nils-herrmann/clean_scirepeval huggingface dataset:
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation

A situation where training and test samples follow different input distributions is calledcovariate shift . Under covariate shift, standard learning methods such as maximum likelihood estimation are no longer consistent—weighted variants according to the ratio of test and training input densities are consistent. Therefore, accurately estimating the density ratio, called the importance, is one of the key issues in covariate shift adaptation. A naive approach to this task is to first estimate training and test input densities separately and then estimate the importance by taking the ratio of the estimated densities. However, this naive approach tends to perform poorly since density estimation is a hard task particularly in high dimensional cases. In this paper, we propose a direct importance estimation method that does not involve density estimation. Our method is equipped with a natural cross validation procedure and hence tuning parameters such as the kernel width can be objectively optimized. Simulations illustrate the usefulness of our approach.
Demystifying Differentiable Programming: Shift/Reset the Penultimate Backpropagator

Deep learning has seen tremendous success over the past decade in computer vision, machine translation, and gameplay. This success rests crucially on gradient-descent optimization and the ability to “learn” parameters of a neural network by backpropagating observed errors. However, neural network architectures are growing increasingly sophisticated and diverse, which motivates an emerging quest for even more general forms of differentiable programming, where arbitrary parameterized computations can be trained by gradient descent. In this paper, we take a fresh look at automatic differentiation (AD) techniques, and especially aim to demystify the reverse-mode form of AD that generalizes backpropagation in neural networks. We uncover a tight connection between reverse-mode AD and delimited continuations, which permits implementing reverse-mode AD purely via operator overloading and without managing any auxiliary data structures. We further show how this formulation of AD can be fruitfully combined with multi-stage programming (staging), leading to an efficient implementation that combines the performance benefits of deep learning frameworks based on explicit reified computation graphs (e.g., TensorFlow) with the expressiveness of pure library approaches (e.g., PyTorch).
Integrated Half-Bridge CLLC Bidirectional Converter for Energy Storage Systems

This paper proposes an integrated half-bridge CLLC (IHBCLLC) resonant bidirectional dc–dc converter suitable as an interface between two dc voltage buses in various applications including energy storage systems. This converter is an integration of a half-bridge CLLC resonant circuit and a buck/boost circuit. Compared with the traditional pulse frequency modulation and phase-shift controlled CLLC resonant converter, for the proposed convertor, the high voltage gain can be obtained from pulse width modulation. Synchronous pulse width modulation is adopted for the converter, and the switching frequency is equal to the resonant frequency. The CLLC resonant circuit can help MOSFETs to achieve soft switching and high voltage gain. The simulation analysis of the modulation schedule and operating principle of the proposed converter prove that the converter is able to achieve zero-voltage switching or zero-current switching. Experimental results obtained from a 1-kW converter prototype confirm the theoretical analysis and the simulation results.
Improved Dropout for Shallow and Deep Learning

Dropout has been witnessed with great success in training deep neural networks by independently zeroing out the outputs of neurons at random. It has also received a surge of interest for shallow learning, e.g., logistic regression. However, the independent sampling for dropout could be suboptimal for the sake of convergence. In this paper, we propose to use multinomial sampling for dropout, i.e., sampling features or neurons according to a multinomial distribution with different probabilities for different features/neurons. To exhibit the optimal dropout probabilities, we analyze the shallow learning with multinomial dropout and establish the risk bound for stochastic optimization. By minimizing a sampling dependent factor in the risk bound, we obtain a distribution-dependent dropout with sampling probabilities dependent on the second order statistics of the data distribution. To tackle the issue of evolving distribution of neurons in deep learning, we propose an efficient adaptive dropout (named evolutional dropout) that computes the sampling probabilities on-the-fly from a mini-batch of examples. Empirical studies on several benchmark datasets demonstrate that the proposed dropouts achieve not only much faster convergence and but also a smaller testing error than the standard dropout. For example, on the CIFAR-100 data, the evolutional dropout achieves relative improvements over 10% on the prediction performance and over 50% on the convergence speed compared to the standard dropout.
Model predictive control for three-level boost converter in photovoltaic systems

In this paper, a three-level Boost (TLB) converter maximum power point tracking (MPPT) control strategy for a two-stage photovoltaic (PV) system based on model predictive control (MPC) is proposed. This method realizes fast and precise control through establishment of a prediction model and minimization of a cost function. In the work, first a three-level predictive model for the boost converter is extracted, then a predictive control algorithm using the Perturb and Observe (P&O) method is proposed, and subsequently voltage balance of the neutral point of the converter is added to the control loop. Based on the proposed method, since the inverter is not obligated to control the DC-link neutral point voltage, its modulation can be used to realize other controls on the AC side to improve grid operation. Results of the control scheme have been verified through Matlab / Simulink, and the results have been in agreement with the expectations.
Neural Photo Editing with Introspective Adversarial Networks

