nils-herrmann/clean_scirepeval huggingface dataset:
Intraoral sebaceous hyperplasia. Diagnostic criteria.

Surprisingly little is understood about the physiologic and pathologic processes that involve intraoral sebaceous glands. Neoplasms are rare. Hyperplasia of these glands is undoubtedly more common, but criteria for the diagnosis of intraoral sebaceous hyperplasia have not been established. These lesions are too often misdiagnosed as large "Fordyce granules" or, when very large, as sebaceous adenomas. On the basis of a series of 31 nonneoplastic sebaceous lesions and on published data, the following definition is proposed: intraoral sebaceous hyperplasia occurs when a lesion, judged clinically to be a distinct abnormality that requires biopsy for diagnosis or confirmation of clinical impression, has histologic features of one or more well-differentiated sebaceous glands that exhibit no fewer than 15 lobules per gland. Sebaceous glands with fewer than 15 lobules that form an apparently distinct clinical lesion on the buccal mucosa are considered normal, whereas similar lesions of other intraoral sites are considered ectopic sebaceous glands. Sebaceous adenomas are less differentiated than sebaceous hyperplasia.
Millimeter-Wave Wideband High-Efficiency Circularly Polarized Planar Array Antenna

A Ka-band circularly polarized (CP) planar array antenna with wide axial ratio (AR) bandwidth and high efficiency is presented in this paper. A crossed slot with four parasitic patches is proposed as the CP element, which is fed by a unique 90° delay line. There are three minimal AR points within the operational frequency band to widen the bandwidth. The 90° delay line has three branches to realize the phase delay, the amplitude compensation, and the impedance matching, respectively. Thus, the design process is clear and simple. Several short-circuited posts are added in the feeding layer to suppress the TEM wave propagating outside the feeding network. Simulated results of the element show an AR bandwidth (AR <; 3 dB) of 21.2% from 25.3 to 31.3 GHz and a gain of 5.7-6.7 dBic over the same frequency band. Measured results of a fabricated 4 × 4 array demonstrate about 14% AR bandwidth and more than 17.4 dBic gain within the frequency band of 26.4-30.3 GHz. The maximum realized gain reaches to 18.2 dBic. The array antenna with a waveguide transition is fabricated through multilayer PCB process and has a size of 72 mm × 48 mm × 2.2 mm.
Sky and Ground Detection Using Convolutional Neural Networks

A critical first step in navigation of unmanned aerial vehicles is the detection of the horizon line. This information can be used for adjusting flight parameters, attitude estimation as well as obstacle detection and avoidance. In this paper, a fast and robust technique for precise detection of the horizon is presented. Our approach is to apply convolutional neural networks to the task, training them to detect the sky and ground regions as well as the horizon line in flight videos. Thorough experiments using large datasets illustrate the significance and accuracy of this technique for various types of terrain as well as seasonal conditions.
Exercise-enhanced neuroplasticity targeting motor and cognitive circuitry in Parkinson's disease

Exercise interventions in individuals with Parkinson's disease incorporate goal-based motor skill training to engage cognitive circuitry important in motor learning. With this exercise approach, physical therapy helps with learning through instruction and feedback (reinforcement) and encouragement to perform beyond self-perceived capability. Individuals with Parkinson's disease become more cognitively engaged with the practice and learning of movements and skills that were previously automatic and unconscious. Aerobic exercise, regarded as important for improvement of blood flow and facilitation of neuroplasticity in elderly people, might also have a role in improvement of behavioural function in individuals with Parkinson's disease. Exercises that incorporate goal-based training and aerobic activity have the potential to improve both cognitive and automatic components of motor control in individuals with mild to moderate disease through experience-dependent neuroplasticity. Basic research in animal models of Parkinson's disease is beginning to show exercise-induced neuroplastic effects at the level of synaptic connections and circuits.
An Analysis of ML Typability

We carry out an analysis of typability of terms in ML. Our main result is that this problem is DEXPTIME-hard, where by DEXPTIME we mean DTIME(2n0(1)). This, together with the known exponential-time algorithm that solves the problem, yields the DEXPTIME-completeness result. This settles an open problem of P. Kanellakis and J. C. Mitchell.
Part of our analysis is an algebraic characterization of ML typability in terms of a restricted form of semi-unification, which we identify as acyclic semi-unification. We prove that ML typability and acyclic semi-unification can be reduced to each other in polynomial time. We believe this result is of independent interest.
Early exercise in critically ill patients enhances short-term functional recovery.

