nils-herrmann/clean_scirepeval huggingface dataset:
Designing Tolerance of Assembled Components Using Weibull Distribution
Year: 2019

Tolerancing is one of the most important tools for planning, controlling, and improving quality in the industry. Tolerancing conducted by design engineers to meet customers' needs is a prerequisite for producing high quality products. Engineers use handbooks to conduct tolerancing. While use of statistical methods for tolerancing is not a new concept, engineers often use known distributions, including the normal distribution. However, if the statistical distribution of the given variable is unknown, a new statistical method will be employed to design tolerance. Therefore, in this study we want to offer a proper statistical method for determining tolerance. The use of statistical methods to design tolerance is not a new concept; however, flexible use of statistical distributions can enhance its performance. In this regard, Weibull distribution is proposed. To illustrate the proposed method first technical characteristics of production parts were selected randomly, and then manufacturing parameters were determined using maximum likelihood method. Finally, the Goodness of Fit test was used to ensure the accuracy of the obtained results.
Sparse Coding Guided Spatiotemporal Feature Learning for Abnormal Event Detection in Large Videos
Venue: IEEE Transactions on Multimedia
Year: 2019

Abnormal event detection in large videos is an important task in research and industrial applications, which has attracted considerable attention in recent years. Existing methods usually solve this problem by extracting local features and then learning an outlier detection model on training videos. However, most previous approaches merely employ hand crafted visual features, which is a clear disadvantage due to their limited representation capacity. In this paper, we present a novel unsupervised deep feature learning algorithm for the abnormal event detection problem. To exploit the spatiotemporal information of the inputs, we utilize the deep three dimensional convolutional network (C3D) to perform feature extraction. Then, the key problem is how to train the C3D network without any category labels. Here, we employ the sparse coding results of the hand crafted features generated from the inputs to guide the unsupervised feature learning. Specifically, we define a multilevel similarity relationship between these inputs according to the statistical information of the shared atoms. In the following, we introduce the quadruplet concept to model the multilevel similarity structure, which could be used to construct a generalized triplet loss for training the C3D network. Furthermore, the C3D network could be utilized to generate the features for sparse coding again, and this pipeline could be iterated for several times. By jointly optimizing between the sparse coding and the unsupervised feature learning, we can obtain robust and rich feature representations. Based on the learned representations, the sparse reconstruction error is applied to predicting the anomaly score of each testing input. Experiments on several publicly available video surveillance datasets in comparison with a number of existing works demonstrate that the proposed algorithm performs favorably against the state of the art methods.
Blending asynchronous discussion groups and peer tutoring in higher education: An exploratory study of online peer tutoring behaviour
Venue: Comput. Educ.
Year: 2008

In the present study cross age peer tutoring was implemented in a higher education context. Fourth year students (N=39) operated as online tutors to support freshmen in discussing cases and solving authentic problems. This study contributes to a better understanding of the supportive interventions of tutors in asynchronous discussion groups. Peer tutor interventions were studied by means of a content analysis scheme based on the e moderating model of Salmon [Salmon, G. (2000) A model for CMC in education and training. E moderating. The key to teaching and learning online.  Kogan Page] The descriptive results reveal that the type of tutor activities varies over the consecutive discussion themes. No evidence was however found for a significant evolution from introductory and social talk to contributions eliciting cognitive processing and critical thinking along the themes. Tutors' social support seems to be of continuous importance. Further, cluster analysis resulted in a classification of the tutors into three different subtypes or tutor styles 'motivators' 'informers' and 'knowledge constructors' which was interpreted as confirmation of Tutor dependent online peer tutoring behaviour.
Transfer of analytical procedures: a panel of strategies selected for risk management, with emphasis on an integrated equivalence based comparative testing approach.
Venue: Journal of pharmaceutical and biomedical analysis
Year: 2011

In 2001, a multidisciplinary team made of analytical scientists and statisticians at Sanofi aventis has published a methodology which has governed, from that time, the transfers from R&D sites to Manufacturing sites of the release monographs. This article provides an overview of the recent adaptations brought to this original methodology taking advantage of our experience and the new regulatory framework, and, in particular, the risk management perspective introduced by ICH Q9. Although some alternate strategies have been introduced in our practices, the comparative testing one, based equivalence testing as statistical approach, remains the standard for assays lying on very critical quality attributes. This is conducted with the concern to control the most important consumer's risk involved at two levels in analytical decisions in the frame of transfer studies: risk, for the receiving laboratory, to take poor release decisions with the analytical method and risk, for the sending laboratory, to accredit such a receiving laboratory on account of its insufficient performances with the method. Among the enhancements to the comparative studies, the manuscript presents the process settled within our company for a better integration of the transfer study into the method life cycle, just as proposals of generic acceptance criteria and designs for assay and related substances methods. While maintaining rigor and selectivity of the original approach, these improvements tend towards an increased efficiency in the transfer operations.
MF59/hBCGFu He Zuo Ji Cu Jin Ju Shi Xi Bao Mu Ji He Fen Mi MCP 1
Year: 2015

