nils-herrmann/clean_scirepeval huggingface dataset:
Qualitative Cleaning of Uncertain Data

We propose a new view on data cleaning: Not data itself but the degrees of uncertainty attributed to data are dirty. Applying possibility theory, tuples are assigned degrees of possibility with which they occur, and constraints are assigned degrees of certainty that say to which tuples they apply. Classical data cleaning modifies some minimal set of tuples. Instead, we marginally reduce their degrees of possibility. This reduction leads to a new qualitative version of the vertex cover problem. Qualitative vertex cover can be mapped to a linear-weighted constraint satisfaction problem. However, any off-the-shelf solver cannot solve the problem more efficiently than classical vertex cover. Instead, we utilize the degrees of possibility and certainty to develop a dedicated algorithm that is fixed parameter tractable in the size of the qualitative vertex cover. Experiments show that our algorithm is faster than solvers for the classical vertex cover problem by several orders of magnitude, and performance improves with higher numbers of uncertainty degrees.
Fourier PCA and robust tensor decomposition

Fourier PCA is Principal Component Analysis of a matrix obtained from higher order derivatives of the logarithm of the Fourier transform of a distribution. To make this algorithmic, we develop a robust tensor decomposition method; this is also of independent interest. Our main application is the first provably polynomial-time algorithm for underdetermined ICA, i.e., learning an n × m matrix A from observations y = Ax where x is drawn from an unknown product distribution with arbitrary non-Gaussian components. The number of component distributions m can be arbitrarily higher than the dimension n and the columns of A only need to satisfy a natural and efficiently verifiable nondegeneracy condition. As a second application, we give an alternative algorithm for learning mixtures of spherical Gaussians with linearly independent means. These results also hold in the presence of Gaussian noise.
The Fast Bilateral Solver Supplement

This supplement provides additional details on the bilateral solver and its extensions in Sections 1–4, and a plethora of experiments and comparisons to prior work in Section 5. In Section 1 we derive the bilateral-space quadratic objective at the core of the bilateral solver. Section 2 details the bilateral-space pyramid representation used for faster initialization and preconditioning. Section 3 extends the bilateral solver to use a robust error function to cope with outliers. Section 4 improves the performance of the bilateral solver when applied to multiple output channels. Finally, in Section 5 we provide additional evaluation of the bilateral solver and extensively compare it to numerous existing and novel baseline techniques.
An analog neural network processor with programmable topology

| The architecture, implementation, and applications of a special purpose neural network processor are described. The chip performs over 2000 multiplications and additions simultaneously. Its datapath is particularly suitable for the convolutional topologies that are typical in classi cation networks, but can also be con gured for fully connected or feedback topologies. Resources can be multiplexed to permit implementation of networks with several hundreds of thousands of connections on a single chip. Computations are performed with 6Bits accuracy for the weights and 3Bits for the neuron states. Analog processing is used internally for reduced power dissipation and higher density, but all input/output is digital to simplify system integration. The practicality of the chip is demonstrated with an implementation of a neural network for optical character recognition. This network contains over 130,000 connections and is evaluated in 1ms.
From Cloud to Fog Computing: A Review and a Conceptual Live VM Migration Framework

Fog computing, an extension of cloud computing services to the edge of the network to decrease latency and network congestion, is a relatively recent research trend. Although both cloud and fog offer similar resources and services, the latter is characterized by low latency with a wider spread and geographically distributed nodes to support mobility and real-time interaction. In this paper, we describe the fog computing architecture and review its different services and applications. We then discuss security and privacy issues in fog computing, focusing on service and resource availability. Virtualization is a vital technology in both fog and cloud computing that enables virtual machines (VMs) to coexist in a physical server (host) to share resources. These VMs could be subject to malicious attacks or the physical server hosting it could experience system failure, both of which result in unavailability of services and resources. Therefore, a conceptual smart pre-copy live migration approach is presented for VM migration. Using this approach, we can estimate the downtime after each iteration to determine whether to proceed to the stop-and-copy stage during a system failure or an attack on a fog computing node. This will minimize both the downtime and the migration time to guarantee resource and service availability to the end users of fog computing. Last, future research directions are outlined.
Empathy decline and its reasons: a systematic review of studies with medical students and residents.

