nils-herrmann/clean_scirepeval huggingface dataset:
The online outpatient booking system 'Choose and Book' improves attendance rates at an audiology clinic: a comparative audit.

The 'Choose and Book' system provides an online booking service which primary care professionals can book in real time or soon after a patient's consultation. It aims to offer patients choice and improve outpatient clinic attendance rates.



An audit comparing attendance rates of new patients booked into the Audiological Medicine Clinic using the 'Choose and Book' system with that of those whose bookings were made through the traditional booking system.



Data accrued between 1 April 2008 and 31 October 2008 were retrospectively analysed for new patient attendance at the department, and the age and sex of the patients, method of appointment booking used and attendance record were collected. Patients were grouped according to booking system used - 'Choose and Book' or the traditional system. The mean ages of the groups were compared by a t test. The standard error of the difference between proportions was used to compare the data from the two groups. A P value of < or = 0.05 was considered to be significant.



'Choose and Book' patients had a significantly better rate of attendance than traditional appointment patients, P < 0.01 (95% CI 4.3, 20.5%). There was no significant difference between the two groups in terms of sex, P > 0.1 (95% CI-3.0, 16.2%). The 'Choose and Book' patients, however, were significantly older than the traditional appointment patients, P < 0.001 (95% CI 4.35, 12.95%).



This audit suggests that when primary care agents book outpatient clinic appointments online it improves outpatient attendance.
Radiographic markers of acetabular retroversion: correlation of the cross-over sign, ischial spine sign and posterior wall sign.

Radiological diagnosis of acetabular retroversion is based on the presence of the cross-over sign (COS), the posterior wall sign (PWS), and prominence of the ischial spine (PRISS). The primary purpose of the study was to correlate the quantitative cross-over sign with the presence or absence of the PRISS and PWS signs. The hypothesis was that both, PRISS and PWS are associated with a higher cross-over sign ratio or higher amount of acetabular retroversion. A previous study identified 1417 patients with a positive acetabular cross-over sign. Among these, three radiological parameters were assessed: (1) the amount of acetabular retroversion, quantified as a cross-over sign ratio; (2) the presence of the PRISS sign; (3) the presence of the PWS sign. The relation of these three parameters was analysed using Fisher's exact test, ANOVA, and linear regression analysis. In hips with cross-over sign, the PRISS was present in 61.7%. A direct association between PRISS and the cross-over sign ratio (p < 0.001) was seen. The PWS was positive in 31% of the hips and was also significantly related with the cross-over sign ratio (p < 0.001). In hips with a PRISS, 39.7% had a PWS sign, which was a significant relation (p < 0.001). In patients with positive PWS, 78.8% of the cases also had a PRISS (p < 0.001). Both the PRISS and PWS signs were significantly associated with higher grade cross-over values. Both the PRISS and PWS signs as well as the coexistence of COS, PRISS, and PWS are significantly associated with higher grade of acetabular retroversion. In conjunction with the COS, the PRISS and PWS signs indicate severe acetabular retroversion. Presence and recognition of distinct radiological signs around the hip joint might raise the awareness of possible femoroacetabular impingement (FAI).
Design and Evaluation of Multiple Valued Logic Gates Using Pseudo N-Type Carbon Nanotube FETs

Multiple valued logic (MVL) circuits are particularly attractive for nanoscale implementation as advantages in information density and operating speed can be harvested using emerging technologies. In this paper, a new family of MVL gates is proposed for implementation using carbon nanotube field-effect transistors (CNTFETs). The proposed designs use pseudo N-type CNTFETs and no resistor is utilized for their operation. This approach exploits threshold voltage control of the P-type and N-type transistors, while ensuring correct MVL operation for both ternary and quaternary logic gates. This paper provides a detailed assessment of several figures of merit, such as static power consumption, switching power consumption, propagation delay and the power-delay product (PDP). Compared with resistor-loaded designs, the proposed pseudo-NCNTFET MVL gates show advantages in circuit area, power consumption and energy efficiency, while still incurring a comparable propagation delay. Compared to a complementary logic family, the pseudo-NCNTFET MVL logic family requires a smaller circuit area with a similar propagation delay on average, albeit with a larger PDP and static power consumption. A design methodology and a discussion of issues related to leakage and yield are also provided for the proposed MVL logic family.
Dynamic stability enhancement of power system using fuzzy logic based power system stabilizer

