nils-herrmann/clean_scirepeval huggingface dataset:
Breast volume determination in breast hypertrophy: an accurate method using two anthropomorphic measurements.

Precise determination of breast volume facilitates reconstructive procedures and helps in the planning of tissue removal for breast reduction surgery. Various methods currently used to measure breast size are limited by technical drawbacks and unreliable volume determinations. The purpose of this study was to develop a formula to predict breast volume based on straightforward anthropomorphic measurements.



One hundred one women participated in this study. Eleven anthropomorphic measurements were obtained on 202 breasts. Breast volumes were determined using a water displacement technique. Multiple stepwise linear regression was used to determine predictive variables and a unifying formula.



Mean patient age was 37.7 years, with a mean body mass index of 31.8. Mean breast volumes on the right and left sides were 1328 and 1305 cc, respectively (range, 330 to 2600 cc). The final regression model incorporated the variables of breast base circumference in a standing position and a vertical measurement from the inframammary fold to a point representing the projection of the fold onto the anterior surface of the breast. The derived formula showed an adjusted R of 0.89, indicating that almost 90 percent of the variation in breast size was explained by the model.



Surgeons may find this formula a practical and relatively accurate method of determining breast volume.
StructCap: Structured Semantic Embedding for Image Captioning

Image captioning has attracted ever-increasing research attention in multimedia and computer vision. To encode the visual content, existing approaches typically utilize the off-the-shelf deep Convolutional Neural Network (CNN) model to extract visual features, which are sent to Recurrent Neural Network (RNN) based textual generators to output word sequence. Some methods encode visual objects and scene information with attention mechanism more recently. Despite the promising progress, one distinct disadvantage lies in distinguishing and modeling key semantic entities and their relations, which are in turn widely regarded as the important cues for us to describe image content. In this paper, we propose a novel image captioning model, termed StructCap. It parses a given image into key entities and their relations organized in a visual parsing tree, which is transformed and embedded under an encoder-decoder framework via visual attention. We give an end-to-end formulation to facilitate joint training of visual tree parser, structured semantic attention and RNN-based captioning modules. Experimental results on two public benchmarks, Microsoft COCO and Flickr30K, show that the proposed StructCap model outperforms the state-of-the-art approaches under various standard evaluation metrics.
Cost and effort estimation in agile software development

Projects that are over-budget, delivered late, and fall short of user's expectations have been a common problem are a for software development efforts for years. Agile methods, which represent an emerging set of software development methodologies based on the concepts of adaptability and flexibility, are currently touted as a way to alleviate these reoccurring problems and pave the way for the future of development. The estimation in Agile Software Development methods depends on an expert opinion and historical data of project for estimation of cost, size, effort and duration. In absence of the historical data and experts the previous method like analogy and planning poker are not useful. This paper focuses on the research work in Agile Software development and estimation in Agile. It also focuses the problems in current Agile practices thereby proposed a method for accurate cost and effort estimation.
Automatic Attendance System Based On Face Recognition

Face Detection and Recognition is an important area in the field of substantiation. Maintenance of records of students along with monitoring of class attendance is an area of administration that requires significant amount of time and efforts for management. Automated Attendance Management System performs the daily activities of attendance analysis, for which face recognition is an important aspect. The prevalent techniques and methodologies for detecting and recognizing faces by using feature extraction tools like mean, standard deviation etc fail to overcome issues such as scaling, pose, illumination, variations. The proposed system provides features such as detection of faces, extraction of the features, detection of extracted features, and analysis of student’s attendance. The proposed system integrates techniques such as Principal Component Analysis (PCA) for feature extraction and voila-jones for face detection &Euclidian distance classifier. Faces are recognized using PCA, using the database that contains images of students and is used to recognize student using the captured image. Better accuracy is attained in results and the system takes into account the changes that occurs in the face over the period of time.
An Overview of Data Warehousing and OLAP Technology

Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. Many commercial products and services are now available, and all of the principal database management system vendors now have offerings in these areas. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications. This paper provides an overview of data warehousing and OLAP technologies, with an emphasis on their new requirements. We describe back end tools for extracting, cleaning and loading data into a data warehouse; multidimensional data models typical of OLAP; front end client tools for querying and data analysis; server extensions for efficient query processing; and tools for metadata management and for managing the warehouse. In addition to surveying the state of the art, this paper also identifies some promising research issues, some of which are related to problems that the database research community has worked on for years, but others are only just beginning to be addressed. This overview is based on a tutorial that the authors presented at the VLDB Conference, 1996.
The assessment of knowledge and application of proper body mechanics in the workplace.

