nils-herrmann/clean_scirepeval huggingface dataset:
Dentinal Tubule Penetration of Tricalcium Silicate Sealers.
Venue: Journal of endodontics
Year: 2016

The treatments for which mineral trioxide aggregate (MTA) based materials can be used in dentistry are expanding. Smaller particle size and easier handling properties have allowed the advent of tricalcium silicate sealers including EndoSequence BC Sealer (Brasseler USA, Savannah, GA) QuickSet2 (Avalon Biomed, Bradenton, FL) NeoMTA Plus (Avalon Biomed) and MTA Fillapex (Angelus, Londrina, Brazil) The objective of this study was to measure the tubule penetration with these sealers using continuous wave (CW) and single cone (SC) obturation techniques.  Eighty single rooted teeth were randomly divided into 8 groups of 10 and obturated with 1 of the previously mentioned sealers mixed with trace amounts of rhodamine using either the CW or SC technique. Teeth were sectioned at 1 mm and 5 mm from the apex and examined under a confocal laser microscope. The percentage of sealer penetration and the maximum sealer penetration were measured.  The tricalcium silicate sealers penetrated tubules as deep as 2000 mm (2 mm) The percentage of sealer penetration was much higher 5 mm from the apex, with many specimens having 100% penetration for both SC and warm vertical techniques. MTA Fillapex, a resin based sealer with less than 20% MTA particles, had significantly greater tubule penetration with a warm vertical technique versus the SC technique at the 1 mm level.  Within the limitations of this study, the CW and SC techniques produced similar tubule penetration at both the 1 mm and the 5 mm level with the tricalcium silicate sealers BC Sealer, QuickSet2, and NeoMTA Plus.
Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering based segmentation approach
Year: 2020

In medical image processing, brain tumor detection and segmentation is a challenging and time consuming task. Magnetic Resonance Image (MRI) scan analysis is a powerful tool in the recent technology that makes effective detection of the abnormal tissues from the brain. In the brain image, the size of a tumor can be varied for different patients along with the minute details of the tumor. It is a difficult task to diagnose and classify the tumor from numerous images for the radiologists. This paper developed a brain tumor classification using a hybrid deep autoencoder with a Bayesian fuzzy clustering based segmentation approach. Initially, the pre processing stage is performed using the non local mean filter for denoising purposes. Then the BFC (Bayesian fuzzy clustering) approach is utilized for the segmentation of brain tumors. After segmentation, robust features such as, information theoretic measures, scattering transform (ST) and wavelet packet Tsallis entropy (WPTE) methods are used for the feature extraction process. Finally, a hybrid scheme of the DAE (deep autoencoder) based JOA (Jaya optimization algorithm) with a softmax regression technique is utilized to classify the tumor part for the brain tumor classification process. The proposed scheme is implemented in a MATLAB environment. The simulation results are conducted by the BRATS 2015 database which proved that the proposed approach obtained the high classification accuracy (98.5 when compared to other state of art methods.
The big fish little pond effect: Generalizability of social comparison processes over two age cohorts from Western, Asian, and Middle Eastern Islamic countries.
Year: 2015

Extensive support for the seemingly paradoxical negative effects of school and class average achievement on academic self concept (ASC) the big fish little pond effect (BFLPE) is based largely on secondary students in Western countries or on cross cultural Program for International Student Assessment studies. There is little research testing the generalizability of this frame of reference effect based on social comparison theory to primary school students and or to matched samples of primary and secondary students from different countries. Using multigroup multilevel latent variable models, we found support for developmental and cross cultural generalizability of the BFLPE based on Trends in International Mathematics and Science Study data; positive effects of individual student achievement and the negative effects of class average achievement on ASC were significant for each of the 26 groups (nationally representative samples of 4th and 8th grade students from 13 diverse countries; 117,321 students from 6,499 classes)
Arab, Chadia (2009) Les Ait Ayad la circulation migratoire des Marocains entre la France, l'Espagne et l'Italie
Year: 2010

Publie en 2009, ce livre de Chadia Arab est le fruit d'un long travail de recherches dans le cadre d'une these en geographie, soutenue au laboratoire MIGRINTER en decembre 2007. Cet ouvrage apporte des elements majeurs a la comprehension des mouvements migratoires internationaux. En effet, l'auteure ne s'est pas contentee des enquetes et des entretiens qualitatifs des migrants rencontres dans differents pays (Maroc, France, Espagne et Italie) mais elle a voyage avec eux et dans les memes con.
PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text
Venue: EMNLP
Year: 2019