The power system is a dynamic system and it is constantly being subjected to disturbances. It is important that these disturbances do not drive the system to unstable conditions. For this purpose, additional signal derived from deviation, excitation deviation and accelerating power are injected into voltage regulators. The device to provide these signals is referred as power system stabilizer. The use of power system stabilizer has become very common in operation of large electric power systems. The conventional PSS which uses lead-lag compensation, where gain setting designed for specific operating conditions, is giving poor performance under different loading conditions. Therefore, it is very difficult to design a stabilizer that could present good performance in all operating points of electric power systems. In an attempt to cover a wide range of operating conditions, Fuzzy logic control has been suggested as a possible solution to overcome this problem, thereby using linguist information and avoiding a complex system mathematical model, while giving good performance under different operating conditions.
Diminishable visual markers on fabricated projection object for dynamic spatial augmented reality

Spatial augmented reality (SAR) is a projection technology to add optical illusion onto static objects. Generally, in SAR, images are projected on complex everyday surfaces other than a flat projection screen. Thus, geometric correction of images is essential. Many studies have examined geometric correction of projection images on nonplanar surfaces. If a projection surface shape is known, it is possible to correct images geometrically by calibrating intrinsic parameters of the projector and extrinsic parameters (position and pose relationships) between the projector and surfaces. However, it is difficult to directly apply previous geometric correction methods to dynamically moving surfaces, because these methods generally assumed only static surfaces.
On Anaphoric Associations ∗

The paper presents a model for the interpretation of pronomi al anaphors based on the notion of ‘anaphoric association’, that is the a ssociation between a hierarchical structure regarding the discourse alr eady processed and a hierarchical structure regarding the pronouns in inpu t units. In the model a central role is played by various notions derived fro m Sidner’s Focus Theory, as well as concepts borrowed from non-linear p honology and non-concatenative morphology. The model, incremen tal in nature, is capable of tackling facts generally overlooked by t he literature on anaphora interpretation, such as the anaphoric possibil ities offered by complex NPs and nominalizations, or the co-presence of dist inct but interrelated discourse regions, as in discourses containing quoted speech.
Interactive Code Snippet Synthesis Through Repository Mining

Programmers repeatedly reuse code snippets. Retyping boilerplate code, and rediscovering how to correctly sequence API calls, programmers waste time. In this paper, we develop techniques that automatically synthesize code snippets upon a programmer’s request. Our approach is based on discovering snippets located in repositories; we mine repositories offline and suggest discovered snippets to programmers. Upon request, our synthesis procedure uses programmer’s current code to find the best fitting snippets, which are then presented to the programmer. The programmer can then either learn the proper API usage or integrate the synthesized snippets directly into her code. We call this approach interactive code snippet synthesis through repository mining. We show that this approach reduces the time spent developing code for 32% in our experiments.
Comparing Static and Dynamic Code Scheduling for Multiple-Instruction-Issue Processors

This paper examines two alternative approaches to supporting code scheduling for multiple-instruction-issue processors. One is to provide a set of non-trapping instructions so that the compiler can perform aggressive static code scheduling. The application of this approach to existing commercial architectures typically requires extending the instruction set. The other approach is to support out-of-order execution in the microarchitecture so that the hardware can perform aggressive dynamic code scheduling. This approach usually does not require modifying the instruction set but requires complex hardware support. In this paper, we analyze the performance of the two alternative approaches using a set of important nonnumerical C benchmark programs. A distinguishing feature of the experiment is that the code for the dynamic approach has been optimized and scheduled as much as allowed by the architecture. The hardware is only responsible for the additional reordering that cannot be performed by the compiler. The overall result is that the clynamic and static approaches are comparable in performance. When applied to a four-instruction-issue processor, both methods achieve more than two times speedup over a high performance single-instruction-issue processor. However, the performance of each scheme varies among the benchmark programs. To explain this variation, we have identified the conditions in these programs that make one approach perform better than the other.
A Comparison of Classifiers and Document Representations for the Routing Problem