Mu De Chu Bu Tan Tao MF59/h BCGFu He Zuo Ji Zeng Qiang Zhong Zu Jie He Fen Zhi Gan Jun Dan Bai (TP)Mian Yi Fan Ying De Ji Zhi Fang Fa MF59, h BCG, TPJi Qi Bu Tong Pei Wu Fen Bie Ti Wai Ci Ji RAW264.7Xi Bao ,Ci Ji Pei Yang 24 hHou Shou Ji Shang Qing ,ELISAJian Ce Xi Bao Pei Yang Shang Qing Zhong TNF a, MCP 1He IL 1bHan Liang MF59, h BCG, TPJi Qi Pei Wu Fen Bie Yu Xiao Shu Zuo  Zhu She ,Zhu She Hou 24, 48Huo 72 hQu Zhu She Bu Wei De Ji Rou Zu Zhi Jian Sui PBSChong Xi ,Cai Yong Xiao Yuan Dan Bai Shui Jie Mei Xiao Hua Fa He Ji Jie Fa Jie He Jiang Ji Rou Zu Zhi Jie Chi Wei Dan Ge Xi Bao ;Ying Guang Kang Ti Biao Ji Xi Bao ,Liu Shi Xi Bao Zhu Fen Xi Ju Shi Xi Bao Han Liang Jie Guo Ren He Mian Yi Yuan Ci Ji RAW264.7Xi Bao ,Qi Pei Yang Shang Qing Jun Wei Jian Ce Dao IL 1b;MF59He h BCGJun Neng Ci Ji RAW264.7Xi Bao Fen Mi TNF aHe MCP 1,Qie TNF aFen Mi Shui Ping Cheng Ji Liang Yi Lai ;Er TPQue Bu Neng Ci Ji RAW264.7Xi Bao Fen Mi TNF aHe MCP 1. 1:100 MF59/h BCG(h BCGZhong Zhi Liang Nong Du Wei 25, 50mg/ml)Ci Ji RAW264.7Xi Bao Fen Mi TNF aHe MCP 1Shui Ping Jun Xian Zhu Gao Yu 25, 50mg/ml h BCGHe Kong Bai Dui Zhao (Suo You P<0.01);1:250MF59/h BCG(h BCGZhong Zhi Liang Nong Du Wei 25, 50mg/ml)Ci Ji RAW264.7Xi Bao Fen Mi TNF aHe MCP 1Shui Ping Jun Xian Zhu Gao Yu 25mg/ml h BCGHe Kong Bai Dui Zhao (P<0.05) 1:250 MF59/TP(TPZhong Zhi Liang Nong Du Wei 5, 10mg/ml)Ci Ji RAW264.7Xi Bao Fen Mi TNF aShui Ping Xian Zhu Di Yu 1:100 MF59/TP(TPZhong Zhi Liang Nong Du Wei 5, 10mg/ml)(P<0.01) Tong Yi Mian Yi Yuan Pi Xia Zhu  50mg h BCGPi Xia Zhu She Hou 72 hHe 250mg h BCGPi Xia Zhu She Hou 24 h,Qi Zhu She Bu Wei Ji Rou Zu Zhi Zhong Ju Shi Xi Bao Han Liang Zui Gao ;Dan Yu Dui
Fast Approximations to Structured Sparse Coding and Applications to Object Classification
Venue: ECCV
Year: 2012

We describe a method for fast approximation of sparse coding. A given input vector is passed through a binary tree. Each leaf of the tree contains a subset of dictionary elements. The coefficients corresponding to these dictionary elements are allowed to be nonzero and their values are calculated quickly by multiplication with a precomputed pseudoinverse. The tree parameters, the dictionary, and the subsets of the dictionary corresponding to each leaf are learned. In the process of describing this algorithm, we discuss the more general problem of learning the groups in group structured sparse modeling. We show that our method creates good sparse representations by using it in the object recognition framework of [1,2] Implementing our own fast version of the SIFT descriptor the whole system runs at 20 frames per second on 321 x481 sized images on a laptop with a quad core cpu, while sacrificing very little accuracy on the Caltech 101, Caltech 256, and 15 scenes benchmarks.
Knowledge Graphs: An Information Retrieval Perspective
Venue: Found. Trends Inf. Retr.
Year: 2020

