nils-herrmann/clean_scirepeval huggingface dataset:
Noninvasive blood glucose measurements by near-infrared transmission spectroscopy across human tongues.

Noninvasive blood glucose measurements are characterized in human subjects. A series of first overtone transmission spectra are collected across the tongues of five human subjects with type 1 diabetes. The noninvasive human spectra are collected by an experimental protocol that is designed to minimize chance correlations with blood glucose levels. In one treatment of the data, every fifth sample is used as a blind prediction point to validate model performance. In another rearrangement of the data, the spectra collected over the first 29 days are used to build calibration models that are then used to predict in vivo glycemia from spectra collected over the next 10 days. Of the five data sets (one for each subject), one demonstrates a complete inability to predict blood glucose levels and is deemed void of glucose-specific information. Glucose-specific information is evident in the remaining four data sets, albeit to varying degrees. For all data sets, the ability to measure glucose from spectra collected noninvasively from human subjects depends on spectral quality and reproducibility of the tongue-to-spectrometer interface. The standard error of prediction is 3.4 mM for the best calibration model. The significance of this magnitude of prediction error is discussed relative to the situations where: (1) the model is completely void of glucose-specific information and (2) glucose predictions are limited by spectral signal-to-noise and sample thickness. Overall, glucose-specific information is available from noninvasive first-overtone spectra collected across human tongues. Significant improvements are necessary, however, before clinically useful measurements are possible.
The Friendship Problem on Graphs

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CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations

This paper introduces a novel activity dataset which exhibits real-life and diverse scenarios of complex, temporallyextended human activities and actions. The dataset presents a set of videos of actors performing everyday activities in a natural and unscripted manner. The dataset was recorded using a static Kinect 2 sensor which is commonly used on many robotic platforms. The dataset comprises of RGB-D images, point cloud data, automatically generated skeleton tracks in addition to crowdsourced annotations. Furthermore, we also describe the methodology used to acquire annotations through crowdsourcing. Finally some activity recognition benchmarks are presented using current state-of-the-art techniques. We believe that this dataset is particularly suitable as a testbed for activity recognition research but it can also be applicable for other common tasks in robotics/computer vision research such as object detection and human skeleton tracking.
Development of a biped robot with torque controlled joints

This paper gives an overview of the development of a novel biped walking machine. The robot is designed as an experimental system for studying biped locomotion based on torque controlled joints. As an underlying drive technology, the torque controlled joint units of the DLR-KUKA-Lightweight-Robot are employed. The relevant design choices for using this technology in a biped robot with integrated joint torque sensors are highlighted and some first experimental results using a conventional ZMP based control scheme are discussed.
A hybrid cross-language name matching technique using novel modified Levenshtein Distance

Name matching is a key component in various applications in our life like record linkage and data mining applications. This process suffers from multiple complexities such as matching data from different languages or data written by people from different cultures. In this paper, we present a new modified Cross-Language Levenshtein Distance (CLLD) algorithm that supports matching names across different writing scripts and with many-to-many characters mapping. In addition, we present a hybrid cross-language name matching technique that uses phonetic matching technique mixed with our proposed CLLD algorithm to improve the overall f-measure and speed up the matching process. Our experiments demonstrate that this method substantially outperforms a number of well-known standard phonetic and approximate string similarity methods in terms of precision, recall, and f-measure.
A Simulation Framework for Evaluating Interference Mitigation Techniques in Heterogeneous Cellular Environments

Femtocells present an attractive solution for the improvement of a mobile network’s services providing better data rates and coverage. Since their deployment results to a heterogeneous network where two layers must utilize the available spectrum, issues of interference arise. A method to address this challenge, is investigating the locations of the newly installed FBS, and enforcing a power controlled transmission of all FBSs that achieves optimal and fair overall performance. Another option that becomes available in inter-cell interference cancellation (ICIC) macrocell environments, is utilizing the available spectrum to complete or partly avoid co-channel operation. In this work, we provide a simulation framework that allows the creation of custom, high configurable, user defined topologies of femtocells with power control and frequency allocation capabilities. It allows the investigation of the margin of improvement in interference when these methods are applied and may work as a decision tool for planning and evaluating heterogeneous networks. To showcase the framework’s capabilities, we evaluate and study the behaviour of custom deployed femtocells/macrocells networks and examine the cross-tier interference issues. Facilitated by the framework, we enforce and evaluate each interference mitigation technique for different femtocells’ deployment densities. Finally, we compare the results of each method in terms of total throughput, spectral efficiency and cell-edge users’ performance. C. Bouras (B) · V. Kokkinos · A. Papazois Computer Technology Institute and Press “Diophantus”, N. Kazantzaki, 26504 Patras, Greece e-mail: bouras@cti.gr V. Kokkinos e-mail: kokkinos@cti.gr A. Papazois e-mail: papazois@ceid.upatras.gr C. Bouras · G. Diles · V. Kokkinos · K. Kontodimas · A. Papazois Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece e-mail: diles@ceid.upatras.gr K. Kontodimas e-mail: kontodimas@ceid.upatras.gr
Scaling Hop-Based Reachability Indexing for Fast Graph Pattern Query Processing

