nils-herrmann/clean_scirepeval huggingface dataset:
A proposition on memes and meta-memes in computing for higher-order learning

In computational intelligence, the term ‘memetic algorithm’ has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a ‘meme’ has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as ‘memetic algorithm’ is too specific, and ultimately a misnomer, as much as a ‘meme’ is defined too generally to be of scientific use. In this paper, we extend the notion of memes from a computational viewpoint and explore the purpose, definitions, design guidelines and architecture for effective memetic computing. Utilizing two conceptual case studies, we illustrate the power of high-order meme-based learning. With applications ranging from cognitive science to machine learning, memetic computing has the potential to provide much-needed stimulation to the field of computational intelligence by providing a framework for higher order learning.
Real-Time Neural Style Transfer for Videos

Recent research endeavors have shown the potential of using feed-forward convolutional neural networks to accomplish fast style transfer for images. In this work, we take one step further to explore the possibility of exploiting a feed-forward network to perform style transfer for videos and simultaneously maintain temporal consistency among stylized video frames. Our feed-forward network is trained by enforcing the outputs of consecutive frames to be both well stylized and temporally consistent. More specifically, a hybrid loss is proposed to capitalize on the content information of input frames, the style information of a given style image, and the temporal information of consecutive frames. To calculate the temporal loss during the training stage, a novel two-frame synergic training mechanism is proposed. Compared with directly applying an existing image style transfer method to videos, our proposed method employs the trained network to yield temporally consistent stylized videos which are much more visually pleasant. In contrast to the prior video style transfer method which relies on time-consuming optimization on the fly, our method runs in real time while generating competitive visual results.
FCL: A general purpose library for collision and proximity queries

We present a new collision and proximity library that integrates several techniques for fast and accurate collision checking and proximity computation. Our library is based on hierarchical representations and designed to perform multiple proximity queries on different model representations. The set of queries includes discrete collision detection, continuous collision detection, separation distance computation and penetration depth estimation. The input models may correspond to triangulated rigid or deformable models and articulated models. Moreover, FCL can perform probabilistic collision checking between noisy point clouds that are captured using cameras or LIDAR sensors. The main benefit of FCL lies in the fact that it provides a unified interface that can be used by various applications. Furthermore, its flexible architecture makes it easier to implement new algorithms within this framework. The runtime performance of the library is comparable to state of the art collision and proximity algorithms. We demonstrate its performance on synthetic datasets as well as motion planning and grasping computations performed using a two-armed mobile manipulation robot.
Places205-VGGNet Models for Scene Recognition

VGGNets have turned out to be effective for object recognition in still images. However, it is unable to yield good performance by directly adapting the VGGNet models trained on the ImageNet dataset for scene recognition. This report describes our implementation of training the VGGNets on the large-scale Places205 dataset. Specifically, we train three VGGNet models, namely VGGNet-11, VGGNet-13, and VGGNet-16, by using a Multi-GPU extension of Caffe toolbox with high computational efficiency. We verify the performance of trained Places205-VGGNet models on three datasets: MIT67, SUN397, and Places205. Our trained models achieve the state-of-the-art performance o n these datasets and are made public available 1.
Robust Convex Optimization

We study convex optimization problems for which the data is not specified exactly and it is only known to belong to a given uncertainty set U, yet the constraints must hold for all possible values of the data from U. The ensuing optimization problem is called robust optimization. In this paper we lay the foundation of robust convex optimization. In the main part of the paper we show that if U is an ellipsoidal uncertainty set, then for some of the most important generic convex optimization problems (linear programming, quadratically constrained programming, semidefinite programming and others) the corresponding robust convex program is either exactly, or approximately, a tractable problem which lends itself to efficient algorithms such as polynomial time interior point methods.
Differentially private transit data publication: a case study on the montreal transportation system

With the wide deployment of smart card automated fare collection (SCAFC) systems, public transit agencies have been benefiting from huge volume of transit data, a kind of sequential data, collected every day. Yet, improper publishing and use of transit data could jeopardize passengers' privacy. In this paper, we present our solution to transit data publication under the rigorous differential privacy model for the Société de transport de Montréal (STM). We propose an efficient data-dependent yet differentially private transit data sanitization approach based on a hybrid-granularity prefix tree structure. Moreover, as a post-processing step, we make use of the inherent consistency constraints of a prefix tree to conduct constrained inferences, which lead to better utility. Our solution not only applies to general sequential data, but also can be seamlessly extended to trajectory data. To our best knowledge, this is the first paper to introduce a practical solution for publishing large volume of sequential data under differential privacy. We examine data utility in terms of two popular data analysis tasks conducted at the STM, namely count queries and frequent sequential pattern mining. Extensive experiments on real-life STM datasets confirm that our approach maintains high utility and is scalable to large datasets.
Curl-noise for procedural fluid flow