In this paper, we compare learning techniques based on statistical classification to traditional methods of relevance feedback for the document routing problem. We consider three classification techniques which have decision rules that are derived via explicit error minimization linear discriminant analysis, logistic regression, and neuraf networks. We demonstrate that the classifiers perform 1015% better than relevance feedback via Rocchio expansion for the TREC-2 and TREC-3 routing tasks. Error minimization is difficult in high-dimensional feature spaces because the convergence process is slow and the models ~e prone to overfitting. We use two different strategies, latent semantic indexing and optimaJ term selection, to reduce the number of features. Our results indicate that features based on latent semantic indexing are more effective for techniques such as linear discriminant anafysis and logistic regression, which have no way to protect against overfitting. Neural networks perform equally well with either set of features and can take advantage of the additional information available when both feature sets are used as input.
Piggyback CrowdSensing (PCS): energy efficient crowdsourcing of mobile sensor data by exploiting smartphone app opportunities

Fueled by the widespread adoption of sensor-enabled smartphones, mobile crowdsourcing is an area of rapid innovation. Many crowd-powered sensor systems are now part of our daily life -- for example, providing highway congestion information. However, participation in these systems can easily expose users to a significant drain on already limited mobile battery resources. For instance, the energy burden of sampling certain sensors (such as WiFi or GPS) can quickly accumulate to levels users are unwilling to bear. Crowd system designers must minimize the negative energy side-effects of participation if they are to acquire and maintain large-scale user populations.
 To address this challenge, we propose Piggyback CrowdSensing (PCS), a system for collecting mobile sensor data from smartphones that lowers the energy overhead of user participation. Our approach is to collect sensor data by exploiting Smartphone App Opportunities -- that is, those times when smartphone users place phone calls or use applications. In these situations, the energy needed to sense is lowered because the phone need no longer be woken from an idle sleep state just to collect data. Similar savings are also possible when the phone either performs local sensor computation or uploads the data to the cloud. To efficiently use these sporadic opportunities, PCS builds a lightweight, user-specific prediction model of smartphone app usage. PCS uses this model to drive a decision engine that lets the smartphone locally decide which app opportunities to exploit based on expected energy/quality trade-offs.
 We evaluate PCS by analyzing a large-scale dataset (containing 1,320 smartphone users) and building an end-to-end crowdsourcing application that constructs an indoor WiFi localization database. Our findings show that PCS can effectively collect large-scale mobile sensor datasets (e.g., accelerometer, GPS, audio, image) from users while using less energy (up to 90% depending on the scenario) compared to a representative collection of existing approaches.
FML-based prediction agent and its application to game of Go

In this paper, we present a robotic prediction agent including a darkforest Go engine, a fuzzy markup language (FML) assessment engine, an FML-based decision support engine, and a robot engine for game of Go application. The knowledge base and rule base of FML assessment engine are constructed by referring the information from the darkforest Go engine located in NUTN and OPU, for example, the number of MCTS simulations and winning rate prediction. The proposed robotic prediction agent first retrieves the database of Go competition website, and then the FML assessment engine infers the winning possibility based on the information generated by darkforest Go engine. The FML-based decision support engine computes the winning possibility based on the partial game situation inferred by FML assessment engine. Finally, the robot engine combines with the human-friendly robot partner PALRO, produced by Fujisoft incorporated, to report the game situation to human Go players. Experimental results show that the FML-based prediction agent can work effectively.
Covariance discriminative learning: A natural and efficient approach to image set classification

We propose a novel discriminative learning approach to image set classification by modeling the image set with its natural second-order statistic, i.e. covariance matrix. Since nonsingular covariance matrices, a.k.a. symmetric positive definite (SPD) matrices, lie on a Riemannian manifold, classical learning algorithms cannot be directly utilized to classify points on the manifold. By exploring an efficient metric for the SPD matrices, i.e., Log-Euclidean Distance (LED), we derive a kernel function that explicitly maps the covariance matrix from the Riemannian manifold to a Euclidean space. With this explicit mapping, any learning method devoted to vector space can be exploited in either its linear or kernel formulation. Linear Discriminant Analysis (LDA) and Partial Least Squares (PLS) are considered in this paper for their feasibility for our specific problem. We further investigate the conventional linear subspace based set modeling technique and cast it in a unified framework with our covariance matrix based modeling. The proposed method is evaluated on two tasks: face recognition and object categorization. Extensive experimental results show not only the superiority of our method over state-of-the-art ones in both accuracy and efficiency, but also its stability to two real challenges: noisy set data and varying set size.
Neural mechanisms of attentional control in mindfulness meditation

