nils-herrmann/clean_scirepeval huggingface dataset:
Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm

The goal of designing an optimal nearest neighbor classifier is to maximize the classification accuracy while minimizing the sizes of both the reference and feature sets. A novel intelligent genetic algorithm (IGA) superior to conventional GAs in solving large parameter optimization problems is used to effectively achieve this goal. It is shown empirically that the IGA-designed classifier outperforms existing GA-based and non-GA-based classifiers in terms of classification accuracy and total number of parameters of the reduced sets.
Morpheo: Traceable Machine Learning on Hidden data

Morpheo is a transparent and secure machine learning platform collecting and analysing large datasets. It aims at building state-of-the art prediction models in various fields where data are sensitive. Indeed, it offers strong privacy of data and algorithm, by preventing anyone to read the data, apart from the owner and the chosen algorithms. Computations in Morpheo are orchestrated by a blockchain infrastructure, thus offering total traceability of operations. Morpheo aims at building an attractive economic ecosystem around data prediction by channelling cryptomoney from prediction requests to useful data and algorithms providers. Morpheo is designed to handle multiple data sources in a transfer learning approach in order to mutualize knowledge acquired from large datasets for applications with smaller but similar datasets.  This is a white-paper: an introduction to the technology powering Morpheo. It is evolving with the project to reflect the current vision and development status of Morpheo. It introduces Morpheo as a data-agnostic platform, although Morpheo is originally developed for a medical application, namely sleep data analysis. For technical details about Morpheo, see the online documentation and source code.
Rethinking Conventional Collaborative Filtering for Recommending Daily Fashion Outfits

A conventional collaborative ltering approach using a standard utility matrix fails to capture the aspect of matching clothing items when recommending daily fashion outts. Moreover, it is challenged by the new user cold-start problem. In this paper, we describe a novel approach for guiding users in selecting daily fashion outts, by providing outt recommendations from a system consisting of an Internet of ings wardrobe enabled with RFID technology and a corresponding mobile application. We show where a conventional collaborative ltering approach comes short when recommending fashion outts, and how our novel approach—powered by machine learning algorithms—shows promising results in the domain of fashion recommendation. Evaluation of our novel approach using a real-world dataset demonstrates the system’s eectiveness and its ability to provide daily outt recommendations that are relevant to the users. A non-comparable evaluation of the conventional approach is also given.
Relativity of remembering: why the laws of memory vanished.

For 120 years, cognitive psychologists have sought general laws of learning and memory. In this review I conclude that none has stood the test of time. No empirical law withstands manipulation across the four sets of factors that Jenkins (1979) identified as critical to memory experiments: types of subjects, kinds of events to be remembered, manipulation of encoding conditions, and variations in test conditions. Another factor affecting many phenomena is whether a manipulation of conditions occurs in randomized, within-subjects designs rather than between-subjects (or within-subject, blocked) designs. The fact that simple laws do not hold reveals the complex, interactive nature of memory phenomena. Nonetheless, the science of memory is robust, with most findings easily replicated under the same conditions as originally used, but when other variables are manipulated, effects may disappear or reverse. These same points are probably true of psychological research in most, if not all, domains.
Image Super-Resolution via Adaptive  $\ell _{p} (0<p<1)$  Regularization and Sparse Representation

Previous studies have shown that image patches can be well represented as a sparse linear combination of elements from an appropriately selected over-complete dictionary. Recently, single-image super-resolution (SISR) via sparse representation using blurred and downsampled low-resolution images has attracted increasing interest, where the aim is to obtain the coefficients for sparse representation by solving an ℓ0 or ℓ1 norm optimization problem. The ℓ0 optimization is a nonconvex and NP-hard problem, while the ℓ0 optimization usually requires many more measurements and presents new challenges even when the image is the usual size, so we propose a new approach for SISR recovery based on ℓp0 <; p <; 1) regularization nonconvex optimization. The proposed approach is potentially a powerful method for recovering SISR via sparse representations, and it can yield a sparser solution than the ℓ1 regularization method. We also consider the best choice for ℓp regularization with all p in (0, 1), where we propose a scheme that adaptively selects the norm value for each image patch. In addition, we provide a method for estimating the best value of the regularization parameter λ adaptively, and we discuss an alternate iteration method for selecting p and λ. We perform experiments, which demonstrates that the proposed ℓp(0 <; p <; 1) regularization nonconvex optimization method can outperform the convex optimization method and generate higher quality images.
MARKOV CHAIN MONTE CARLO SIMULATION METHODS IN ECONOMETRICS

