nils-herrmann/clean_scirepeval huggingface dataset:
Eye Blink Detection Using Facial Landmarks

A real time algorithm to detect eye blinks in a video sequence from a standard camera is proposed. Recent landmark detectors, trained on in the wild datasets exhibit excellent robustness against face resolution, varying illumination and facial expressions. We show that the landmarks are detected precisely enough to reliably estimate the level of the eye openness. The proposed algorithm therefore estimates the facial landmark positions, extracts a single scalar quantity eye aspect ratio (EAR) characterizing the eye openness in each frame. Finally, blinks are detected either by an SVM classifier detecting eye blinks as a pattern of EAR values in a short temporal window or by hidden Markov model that estimates the eye states followed by a simple state machine recognizing the blinks according to the eye closure lengths. The proposed algorithm has comparable results with the state of the art methods on three standard datasets.
The Influence of School Environment, Social Intelligence, and Self Esteem toward Academic Achievement of Student in Rural Area

Academic achievement of students is affected by several factors. This study aimed to analyze the effect of adolescent and family characteristics, school environment, social intelligence, and self esteem toward academic achievement of rural adolescents. This study used the cross sectional study design. The purposive method was used in selecting the research location. Data were collected from two senior high school students in Bogor District (n=150) chosen by proportional random sampling. The result showed that gender and school environment had significantly positive effect on academic achievement. Conversely, family income had negative effect on academic achievement of rural adolescents.  academic achievement, adolescent, self esteem, school environment, social intelligence, rural area Pencapaian remaja untuk meraih prestasi akademik merupakan hasil dari interaksi antara berbagai faktor yang mempengaruhinya. Tujuan penelitian ini adalah untuk menganalisis pengaruh karakeristik remaja, karakteristik keluarga, lingkungan sekolah, kecerdasan sosial, dan self esteem terhadap prestasi akademik remaja di wilayah perdesaan. Penelitian ini menggunakan desain cross sectional study Responden dalam penelitian ini berjumlah 150 siswa SMA di wilayah Kabupaten Bogor, Jawa Barat. Pemilihan lokasi penelitian ini dilakukan secara purposif dan penarikan contoh dilakukan dengan teknik proportional random sampling Hasil uji regresi menunjukkan bahwa kecerdasan sosial dan self esteem tidak memiliki pengaruh yang signifikan terhadap prestasi akademik. Jenis kelamin dan lingkungan sekolah ditemukan memiliki pengaruh positif signifikan terhadap prestasi akademik. Sebaliknya, pendapatan per kapita keluarga mempunyai pengaruh negatif signifikan terhadap prestasi akademik remaja perdesaan.  kecerdasan sosial, lingkungan sekolah, perdesaan, prestasi akademik, remaja, self esteem
The TileDB Array Data Storage Manager
Venue: Proc. VLDB Endow.

We present a novel storage manager for multi dimensional arrays that arise in scientific applications, which is part of a larger scientific data management system called TileDB. In contrast to existing solutions, TileDB is optimized for both dense and sparse arrays. Its key idea is to organize array elements into ordered collections called fragments. Each fragment is dense or sparse, and groups contiguous array elements into data tiles of fixed capacity. The organization into fragments turns random writes into sequential writes, and, coupled with a novel read algorithm, leads to very efficient reads. TileDB enables parallelization via multi threading and multi processing, offering thread /process safety and atomicity via lightweight locking. We show that TileDB delivers comparable performance to the HDF5 dense array storage manager, while providing much faster random writes. We also show that TileDB offers substantially faster reads and writes than the SciDB array database system with both dense and sparse arrays. Finally, we demonstrate that TileDB is considerably faster than adaptations of the Vertica relational column store for dense array storage management, and at least as fast for the case of sparse arrays.
Study on contribution of the asymmetric stress to the birefringence induced by an ultrashort single laser pulse inside fused silica glass

Stress induced birefringence due to asymmetry in axial and radial directions that is generated because of the interaction of ultrashort laser pulses with a transparent material is numerically studied. The coupled equations of nonlinear Schrodinger and plasma density evolution are solved to calculate the deposited energy density and initial temperature profile. Fourier's heat equation and the equations related to the thermo elasto plastic model are solved to calculate the temperature evolution and distribution of induced displacement inside the material, respectively. Finally, by numerically calculating the distribution of the induced refractive index changes experienced by both axially and radially probe beams, induced stress birefringence is calculated for different characteristics of writing pulses. Furthermore, the induced stress birefringence is experimentally realized, and the effect of the energy of the writing pulse is investigated. To know how the induced refractive index changes and birefringence.
TensorFlow: A system for large scale machine learning
Venue: OSDI

TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor Flow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom designed ASICs known as Tensor Processing Units (TPUs) This architecture gives flexibility to the application developer: whereas in previous "parameter server" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real world applications.
TensorFlow: Large Scale Machine Learning on Heterogeneous Distributed Systems
Venue: ArXiv

TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The TensorFlow API and a reference implementation were released as an open source package under the Apache 2.0 license in November, 2015 and are available at www.tensorflow.org.
Traffic speed prediction using deep learning method
Venue: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

Successful traffic speed prediction is of great importance for the benefits of both road users and traffic management agencies. To solve the problem, traffic scientists have developed a number of time series speed prediction approaches, including traditional statistical models and machine learning techniques. However, existing methods are still unsatisfying due to the difficulty to reflect the stochastic traffic flow characteristics. Recently, various deep learning models have been introduced to the prediction field. In this paper, a deep learning method, the Deep Belief Network (DBN) model, is proposed for short term traffic speed information prediction. The DBN model is trained in a greedy unsupervised method and fine tuned by labeled data. Based on traffic speed data collected from one arterial in Beijing, China, the model is trained and tested for different prediction time horizons. From experiment analysis, it is concluded that the DBN can outperform Back Propagation Neural Network (BPNN) and Auto Regressive Integrated Moving Average (ARIMA) for all time horizons. The advantages of DBN indicate that deep learning is promising in traffic research area.
An Architecture for QoS Enabled Mobile Video Surveillance Applications in a 4G EPC and M2M Environment
Venue: IEEE Access

Mobile video surveillance applications are used widely nowadays. They offer real time video monitoring for homes, offices, warehouses, airports, and so on with live and pre recorded on demand video streaming. Quality of service (QoS) remains a key challenge faced by most of these applications. In this article, we propose an architecture for mobile video surveillance applications with a guaranteed and differentiated QoS support. The architecture relies on the 3GPP 4G evolved packet core (EPC) The main components are the QoS enabler, media server, and machine to machine gateway and surveillance application. To demonstrate its feasibility, a proof of concept prototype has been implemented and deployed. We also took measurements to evaluate the performance. Several lessons were learned. For instance, multimedia frameworks must allow for buffering controls in media streaming to reduce live streaming delay. In addition, we have learned that publicly available materials related to the EPC prototyping platform we have used (i.e. OpenEPC) are scarce. This has made our prototyping task rather difficult.
Revisiting Distributed Synchronous SGD
Venue: ArXiv

Distributed training of deep learning models on large scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the cost of additional noise introduced from asynchrony. In contrast, the synchronous approach is often thought to be impractical due to idle time wasted on waiting for straggling workers. We revisit these conventional beliefs in this paper, and examine the weaknesses of both approaches. We demonstrate that a third approach, synchronous optimization with backup workers, can avoid asynchronous noise while mitigating for the worst stragglers. Our approach is empirically validated and shown to converge faster and to better test accuracies.
Synchronous Hybrid Event and Time Driven Consensus in Multiagent Networks With Time Delays
Venue: IEEE Transactions on Cybernetics

This paper studies the delay robustness of a class of synchronous hybrid event and time driven consensus protocols in undirected networks. These protocols can ensure the system performance at reduced data sampling rates. We consider three types of time delays in feedbacks, including one common time delay, multiple time invariant delays, and multiple time varying delays; and by sampled data control techniques, we characterize the maximum allowable time delay and the event detecting period for solving the average consensus problem in terms of the algebraic structure of interaction topologies. Simulations are given to show the effectiveness of theoretical results.
Observer Based Adaptive Backstepping Consensus Tracking Control for High Order Nonlinear Semi Strict Feedback Multiagent Systems
Venue: IEEE Transactions on Cybernetics

Combined with backstepping techniques, an observer based adaptive consensus tracking control strategy is developed for a class of high order nonlinear multiagent systems, of which each follower agent is modeled in a semi strict feedback form. By constructing the neural network based state observer for each follower, the proposed consensus control method solves the unmeasurable state problem of high order nonlinear multiagent systems. The control algorithm can guarantee that all signals of the multiagent system are semi globally uniformly ultimately bounded and all outputs can synchronously track a reference signal to a desired accuracy. A simulation example is carried out to further demonstrate the effectiveness of the proposed consensus control method.
The importance of amylose and amylopectin fine structure for textural properties of cooked rice grains.
Venue: Food chemistry

