nils-herrmann/clean_scirepeval huggingface dataset:
Dorsal Scaphoid Subluxation on Sagittal Magnetic Resonance Imaging as a Marker for Scapholunate Ligament Tear.

To evaluate the diagnostic utility of scaphoid dorsal subluxation on magnetic resonance imaging (MRI) as a predictor of scapholunate interosseous ligament (SLIL) tears and compare this with radiographic findings.



Thirty-six MRIs were retrospectively reviewed: 18 with known operative findings of complete Geissler IV SLIL tears that were surgically repaired, and 18 MRIs performed for ulnar-sided wrist pain but no SLIL tear. Dorsal subluxation of the scaphoid was measured on the sagittal MRI cut, which demonstrated the maximum subluxation. Independent samples t tests were used to compare radiographic measurements of scapholunate (SL) gap, SL angle, and capitolunate/third metacarpal-lunate angles between the SLIL tear and the control groups and to compare radiographic measurements between wrists that had dorsal subluxation of the scaphoid and wrists that did not have dorsal subluxation. Interrater reliability of subluxation measurements on lateral radiographs and on MRI were calculated using kappa coefficients.



Thirteen of 18 wrists with complete SLIL tears had greater than 10% dorsal subluxation of the scaphoid relative to the scaphoid facet. Average subluxation in this group was 34%. Four of 18 wrists with known SLIL tears had no subluxation. No wrists without SLIL tears (control group) had dorsal subluxation. The SL angle, capitolunate/third metacarpal-lunate angle and SL gap were greater in wrists that had dorsal subluxation of the scaphoid on MRI. Interrater reliability of measurements of dorsal subluxation of the scaphoid was superior on MRI than on lateral x-ray.



An MRI demonstration of dorsal subluxation of the scaphoid, of as little as 10%, as a predictor of SLIL tear had a sensitivity of 72% and a specificity of 100%. The high positive predictive value indicates that the presence of dorsal subluxation accurately predicts SLIL tear.



Diagnostic II.
Design and construction of continuous alternate wheels for an omnidirectional mobile robot

Many types of omnidirectional wheels with passive rollers have gaps between rollers. Since these gaps cause a wheel to make discontinuous contact with the ground, they lead to vertical and/or horizontal vibrations during wheel operation. In addition, the radii of passive rollers are related to the height of a bump an omnidirectional wheel can surmount. In this research a new design of the alternate wheel is proposed. Because this wheel makes continuous contact with the ground and has alternating large and small rollers around the wheel, it is termed a continuous alternate wheel (CAW). In this paper a design procedure is also presented to determine the optimum number of rollers, the radii of rollers and the inside inclination angle of an outer roller for given design specifications. The CAW based on this design is compared to the existing alternate wheels in terms of design. Finally, an actual continuous alternate wheel is constructed to verify validity of the design guidelines.
When CSI Meets Public WiFi: Inferring Your Mobile Phone Password via WiFi Signals

In this study, we present WindTalker, a novel and practical keystroke inference framework that allows an attacker to infer the sensitive keystrokes on a mobile device through WiFi-based side-channel information. WindTalker is motivated from the observation that keystrokes on mobile devices will lead to different hand coverage and the finger motions, which will introduce a unique interference to the multi-path signals and can be reflected by the channel state information (CSI). The adversary can exploit the strong correlation between the CSI fluctuation and the keystrokes to infer the user's number input. WindTalker presents a novel approach to collect the target's CSI data by deploying a public WiFi hotspot. Compared with the previous keystroke inference approach, WindTalker neither deploys external devices close to the target device nor compromises the target device. Instead, it utilizes the public WiFi to collect user's CSI data, which is easy-to-deploy and difficult-to-detect. In addition, it jointly analyzes the traffic and the CSI to launch the keystroke inference only for the sensitive period where password entering occurs. WindTalker can be launched without the requirement of visually seeing the smart phone user's input process, backside motion, or installing any malware on the tablet. We implemented Windtalker on several mobile phones and performed a detailed case study to evaluate the practicality of the password inference towards Alipay, the largest mobile payment platform in the world. The evaluation results show that the attacker can recover the key with a high successful rate.
Reconstruction of microtia.