Procedural methods for animating turbulent fluid are often preferred over simulation, both for speed and for the degree of animator control. We offer an extremely simple approach to efficiently generating turbulent velocity fields based on Perlin noise, with a formula that is exactly incompressible (necessary for the characteristic look of everyday fluids), exactly respects solid boundaries (not allowing fluid to flow through arbitrarily-specified surfaces), and whose amplitude can be modulated in space as desired. In addition, we demonstrate how to combine this with procedural primitives for flow around moving rigid objects, vortices, etc.
An L1-based variational model for Retinex theory and its application to medical images

Human visual system (HVS) can perceive constant color under varying illumination conditions while digital images record information of both reflectance (physical color) of objects and illumination. Retinex theory, formulated by Edwin H. Land, aimed to simulate and explain this feature of HVS. However, to recover the reflectance from a given image is in general an ill-posed problem. In this paper, we establish an L1-based variational model for Retinex theory that can be solved by a fast computational approach based on Bregman iteration. Compared with previous works, our L1-Retinex method is more accurate for recovering the reflectance, which is illustrated by examples and statistics. In medical images such as magnetic resonance imaging (MRI), intensity inhomogeneity is often encountered due to bias fields. This is a similar formulation to Retinex theory while the MRI has some specific properties. We then modify the L1-Retinex method and develop a new algorithm for MRI data. We demonstrate the performance of our method by comparison with previous work on simulated and real data.
SEGMENTATION OF SUBCELLULAR COMPARTIMENTS COMBINING SUPERPIXEL REPRESENTATION WITH VORONOI DIAGRAMS

We propose and implement a computer vision algorithm for detecting individual cells, including the ability to distinguish subcellular compartments, such as nucleus and cytoplasm. Our approach consists of three main steps: (a) cellular mass estimation, and (b) nuclei detection through superpixel representation and, (c) cytoplasm detection through nuclear narrow-band seeding, graph-based region growing and Voronoi diagrams. We test our implementation on both real and simulated cervical cell images, containing an assortment of cells and configurations that often present occlusion and/or poor contrast. Our results show both qualitative and quantitative assessment of the datasets, using a completely automated computer program. The quantitative performance presents average Dice Coefficient equals to 0.85.
Algorithm-Induced Prior for Image Restoration

This paper studies a type of image priors that are constructed implicitly through the alternating direction method of multiplier (ADMM) algorithm, called the algorithm-induced prior. Different from classical image priors which are defined before running the reconstruction algorithm, algorithm-induced priors are defined by the denoising procedure used to replace one of the two modules in the ADMM algorithm. Since such prior is not explicitly defined, analyzing the performance has been difficult in the past. Focusing on the class of symmetric smoothing filters, this paper presents an explicit expression of the prior induced by the ADMM algorithm. The new prior is reminiscent to the conventional graph Laplacian but with stronger reconstruction performance. It can also be shown that the overall reconstruction has an efficient closed-form implementation if the associated symmetric smoothing filter is low rank. The results are validated with experiments on image inpainting.
X-Band Robust AlGaN/GaN Receiver MMICs with over 41 dBm Power Handling

Gallium-Nitride technology is known for its high power density and power amplifier designs, but is also very well suited to realize robust receiver components. This paper presents the design and measurement of a robust AlGaN/GaN Low Noise Amplifier and Transmit/Receive Switch MMIC. Two versions of both MMICs have been designed in the Alcatel-Thales III-V lab AlGaN/GaN microstrip technology. One chipset version operates at X-band and the second also shows wideband performance. Input power handling of >46 dBm for the switch and >41 dBm for the LNA have been measured.
Natural color image enhancement and evaluation algorithm based on human visual system

To a significant degree, multimedia applications derive their effectiveness from the use of color graphics, images, and videos. In these applications, human visual system (HVS) often gives the final evaluation of the processed results. In this paper, we first propose a novel color image enhancement method, which is named HVS Controlled Color Image Enhancement and Evaluation algorithm (HCCIEE algorithm). We then applied the HCCIEE to color image by considering natural image quality metrics. This HCCIEE algorithm is base on multiscale representation of pattern, luminance, and color processing in the HVS. Experiments illustrated that the HCCIEE algorithm can produce distinguished details without ringing or halo artifacts. These two problems often occur in conventional multiscale enhancement techniques.) As a result, the experimental results appear as similar as possible to the viewers’ perception of the actual scenes.
Web search query privacy: Evaluating query obfuscation and anonymizing networks