Empathy is a key element of patient-physician communication; it is relevant to and positively influences patients' health. The authors systematically reviewed the literature to investigate changes in trainee empathy and reasons for those changes during medical school and residency.



The authors conducted a systematic search of studies concerning trainee empathy published from January 1990 to January 2010, using manual methods and the PubMed, EMBASE, and PsycINFO databases. They independently reviewed and selected quantitative and qualitative studies for inclusion. Intervention studies, those that evaluated psychometric properties of self-assessment tools, and those with a sample size <30 were excluded.



Eighteen studies met the inclusion criteria: 11 on medical students and 7 on residents. Three longitudinal and six cross-sectional studies of medical students demonstrated a significant decrease in empathy during medical school; one cross-sectional study found a tendency toward a decrease, and another suggested stable scores. The five longitudinal and two cross-sectional studies of residents showed a decrease in empathy during residency. The studies pointed to the clinical practice phase of training and the distress produced by aspects of the "hidden," "formal," and "informal" curricula as main reasons for empathy decline.



The results of the reviewed studies, especially those with longitudinal data, suggest that empathy decline during medical school and residency compromises striving toward professionalism and may threaten health care quality. Theory-based investigations of the factors that contribute to empathy decline among trainees and improvement of the validity of self-assessment methods are necessary for further research.
Fingerprint matching by thin-plate spline modelling of elastic deformations

This paper presents a novel minutiae matching method that describes elastic distortions in 4ngerprints by means of a thin-plate spline model, which is estimated using a local and a global matching stage. After registration of the 4ngerprints according to the estimated model, the number of matching minutiae can be counted using very tight matching thresholds. For deformed 4ngerprints, the algorithm gives considerably higher matching scores compared to rigid matching algorithms, while only taking 100 ms on a 1 GHz P-III machine. Furthermore, it is shown that the observed deformations are di6erent from those described by theoretical models proposed in the literature. ? 2003 Pattern Recognition Society.
Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it’s often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique.  Corresponding author
A Polymorphic Future and First-class Function Typefor Concurrent Object-Oriented Programming

A \future" is a typed synchronization mechanism used to represent a value that will come into existence at some point in time after the creation of the future. In this paper, a polymorphic future type is introduced that di ers from previous mechanisms in that it provides a rst-class, extensible facility for expressing synchronized access to the typed result value of a concurrent computation. The proposed mechanism requires write-once/read-many synchronization, which is implemented in terms of common thread synchronization primitives, thereby ensuring a high-degree of e ciency. The type de nition and type specialization features of C++ are used to illustrate how one would implement a polymorphic future type in a strongly typed object-oriented language supporting polymorphic type de nitions.
Full- and Reduced-Order Linear Observer Implementations in Matlab\/Simulink [Lecture Notes]

The design of observers is usually considered a graduate-level topic and therefore tends to be taught in a graduate-level control engineering course. However, several recent editions of standard undergraduate controlsystem textbooks cover full-order, and even reduced-order, observers [1]-[9]. Observers are also used in their own right to strictly observe the state variables of a dynamic system rather than to be used for feedback control (for example, in an experiment whose state variables have to be monitored, observed, or estimated at all times).
The Conversation: Deep Audio-Visual Speech Enhancement

The enhancement model consists of two modules: A phase and a magnitude sub-network: ●  Noisy audio & cropped video of lip region;  Clean audio ● Visual features are extracted with a spatio-temporal (3D & 2D convolutions) residual network. ● Linear scale spectrograms are extracted through STFT ● The magnitude sub-network predicts a soft-mask that filters the noisy magnitude spectrogram ● The phase sub-network produces a phase residual that corrects the noisy phase Our goal is to isolate individual voices in videos with multiple simultaneous speakers:
Tagoram: real-time tracking of mobile RFID tags to high precision using COTS devices

