org/10.22363/2312-9182-2019-23-2-473-486.
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This article is dedicated to some aspects of translation of vocabulary with a cultural component (realia, idioms, metaphors, comparisons, etc.). In the frame of our Spanish translation of Evgeny Vodolazkin's novel Aviator are analyzed some issues of literary translation of this kind of words. This translation (first Evgeny Vodolazkin's work translated to this language), financed by a a grant of the Institute of Translation in Moscow, was published in November 2018. When answering the question which instruments have the translator to make possible the transfer of words with a cultural component from one language to another and whether it is possible to convey cultural realities without losing a significant share of sense, our research shows with a rich variety of examples taken from Aviator that the translation text does not always create the same effect and does not always have the same emotional effect on the recipient, since vocabulary with a cultural component is closely related to the culture and language of the realities of the text culture. In the article shows some of the strategies, used by the translator to translate these words and how the cultural factor and the lack of prior knowledge create great difficulties, requiring more attention from the translator and serious operational intervention in the literary text he works with.
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Maynard, Alex, and Katsumi Shimotsu. "COVARIANCE-BASED ORTHOGONALITY TESTS FOR REGRESSORS WITH UNKNOWN PERSISTENCE." Econometric Theory 25, no. 1 (February 2009): 63–116. http://dx.doi.org/10.1017/s0266466608090038.
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This paper develops a new test of orthogonality based on a zero restriction on the covariance between the dependent variable and the predictor. The test provides a useful alternative to regression-based tests when conditioning variables have roots close or equal to unity. In this case standard predictive regression tests can suffer from well-documented size distortion. Moreover, under the alternative hypothesis, they force the dependent variable to share the same order of integration as the predictor, whereas in practice the dependent variable often appears stationary and the predictor may be near-nonstationary. By contrast, the new test does not enforce the same orders of integration and is therefore capable of detecting a rich set of alternatives to orthogonality that are excluded by the standard predictive regression model. Moreover, the test statistic has a standard normal limit distribution for both unit root and local-to-unity conditioning variables, without prior knowledge of the local-to-unity parameter. If the conditioning variable is stationary, the test remains conservative and consistent. Simulations suggest good small-sample performance. As an empirical application, we test for the predictability of stock returns using two persistent predictors, the dividend-price ratio and short-term interest rate.
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Clerk, Saloni, Daniel T. Selbie, and John P. Smol. "Cage aquaculture and water-quality changes in the LaCloche Channel, Lake Huron, Canada: a paleolimnological assessment." Canadian Journal of Fisheries and Aquatic Sciences 61, no. 9 (September 1, 2004): 1691–701. http://dx.doi.org/10.1139/f04-099.
Full textAbstract:
Lake eutrophication due to cage aquaculture is an area of concern in Ontario; however, without knowledge of pre-impact conditions, it is difficult to determine the extent and magnitude of environmental change. Paleolimnological techniques were used to estimate water-quality conditions prior to, during, and briefly following aquaculture operation in the LaCloche Channel, Lake Huron. Past oxygen and nutrient levels were inferred from assemblages of chironomids and diatoms, respectively, to determine whether recent low-oxygen and nutrient-rich conditions were related to cage aquaculture in operation from 1989 to 1998. Chironomid assemblages exhibited trends consistent with decreased hypolimnetic oxygen levels, with reductions in oxic-type profundal taxa and increased relative abundances of littoral communities. Diatom assemblages reflected a period of nutrient enrichment by increased relative abundances of meso-eutrophic taxa. Improvements in water quality are inferred from assemblages of diatoms in surface sediments, which may correspond to the cessation of fish-farming activities in 1998. In contrast, no sign of deep-water oxygen recovery is recorded by chironomids. These trends are consistent with eutrophication, and suggest that the LaCloche Channel was sensitive to nutrient loading from the cage-aquaculture operation. This study demonstrates the potential of using paleolimnological techniques to track water-quality changes associated with cage farming.
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Stones, Rob. "Sociology’s unspoken weakness: Bringing epistemology back in." Journal of Sociology 53, no. 4 (December 2017): 730–52. http://dx.doi.org/10.1177/1440783317744447.
Full textAbstract:
Following the sustained criticism of positivism and empiricism in the social sciences through the 1960s and 1970s, social theory turned its attention resolutely towards ontology. This ‘ontological turn’ has provided sociology with an enviably rich and diverse palette of understandings emerging from a variety of theoretical traditions. This development has been accompanied, however, by a failure to construct a parallel epistemology with which to translate the variety, fullness and nuance of ontological concepts into strong and defensible empirical accounts. The article signals the complex nature of the consequent challenge and presents the components of a new epistemological framework designed to enable the social sciences to respond to it. Grounded theory is taken as an example of how an influential prior approach attuned to the role of concepts in making sense of empirical data could be constructively integrated into the new epistemology while being greatly strengthened by it. The article concludes with a critical discussion of John Law’s After Method. Closely associated with Actor Network Theory (ANT), Law offers a ‘hard case’ against which to pitch my argument. This is because he holds that the subjective, contingent and ‘assembled’ character of knowledge renders both undesirable and impossible the project of epistemological rigour I present as both possible and essential.
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Lewis, Sarah, Robb Lindgren, Shuai Wang, and Roy D. Pea. "Learning With Media." Journal of Media Psychology 31, no. 3 (July 2019): 128–36. http://dx.doi.org/10.1027/1864-1105/a000235.
Full textAbstract:
Abstract. Digital media, such as interactive video, games, and immersive worlds, offer rich visual perspectives, often allowing one to experience events through another’s eyes. While prior research indicates that considering alternative perspectives facilitates understanding, little is known about how media-enhanced perspectives affect learning processes for higher-order concepts that require synthesis of ideas and making inferences such as reasoning about problems in science. Two experiments used digital video of a science instructional event to investigate features of visual perspective on engagement and knowledge construction. Study 1 showed that an embodied first-person viewpoint achieved using a head-mounted camera better supported learning than a traditional third-person view of the same event. In Study 2, applying a motion algorithm to both a first-person and third-person video allowed us to isolate the effects of viewpoint and camera motion. While the addition of artificial motion benefited learning for third-person viewers, only motion that is aligned with the actor’s actions and affect enhances first-person viewing. Findings are considered in terms of how certain media position learners in relation to educational content. Specifically, we argue that media features such as viewpoint and motion can be configured in ways to create “fields of potential action” that engage viewers and optimize conditions for learning.