The reconstruction of microtia continues to represent one of the more challenging plastic surgery procedures. The limitations of the reconstruction are partly inherent in the soft-tissue deficiencies present in increasing degrees from large conchal remnant microtia, to lobular microtia, to the displaced remnant in auricular dystopia, and partly because of the high level of technical expertise required. After a brief review of the history of ear reconstruction in general and microtia specifically, issues related to the transition in the popularity from techniques involving three or more stages (Tanzer and Brent) to the current popularity of two-stage procedures (Nagata, Firmin, and Park) are discussed in detail. Each of the popular procedures is viewed in relation to timing of the reconstruction, procedure planning, and how both the soft tissues and framework construction are handled, in each of the stages. The most significant differences include whether the autogenous cartilage framework is constructed with or without the tragal construct (for the lobular-type microtia), whether or not the lobule is rotated in the first-stage reconstruction, whether an additional cartilage block is placed behind the framework for added ear projection, and how that added block is covered (choice of fascia flap and skin graft). Each of the techniques has to be varied in reconstruction of auricular dystopia in light of the associated skeletal and soft-tissue hypoplasia. In this article, the author demonstrates that although there may be significant advantages to the two-stage reconstructions of Nagata and Firmin, some may feel that the larger amount of cartilage harvested, the later optimal age for beginning the reconstruction, the additional scalp scars engendered by using the temporoparietal fascia flap in the second-stage elevation of the framework, and even the exchange of lobule tissue (and ability to later pierce ears) to obtain better coverage of the concha and tragus are unacceptable. Having gained experience with each of the varied approaches and having modified them when unusual variations in deformities have required it, the author discusses his current preferences. Clearly, this author feels that there may be significant advantages to delaying the reconstruction to age 10 years or older, yet experience gained over the past 25 years would seem to indicate that as in all other aspects of plastic and reconstructive surgery one must never be wed to a single approach, and experience and flexibility are essential in obtaining the optimal outcome in all variations of the deformity.
Is it wrong to play violent video games?

Many people have a strong intuition that there is something morallyobjectionable about playing violent video games, particularly withincreases in the number of people who are playing them and the games'alleged contribution to some highly publicized crimes. In this paper,I use the framework of utilitarian, deontological, and virtue ethicaltheories to analyze the possibility that there might be some philosophicalfoundation for these intuitions. I raise the broader question of whetheror not participating in authentic simulations of immoral acts in generalis wrong. I argue that neither the utilitarian, nor the Kantian hassubstantial objections to violent game playing, although they offersome important insights into playing games in general and what it ismorally to be a ``good sport.'' The Aristotelian, however, has a plausibleand intuitive way to protest participation in authentic simulations ofviolent acts in terms of character: engaging in simulated immoral actserodes one's character and makes it more difficult for one to live afulfilled eudaimonic life.
Formation and psychometric evaluation of a health-related quality of life instrument for children living with HIV in India.

In-depth interviews and focus group discussions were conducted to inform the development of an instrument to measure the health-related quality of life of children living with HIV. The QOL-CHAI instrument consists of four generic core scales of the "Pediatric Quality of Life Inventory" and two HIV-targeted scales-"symptoms" and "discrimination." A piloting exercise involving groups of children living with HIV and HIV-negative children born to HIV-infected parents provided evidence for the acceptable psychometric properties and usability of the instrument. It is expected that the QOL-CHAI can serve well as a brief, standardized, and culturally appropriate instrument for assessing health-related quality of life of Indian children living with HIV.
A systematic and comprehensive investigation of methods to build and evaluate fault prediction models

This paper describes a study performed in an industrial setting that attempts to build predictive models to identify parts of a Java system with a high fault probability. The system under consideration is constantly evolving as several releases a year are shipped to customers. Developers usually have limited resources for their testing and would like to devote extra resources to faulty system parts. The main research focus of this paper is to systematically assess three aspects on how to build and evaluate fault-proneness models in the context of this large Java legacy system development project: (1) compare many data mining and machine learning techniques to build fault-proneness models, (2) assess the impact of using different metric sets such as source code structural measures and change/fault history (process measures), and (3) compare several alternative ways of assessing the performance of the models, in terms of (i) confusion matrix criteria such as accuracy and precision/recall, (ii) ranking ability, using the receiver operating characteristic area (ROC), and (iii) our proposed cost-effectiveness measure (CE). The results of the study indicate that the choice of fault-proneness modeling technique has limited impact on the resulting classification accuracy or cost-effectiveness. There is however large differences between the individual metric sets in terms of cost-effectiveness, and although the process measures are among the most expensive ones to collect, including them as candidate measures significantly improves the prediction models compared with models that only include structural measures and/or their deltas between releases – both in terms of ROC area and in terms of CE. Further, we observe that what is considered the best model is highly dependent on the criteria that are used to evaluate and compare the models. And the regular confusion matrix criteria, although popular, are not clearly related to the problem at hand, namely the cost-effectiveness of using fault-proneness prediction models to focus verification efforts to deliver software with less faults at less cost. ! 2009 Elsevier Inc. All rights reserved.
Design Automation for Binarized Neural Networks: A Quantum Leap Opportunity?