: To investigate whether a daily exercise session, using a bedside cycle ergometer, is a safe and effective intervention in preventing or attenuating the decrease in functional exercise capacity, functional status, and quadriceps force that is associated with prolonged intensive care unit stay. A prolonged stay in the intensive care unit is associated with muscle dysfunction, which may contribute to an impaired functional status up to 1 yr after hospital discharge. No evidence is available concerning the effectiveness of an early exercise training intervention to prevent these detrimental complications.


 Randomized controlled trial.



: Medical and surgical intensive care unit at University Hospital Gasthuisberg.



: Ninety critically ill patients were included as soon as their cardiorespiratory condition allowed bedside cycling exercise (starting from day 5), given they still had an expected prolonged intensive care unit stay of at least 7 more days.



: Both groups received respiratory physiotherapy and a daily standardized passive or active motion session of upper and lower limbs. In addition, the treatment group performed a passive or active exercise training session for 20 mins/day, using a bedside ergometer.



: All outcome data are reflective for survivors. Quadriceps force and functional status were assessed at intensive care unit discharge and hospital discharge. Six-minute walking distance was measured at hospital discharge. No adverse events were identified during and immediately after the exercise training. At intensive care unit discharge, quadriceps force and functional status were not different between groups. At hospital discharge, 6-min walking distance, isometric quadriceps force, and the subjective feeling of functional well-being (as measured with "Physical Functioning" item of the Short Form 36 Health Survey questionnaire) were significantly higher in the treatment group (p < .05).



: Early exercise training in critically ill intensive care unit survivors enhanced recovery of functional exercise capacity, self-perceived functional status, and muscle force at hospital discharge.
Backprop as Functor: A compositional perspective on supervised learning

A supervised learning algorithm searches over a set of functions A→ B parametrised by a space P to find the best approximation to some ideal function f : A→ B. It does this by taking examples (a, f (a)) ∈ A×B, and updating the parameter according to some rule. We define a category where these update rules may be composed, and show that gradient descent—with respect to a fixed step size and an error function satisfying a certain property— defines a monoidal functor from a category of parametrised functions to this category of update rules. A key contribution is the notion of request function. This provides a structural perspective on backpropagation, as well as a broad generalisation of neural networks.
Pr epr in t Supporting Application Deployment and Management in Fog Computing

Deploying and managing multi-component IoT applications in Fog computing scenarios is challenging due to the heterogeneity, scale and dynamicity of Fog infrastructures, as well as to the complexity of modern software systems. When deciding on where/how to (re-)allocate application components over the continuum from the IoT to the Cloud, application administrators need to find the best deployment, satisfying all application (hardware, software, QoS, IoT) requirements over the contextually available resources, also trading-off non-functional desiderata (e.g., financial costs, security). This PhD thesis proposal aims at devising models, algorithms and methodologies to support the adaptive deployment and management of Fog applications.
Combining Fuzzy Logic and Dempster-Shafer Theory

This research aims to combine the mathematical theory of evidence with the rule based logics to refine the predictable output. Integrating Fuzzy Logic and Dempster-Shafer theory is calculated from the similarity of Fuzzy membership function. The novelty aspect of this work is that basic probability assignment is proposed based on the similarity measure between membership function. The similarity between Fuzzy membership function is calculated to get a basic probability assignment. The DempsterShafer mathematical theory of evidence has attracted considerable attention as a promising method of dealing with some of the basic problems arising in combination of evidence and data fusion. DempsterShafer theory provides the ability to deal with ignorance and missing information. The foundation of Fuzzy logic is natural language which can help to make full use of expert information.
Practical delegation of computation using multiple servers

The current move to Cloud Computing raises the need for verifiable delegation of computations, where a weak client delegates his computation to a powerful server, while maintaining the ability to verify that the result is correct. Although there are prior solutions to this problem, none of them is yet both general and practical for real-world use. We demonstrate a relatively efficient and general solution where the client delegates the computation to several servers, and is guaranteed to determine the correct answer as long as even a single server is honest.  A protocol for any efficiently computable function, with logarithmically many rounds, based on any collision-resistant hash family. The protocol is set in terms of Turing Machines but can be adapted to other computation models. An adaptation of the protocol for the X86 computation model and a prototype implementation, called Quin, for Windows executables. We describe the architecture of Quin and experiment with several parameters on live clouds. We show that the protocol is practical, can work with nowadays clouds, and is efficient both for the servers and for the client.
Do confessions taint perceptions of handwriting evidence? An empirical test of the forensic confirmation bias.