We consider open domain question answering (QA) where answers are drawn from either a corpus, a knowledge base (KB) or a combination of both of these. We focus on a setting in which a corpus is supplemented with a large but incomplete KB, and on questions that require non trivial (e.g. "multi hop" reasoning. We describe PullNet, an integrated framework for (1) learning what to retrieve and (2) reasoning with this heterogeneous information to find the best answer. PullNet uses an iterative process to construct a question specific subgraph that contains information relevant to the question. In each iteration, a graph convolutional network (graph CNN) is used to identify subgraph nodes that should be expanded using retrieval (or "pull" operations on the corpus and/or KB. After the subgraph is complete, another graph CNN is used to extract the answer from the subgraph. This retrieve and reason process allows us to answer multi hop questions using large KBs and corpora. PullNet is weakly supervised, requiring question answer pairs but not gold inference paths. Experimentally PullNet improves over the prior state of the art, and in the setting where a corpus is used with incomplete KB these improvements are often dramatic. PullNet is also often superior to prior systems in a KB only setting or a text only setting.
Profit Aware Load Balancing for Distributed Cloud Data Centers
Venue: 2013 IEEE 27th International Symposium on Parallel and Distributed Processing
Year: 2013

The advent of cloud systems has spurred the emergence of an impressive assortment of Internet services. Recent pressures on enhancing the profitability by curtailing surging dollar costs on energy have posed challenges to, as well as placed a new emphasis on, designing energy efficient request dispatching and resource management algorithms. What further adds to the design challenge is the highly diverse nature of Internet service requests in terms of Quality of Service (QoS) constraints and business values. Nonetheless, most of the existing job scheduling and resource management solutions are for a single type of request and are profit oblivious. They are unable to reap the benefit of multi service profit aware algorithm designs. In this paper, we consider a cloud service provider operating geographically distributed data centers in a multi electricity market environment, and propose an energy efficient, profit and cost aware request dispatching and resource allocation algorithm to maximize a service provider's net profit. We formulate the net profit maximization issue as a constrained optimization problem, using a unified task model capturing multiple cloud layers (e.g. SaaS, PaaS, IaaS. The proposed approach maximizes a service provider's net profit by judiciously distributing service requests to data centers, powering on/off an appropriate number of servers, and allocating server resources to dispatched requests. We conduct extensive experiments to validate our proposed algorithm. Results show that our proposed approach can improve a service provider's net profit significantly.
The Language Model for Legal Retrieval and Bert based Model for Rhetorical Role Labeling for Legal Judgments
Venue: FIRE
Year: 2020

This paper mainly introduces the solutions to the two tasks published in FIRE2020(forum for information retrieval evaluation) For Task1 (statistic retrieval) The task 1 is, for a given query(description of a situation) identify relevant statutes and prior cases. This task includes two subtasks, Task1a (identifying relevant prior cases) and Task1b (identifying relevant statistics) For these two subtasks, we use the language model to score each query, and then rank them according to the score. For Task2(rhetorical role labeling for legal judgments) It requires us to classify sentences. We think it's a multi classification problem, and finally, we use Bert to complete the classification task. In the final result, the score of Task1a is 0.125, the score of Task1b is 0.2003, and the accuracy of Task2 is 0.549. The results and experiments show that the language model is a better way to complete Task1 and Bert is better to complete task2.
Are Multilingual BERT models robust? A Case Study on Adversarial Attacks for Multilingual Question Answering
Venue: ArXiv
Year: 2021

Recent approaches have exploited weaknesses in monolingual question answering (QA) models by adding adversarial statements to the passage. These attacks caused a reduction in stateof the art performance by almost 50% In this paper, we are the first to explore and successfully attack a multilingual QA (MLQA) system pre trained on multilingual BERT using several attack strategies for the adversarial statement reducing performance by as much as 85% We show that the model gives priority to English and the language of the question regardless of the other languages in the QA pair. Further, we also show that adding our attack strategies during training helps alleviate the attacks.
Improvements in dental care using a new mobile app with cloud services.
Venue: Journal of the Formosan Medical Association Taiwan yi zhi
Year: 2014

Traditional dental care, which includes long term oral hygiene maintenance and scheduled dental appointments, requires effective communication between dentists and patients. In this study, a new system was designed to provide a platform for direct communication between dentists and patients.  A new mobile app, Dental Calendar, combined with cloud services specific for dental care was created by a team constituted by dentists, computer scientists, and service scientists. This new system would remind patients about every scheduled appointment, and help them take pictures of their own oral cavity parts that require dental treatment and send them to dentists along with a symptom description. Dentists, by contrast, could confirm or change appointments easily and provide professional advice to their patients immediately. In this study, 26 dentists and 32 patients were evaluated by a questionnaire containing eight dental service items before and after using this system. Paired sample t test was used for statistical analysis.  After using the Dental Calendar combined with cloud services, dentists were able to improve appointment arrangements significantly, taking care of the patients with sudden worse prosthesis (p 0.05) Patients also achieved significant improvement in appointment reminder systems, rearrangement of appointments in case of sudden worse prosthesis, and establishment of a direct relationship with dentists (p 0.05) CONCLUSION Our new mobile app, Dental Calendar, in combination with cloud services, provides efficient service to both dentists and patients, and helps establish a better relationship between them. It also helps dentists to arrange appointments for patients with sudden worsening of prosthesis function.
The eXpress data path: fast programmable packet processing in the operating system kernel
Venue: CoNEXT
Year: 2018