Back pain is the most expensive industrial injury. Representatives at DeRoyal, a manufacturer of orthopaedic soft goods, believed a total back hygiene program that included aggressive training in body mechanics would reduce the cost associated with back injury. Prior to implementing a back educational program, the knowledge base of employees was assessed. A survey designed to measure knowledge and application of proper body mechanics was developed and distributed to 100 randomly selected workers. Most workers know the best way to lift. But, they had less knowledge of the best way to push. They also did not always use proper technique in lifting without twisting, planning lifts, and proper standing. A subset of workers that has previously attended back training sessions (n = 6) all gave the correct answers on 4 or the 5 survey items. Orthopaedic nurses could play a critical role in the industrial setting through assessment of body mechanic knowledge and implementation of educational programs.
Basic properties of the Multivariate Fractional Brownian Motion

— This paper reviews and extends some recent results on the multivariate fractional Brownian motion (mfBm) and its increment process. A characterization of the mfBm through its covariance function is obtained. Similarly, the correlation and spectral analyses of the increments are investigated. On the other hand we show that (almost) all mfBm’s may be reached as the limit of partial sums of (super)linear processes. Finally, an algorithm to perfectly simulate the mfBm is presented and illustrated by some simulations. Résumé (Propriétés du mouvement brownien fractionnaire multivarié) Cet article constitue une synthèse des propriétés du mouvement brownien fractionnaire multivarié (mBfm) et de ses accroissements. Différentes caractérisations du mBfm sont présentées à partir soit de la fonction de covariance, soit de représentations intégrales. Nous étudions aussi les propriétés temporelles et spectrales du processus des accroissements. D’autre part, nous montrons que (presque) tous les mBfm peuvent être atteints comme la limite (au sens de la convergence faible) des sommes partielles de processus (super)linéaires. Enfin, un algorithme de simulation exacte est présenté et quelques simulations illustrent les propriétés du mBfm.
Fabrication of hemispherical fused silica micro-resonator with tailored stiffness and mass distribution

This paper presents initial progress toward a precision shell integrating (PSI) gyroscope designed for high Coriolis force sensitivity and high shock and vibration tolerance. We demonstrate control over the stiffness and mass distribution of a 3D hemispherical shell resonator formed with blowtorch reflow molding of fused silica (FS). The thickness profile of the resonator is designed to reduce the frequency of the n = 2 wine-glass (WG) modes and to maximize the parasitic mode frequencies, which increases shock and vibration tolerance. We simulate the dynamic reflow process with a non-isothermal finite element model, allowing us to design a substrate pattern that will reflow into the desired geometry. The PSI resonator is simulated to have a larger effective mass (Meff) than other MEMS gyroscope resonator designs.
Tracking a large number of objects from multiple views

We propose a multi-object multi-camera framework for tracking large numbers of tightly-spaced objects that rapidly move in three dimensions. We formulate the problem of finding correspondences across multiple views as a multidimensional assignment problem and use a greedy randomized adaptive search procedure to solve this NP-hard problem efficiently. To account for occlusions, we relax the one-to-one constraint that one measurement corresponds to one object and iteratively solve the relaxed assignment problem. After correspondences are established, object trajectories are estimated by stereoscopic reconstruction using an epipolar-neighborhood search. We embedded our method into a tracker-to-tracker multi-view fusion system that not only obtains the three-dimensional trajectories of closely-moving objects but also accurately settles track uncertainties that could not be resolved from single views due to occlusion. We conducted experiments to validate our greedy assignment procedure and our technique to recover from occlusions. We successfully track hundreds of flying bats and provide an analysis of their group behavior based on 150 reconstructed 3D trajectories.
Small-Scale Modeling of Pharmaceutical Powder Compression from Tap Density Testers, to Roller Compactors, and to the Tablet Press Using Big Data