In many applications, we have to identify an object and then locate the object to within high precision (centimeter- or millimeter-level). Legacy systems that can provide such accuracy are either expensive or suffering from performance degradation resulting from various impacts, e.g., occlusion for computer vision based approaches.
 In this work, we present an RFID-based system, Tagoram, for object localization and tracking using COTS RFID tags and readers. Tracking mobile RFID tags in real time has been a daunting task, especially challenging for achieving high precision. Our system achieves these three goals by leveraging the phase value of the backscattered signal, provided by the COTS RFID readers, to estimate the location of the object. In Tagoram, we exploit the tag's mobility to build a virtual antenna array by using readings from a few physical antennas over a time window. To illustrate the basic idea of our system, we firstly focus on a simple scenario where the tag is moving along a fixed track known to the system. We propose Differential Augmented Hologram (DAH) which will facilitate the instant tracking of the mobile RFID tag to a high precision. We then devise a comprehensive solution to accurately recover the tag's moving trajectories and its locations, relaxing the assumption of knowing tag's track function in advance.
 We have implemented the Tagoram system using COTS RFID tags and readers. The system has been tested extensively in the lab environment and used for more than a year in real airline applications. For lab environment, we can track the mobile tags in real time with a millimeter accuracy to a median of 5mm and 7.29mm using linear and circular track respectively. In our year- long large scale baggage sortation systems deployed in two airports, our results from real deployments show that Tagoram can achieve a centimeter-level accuracy to a median of 6.35cm in these real deployments.
Joint Alignment of Multiple Point Sets with Batch and Incremental Expectation-Maximization

This paper addresses the problem of registering multiple point sets. Solutions to this problem are often approximated by repeatedly solving for pairwise registration, which results in an uneven treatment of the sets forming a pair: a model set and a data set. The main drawback of this strategy is that the model set may contain noise and outliers, which negatively affects the estimation of the registration parameters. In contrast, the proposed formulation treats all the point sets on an equal footing. Indeed, all the points are drawn from a central Gaussian mixture, hence the registration is cast into a clustering problem. We formally derive batch and incremental EM algorithms that robustly estimate both the GMM parameters and the rotations and translations that optimally align the sets. Moreover, the mixture's means play the role of the registered set of points while the variances provide rich information about the contribution of each component to the alignment. We thoroughly test the proposed algorithms on simulated data and on challenging real data collected with range sensors. We compare them with several state-of-the-art algorithms, and we show their potential for surface reconstruction from depth data.
Union and Difference of Models, 10 years later

This paper contains a summary of the talk given by the author on the occasion of the MODELS 2013 most influential paper award. The talk discussed the original paper as published in 2003, the research work done by others afterwards and the author’s personal reflection on the award. 1 Version Control of Software and System Models There are two main usage scenarios for design models in software and system development: models as sketches, that serve as a communication aid in informal discussions, and models as formal artifacts, to be analyzed, transformed into other artifacts, maintained and evolved during the whole software and system development process. In this second scenario, models are valuable assets that should be kept in a trusted repository. In a complex development project, these models will be updated often and concurrently by different developers. Therefore, there is a need for a version control system for models with optimistic locking. This is a system to compare, merge and store all versions of all models created within a development project. We can illustrate the use of a version control system for models as follows. Let us assume that the original model shown at the top of Figure 1 is edited simultaneously by two developers. One developer has decided that the subclass B is no longer necessary in the model. Simultaneously, the other developer has decided that class C should have a subclass D. The problem is to combine the contributions of both developers into a single model. This is the model shown at the bottom of Fig. 1. We presented the basic algorithms to solve this problem in the original paper published in the proceedings of the UML 2003 conference . The proposed solution is based on calculating the final model as the merge of the differences between the original and the edited models. Figure 2 shows an example of the difference of two models, in this case the difference between the models edited by the developers and the original model. The result of the difference is not always a model, in a similar way that the difference between two natural numbers is not a natural number but a negative one. An example of this is shown in the bottom B C A Original Model
PubViz: Lightweight Visual Presentation of Publication Data