In this survey, we provide an overview of the literature on knowledge graphs (KGs) in the context of information retrieval (IR) Modern IR systems can benefit from information available in KGs in multiple ways, independent of whether the KGs are publicly available or proprietary ones. We provide an overview of the components required when building IR systems that leverage KGs and use a task oriented organization of the material that we discuss. As an understanding of the intersection of IR and KGs is beneficial to many researchers and practitioners, we consider prior work from two complementary angles: leveraging KGs for information retrieval and enriching KGs using IR techniques. We start by discussing how KGs can be employed to support IR tasks, including document and entity retrieval. We then proceed by describing how IR and language technology in general can be utilized for the construction and completion of KGs. This includes tasks such as entity recognition, typing, and relation extraction. We discuss common issues that appear across the tasks that we consider and identify future directions for addressing them. We also provide pointers to datasets and other resources that should be useful for both newcomers and experienced researchers in the area. Ridho Reinanda, Edgar Meij and Maarten de Rijke (2020) "Knowledge Graphs: An Information Retrieval Perspective" Foundations and Trends(r) in Information Retrieval: Vol. 14, No. 4,
The Relationship between Innovation Culture and Innovation Outcomes: Exploring the Effects of Sustainability Orientation and Firm Size
Year: 2018

Being sustainability oriented has become a key strategy for many firms. Equally, innovation culture and innovation outcomes have long been recognized as important contributors to the growth of firms. However, the literature on sustainability and innovation provides limited understanding of the important relationship between sustainability orientation, innovation culture and innovation outcomes. Given that large firms and small firms differ in building and employing their strategic assets, firm size matters in understanding the relationship. Through the lens of resource based view, we develop a theoretical model embedding the four components and test it using data from a global survey: the 2012 Comparative Performance Assessment Study. Our research contributes to sustainability literature and innovation theory by providing an integrated framework to explicate the mechanism through which the innovation culture of the firm impacts on innovative performance through the sustainability orientation of the firm. The findings advance our understanding of the extent to which sustainable orientation can explain the relationship between innovation culture and innovation outcomes. Our evidence shows that the innovation culture of a firm facilitates the sustainability orientation of the firm and that the converse also applies. The research also contributes to our knowledge of the differences between large and small firms in leveraging their strategic assets in terms of innovation culture and sustainability orientation to facilitate superior innovation outcomes. Although firm size moderates the relationship between innovation culture and innovation outcomes, the research shows that this no longer holds when sustainability orientation is included in the relationship. A strong sustainability orientation can be a competitive advantage for firms in the R&D Management delivery of superior innovation outcomes.
Predicting optimal in vitro culture medium for Pistacia vera micropropagation using neural networks models
Venue: Plant Cell, Tissue and Organ Culture (PCTOC)
Year: 2016

In this study, artificial intelligence techniques specifically artificial neural networks (ANNs) in combination with fuzzy logic (neurofuzzy logic) or with genetic algorithms (ANNs GA) have been employed, as modeling tools, to get insight, to predict and to optimize the effect of several independent factors on four growth parameters during Pistacia vera micropropagation. Twenty six media ingredients, including mineral ions (or salts) glycine, vitamins and plant growth regulators (PGRs) at different concentrations, were used as inputs and four growth parameters: proliferation rate, shoot length, total and healthy fresh weight as outputs on the models. The IF THEN rules from neurofuzzy logic models have allowed discovering the positive (BAP, nicotinic acid and pyridoxine HCl) and negative (NO3 Mg2+ Ag+ and gluconate effects on the growth parameters and the fundamental role of BAP over all of them. Also, ANNs GA technology has permitted to estimate the best combination of media ingredients to simultaneously maximize the four parameters of growth: 4.4 new shoots per explant; 28.7 mm length; 1.1 and 0.53 g total and healthy fresh weight, respectively, minimizing physiological disorders. In our opinion, the information obtained in this study is extremely useful to improve the massive multiplication of pistachio plant, in particular, but also demonstrate the ability of artificial intelligence technology to design plant tissue culture media with predictable and tailorable characteristics.
Raspberry Pi Based Intelligent Robot that Recognizes and Places Puzzle Objects
Venue: ArXiv
Year: 2021

In this study, computer vision application was realized by using Raspberry Pi, USB camera and 3D robotic arm system, which are designed with 3D modeling programs and output from 3D printer. The system automatically determines the position information of the detected objects from the camera and calculates the directions and angles of the robot joints using the position information. Because of calculations, the robot Arm moves to the target. The robot arm is 6 axis industrial type. The Raspberry PI based system uses python programming language and OpenCV library for image processing.  Raspberry PI; 3D modelling; 6 axis robot arm; image processing; Python; OpenCV.
Motivating Students in Classrooms
Year: 2010

Many motivational principles are theoretically clear and well supported by research, yet difficult to apply in classrooms. This is especially true of principles for supporting students' intrinsic motivation because attendance is required, curricula are externally determined, and students' performance is subject to grading and other extrinsic consequences. This article offers guidelines about how teachers can respond to these challenges and encourage motivation to learn by establishing their classes as collaborative learning communities, supporting their students' confidence as learners, and helping them to appreciate curricular content as worth learning and applicable to their lives outside of school.
Exploring non native English speaking teachers' cognitions about corrective feedback in teaching English oral communication
Year: 2015