Roller compaction is commonly used in the pharmaceutical industry to improve powder flow and compositional uniformity. The process produces ribbons which are milled into granules. The ribbon solid fraction (SF) can affect both the granule size and the tensile strength of downstream tablets. Roll force, which is directly related to the applied stress on the powder in the nip region, is typically the most dominant process parameter controlling the ribbon solid fraction. This work is an extension of a previous study, leveraging mathematical modeling as part of a Quality by Design development strategy (Powder Technology, 2011, 213: 1–13). In this paper, a semi-empirical unified powder compaction model is postulated describing powder solid fraction evolution as a function of applied stress in three geometries: the tapped cylinder (uniaxial strain—part of a standard tapped density measurement), the roller compaction geometry (plane strain deformation), and tablet compression (uniaxial strain). A historical database (CRAVE) containing data from many different formulations was leveraged to evaluate the model. The internally developed CRAVE database contains all aspects of drug product development batch records and was queried to retrieve tablet compression data along with corresponding roller compaction and tap density measurements for the same batch. Tablet compaction data and tap density data were used to calibrate a quadratic relationship between stress and the reciprocal of porosity. The quadratic relationship was used to predict the roll stress and corresponding roll force required to attain the reported ribbon SF. The predicted roll force was found to be consistent with the actual roll force values recorded across 136 different formulations in 136 batch records. In addition, significant correlations were found between the first and the second order constants of the quadratic relationship, suggesting that a single formulation-dependent fitting parameter may be used to define the complete SF versus stress relationship. The fitting parameter could be established by compressing a single tablet and measuring the powder tapped density. It was concluded that characterization of this parameter at a small scale can help define the required process parameters for both roller compactors and tablet presses at a large scale.
Efficient public integrity checking for cloud data sharing with multi-user modification

In past years a body of data integrity checking techniques have been proposed for securing cloud data services. Most of these works assume that only the data owner can modify cloud-stored data. Recently a few attempts started considering more realistic scenarios by allowing multiple cloud users to modify data with integrity assurance. However, these attempts are still far from practical due to the tremendous computational cost on cloud users. Moreover, collusion between misbehaving cloud servers and revoked users is not considered. This paper proposes a novel data integrity checking scheme characterized by multi-user modification, collusion resistance and a constant computational cost of integrity checking for cloud users, thanks to our novel design of polynomial-based authentication tags and proxy tag update techniques. Our scheme also supports public checking and efficient user revocation and is provably secure. Numerical analysis and extensive experimental results show the efficiency and scalability of our proposed scheme.
Active hepatic capsulitis caused by Paragonimus westermani infection.

Paragonimiasis is an important re-emerging parasitosis in Japan. Although the lungs and pleural cavity are the principal sites affected with the parasite, ectopic infection can occur in unexpected sites such as skin and brain. This case report describes a patient with active hepatic capsulitis due to Paragonimus westermani infection. The patient was successfully treated with praziquantel at the dose of 75 mg/kg/day for 3 days.
Intrasacral rod fixation for pediatric long spinal fusion: results of a prospective study with a minimum 5-year follow-up.

Lumbosacral fusion is often needed in cases of pediatric neuromuscular spinal deformities. Despite the numerous fixation techniques described, the procedure remains challenging. Jackson has described a method of intrasacral fixation providing immediate 3-dimensional stability and promising clinical results. The purpose of this study was to report our experience with long spinal fusion using Jackson intrasacral fixation in pediatric patients.



All patients with at least 5 years of follow-up were reviewed. No brace was used postoperatively. Clinical data and radiographs were collected and analyzed preoperatively, postoperatively, and at latest follow-up. Intraoperative and postoperative complications were reported. Paired t test was used for statistical analysis.



Fifty-six patients were included. The average age at surgery was 15.3 years. Mean follow-up period was 10.3 years and no patient was lost to follow-up. All radiographic parameters (frontal balance, frontal Cobb angle of the primary curve, iliolumbar angle, pelvic obliquity, sagittal balance, lumbosacral lordosis, and sacral slope) were significantly improved postoperatively (P<0.001), without significant loss of correction at latest follow-up. Four early infections, 1 pressure sore, and 4 cases of radicular pain, which resolved without intervention, were reported postoperatively. At latest follow-up, no patient complained of lumbar pain, and neither ambulatory status nor activity level ability worsened in any case. Sixteen of the 20 patients who needed a sitting orthosis preoperatively achieved a functional sitting posture without bracing.



Jackson fixation is a safe and reliable technique providing immediate stability. In our series, no mechanical complication occurred and no loss of correction was observed, despite immediate unprotected mobilization. The method provides reliable good sacral fixation for pediatric neuromuscular spinal deformities, especially when the correction of severe pelvic obliquity is necessary.