We present the Neural Photo Editor, an interface for exploring the latent space of generative image models and making large, semantically coherent changes to existing images. Our interface is powered by the Introspective Adversarial Network, a hybridization of the Generative Adversarial Network and the Variational Autoencoder designed for use in the editor. Our model makes use of a novel computational block based on dilated convolutions, and Orthogonal Regularization, a novel weight regularization method. We validate our model on CelebA, SVHN, and ImageNet, and produce samples and reconstructions with high visual fidelity.
A recommender system for job seeking and recruiting website

In this paper, a hybrid recommender system for job seeking and recruiting websites is presented. The various interaction features designed on the website help the users organize the resources they need as well as express their interest. The hybrid recommender system exploits the job and user profiles and the actions undertaken by users in order to generate personalized recommendations of candidates and jobs. The data collected from the website is modeled using a directed, weighted, and multi-relational graph, and the 3A ranking algorithm is exploited to rank items according to their relevance to the target user. A preliminary evaluation is conducted based on simulated data and production data from a job hunting website in Switzerland.
A Study on Developing CRM Scorecard

As more firms embrace CRM as a core business strategy, it is getting more important to assess firms' CRM readiness and performance for diagnosing, guiding, managing, and evaluating their current CRM practices. Previous CRM studies have been limited to extracting a few success factors or focus only on specific implementation methodologies without any theoretical underpinning. In this paper, we suggest a performance measurement framework called CRM scorecard as an integrative organizational artifact to diagnose and assess a firm's CRM practice. The CRM scorecard was developed through comprehensive literature review and in-depth interviews with CRM practitioners, with specific objective and subjective instruments added to measure corporate CRM capability and readiness. Additionally, we conducted an analytic hierarchy process (AHP) analysis to prioritize the evaluation factors in the CRM scorecard. To illustrate the applicability of the proposed CRM scorecard, we apply the artifact to a consumer bank, which is well known for an exemplary CRM strategy in Korea
Degree of Modularity in Engineering Systems and Products with Technical and Business Constraints

There is consensus that modularity has many benefits from cost savings due to increased commonality to enabling a higher variety of products. Full modularity is, however, not always achievable. How engineering systems and products whose design is heavily influenced by technical constraints, such as weight or size limitations, tend to exhibit rather integral architectures is shown in this study. For this, two metrics are defined on the basis of a binary design structure matrix (DSM) representation of a system or product. The non-zero fraction (NZF) captures the sparsity of the interrelationships between components between zero and one, while the singular value modularity index (SMI) captures the degree of internal coupling, also between zero and one. These metrics are first developed using idealized canonical architectures and are then applied to two different product pairs that are functionally equivalent, but different in terms of technical constraints. Empirical evidence is presented that the lightweight variant of the same product tends to be more integral, presumably to achieve higher mass efficiency. These observations are strengthened by comparing the results to another, previously published, modularity metric as well as by comparing sparsity and modularity of a set of 15 products against a control population of randomly generated architectures of equivalent size and density. The results suggest that, indeed, some products are inherently less modular than others due to technological factors. The main advantage of SMI is that it enables analysis of the degree of modularity of any architecture independent of subjective module choices.
Automatic medical image annotation in ImageCLEF 2007: Overview, results, and discussion

In this paper, the automatic medical annotation task of the 2007 CLEF cross-language image retrieval campaign (ImageCLEF) is described. The paper focusses on the images used, the task setup, and the results obtained in the evaluation campaign. Since 2005, the medical automatic image annotation task exists in ImageCLEF with increasing complexity to evaluate the performance of state-of-the-art methods for completely automatic annotation of medical images based on visual properties. The paper also describes the evolution of the task from its origin in 2005 to 2007. The 2007 task, comprising 11,000 fully annotated training images and 1,000 test images to be annotated, is a realistic task with a large number of possible classes at different levels of detail. Detailed analysis of the methods across participating groups is presented with respect to the (i) image representation, (ii) classification method, and (iii) use of the class hierarchy. The results show that methods which build on local image descriptors and discriminative models are able to provide good predictions of the image classes, mostly by using techniques that were originally developed in the machine learning and computer vision domain for object recognition in non-medical images.
Awareness Qualification Level Measurement Model