Statistically and causally meaningful relationships are established between starch molecular structure (the molecular distribution of branched starch and the chain length distribution of debranched starch) and texture (hardness and stickiness) of cooked rice grains. The amounts of amylose chains with degree of polymerization (DP) 100 20,000, and of long amylopectin chains, positively correlated with hardness, while amylopectin chains with DP<70 and amylose molecular size both showed negative correlations with hardness (p<0.05) There was also a significant negative correlation between stickiness and the amounts of long amylopectin chains (p<0.01) For rices with similar amylose content, the amount of amylose chains with DP 1000 2000 positively correlated with hardness while size negatively correlated with hardness (p<0.05) This indicates for the first time that, regardless of amylose content, rice varieties with smaller amylose molecular sizes and with higher proportions of long amylose chains have a harder texture after cooking.
Demographic buffering of life histories? Implications of the choice of measurement scale.
Venue: Ecology

Life history theory predicts that the vital rates that influence population growth the most should be buffered against environmental fluctuations due to selection for reduced variation. However, it remains unclear whether populations actually are influenced by such "demographic buffering," because variation in vital rates can be compared on different measurement scales, and there has been little attempt to investigate whether the choice of scale influences the chance of detecting demographic buffering. We compared two statistical approaches to examine whether demographic buffering has influenced vital rates in wild Svalbard reindeer (Rangifer tarandus platyrhynchus) To account for statistical variance constraints on vital rates limited between 0 and 1 in analyses of demographic buffering, one approach is to scale observed variation by the maximum possible variation on the arithmetic scale. When applying this approach, the results suggested that demographic buffering was occurring. However, when we applied an alternative approach that identified statistical variance constraints on the logit scale, there was no evidence for demographic buffering. Thus, the choice of measurement scale must be carefully considered before one can fully understand whether demographic buffering influences life histories. Defining the appropriate scale may require an understanding of the mechanisms through which demographic buffering may have evolved.
A variational model with hybrid images data fitting energies for segmentation of images with intensity inhomogeneity
Venue: Pattern Recognit.

Level set functions based variational image segmentation models provide reliable methods to capture boundaries of objects/regions in a given image, provided that the underlying intensity has homogeneity. The case of images with essentially piecewise constant intensities is satisfactorily dealt with in the well known work of Chan Vese (2001) and its many variants. However for images with intensity inhomogeneity or multiphases within the foreground of objects, such models become inadequate because the detected edges and even phases do not represent objects and are hence not meaningful. To deal with such problems, in this paper, we have proposed a new variational model with two fitting terms based on regions and edges enhanced quantities respectively from multiplicative and difference images. Tests and comparisons will show that our new model outperforms two previous models. Both synthetic and real life images are used to illustrate the reliability and advantages of our new model. Graphical abstractDisplay Omitted HighlightsWe model image segmentation problem using product and difference images.The convex formulation of the model is also presented.The proposed model takes care of the minute details and inhomogeneity in an image.This model is compared with LCV and FGM of the active contour model.The competing energies are qualitatively tested by using Jaccord Similarity Index.
An Analysis of Auditor Litigation and Audit Service Quality