Citing classic psychological research and a smattering of recent studies, Kassin, Dror, and Kukucka (2013) proposed the operation of a forensic confirmation bias, whereby preexisting expectations guide the evaluation of forensic evidence in a self-verifying manner. In a series of studies, we tested the hypothesis that knowing that a defendant had confessed would taint people's evaluations of handwriting evidence relative to those not so informed. In Study 1, participants who read a case summary in which the defendant had previously confessed were more likely to erroneously conclude that handwriting samples from the defendant and perpetrator were authored by the same person, and were more likely to judge the defendant guilty, compared with those in a no-confession control group. Study 2 replicated and extended these findings using a within-subjects design in which participants rated the same samples both before and after reading a case summary. These findings underscore recent critiques of the forensic sciences as subject to bias, and suggest the value of insulating forensic examiners from contextual information.
Task-technology Fit Aware Expectation-confirmation Model towards Understanding of MOOCs Continued Usage Intention

Massive Open Online Courses (MOOCs) have been playing a pivotal role among the latest e-learning initiative and obtain widespread popularity in many universities. But the low course completion rate and the high midway dropout rate of students have puzzled some researchers and designers of MOOCs. Therefore, it is important to explore the factors affecting students’ continuance intention to use MOOCs. This study integrates task-technology fit which can explain how the characteristics of task and technology affect the outcome of technology utilization into expectationconfirmation model to analyze the factors influencing students’ keeping using MOOCs and the relationships of constructs in the model, then it will also extend our understandings of continuance intention about MOOCs. We analyze and study 234 respondents, and results reveal that perceived usefulness, satisfaction and task-technology fit are important precedents of the intention to continue using MOOCs. Researchers and designers of MOOCs may obtain further insight in continuance intention about MOOCs.
Vehicle color recognition using deep learning for hazy images

Recent years the number of vehicles increases tremendously. Because of that to identify the vehicle is significant task. Vehicle color and number plate recognition are various ways to identify the vehicle. So Vehicle color recognition essential part of an intelligent transportation system. There are several methods for recognizing the color of the vehicle like feature extract, template matching, convolutional neural network (CNN), etc. CNN is emerging technique within the field of Deep learning. The survey concludes that compared to other techniques CNN gives more accurate results with less training time even for large dataset. The images taken from roads or hill areas aren't visible because of haze. Consequently, removing haze may improve the color recognition. The proposed system combines both techniques and it adopts the dark channel prior technique to remove the haze, followed by feature learning using CNN. After feature learning, classification can be performed by effective classification technique like SVM.
Privacy, Anonymity, and Perceived Risk in Open Collaboration: A Study of Tor Users and Wikipedians

This qualitative study examines privacy practices and concerns among contributors to open collaboration projects. We collected interview data from people who use the anonymity network Tor who also contribute to online projects and from Wikipedia editors who are concerned about their privacy to better understand how privacy concerns impact participation in open collaboration projects. We found that risks perceived by contributors to open collaboration projects include threats of surveillance, violence, harassment, opportunity loss, reputation loss, and fear for loved ones. We explain participants' operational and technical strategies for mitigating these risks and how these strategies affect their contributions. Finally, we discuss chilling effects associated with privacy loss, the need for open collaboration projects to go beyond attracting and educating participants to consider their privacy, and some of the social and technical approaches that could be explored to mitigate risk at a project or community level.
A generic and distributed privacy preserving classification method with a worst-case privacy guarantee

It is often necessary for organizations to perform data mining tasks collaboratively without giving up their own data. This necessity has led to the development of privacy preserving distributed data mining. Several protocols exist which deal with data mining methods in a distributed scenario but most of these methods handle a single data mining task. Therefore, if the participating parties are interested in more than one classification methods they will have to go through a series of distributed protocols every time, thus increasing the overhead substantially. A second significant drawback with existing methods is that they are often quite expensive due to the use of encryption operations. In this paper a method has been proposed that addresses both these issues and provides a generic approach to efficient privacy preserving classification analysis in a distributed setting with a worst-case privacy guarantee. The experimental results demonstrate the effectiveness of this method.
Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search