Programmable packet processing is increasingly implemented using kernel bypass techniques, where a userspace application takes complete control of the networking hardware to avoid expensive context switches between kernel and userspace. However, as the operating system is bypassed, so are its application isolation and security mechanisms; and well tested configuration, deployment and management tools cease to function. To overcome this limitation, we present the design of a novel approach to programmable packet processing, called the eXpress Data Path (XDP) In XDP, the operating system kernel itself provides a safe execution environment for custom packet processing applications, executed in device driver context. XDP is part of the mainline Linux kernel and provides a fully integrated solution working in concert with the kernel's networking stack. Applications are written in higher level languages such as C and compiled into custom byte code which the kernel statically analyses for safety, and translates into native instructions. We show that XDP achieves single core packet processing performance as high as 24 million packets per second, and illustrate the flexibility of the programming model through three example use cases: layer 3 routing, inline DDoS protection and layer 4 load balancing.
Anomaly Threat Detection System and Role
Year: 2018

In network security the organizations are ever growing to identify insider threats. Those who have authorized access to sensitive organizational placed in a position of power that could well be abused and could cause significant damage to an organization. Traditional intrusion detection systems are neither designed nor capable of identifying those who act maliciously within an organization describe an automated system that is capable of detecting insider threats within an organization. We define a tree structure profiling approach that incorporates the details of activities conducted by each user and each job role and then use this to o a consistent representation of features that provide a rich description of the user's behavior. Deviation can be assessed based on the amount of variance that each user exhibits across multiple attributes, compared against their peers. We have perfor experimentation using that the system can identify anomalous behavior that may be indicative of a potential threat. We also show how our detection system can be combined with visual analytics tools to support further investigation by an analyst.
Examination Incentives, Learning, and Patent Office Outcomes: The Use of Examiner's Amendments at the USPTO
Year: 2020

We investigate how U.S. Patent and Trademark Office (USPTO) patent examiner experience and seniority based incentives affect the innovation ecosystem. First, we show that examiners respond to production incentives and demonstrate learning by increasing the use of examiner's amendments in both experience and seniority, a mechanism not previously studied. Second, this examination procedure directly benefits innovators and firms by significantly reducing prosecution processing time without impacting patent quality. Finally, after considering *The views expressed are those of the individual authors and do not necessarily reflect official positions of the Office of Chief Economist or the U. S. Patent and Trademark Office. USPTO Economic Working Papers are preliminary research being shared in a timely manner with the public in order to stimulate discussion, scholarly debate, and critical comment. +Authors listed alphabetically examiner's amendments, the negative relationship between examiner characteristics and patent examination quality found in the previous literature does not persist at first action, a decision point that allows for the clear measurement of examiner behavior. Our results demonstrate a need for reformulated policy recommendations related to the structure of examination at the USPTO.
Research on Collaborative Development of Financial Science and Technology and Risk Supervision Based on Big Data Era
Year: 2020

with the Rapid Development of Internet Finance, Our Government Has Paid Great Attention to the Standardized Development of Internet Finance. Financial Science and Technology, through the Rapid Integration of Information Technology and Traditional Financial Services, Has Greatly Affected the Business Models and Financial Products of Financial Markets, Financial Institutions and Financial Services, and Has Become the Mainstream Trend of Current Financial Innovation. the Financial Market is a Place Where the Fund Suppliers and Demanders Who Take Financial Assets as Trading Objects Meet with Each Other to Jointly Determine the Rate of Return According to the Principle of Competition and Finally Complete the Fund Transaction. Compared with Traditional Internet Finance, Financial Technology is More in Depth in Concept and Business. Financial Innovation Has Created Various Types of Institutional Investors. Meanwhile, the Rapid Growth of Institutional Investors Has Accelerated the Process of Financial Innovation. from the Perspective of Big Data Era, This Paper Analyzes and Discusses the Collaborative Development of Financial Technology and Risk Supervision in the Era of Big Data.
Reverse Solidification Path Methodology for Dewaxing Ceramic Shells in Investment Casting Process
Year: 2010