This study compares litigation activities of independent auditors to assess litigation as a means for making quality distinctions among auditors. The study provides a framework suggesting auditors with relatively low (high) litigation activity represent higher (lower) quality suppliers. The empirical analysis examines a sample of legal cases (n 472) and resolutions for these cases (if available, n= 183) The cases involve audit related litigation against both Big Eight and large non Big Eight firms during 1960 1985. The results indicate that nonBig Eight firms as a group have higher litigation activity than Big Eight firms. This result is consistent with existing research supporting the Big Eight as quality differentiated auditors. Comparisons among Big Eight firms reveal some significant differences. However, identification of particular Big Eight firms as low (high) litigation activity auditors appears sensitive to the type of analysis. T HISstudy compares litigation activities among the largest U.S. independent audit firms. Litigation against auditors typically involves a process, from initial discovery of potentially false or misleading financial statements, to filing of lawsuits, and eventual resolution of such suits. In investigating differences in litigation among auditors, the study utilizes data on both litigation occurrences and resolutions. The primary objective of the comparisons is to assess litigation as a means for making quality distinctions among independent audit firms. Previous empirical research on auditor litigation (Kellogg [1984] Palmrose [1987] and St. Pierre and Anderson [1984])1 and on audit quality (Francis and Simon [1987] Nichols and Smith [1983] Palmrose Financial assistance of the University of California at Berkeley Professional Accounting Program and the research assistance of Hay Young Chung, Changyong Ham, and Richard Taylor are gratefully acknowledged. I would like to thank Mike Duffy and Robert Kellogg for contributions during the data gathering process. In addition, I would like to acknowledge the helpful comments of various individuals and groups including, William L. Felix, Jr. Marcia Niles, Kent St. Pierre, participants in workshops at the University of Washington and the University of Arizona, and especially William R. Kinney, Jr. ZoeVonna Palmrose is Assistant Professor at the School of Business Administration, University of California at Berkeley. Manuscript received February 1987. Revision received July 1987.  Taxes and Off Balance Sheet Financing: Research and Development Limited Partnerships" by Terry Shevlin that appeared in the July 1987 issue of
Measuring service quality in higher education: Development of a hierarchical model (HESQUAL)

This paper aims to develop and empirically test a hierarchical model for measuring service quality in higher education.  The first phase of the study consisted of qualitative research methods and a comprehensive literature review, which allowed the development of a conceptual model comprising 53 service quality attributes. Quantitative methods were used for the second phase so as to test the dimensionality of the measurement instrument and assess its validity and reliability. A sample of 207 students was surveyed, and data were analysed using exploratory factor analysis and Cronbach's alpha test.  The results of the factor analysis revealed the presence of sub dimensions. A hierarchical model was therefore considered most appropriate. The final model consisted of five primary dimensions, which are administrative quality, physical environment quality, core educational quality, support facilities quality and transformative quality. The instrument contained a
[A Case of Angioimmunoblastic T Cell Lymphoma Merged with Colorectal Cancer That We Were Able to Resect after a Chemotherapy Response]
Venue: Gan to kagaku ryoho. Cancer chemotherapy

The patient was 77 year old man. He visited our hospital with the chief complaint of an abdominal mass in March 2015. We diagnosed the patient with transverse colon cancer and he was suspected of having malignant lymphoma. In March 2016, we attempted to perform right hemicolectomy for the transverse colon cancer, but it was difficult because swollen lymph nodes had formed a large mass with the surrounding tissue, including vessels of the mesentery. We could only complete the surgery after mesenteric lymph node biopsy. On the basis of the results of this biopsy, we diagnosed angioimmunoblastic Tcell lymphoma. At first, we administered THP COP therapy for malignant lymphoma. However, after 3 courses of this therapy, the patient suddenly developed ileus due to the aforementioned colon cancer. According to enhanced CT performed at the onset of intestinal obstruction, chemotherapy dramatically reduced the size of the intraperitoneal lymph nodes. We therefore concluded that it was able to treat the colon cancer. We performed right hemicolectomy in June 2015. Angioimmunoblastic T cell lymphoma comprises only 2 3% of all malignant lymphoma cases. We experienced a rare case of complications from angioimmunoblastic T cell lymphoma and colorectal cancer.
Stakeholders' perceptions of the overall effectiveness of early warning systems and risk assessments for weather related hazards in Africa, the Caribbean and South Asia
Venue: Natural Hazards

Low income countries are significantly more vulnerable than high income countries to the risks posed by natural hazards. This paper describes the findings of research into stakeholders' perceptions of the overall effectiveness of early warnings and risk assessments for weather related hazards (i.e. cyclones, floods, droughts and landslides) for humanitarian and development purposes in low income countries in Africa, the Caribbean and South Asia. New findings are derived from a survey and consultations with some 400 practitioners, scientists, researchers and decision makers in the regions. Our findings, which were based upon the collated results of a literature review, stakeholder interviews together with other reviews and surveys, show that although there would appear to have been some progress in improving early warning and risk assessments for weather related hazards, it is highly variable across the three regions. The findings are inconsistent with the self reporting of progress against the Hyogo Framework for Action, which in many cases give a more positive view of their status. Significantly, more work is required to produce robust, reliable and accessible information to reduce vulnerability and manage risks, and this should concentrate on understanding aspects of vulnerability, effective risk communication and community level actions, as well as some well focused improvements in technical, and underpinning scientific aspects of early warning systems and risk assessments.