This consecutive series provides level IV evidence.
Toward Context-Aware Computing: Experiences and Lessons

person’s ability to focus on his or her primary task. Distractions occur especially in mobile environments, because walking, driving, or other real-world interactions often preoccupy the user. A pervasivecomputing environment that minimizes distraction must be context aware, and a pervasive-computing system must know the user’s state to accommodate his or her needs. Context-aware applications provide at least two fundamental services: spatial awareness and temporal awareness. Spatially aware applications consider a user’s relative and absolute position and orientation. Temporally aware applications consider the time schedules of public and private events. With an interdisciplinary class of Carnegie Mellon University (CMU) students, we developed and implemented a context-aware, pervasive-computing environment that minimizes distraction and facilitates collaborative design.
Modeling Progress in AI

Participants in recent discussions of AI-related issues ranging from intelligence explosion to technological unemployment have made diverse claims about the nature, pace, and drivers of progress in AI. However, these theories are rarely specified in enough detail to enable systematic evaluation of their assumptions or to extrapolate progress quantitatively, as is often done with some success in other technological domains. After reviewing relevant literatures and justifying the need for more rigorous modeling of AI progress, this paper contributes to that research program by suggesting ways to account for the relationship between hardware speed increases and algorithmic improvements in AI, the role of human inputs in enabling AI capabilities, and the relationships between different sub-fields of AI. It then outlines ways of tailoring AI progress models to generate insights on the specific issue of technological unemployment, and outlines future directions for research on AI progress.
Deep Leaming for Stock Market Prediction Using Technical Indicators and Financial News Articles

This work uses deep learning models for daily directional movements prediction of a stock price using financial news titles and technical indicators as input. A comparison is made between two different sets of technical indicators, set 1: Stochastic %K, Stochastic %D, Momentum, Rate of change, William’s %R, Accumulation/Distribution (A/D) oscillator and Disparity 5; set 2: Exponential Moving Average, Moving Average Convergence-Divergence, Relative Strength Index, On Balance Volume and Bollinger Bands. Deep learning methods can detect and analyze complex patterns and interactions in the data allowing a more precise trading process. Experiments has shown that Convolutional Neural Network (CNN) can be better than Recurrent Neural Networks (RNN) on catching semantic from texts and RNN is better on catching the context information and modeling complex temporal characteristics for stock market forecasting. So, there are two models compared in this paper: a hybrid model composed by a CNN for the financial news and a Long Short-Term Memory (LSTM) for technical indicators, named as SI-RCNN; and a LSTM network only for technical indicators, named as I-RNN. The output of each model is used as input for a trading agent that buys stocks on the current day and sells the next day when the model predicts that the price is going up, otherwise the agent sells stocks on the current day and buys the next day. The proposed method shows a major role of financial news in stabilizing the results and almost no improvement when comparing different sets of technical indicators.
A Qualitative Approach to Examine Technology Acceptance

The research field of technology acceptance and software acceptance is a fertile field in the discipline of MIS. Acceptance research is mainly affected by the technology acceptance model (TAM). The TAM is counted as the major guideline for acceptance research. But recently more researchers discover the deficits of former acceptance research. The main cause of the criticism is the focus on quantitative research methods. We will show this with the help of former meta-studies and a literature review. Quantitative approaches are basically appropriate for the testing of theories. The development of new theories or constructs is followed to a lesser intent. In the article we will show how a qualitative approach can be used for theory-construction. We will introduce a qualitative research design and show how this approach can be used to develop new constructs of acceptance while some existing constructs taken from TAM and related theories cannot be confirmed.
Chronic obstructive pulmonary disease in Portugal: Pneumobil (1995) and 2002 prevalence studies revisited.

Chronic Obstructive Pulmonary Disease (COPD) has been a leading cause of morbidity and mortality worldwide, over the years. In 1995, the implementation of a respiratory function survey seemed to be an adequate way to draw attention to neglected respiratory symptoms and increase the awareness of spirometry surveys. By 2002 there were new consensual guidelines in place and the awareness that prevalence of COPD depended on the criteria used for airway obstruction definition. The purpose of this study is to revisit the two studies and to turn public some of the data and respective methodologies.



From Pneumobil study database of 12,684 subjects, only the individuals with 40+ years old (n = 9.061) were selected. The 2002 study included a randomized representative sample of 1,384 individuals with 35-69 years old.



The prevalence of COPD was 8.96% in Pneumobil and 5.34% in the 2002 study. In both studies, presence of COPD was greater in males and there was a positive association between presence of COPD and older age groups. Smokers and ex-smokers showed a higher proportion of cases of COPD.



Prevalence in Portugal is lower than in other European countries. This may be related to lower smokers' prevalence. Globally, the most important risk factors associated with COPD were age over 60 years, male gender and smoking exposure. All aspects and limitations regarding different recruitment methodologies and different criteria for defining COPD cases highlight the need of a standardized method to evaluate COPD prevalence and associated risks factors, whose results can be compared across countries, as it is the case of BOLD project.