Recent work in network quantization has substantially reduced the time and space complexity of neural network inference, enabling their deployment on embedded and mobile devices with limited computational and memory resources. However, existing quantization methods often represent all weights and activations with the same precision (bit-width). In this paper, we explore a new dimension of the design space: quantizing different layers with different bit-widths. We formulate this problem as a neural architecture search problem and propose a novel differentiable neural architecture search (DNAS) framework to efficiently explore its exponential search space with gradient-based optimization. Experiments show we surpass the state-of-the-art compression of ResNet on CIFAR-10 and ImageNet. Our quantized models with 21.1x smaller model size or 103.9x lower computational cost can still outperform baseline quantized or even full precision models.
Improved complementary split-ring resonator for nondestructive testing of materials

This paper presents the design and applications of an improved complementary split-ring resonator (CSRR) sensor for nondestructive testing of dielectric samples in the S-band (2–4 GHz). Two sensors are presented here, first sensor is designed by loading the microstrip line with a single CSRR unit cell and the second sensor is designed by loading the line with a pair of identical CSRRs unit cell. The later shows the higher sensitivity towards the loss tangent of the material under test and hence appears to be more appropriate for the characterization of low loss dielectric samples and small impurity detection. Numerical models are developed for both the sensors to calculate the complex permittivity of sample. The sensors are designed and numerically tested using a 3-D full wave electromagnetic simulator, the Computer Simulation Technology Microwave Studio (CST-MWS). The determined complex permittivity results of various reference samples from both the sensors show good agreement with reference data and the results also illustrate that the microstrip line sensor loaded with double CSRRs unit cell is more sensitive and accurate than the microstrip line sensor loaded with single CSRR unit cell.
Software risk management: principles and practices

The emerging discipline of software risk management is described. It is defined as an attempt to formalize the risk-oriented correlates of success into a readily applicable set of principles and practices. Its objectives are to identify, address, and eliminate risk items before they become either threats to successful software operation or major sources of software rework. The basic concepts are set forth, and the major steps and techniques involved in software risk management are explained. Suggestions for implementing risk management are provided.<<ETX>>
Racial Differences in the Impact of Adolescent Depression on Families in TADS

This manuscript examines racial differences in the impact of adolescent depression between White, African American, and Latino families. Findings are from the Treatment for Adolescents with Depression Study (TADS), a multi-site clinical research study evaluating the efficacy of CBT and Fluoxetine for treating adolescents with major depressive disorder. Primary caretakers of 439 adolescents with depression were interviewed. Results demonstrated that compared to White families, African American and Latino families were more likely to report increased feelings of tiredness or anergia (p<.05) and more physical health problems (p<.01) associated with caring for a depressed teen. The conclusion suggests the need for a better understanding of depression in people and families of color and offers recommendations for further research.
AMT: A Property-Based Monitoring Tool for Analog Systems

In this paper we describe AMT, a tool for monitoring temporal properties of continuous signals. We first introduce S TL /PSL, a specification formalism based on the industrial standard language P SL and the real-time temporal logic MITL , extended with constructs that allow describing behaviors of real-valued variables. The tool automatically builds property observers from an STL /PSL specification and checks, in an offlineor incrementalfashion, whether simulation traces satisfy the property. The AMT tool is validated through a Fla sh memory case-study.
Detecting Uyghur text in complex background images with convolutional neural network

Uyghur text detection is crucial to a variety of real-world applications, while little researches put their attention on it. In this paper, we develop an effective and efficient region-based convolutional neural network for Uyghur text detection in complex background images. The characteristics of the network include: (1) Three region proposal networks are used to improve the recall, which simultaneously utilize feature maps from different convolutional layers. (2) The overall architecture of our network is in the form of fully convolutional network, and global average pooling is applied to replace the fully connected layers in the classification and bounding box regression layers. (3) To fully utilize the baseline information, Uyghur text lines are detected directly by the network in an end-to-end fashion. Experiment results on benchmark dataset show that our method achieves an F-measure of 0.83 and detection time of 0.6 s for each image in a single K20c GPU, which is much faster than the state-of-the-art methods while keeps competitive accuracy.