To be able to talk about effective information security against all types of attacks, from a simple cyber attack to cyber terrorism and even cyber warfare, awareness must be defined as a problem, not a solution. To solve this problem “You can not manage what you can not measure!” point of view must be the starting point. Information security problem can be solved if security awareness level can be measured accurately and raising the awareness level according to the measurements. After studies in the literature are examined, it is seen that there is no dynamic model for measuring the cyber security awareness, score based leveling, survey and focus area based approaches are used, measurements are made for certain levels/groups and do not have a certain leveling standardization. In this study, a “capability based dynamic model” has been proposed for the first time in order to measure awareness level. It is seen that the proposed model is superior to the models in literature by these specifications; designed in a stepped structure with leveling standardization, applicable to all groups/levels and capability based approach used. It is considered that the developed model will contribute to the increase of cyber security awareness.
Pushover Analysis of 4 Storey ’ s Reinforced Concrete Building

The earthquakes in the Indian subcontinent have led to an increase in the seismic zoning factor over many parts of the country. Also, ductility has become an issue for all building that was designed and detailed using earlier versions of the codes. Under such circumstances, seismic qualification of building has become extremely important. The structural engineering profession has been using the nonlinear static procedure (NSP) or pushover analysis. Modeling for such analysis requires the determination of the nonlinear properties of each component in the structure, quantified by strength and deformation capacities, which depend on the modeling assumptions. Pushover analysis is carried out for either user-defined nonlinear hinge properties or default-hinge properties, available in some programs based on the FEMA-356 and ATC-40 guidelines. This paper aims to evaluate the zone –IV selected reinforced concrete building to conduct the non-linear static analysis (Pushover Analysis). The pushover analysis shows the pushover curves, capacity spectrum, plastic hinges and performance level of the building. The non-linear static analysis gives better understanding and more accurate seismic performance of buildings of the damage or failure element.
PAMM - A Robotic Aid to the Elderly for Mobility Assistance and Monitoring: A Helping-Hand for the Elderly

Meeting the needs of the elderly presents important technical challenges. In this research, a system concept for a robotic aid to provide mobility assistance and monitoring for the elderly and its enabling technologies are being developed. The system, called PAMM (Personal Aid for Mobility and Monitoring), is intended to assist the elderly living independently or in senior Assisted Living Facilities. It provides physical support and guidance, and it monitors the user's basic vital signs. An experimental test-bed used to evaluate the PAMM technology is described. This test-bed has a cane based configuration with a non-holonomic drive. Preliminary field trials at an eldercare facility are presented.
Dermoscopic findings in Laugier-Hunziker syndrome.

Laugier-Hunziker syndrome (LHS) is a rare, acquired mucocutaneous hyperpigmentation often associated with longitudinal melanonychia. The clinical behavior of mucocutaneous pigmented lesions ranges from benign to highly malignant. Therefore, in most cases, the clinical diagnosis should be confirmed by further diagnostic methods. Dermoscopy is a noninvasive technique that has been used to make more accurate diagnoses of pigmented skin lesions. Nevertheless, to our knowledge, the dermoscopic features of the pigmented lesions in LHS have not been described previously. Herein, we report a case of LHS together with its dermoscopic features.



The clinical examination revealed macular hyperpigmentation on the oral and genital mucosa, conjunctiva, and palmoplantar region together with longitudinal melanonychia. Dermoscopic examination of mucosal lesions on the patient's lips and vulva revealed a parallel pattern. Longitudinal homogeneous pigmentation was observed on the toenails. The pigmented macules on the palms and the sole showed a parallel furrow pattern. A skin biopsy sample taken from the labial lesion was compatible with a diagnosis of mucosal melanosis.



By means of this case report, the dermoscopic features of the pigmented lesions in LHS are described for the first time, which facilitates diagnosis with a noninvasive technique. Future reports highlighting the dermoscopic features of this syndrome may simplify the diagnosis of LHS, which is thought to be underdiagnosed.
Pair-Wise Temporal Pooling Method for Rapid Training of the HTM Networks Used in Computer Vision Applications