Design automation in general, and in particular logic synthesis, can play a key role in enabling the design of application-specific Binarized Neural Networks (BNN). This paper presents the hardware design and synthesis of a purely combinational BNN for ultra-low power near-sensor processing. We leverage the major opportunities raised by BNN models, which consist mostly of logical bit-wise operations and integer counting and comparisons, for pushing ultra-low power deep learning circuits close to the sensor and coupling them with binarized mixed-signal image sensor data. We analyze area, power and energy metrics of BNNs synthesized as combinational networks. Our synthesis results in GlobalFoundries 22 nm SOI technology shows a silicon area of 2.61 mm2 for implementing a combinational BNN with 32×32 binary input sensor receptive field and weight parameters fixed at design time. This is 2.2× smaller than a synthesized network with re-configurable parameters. With respect to other comparable techniques for deep learning near-sensor processing, our approach features a 10× higher energy efficiency.
Multi-stream Processing for Noise Robust Speech Recognition

In this thesis, the framework of multi-stream combination has been explored to improve the noise robustness of automatic speech recognition (ASR) systems. The central idea of multi-stream ASR is to combine information from several sources to improve the performance of a system. The two important issues of multi-stream systems are which information sources (feature representations) to combine and what importance (weights) be given to each information source. In the framework of hybrid hidden Markov model/artificial neural network (HMM/ANN) and Tandem systems, several weighting strategies are investigated in this thesis to merge the posterior outputs of multi-layered perceptrons (MLPs) trained on different feature representations. The best results were obtained by inverse entropy weighting in which the posterior estimates at the output of the MLPs were weighted by their respective inverse output entropies. In the second part of this thesis, two feature representations have been investigated, namely pitch frequency and spectral entropy features. The pitch frequency feature is used along with perceptual linear prediction (PLP) features in a multi-stream framework. The second feature proposed in this thesis is estimated by applying an entropy function to the normalized spectrum to produce a measure which has been termed spectral entropy. The idea of the spectral entropy feature is extended to multi-band spectral entropy features by dividing the normalized full-band spectrum into sub-bands and estimating the spectral entropy of each sub-band. The proposed multi-band spectral entropy features were observed to be robust in high noise conditions. Subsequently, the idea of embedded training is extended to multi-stream HMM/ANN systems. To evaluate the maximum performance that can be achieved by frame-level weighting, we investigated an “oracle test”. We also studied the relationship of oracle selection to inverse entropy weighting and proposed an alternative interpretation of the oracle test to analyze the complementarity of streams in multi-stream systems. The techniques investigated in this work gave a significant improvement in performance for clean as well as noisy test conditions.
Trends in postpartum hemorrhage: United States, 1994-2006.

The purpose of this study was to estimate the incidence of postpartum hemorrhage (PPH) in the United States and to assess trends.



Population-based data from the 1994-2006 National Inpatient Sample were used to identify women who were hospitalized with postpartum hemorrhage. Data for each year were plotted, and trends were assessed. Multivariable logistic regression was used in an attempt to explain the difference in PPH incidence between 1994 and 2006.



PPH increased 26% between 1994 and 2006 from 2.3% (n = 85,954) to 2.9% (n = 124,708; P < .001). The increase primarily was due to an increase in uterine atony, from 1.6% (n = 58,597) to 2.4% (n = 99,904; P < .001). The increase in PPH could not be explained by changes in rates of cesarean delivery, vaginal birth after cesarean delivery, maternal age, multiple birth, hypertension, or diabetes mellitus.



Population-based surveillance data signal an apparent increase in PPH caused by uterine atony. More nuanced clinical data are needed to understand the factors that are associated with this trend.
Micronutrient Needs of the Elderly.