Web Search is one of the most rapidly growing applications on the internet today. However, the current practice followed by most search engines – of logging and analyzing users’ queries – raises serious privacy concerns. In this paper, we concentrate on two existing solutions which are relatively easy to deploy – namely Query Obfuscation and Anonymizing Networks. In query obfuscation, a client-side software attempts to mask real user queries via injection of certain noisy queries. Anonymizing networks route the user queries through a series of relay servers, hiding the actual query source from the search engine. A fundamental problem with these solutions, however, is that user queries are still obviously revealed to the search engine, although they are “mixed” among queries generated either by a machine or by other users. We focus on TrackMeNot (TMN), a popular query obfuscation tool, and the Tor anonymizing network, and try to analyse whether these solutions can actually preserve users’ privacy in practice against an adversarial search engine. We demonstrate that a search engine, equipped with only a short-term history of a user’s search queries, can break the privacy guarantees of TMN and Tor by only utilizing off-the-shelf machine learning techniques.
Conformal Prediction Using Decision Trees

Conformal prediction is a relatively new framework in which the predictive models output sets of predictions with a bound on the error rate, i.e., in a classification context, the probability of excluding the correct class label is lower than a predefined significance level. An investigation of the use of decision trees within the conformal prediction framework is presented, with the overall purpose to determine the effect of different algorithmic choices, including split criterion, pruning scheme and way to calculate the probability estimates. Since the error rate is bounded by the framework, the most important property of conformal predictors is efficiency, which concerns minimizing the number of elements in the output prediction sets. Results from one of the largest empirical investigations to date within the conformal prediction framework are presented, showing that in order to optimize efficiency, the decision trees should be induced using no pruning and with smoothed probability estimates. The choice of split criterion to use for the actual induction of the trees did not turn out to have any major impact on the efficiency. Finally, the experimentation also showed that when using decision trees, standard inductive conformal prediction was as efficient as the recently suggested method cross-conformal prediction. This is an encouraging results since cross-conformal prediction uses several decision trees, thus sacrificing the interpretability of a single decision tree.
Anatomy of a Failed Knowledge Management Initiative: Lessons from PharmaCorp's Experiences

On a sunny morning in July 1999, Samuel Parsons, Head of Knowledge Management at PharmaCorp, convened his regular Monday team meeting. He looked stressed. After dealing with a couple of administrative issues he said: ‘Last Friday evening I was informed that Wilco Smith, Head of Pharma Global Order Handling Services, no longer wants knowledge management. His only question now is how to off-board the knowledge management staff.’ Thus came to an end a three-year initiative that at the outset was considered to be ‘the knowledge management showcase for the firm’. This paper is for managers who have an interest in operationalizing knowledge and want to avoid the traps others have fallen into. It examines the case of PharmaCorp, a global organization and one of the largest in its industry. The case provides managers with five key lessons. First, manage knowledge interdependencies across communities of practice; second, contextualize knowledge within natural groups of activities; third, avoid an over-emphasis on explicit knowledge; fourth, let knowledge management recipients determine tacit and explicit knowledge; and fifth, manage the input from external consultants.
Unsupervised DRG Upcoding Detection in Healthcare Databases

Diagnosis Related Group (DRG) upcoding is an anomaly in healthcare data that costs hundreds of millions of dollars in many developed countries. DRG upcoding is typically detected through resource intensive auditing. As supervised modeling of DRG upcoding is severely constrained by scope and timeliness of past audit data, we propose in this paper an unsupervised algorithm to filter data for potential identification of DRG upcoding. The algorithm has been applied to a hip replacement/revision dataset and a heart-attack dataset. The results are consistent with the assumptions held by domain experts.
Cuckoo hashing

We present a simple dictionary with worst case constant lookup time, equaling the theoretical performance of the classic dynamic perfect hashing scheme of Dietzfelbinger et al. (Dynamic perfect hashing: Upper and lower bounds.  1994). The space usage is similar to that of binary search trees, i.e., three words per key on average. Besides being conceptually much simpler than previous dynamic dictionaries with worst case constant lookup time, our data structure is interesting in that it does not use perfect hashing, but rather a variant of open addressing where keys can be moved back in their probe sequences. An implementation inspired by our algorithm, but using weaker hash functions, is found to be quite practical. It is competitive with the best known dictionaries having an average case (but no nontrivial worst case) guarantee.
Learning Sparse FRAME Models for Natural Image Patterns