Publications play a central role in presenting the outcome of scientific research but are typically presented as textual lists, whereas related work in visualization of publication focuses on exploration – not presentation. To bridge this gap, we conducted a design study of an interactive visual representation of publication data in a BibTeX file. This paper reports our domain and problem characterization as well as our visualization design decisions in light of our user-centered design process including interviews, two user studies with a paper prototype and a d3.js prototype, and practical application at our group’s website.
Hands Deep in Deep Learning for Hand Pose Estimation

We introduce and evaluate several architectures for Convolutional Neural Networks to predict the 3D joint locations of a hand given a depth map. We first show that a prior on the 3D pose can be easily introduced and significantly improves the accuracy and reliability of the predictions. We also show how to use context efficiently to deal with ambiguities between fingers. These two contributions allow us to significantly outperform the state-of-theart on several challenging benchmarks, both in terms of accuracy and computation times. The code can be found at https://github.com/moberweger/ deep-prior/.
High-Speed Parallel Architecture of the Whirlpool Hash Function

Whirlpool hash function should be capable of processing the input data streams at high speeds. We propose a fully synchronous parallel pipelined architecture with ten stages of pipelining between rounds, and internal pipelining within each round stage. The proposed architecture can tremendously improve the performance of Whirlpool hash function. Our final implementation can encrypt continuous bit streams seamlessly, achieving a throughput of 56.89 G bps, which is considerably high compared to existing implementations in literature.
Solving molecular distance geometry problems in OpenCL

We focus on the following computational chemistry problem: Given a subset of the exact distances between atoms, reconstruct the three-dimensional position of each atom in the given molecule. The distance matrix is generally sparse. This problem is both important and challenging. Our contribution is a novel combination of two known techniques (parallel breadth-first search and geometric buildup) and its OpenCL parallel implementation. The approach has the potential to speed up computation of three-dimensional structures of molecules - a critical process in computational chemistry. From experiments on multi-core CPUs and graphic processing units, we conclude that, for sufficient large problems, our implementation shows a moderate scalability.
Temporal recommendation on graphs via long- and short-term preference fusion

Accurately capturing user preferences over time is a great practical challenge in recommender systems. Simple correlation over time is typically not meaningful, since users change their preferences due to different external events. User behavior can often be determined by individual's long-term and short-term preferences. How to represent users' long-term and short-term preferences? How to leverage them for temporal recommendation? To address these challenges, we propose Session-based Temporal Graph (STG) which simultaneously models users' long-term and short-term preferences over time. Based on the STG model framework, we propose a novel recommendation algorithm Injected Preference Fusion (IPF) and extend the personalized Random Walk for temporal recommendation. Finally, we evaluate the effectiveness of our method using two real datasets on citations and social bookmarking, in which our proposed method IPF gives 15%-34% improvement over the previous state-of-the-art.
Culture, corruption, suicide, happiness and global social media use: a cross-cultural perspective

This study was conducted to answer this simple question: ‘Can cultural values explain global social media use?’ Along with cultural dimensions introduced by past studies we also added several demographic, socio-economic and personality variables into this study that generated quite interesting findings. We found that there are low levels of suicide, more happiness and more corruption in societies that use social media heavily. We also observed that GDP per capita and median age are negatively related with social media use. Self-esteem stood out as important variable related to social media use intensity along with emotional expressiveness, openness and conscientiousness. Contrary to the common view, nation-level social capital achievement was negatively related with social media use and there was absolutely no relationship between high-context and low-context communication characteristics and local social media use. Some other findings also indicated that conservative and collectivistic countries use social media more often than do individualistic and developed countries. Schwartz’s cultural dimensions and the results of the GLOBE study accounted for a considerable amount of variation in country-level social media use where Hofstede and Trompenaars’ cultural dimensions were insignificant. Since most of the cultural values failed to explain the intensity of social media use, we also developed a cross-cultural online communication framework called cross-cultural self and others’ worth.