Dewaxing of investment shells is the most critical step in the investment casting process. It defines the fate of casting because the surface and dimensional characteristics of the wax are transferred to the ceramic shell and so to the final casting. A novel route of dewaxing of investment shells through a modified thermal heating method and the effect of such thermal heating on investment shells has been investigated. The experiments have shown firstly the formation of a "dilatation gap" followed by the reversal of the solidification path, and both facilitate the management of thermal expansion related problems, resulting in smooth discharge of molten wax. The shells made by this methodology of Reversed Solidification Path Dewaxing (RSPD) enable production with low rejection rates. The investment has been shown to be of high strength, sustaining thermal shock of more than one casting trial. The process can therefore be of great industrial importance pertaining to cost and energy efficiency, reduced defects, and high production rates.
Neural mechanisms of female sexual behavior in the rat; comparison with male ejaculatory control
Venue: Pharmacology Biochemistry and Behavior
Year: 2014

The sequential organization of sexual behavior of the female rat is described, eventually leading to the lordotic posture, shown during mating. A complex set of signals: olfactory, cutaneous sensory as well as genitosensory, is guiding the female to this specific posture, eventually. Genitosensory signals converge in the lumbosacral levels of the spinal cord, from where they are dispersed to a series of supraspinal brain areas, in the brainstem, thalamus, hypothalamus and limbic system. The similarity with the neural activation patterns observed in the male rat is remarkable. In a number of brain areas, however: the midbrain periaqueductal gray, the ventrolateral part of the ventromedial hypothalamic nucleus (VMHvl) and the medial preoptic lateral septum regions, specific male female differences have been observed. Especially the VMHvl is an intriguing area, as it has been shown that the same neurons may be involved in 'opposite behavior' like aggression and the induction of lordosis. The motor mechanisms controlling the lordosis posture in the rat as well as in some other mammals are discussed, as well as some aspects of the reward mechanisms contributing to female sex. We conclude that we have collected a great amount of neurophysiological knowledge over the last 20 years, but that the unresolved questions are still numerous. In this field, there is still much to explore.
Perception of transformational leadership style and its effectiveness on virtual work teams (VWT) A literature review in the organizational context
Year: 2018

This article presents the development of different studies in perception of the transformational leadership style applied in virtual work teams (VWT) The approached phenomenon's study method was conducted through a literature review in transformational leadership and VWT's, to comprehend and stablish their importance in organizations. Results could provide VWT leaders with pertinent information to counteract distance and time difficulties. Findings determined leadership capability generates effectiveness in changing global business scenarios, as an adequate response of VWT's at the organizational arena.
Semi implicit Galerkin Legendre Spectral Schemes for Nonlinear Time Space Fractional Diffusion Reaction Equations with Smooth and Nonsmooth Solutions
Venue: J. Sci. Comput.
Year: 2020

For the first time in literature, semi implicit spectral approximations for nonlinear Caputo time and Riesz space fractional diffusion equations with both smooth and non smooth solutions are proposed. More precisely, the governing partial differential equation generalizes the Hodgkin Huxley, the Allen Cahn and the Fisher Kolmogorov Petrovskii Piscounov equations. The schemes employ a Legendre based Galerkin spectral method for the Riesz space fractional derivative, and L 1 type approximations with both uniform and graded meshes for the Caputo time fractional derivative. More importantly, by using fractional Gronwall inequalities and their associated discrete forms, sharp error estimates are proved which show an enhancement in the convergence rate compared with the standard L 1 approximation on uniform meshes. This analysis encompasses both uniform meshes as well as meshes that are graded in time, and guarantees the unconditional stability. The numerical results that accompany our analysis confirm our theoretical error estimates, and give significant insights into the convergence behavior of our schemes for problems with smooth and non smooth solutions.
Cumulative Nets for Edge Detection
Venue: ACM Multimedia
Year: 2018

Lots of recent progress have been made by using Convolutional Neural Networks (CNN) for edge detection. Due to the nature of hierarchical representations learned in CNN, it is intuitive to design side networks utilizing the richer convolutional features to improve the edge detection. However, different side networks are isolated, and the final results are usually weighted sum of the side outputs with uneven qualities. To tackle these issues, we propose a Cumulative Network (C Net) which learns the side network cumulatively based on current visual features and low level side outputs, to gradually remove detailed or sharp boundaries to enable high resolution and accurate edge detection. Therefore, the lower level edge information is cumulatively inherited while the superfluous details are progressively abandoned. In fact, recursively Learningwhere to remove superfluous details from the current edge map with the supervision of a higher level visual feature is challenging. Furthermore, we employ atrous convolution (AC) and atrous convolution pyramid pooling (ASPP) to robustly detect object boundaries at multiple scales and aspect ratios. Also, cumulatively refining edges using high level visual information and lower lever edge maps is achieved by our designed cumulative residual attention (CRA) block. Experimental results show that our C Net sets new records for edge detection on both two benchmark datasets: BSDS500 (i.e. .819 ODS, .835 OIS and .862 AP) and NYUDV2 (i.e. .762 ODS, .781 OIS, .797 AP) C Net has great potential to be applied to other deep learning based applications, e.g. image classification and segmentation.