It is well known that natural images admit sparse representations by redundant dictionaries of basis functions such as Gabor-like wavelets. However, it is still an open question as to what the next layer of representational units above the layer of wavelets should be. We address this fundamental question by proposing a sparse FRAME (Filters, Random field, And Maximum Entropy) model for representing natural image patterns. Our sparse FRAME model is an inhomogeneous generalization of the original FRAME model. It is a non-stationary Markov random field model that reproduces the observed statistical properties of filter responses at a subset of selected locations, scales and orientations. Each sparse FRAME model is intended to represent an object pattern and can be considered a deformable template. The sparse FRAME model can be written as a shared sparse coding model, which motivates us to propose a two-stage algorithm for learning the model. The first stage selects the subset of wavelets from the dictionary by a shared matching pursuit algorithm. The second stage then estimates the parameters of the model given the selected wavelets. Our experiments show that the sparse FRAME models are capable of representing a wide variety of object patterns in natural images and that the learned models are useful for object classification.
Densest Subgraph in Dynamic Graph Streams

In this paper, we consider the problem of approximating the densest subgraph in the dynamic graph stream model. In this model of computation, the input graph is defined by an arbitrary sequence of edge insertions and deletions and the goal is to analyze properties of the resulting graph given memory that is sub-linear in the size of the stream. We present a single-pass algorithm that returns a (1 + ) approximation of the maximum density with high probability; the algorithm uses O( −2npolylog n) space, processes each stream update in polylog(n) time, and uses poly(n) post-processing time where n is the number of nodes. The space used by our algorithm matches the lower bound of Bahmani et al. (PVLDB 2012) up to a poly-logarithmic factor for constant . The best existing results for this problem were established recently by Bhattacharya et al. . They presented a (2 + ) approximation algorithm using similar space and another algorithm that both processed each update and maintained a (4 + ) approximation of the current maximum density in polylog(n) time per-update.
Measuring mindfulness — the Freiburg Mindfulness Inventory ( FMI )

Mindfulness, a concept originally derived from Buddhist psychology, is essential for some well-known clinical interventions. Therefore an instrument for measuring mindfulness is useful. We report here on two studies constructing and validating the Freiburg Mindfulness Inventory (FMI) including a short form. A preliminary questionnaire was constructed through expert interviews and extensive literature analysis and tested in 115 subjects attending mindfulness meditation retreats. This psychometrically sound 30-item scale with an internal consistency of Cronbach alpha = .93 was able to significantly demonstrate the increase in mindfulness after the retreat and to discriminate between experienced and novice meditators. In a second study we broadened the scope of the concept to 86 subjects without meditation experience, 117 subjects with clinical problems, and 54 participants from retreats. Reducing the scale to a short form with 14 items resulted in a semantically robust and psychometrically stable (alpha = .86) form. Correlation 0191-8869/$ see front matter 2006  Corresponding author.  University of Northampton, School of Social Sciences, Division of Psychology and Samueli Institute—European Office, Boughton Green Road, Northampton NN2 7AL, UK. E-mail address: harald.walach@northampton.ac.uk (H. Walach). www.elsevier.com/locate/paid Personality and Individual Differences 40 (2006) 1543–1555 with other relevant constructs (self-awareness, dissociation, global severity index, meditation experience in years) was significant in the medium to low range of correlations and lends construct validity to the scale. Principal Component Analysis suggests one common factor. This short scale is sensitive to change and can be used also with subjects without previous meditation experience.
ANFIS Approach for Navigation of Mobile Robots

This paper, discusses about navigation control of mobile robot using adaptive neuro-fuzzy inference system (ANFIS) in a real word dynamic environment. In the ANFIS controller after the input layer there is a fuzzy layer and rest of the layers are neural network layers. The adaptive neuro-fuzzy hybrid system combines the advantages of fuzzy logic system, which deal with explicit knowledge that can be explained and understood, and neural network, which deal with implicit knowledge, which can be acquired by learning. The inputs to fuzzy logic layer are front obstacle distance, left obstacle distance, right obstacle distance and target steering. A learning algorithm based on neural network technique has been developed to tune the parameters of fuzzy membership functions, which smooth the trajectory generated by the fuzzy logic system. Using the developed ANFIS controller, the mobile robots are able to avoid static and dynamic obstacles, and reach the target successfully in cluttered environments. The experimental results agree well with the simulation results, proves the authenticity of the theory developed.
Are GANs Created Equal? A Large-Scale Study

Generative adversarial networks (GAN) are a powerful subclass of generative models. Despite a very rich research activity leading to numerous interesting GAN algorithms, it is still very hard to assess which algorithm(s) perform better than others. We conduct a neutral, multi-faceted large-scale empirical study on state-of-the art models and evaluation measures. We find that most models can reach similar scores with enough hyperparameter optimization and random restarts. This suggests that improvements can arise from a higher computational budget and tuning more than fundamental algorithmic changes. To overcome some limitations of the current metrics, we also propose several data sets on which precision and recall can be computed. Our experimental results suggest that future GAN research should be based on more systematic and objective evaluation procedures. Finally, we did not find evidence that any of the tested algorithms consistently outperforms the non-saturating GAN introduced in .