We present several Markov chain Monte Carlo simulation methods that have been widely used in recent years in econometrics and statistics. Among these is the Gibbs sampler, which has been of particular interest to econometricians. Although the paper summarizes some of the relevant theoretical literature, its emphasis is on the presentation and explanation of applications to important models that are studied in econometrics. We include a discussion of some implementation issues, the use of the methods in connection with the EM algorithm, and how the methods can be helpful in model specification questions. Many of the applications of these methods are of particular interest to Bayesians, but we also point out ways in which frequentist statisticians may find the techniques useful.
Phonetic posteriorgrams for many-to-one voice conversion without parallel data training

This paper proposes a novel approach to voice conversion with non-parallel training data. The idea is to bridge between speakers by means of Phonetic PosteriorGrams (PPGs) obtained from a speaker-independent automatic speech recognition (SI-ASR) system. It is assumed that these PPGs can represent articulation of speech sounds in a speaker-normalized space and correspond to spoken content speaker-independently. The proposed approach first obtains PPGs of target speech. Then, a Deep Bidirectional Long Short-Term Memory based Recurrent Neural Network (DBLSTM) structure is used to model the relationships between the PPGs and acoustic features of the target speech. To convert arbitrary source speech, we obtain its PPGs from the same SI-ASR and feed them into the trained DBLSTM for generating converted speech. Our approach has two main advantages: 1) no parallel training data is required; 2) a trained model can be applied to any other source speaker for a fixed target speaker (i.e., many-to-one conversion). Experiments show that our approach performs equally well or better than state-of-the-art systems in both speech quality and speaker similarity.
Using Retweets to Shape our Online Persona: a Topic Modeling Approach

Online socializing technologies have given rise to new social behaviors. We focus on the effect of reiteration tool (tools that enable us to redistribute a copy of content that others have posted) on users’ online persona building. Specifically, we study retweeting behavior on Twitter and ask: (1) Do users expand the breadth of topics they discuss? (2) Do users change the distribution of the topics they discuss? (3) Does the behaviors of experts differ from those of non-experts? We use data about 2,435 non-expert users, and 415 expert users and the users whom they follow and use LDA topic modeling to derive the topics in both self-tweets and retweets. We find that users rarely add new topics when retweeting and they do not alter significantly the distribution of topics. Also, this tendency is stronger among expert users, indicating that they rely more on their own words for impression management.
The research and implementation of metadata cache backup technology based on CEPH file system

Based on research and analysis of the Ceph file system, the log cache backup scheme is proposed. The log cache backup scheme reduces metadata access delays by caching logs and reducing the time of storing logs to server clusters. In order to prevent the loss of cached data in metadata servers, a cached data backup scheme is proposed. Compared to the metadata management subsystem of the Ceph, the log cache backup scheme can effectively improve the access performance of metadata. Finally, based on open source codes of the Ceph file system, the log cache backup scheme is implemented. Compared to the performance of the Ceph metadata management subsystem, experiment results show that performance improvements of the log cache backup scheme are up to 11.5%.
CleanM: An Optimizable Query Language for Unified Scale-Out Data Cleaning

Data cleaning has become an indispensable part of data analysis due to the increasing amount of dirty data. Data scientists spend most of their time preparing dirty data before it can be used for data analysis. At the same time, the existing tools that attempt to automate the data cleaning procedure typically focus on a specific use case and operation. Still, even such specialized tools exhibit long running times or fail to process large datasets. Therefore, from a user’s perspective, one is forced to use a different, potentially inefficient tool for each category of errors. This paper addresses the coverage and efficiency problems of data cleaning. It introduces CleanM (pronounced clean’em), a language which can express multiple types of cleaning operations. CleanM goes through a three-level translation process for optimization purposes; a different family of optimizations is applied in each abstraction level. Thus, CleanM can express complex data cleaning tasks, optimize them in a unified way, and deploy them in a scaleout fashion. We validate the applicability of CleanM by using it on top of CleanDB, a newly designed and implemented framework which can query heterogeneous data. When compared to existing data cleaning solutions, CleanDB a) covers more data corruption cases, b) scales better, and can handle cases for which its competitors are unable to terminate, and c) uses a single interface for querying and for data cleaning.
A Geometric Framework for Rectangular Shape Detection

Rectangular shape detection has a wide range of applications, such as license plate detection, vehicle detection, and building detection. In this paper, we propose a geometric framework for rectangular shape detection based on the channel-scale space of RGB images. The framework consists of algorithms developed to address three issues of a candidate shape (i.e., a connected component of edge points), including: 1) outliers; 2) open shape; and 3) fragmentation. Furthermore, we propose an interestness measure for rectangular shapes by integrating imbalanced points (one type of interest points). Our experimental study shows the promise of the proposed framework.
Smart Health Care: An Edge-Side Computing Perspective