The scientific interest in meditation and mindfulness practice has recently seen an unprecedented surge. After an initial phase of presenting beneficial effects of mindfulness practice in various domains, research is now seeking to unravel the underlying psychological and neurophysiological mechanisms. Advances in understanding these processes are required for improving and fine-tuning mindfulness-based interventions that target specific conditions such as eating disorders or attention deficit hyperactivity disorders. This review presents a theoretical framework that emphasizes the central role of attentional control mechanisms in the development of mindfulness skills. It discusses the phenomenological level of experience during meditation, the different attentional functions that are involved, and relates these to the brain networks that subserve these functions. On the basis of currently available empirical evidence specific processes as to how attention exerts its positive influence are considered and it is concluded that meditation practice appears to positively impact attentional functions by improving resource allocation processes. As a result, attentional resources are allocated more fully during early processing phases which subsequently enhance further processing. Neural changes resulting from a pure form of mindfulness practice that is central to most mindfulness programs are considered from the perspective that they constitute a useful reference point for future research. Furthermore, possible interrelations between the improvement of attentional control and emotion regulation skills are discussed.
Chemical and instrumental approaches to treat hyperpigmentation.

Many modalities of treatment for acquired skin hyperpigmentation are available including chemical agents or physical therapies, but none are completely satisfactory. Depigmenting compounds should act selectively on hyperactivated melanocytes, without short- or long-term side-effects, and induce a permanent removal of undesired pigment. Since 1961 hydroquinone, a tyrosinase inhibitor, has been introduced and its therapeutic efficacy demonstrated, and other whitening agents specifically acting on tyrosinase by different mechanisms have been proposed. Compounds with depigmenting activity are now numerous and the classification of molecules, based on their mechanism of action, has become difficult. Systematic studies to assess both the efficacy and the safety of such molecules are necessary. Moreover, the evidence that bleaching compounds are fairly ineffective on dermal accumulation of melanin has prompted investigations on the effectiveness of physical therapies, such as lasers. This review which describes the different approaches to obtain depigmentation, suggests a classification of whitening molecules on the basis of the mechanism by which they interfere with melanogenesis, and confirms the necessity to apply standardized protocols to evaluate depigmenting treatments.
Neuro-fuzzy speed control of traveling-wave type ultrasonic motor drive using frequency and phase modulation.

This paper presents a Fuzzy Neural Network (FNN) control system for a traveling-wave ultrasonic motor (TWUSM) driven by a dual mode modulation non-resonant driving circuit. First, the motor configuration and the proposed driving circuit of a TWUSM are introduced. To drive a TWUSM effectively, a novel driving circuit, that simultaneously employs both the driving frequency and phase modulation control scheme, is proposed to provide two-phase balance voltage for a TWUSM. Since the dynamic characteristics and motor parameters of the TWUSM are highly nonlinear and time-varying, a FNN control system is therefore investigated to achieve high-precision speed control. The proposed FNN control system incorporates neuro-fuzzy control and the driving frequency and phase modulation to solve the problem of nonlinearities and variations. The proposed control system is digitally implemented by a low-cost digital signal processor based microcontroller, hence reducing the system hardware size and cost. The effectiveness of the proposed driving circuit and control system is verified with hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a conventional proportional-integral control system.
Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization

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Automatically finding patches using genetic programming

Automatic program repair has been a longstanding goal in software engineering, yet debugging remains a largely manual process. We introduce a fully automated method for locating and repairing bugs in software. The approach works on off-the-shelf legacy applications and does not require formal specifications, program annotations or special coding practices. Once a program fault is discovered, an extended form of genetic programming is used to evolve program variants until one is found that both retains required functionality and also avoids the defect in question. Standard test cases are used to exercise the fault and to encode program requirements. After a successful repair has been discovered, it is minimized using structural differencing algorithms and delta debugging. We describe the proposed method and report experimental results demonstrating that it can successfully repair ten different C programs totaling 63,000 lines in under 200 seconds, on average.
A fast algorithm for Steiner trees

Given an undirected distance graph G=(V, E, d) and a set S, where V is the set of vertices in G, E is the set of edges in G, d is a distance function which maps E into the set of nonnegative numbers and S⊑V is a subset of the vertices of V, the Steiner tree problem is to find a tree of G that spans S with minimal total distance on its edges. In this paper, we analyze a heuristic algorithm for the Steiner tree problem. The heuristic algorithm has a worst case time complexity of O(¦S¦¦V¦ 2) on a random access computer and it guarantees to output a tree that spans S with total distance on its edges no more than 2(1−1/l) times that of the optimal tree, where l is the number of leaves in the optimal tree.