Exploring teachers' cognitions is a significant endeavour, as teachers' cognitions inform their classroom practice, and, in turn, their cognitions are influenced by their teaching experiences. This study explored the differences between novice and experienced non native English speaking teachers' cognitions about corrective feedback (CF) in teaching English oral communication. Data were collected from 20 novice and 20 experienced teachers through a questionnaire and follow up interviews. Results show statistically significant differences between the two groups. Teachers' personal experiences influenced their cognitions about the necessity of CF and the effectiveness of different CF types and timing. Interview data show teachers' teaching experiences raise their awareness of the role of mediating factors, namely learner factors, error frequency, types, and severity, target form difficulty, instructional focus, and task types in their cognitions about the necessity, timing, and types of CF. In contrast, novice teachers, partially due to their insufficient teaching experiences, had rigid cognitions about CF. Novice teachers attributed their cognitions to their personal language learning experiences. As a pedagogical implication, we recommend that student teachers be provided with opportunities for acquiring theoretical understanding about CF and translating it into classroom practice.
Sensor Positioning for Activity Recognition Using Wearable Accelerometers
Venue: IEEE Transactions on Biomedical Circuits and Systems
Year: 2011

Activities of daily living are important for assessing changes in physical and behavioral profiles of the general population over time, particularly for the elderly and patients with chronic diseases. Although accelerometers have been used widely in wearable devices for activity classification, the positioning of the sensors and the selection of relevant features for different activity groups still pose significant research challenges. This paper investigates wearable sensor placement at different body positions and aims to provide a systematic framework that can answer the following questions: 1) What is the ideal sensor location for a given group of activities? and 2) Of the different time frequency features that can be extracted from wearable accelerometers, which ones are the most relevant for discriminating different activity types?
Combining convolutional side outputs for road image segmentation
Venue: 2019 International Joint Conference on Neural Networks (IJCNN)
Year: 2019

Image segmentation consists in creating partitions within an image into meaningful areas and objects. It can be used in scene understanding and recognition, in fields like biology, medicine, robotics, satellite imaging, amongst others. In this work we take advantage of the learned model in a deep architecture, by extracting side outputs at different layers of the network for the task of image segmentation. We study the impact of the amount of side outputs and evaluate strategies to combine them. A post processing filtering based on mathematical morphology idempotent functions is also used in order to remove some undesirable noises. Experiments were performed on the publicly available KITTI Road Dataset for image segmentation. Our comparison shows that the use of multiples side outputs can increase the overall performance of the network, making it easier to train and more stable when compared with a single output in the end of the network. Also, for a small number of training epochs (500) we achieved a competitive performance when compared to the best algorithm in KITTI Evaluation Server.
Multiple Tls Point Cloud Registration Based on Point Projection Images
Year: 2018

In this paper, an efficient and robust registration method of multiple point clouds is proposed. In our research, we assume that point clouds are acquired by Terrestrial Laser Scanning (TLS) systems, and the scanned environments have a relatively flat base plane such as the ground or a floor. Our method is based on an existing pairwise registration method based on point projection images, which can quickly register the point clouds under the above assumptions. In the method, sliced point clouds are projected onto the base plane, and a binary image with feature points is created. The registration is done by using feature points of the images based on the sample consensus strategy. In this paper, first, we improve the efficiency of the pairwise registration method by introducing height and occlusion information to the image. Then, a validity check method of pairwise registration using space classified images is proposed to avoid exhaustive pairwise registration in the multiple point cloud registration process. Finally, an efficient multiple point cloud registration algorithm based on progressive creation of a point cloud connectivity graph using iterative rough and precise pairwise registration and the validity check method is proposed. The effectiveness of our method is shown through its application to three datasets of outdoor environments.
Autonomous Underwater Pipeline Tracking Control Based on Visual Images
Venue: 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)
Year: 2018

In order to realize the underwater pipeline tracking with an Autonomous underwater vehicle (AUV) under environment disturbances, a hybrid control method is presented based on vision navigation information of the monocular camera. Since the original image may be distorted, the information of the pipeline in images needs to be transformed first. Moreover, a robust controller plays an important role in the motion control of an AUV in disturbances. Therefore, a DRNN (Diagonal Recurrent Neural Networks) and S nonlinear hybrid controller is established. Compared with the PD controller, the result shows that the controller has smaller steady state error and better self adaptive capacity. The simulation results show that the control method is effective with strong robust and adaptive capability for the process for autonomous underwater pipeline tracking with fixed tracking height from the pipeline. Moreover, the method removing the distortion of images is concise and efficient, and the parameters of the controller are easy to be adjusted.