Increasing health awareness and rapid technological advancements have resulted in significant growth in an emerging sector: smart health care. By the end of 2020, the total number of smart health-care devices is expected to reach 808.9 million (646 million devices without wearables and the remaining with wearables) , . This proliferation of devices is expected to revolutionize health care by speeding up treatment and diagnostic processes, decreasing physician visit costs, and enhancing patient care quality.
Learned optimizers that outperform SGD on wall-clock and test loss

Deep learning has shown that learned functions can dramatically outperform handdesigned functions on perceptual tasks. Analogously, this suggests that learned optimizers may similarly outperform current hand-designed optimizers, especially for specific problems. However, learned optimizers are notoriously difficult to train and have yet to demonstrate wall-clock speedups over hand-designed optimizers, and thus are rarely used in practice. Typically, learned optimizers are trained by truncated backpropagation through an unrolled optimization process. The resulting gradients are either strongly biased (for short truncations) or have exploding norm (for long truncations). In this work we propose a training scheme which overcomes both of these difficulties, by dynamically weighting two unbiased gradient estimators for a variational loss on optimizer performance. This allows us to train neural networks to perform optimization of a specific task faster than well tuned first-order methods. Moreover, by training the optimizer against validation loss (as opposed to training loss), we are able to learn optimizers that train networks to better generalization than first order methods. We demonstrate these results on problems where our learned optimizer trains convolutional networks in a fifth of the wall-clock time compared to tuned first-order methods, and with an improvement in test loss.
Anxious and avoidant attachment, vibrator use, anal sex, and impaired vaginal orgasm.

Disturbances in intimate relationships are among the risk factors for female sexual dysfunction. Insecure styles of anxious attachment (preoccupations about abandonment) and avoidant attachment (avoidance of closeness in relationships) are robustly associated with sexual problems, relationship difficulties, and several indices of poorer physical and mental health. Similar indices of poorer sexual, relationship, and health functioning are associated with impairment of orgasm triggered by penile-vaginal stimulation (vaginal orgasm), but unrelated or related to greater frequency of other sexual behaviors. However, research examining the differential association of sexual activities with insecure attachment styles has been lacking.



The aim of this study was to test the hypotheses that insecure attachment styles are associated with lesser vaginal orgasm consistency, and are unrelated or directly related to greater frequency of other sexual behaviors.



Seventy coitally experienced women recruited at a Scottish university completed the Revised Experience in Close Relationships scale, and reported their frequency of various sexual behaviors (and corresponding orgasms) in a recent representative month.



The main outcome measures for this study are multivariate correlations of various sexual activities with insecure attachment styles, age, and social desirability response bias.



Anxious attachment was associated with lesser vaginal orgasm consistency, but with higher frequency of vibrator and anal sex orgasms. Avoidant attachment was associated with higher frequency of vibrator orgasms. Neither anxious nor avoidant attachment was associated with lifetime number of penile-vaginal intercourse partners.



The results provide evidence that inability to attain a vaginal orgasm is associated with anxious attachment, among other indices of poorer mental health and relatedness. Vaginal orgasm might be the relevant sexual activity for the maintenance of a secure attachment style with a sexual partner and/or more insecurely attached women might have difficulties in attaining vaginal orgasms and be more inclined to sexual activities characterized by more emotional and physical detachment, as part of a characterological discomfort with intimacy.
Cloud RAN challenges and solutions

In this paper we take an overall look at key technical challenges in the evolution of the Radio Access Network (RAN) architecture towards Cloud RAN, and solutions to overcome them. To address fronthaul limitations, we examine the implications and tradeoffs enabled by functional splits on fronthaul needs, system performance, and centralization scale. We examine the architecture of algorithms for multi-cell coordination and implications in a Cloud RAN environment. To maximize the use of General- Processors (GPP) and operating systems such as Linux for Cloud RAN, we propose methods of achieving real-time performance suitable for RAN functions. To enable right-sizing the amount of compute used for various RAN functions based on the workload, we propose methods of pooling and elastic scaling for RAN functions that exploit the fact that certain RAN functions perform per-user operations while others perform per-cell operations. Cloud RAN also aims to use cloud management technologies such as virtualized infrastructure management (VIM) and orchestration for automating the instantiation and scaling of RAN functions. We identify special needs for RAN arising from real-time constraints and a mix of GPP and non-GPP hardware. Cloud RAN, fronthaul, real-time, pooling, elastic scaling, multi-cell coordination, orchestration, NFV, 5G.
YGGDRASIL - A Statistical Package for Learning Split Models