Older adults are becoming a significant percentage of the world's population. A multitude of factors, from the normal aging process to the progression of chronic disease, influence the nutrition needs of this very diverse group of people. Appropriate micronutrient intake is of particular importance but is often suboptimal. Here we review the available data regarding micronutrient needs and the consequences of deficiencies in the ever growing aged population.
Impact of Frequency Ramp Nonlinearity, Phase Noise, and SNR on FMCW Radar Accuracy

One of the main disturbances in a frequency-modulated continuous wave radar system for range measurement is nonlinearity in the frequency ramp. The intermediate frequency (IF) signal and consequently the target range accuracy are dependent on the type of the nonlinearity present in the frequency ramp. Moreover, the type of frequency ramp nonlinearity cannot be directly specified, which makes the problem even more challenging. In this paper, the frequency ramp nonlinearity is investigated with the modified short-time Fourier transform method by using the short-time Chirp-Z transform method with high accuracy. The random and periodic nonlinearities are characterized and their sources are identified as phase noise and spurious. These types of frequency deviations are intentionally increased, and their influence on the linearity and the IF-signal is investigated. The dependence of target range estimation accuracy on the frequency ramp nonlinearity, phase noise, spurious, and signal-to-noise ratio in the IF-signal are described analytically and are verified on the basis of measurements.
Learning Quick Fixes from Code Repositories

Code analyzers such as Error Prone and FindBugs detect code patterns symptomatic of bugs, performance issues, or bad style. These tools express patterns as quick fixes that detect and rewrite unwanted code. However, it is difficult to come up with new quick fixes and decide which ones are useful and frequently appear in real code. We propose to rely on the collective wisdom of programmers and learn quick fixes from revision histories in software repositories. We present REVISAR, a tool for discovering common Java edit patterns in code repositories. Given code repositories and their revision histories, REVISAR (i) identifies code edits from revisions and (ii) clusters edits into sets that can be described using an edit pattern. The designers of code analyzers can then inspect the patterns and add the corresponding quick fixes to their tools. We ran REVISAR on nine popular GitHub projects, and it discovered 89 useful edit patterns that appeared in 3 or more projects. Moreover, 64% of the discovered patterns did not appear in existing tools. We then conducted a survey with 164 programmers from 124 projects and found that programmers significantly preferred eight out of the nine of the discovered patterns. Finally, we submitted 16 pull requests applying our patterns to 9 projects and, at the time of the writing, programmers accepted 6 (60%) of them. The results of this work aid toolsmiths in discovering quick fixes and making informed decisions about which quick fixes to prioritize based on patterns programmers actually apply in practice.
A Survey on Security Threats and Vulnerability attacks on Bluetooth Communication

In this article we present a survey on threats and vulnerability attacks on Bluetooth security mechanism. Bluetooth is the personal area network (PAN). It is the kind of wireless Ad hoc network. Low cost, low power, low complexity and robustness are the basic features of Bluetooth. It works on Radio frequency. Bluetooth Technology has many benefits like replacement of cable, easy file sharing, wireless synchronization and internet connectivity. As Bluetooth Technology becomes widespread, vulnerabilities in its security protocols are increasing which can be potentially dangerous to the privacy of a user’s personal information. Security in Bluetooth communication has been an active area of research for last few years. The article presents various security threats and vulnerability attacks on Bluetooth technology.  Bluetooth security; security protocol; vulnerability; security threats; bluejacking; eavesdropping; malicious attackers.
Minimum Word Error Rate Training for Attention-Based Sequence-to-Sequence Models

Sequence-to-sequence models, such as attention-based models in automatic speech recognition (ASR), are typically trained to optimize the cross-entropy criterion which corresponds to improving the log-likelihood of the data. However, system performance is usually measured in terms of word error rate (WER), not log-likelihood. Traditional ASR systems benefit from discriminative sequence training which optimizes criteria such as the state-level minimum Bayes risk (sMBR) which are more closely related to WER. In the present work, we explore techniques to train attention-based models to directly minimize expected word error rate. We consider two loss functions which approximate the expected number of word errors: either by sampling from the model, or by using N-best lists of decoded hypotheses, which we find to be more effective than the sampling-based method. In experimental evaluations, we find that the proposed training procedure improves performance by up to 8.2% relative to the baseline system. This allows us to train grapheme-based, uni-directional attention-based models which match the performance of a traditional, state-of-the-art, discriminative sequence-trained system on a mobile voice-search task.