In the paper, several modifications to the conventional learning algorithms of the Hierarchical Temporal Memory (HTM) – a biologically inspired largescale model of the neocortex by Numenta – have been proposed. Firstly, an alternative spatial pooling method has been introduced, which makes use of a random pattern generator exploiting the Metropolis-Hastings algorithm. The original inference algorithm by Numenta has been reformulated, in order to reduce a number of tunable parameters and to optimize its computational efficiency. The main contribution of the paper consists in the proposal of a novel temporal pooling method – the pair-wise explorer – which allows faster and more reliable training of the HTM networks using data without inherent temporal information (e.g., static images). While the conventional temporal pooler trains the HTM network on a finite segment of the smooth Brownian-like random walk across the training images, the 902 S. Štolc, I. Bajla, K. Valent́ın, R. Škoviera proposed method performs training by means of the pairs of patterns randomly sampled (in a special manner) from a virtually infinite smooth random walk. We have conducted a set of experiments with the single-layer HTM network applied to the position, scale, and rotation-invariant recognition of geometric objects. The obtained results provide a clear evidence that the pair-wise method yields significantly faster convergence to the theoretical maximum of the classification accuracy with respect to both the length of the training sequence (defined by the maximum allowed number of updates of the time adjacency matrix – TAM) and the number of training patterns. The advantage of the proposed explorer manifested itself mostly in the lower range of TAM updates where it caused up to 10% relative accuracy improvement over the conventional method. Therefore we suggest to use the pair-wise explorer, instead of the smooth explorer, always when the HTM network is trained on a set of static images, especially when the exhaustive training is impossible due to the complexity of the given task.
Improving Gait Cryptosystem Security Using Gray Code Quantization and Linear Discriminant Analysis

Gait has been considered as an efficient biometric trait for user authentication. Although there are some studies that address the task of securing gait templates/models in gait-based authentication systems, they do not take into account the low discriminability and high variation of gait data which significantly affects the security and practicality of the proposed systems. In this paper, we focus on addressing the aforementioned deficiencies in inertial-sensor based gait cryptosystem. Specifically, we leverage Linear Discrimination Analysis to enhance the discrimination of gait templates, and Gray code quantization to extract high discriminative and stable binary template. The experimental results on 38 different users showed that our proposed method significantly improve the performance and security of the gait cryptosystem. In particular, we achieved the False Acceptant Rate of 6×10−5% (i.e., 1 fail in 16983 trials) and False Rejection Rate of 9.2% with 148-bit security.
A unifying computational framework for motor control and social interaction.

Recent empirical studies have implicated the use of the motor system during action observation, imitation and social interaction. In this paper, we explore the computational parallels between the processes that occur in motor control and in action observation, imitation, social interaction and theory of mind. In particular, we examine the extent to which motor commands acting on the body can be equated with communicative signals acting on other people and suggest that computational solutions for motor control may have been extended to the domain of social interaction.
Graph Drawing by Stress Majorization

One of the most popular graph drawing methods is based of achieving graphtheoretic target ditsances. This method was used by Kamada and Kawai , who formulated it as an energy optimization problem. Their energy is known in the multidimensional scaling (MDS) community as the stress function. In this work, we show how to draw graphs by stress majorization, adapting a technique known in the MDS community for more than two decades. It appears that majorization has advantages over the technique of Kamada and Kawai in running time and stability. We also present a few extensions to the basic energy model which can improve layout quality and computation speed in practice. Majorization-based optimization is essential to these extensions.
Network Analysis with the Enron Email Corpus

We use the Enron email corpus to study relationships in a network by applying six different measures of centrality. Our results came out of an in-semester undergraduate research seminar. The Enron corpus is well suited to statistical analyses at all levels of undergraduate education. Through this article’s focus on centrality, students can explore the dependence of statistical models on initial assumptions and the interplay between centrality measures and hierarchical ranking, and they can use completed studies as springboards for future research. The Enron corpus also presents opportunities for research into many other areas of analysis, including social networks, clustering, and natural language processing.
Improving Data Augmentation for Medical Image Segmentation

Medical image segmentation is often constrained by the availability of labelled training data. ‘Data augmentation’ helps to prevent memorisation of training data and helps the network’s performance on data from outside the training set. As such, it is vital in building robust deep learning pipelines. Augmentation in medical imaging typically involves applying small transformations to images during training to create variety. However, it is also possible to use linear combinations of training images and labels to augment the dataset using the recently-proposed ‘mixup’ algorithm. Here, we apply this algorithm for use in medical imaging segmentation. We show that it increases performance in segmentation tasks, and also offer a theoretical suggestion for the efficacy of this technique.
Enabling ∼100fps detection on a landing unmanned aircraft for its on-ground vision-based recovery

In this paper, a deep learning inspired solution is proposed and developed to enable timeliness and practicality of the pre-existing ground stereo vision guidance system for flxed-wing UAVs' safe landing. Since the ground guidance prototype was restricted within applications due to its untimeliness, eventually the vision-based detection less than 15 fps (frame per second). Under such circumstances, we employ a regression based deep learning algorithm into automatic detection on the flying aircraft in the landing sequential images. The system architecture is upgraded so as to be compatible with the novel deep learning requests, and furthermore, annotated datasets are conducted to support training and testing of the regression-based learning detection algorithm. Experimental results validate that the detection attaches 100 fps or more while the localization accuracy is kept in the same level.