There are two main objectives of this paper. The first is to present a statistical framework for models with context specific indepen­ dence structures, i.e. conditional independen­ cies holding only for specific values of the con­ ditioning variables. This framework is consti­ tuted by the class of split models. Split mod­ els are an extension of graphical models for contingency tables and allow for a more so­ phisticated modelling than graphical models. The treatment of split models include estima­ tion, representation and a Markov property for reading off those independencies holding in a specific context. The second objective is to present a software package named YG­ GDRASIL which is designed for statistical in­ ference in split models, i.e. for learning such models on the basis of data.
Robust video denoising using low rank matrix completion

Most existing video denoising algorithms assume a single statistical model of image noise, e.g. additive Gaussian white noise, which often is violated in practice. In this paper, we present a new patch-based video denoising algorithm capable of removing serious mixed noise from the video data. By grouping similar patches in both spatial and temporal domain, we formulate the problem of removing mixed noise as a low-rank matrix completion problem, which leads to a denoising scheme without strong assumptions on the statistical properties of noise. The resulting nuclear norm related minimization problem can be efficiently solved by many recently developed methods. The robustness and effectiveness of our proposed denoising algorithm on removing mixed noise, e.g. heavy Gaussian noise mixed with impulsive noise, is validated in the experiments and our proposed approach compares favorably against some existing video denoising algorithms.
Perceived qualities of smart wearables: determinants of user acceptance

Wearable computers are one of the new technologies that are expected to be a part of users' lives extensively in near future. While some of the users have positive attitudes towards these new products, some users may reject to use them due to different reasons. User experience is subjective, and effected by various parameters. Among these the first impression, namely the perceived qualities has an important impact on product acceptance. This paper aims to explore the perceived qualities of wearables and define the relations between them. An empirical study is conducted, to find out the hierarchy and meaningful relationships between the perceived qualities of smart wearables. The study is based on personal construct theory and data is presented by  The patterns behind affection and affected qualities are explored to understand the design requirements for the best integration of wearables into daily lives.
Gossip in Evolutionary Perspective

Conversation is a uniquely human phenomenon. Analyses of freely forming conversations indicate that approximately two thirds of conversation time is devoted to social topics, most of which can be given the generic label gossip. This article first explores the origins of gossip as a mechanism for bonding social groups, tracing these origins back to social grooming among primates. It then asks why social gossip in this sense should form so important a component of human interaction and presents evidence to suggest that, aside from servicing social networks, a key function may be related explicitly to controlling free riders. Finally, the author reviews briefly the role of social cognition in facilitating conversations of this kind.
Sustainable Innovation , Management Accounting and Control Systems , and International Performance

This study analyzes how Management Accounting and Control Systems (MACS) facilitate the appropriation of the benefits of sustainable innovations in organizations. In particular, this paper examines the moderating role of different types of MACS in the relationships between sustainable innovation and international performance at an organizational level. We collected survey data from 123 Spanish and Portuguese organizations. Partial Least Square was used to analyze the data. Results show that the effect of sustainable innovations on international performance is enhanced by contemporary rather than traditional types of MACS. Overall our findings show that MACS can help managers to develop and monitor organizational activities (e.g., costumer services and distribution activities), which support the appropriation of the potential benefits from sustainable innovation. This paper responds to recent calls for in-depth studies about the organizational mechanism that may enhance the success of sustainable innovation.
Graph Signal Processing – A Probabilistic Framework

This theoretical paper aims to provide a probabilistic framework for graph signal processing. By modeling signals on graphs as Gaussian Markov Random Fields, we present numerous important aspects of graph signal processing, including graph construction, graph transform, graph downsampling, graph prediction, and graph-based regularization, from a probabilistic point of view. As examples, we discuss a number of methods for constructing graphs based on statistics from input data sets; we show that the graph transform is the optimal linear transform to decorrelate the signal; we describe the optimality of the Kron reduction for graph downsampling in a probabilistic sense; and we derive the optimal predictive transform coding scheme applicable to both motion prediction and intra predictive coding.
An improved algorithm on Viola-Jones object detector

In image processing, Viola-Jones object detector  is one of the most successful and widely used object detectors. A popular implementation used by the community is the one in OpenCV. The detector shows its strong power in detecting faces, but we found it hard to be extended to other kinds of objects. The convergence of the training phase of this algorithm depends a lot on the training data. And the prediction precision stays low. In this paper, we have come up with new ideas to improve its performance for diverse object categories. We incorporated six different types of feature images into the Viola and Jones' framework. The integral image  used by the Viola-Jones detector is then computed on these feature images respectively instead of only on the gray image. The stage classifier in Viola-Jones detector is now trained on one of these feature images. We also present a new stopping criterion for the stage training. In addition, we integrate a key points based SVM  predictor into the prediction phase to improve the confidence of the detection result.