Graphs are becoming increasingly dominant in modeling real-life networked data including social and biological networks, the WWW and the Semantic Web, etc. Graph pattern queries are useful for gathering information with expressive semantics from these graph-structured data. Current methods for graph pattern query processing have performance deficiency caused by inefficiencies of the underlying reachability index and costly merge-join operations on huge amounts of tuple-formatted intermediate results. To overcome the above problems, this paper contributes in the following aspects to boost graph pattern query evaluation. First, we propose an improved hop-based reachability indexing scheme 3-Hop which gains faster reachability query evaluation, less indexing costs and better scalabilities than state-of-the-art hop-based methods. Second, we propose a two-stage node filtering algorithm based on 3-Hop to answer tree pattern queries more efficiently. Tree pattern queries serve as the underlying facility for graph pattern query evaluation. Furthermore, we use a graph representation of the intermediate results during node filtering and final results enumeration. Experiments on real-life and synthetic datasets demonstrate the effectiveness of the proposed methods.
Generating Counterfactual Explanations with Natural Language

Natural language explanations of deep neural network decisions provide an intuitive way for a AI agent to articulate a reasoning process. Current textual explanations learn to discuss class discriminative features in an image. However, it is also helpful to understand which attributes might change a classification decision if present in an image (e.g., “This is not a Scarlet Tanager because it does not have black wings.”) We call such textual explanations counterfactual explanations, and propose an intuitive method to generate counterfactual explanations by inspecting which evidence in an input is missing, but might contribute to a different classification decision if present in the image. To demonstrate our method we consider a fine-grained image classification task in which we take as input an image and a counterfactual class and output text which explains why the image does not belong to a counterfactual class. We then analyze our generated counterfactual explanations both qualitatively and quantitatively using proposed automatic metrics.
Linear tSNE optimization for the Web

The t-distributed Stochastic Neighbor Embedding (tSNE) algorithm has become in recent years one of the most used and insightful techniques for the exploratory data analysis of high-dimensional data. tSNE reveals clusters of high-dimensional data points at different scales while it requires only minimal tuning of its parameters. Despite these advantages, the computational complexity of the algorithm limits its application to relatively small datasets. To address this problem, several evolutions of tSNE have been developed in recent years, mainly focusing on the scalability of the similarity computations between data points. However, these contributions are insufficient to achieve interactive rates when visualizing the evolution of the tSNE embedding for large datasets. In this work, we present a novel approach to the minimization of the tSNE objective function that heavily relies on modern graphics hardware and has linear computational complexity. Our technique does not only beat the state of the art, but can even be executed on the client side in a browser. We propose to approximate the repulsion forces between data points using adaptive-resolution textures that are drawn at every iteration with WebGL. This approximation allows us to reformulate the tSNE minimization problem as a series of tensor operation that are computed with TensorFlow.js, a JavaScript library for scalable tensor computations.
Island Loss for Learning Discriminative Features in Facial Expression Recognition

Over the past few years, Convolutional Neural Networks (CNNs) have shown promise on facial expression recognition. However, the performance degrades dramatically under real-world settings due to variations introduced by subtle facial appearance changes, head pose variations, illumination changes, and occlusions. In this paper, a novel island loss is proposed to enhance the discriminative power of deeply learned features. Specifically, the island loss is designed to reduce the intra-class variations while enlarging the inter-class differences simultaneously. Experimental results on four benchmark expression databases have demonstrated that the CNN with the proposed island loss (IL-CNN) outperforms the baseline CNN models with either traditional softmax loss or center loss and achieves comparable or better performance compared with the state-of-the-art methods for facial expression recognition.
Recognizing Semantic Features in Faces using Deep Learning

Human face constantly conveys information, both consciously and subconsciously. However, as basic as it is for humans to visually interpret this information, it is quite a big challenge for machines. Conventional semantic facial feature recognition and analysis techniques mostly lack robustness and suffer from high computation time. This paper aims to explore ways for machines to learn to interpret semantic information available in faces in an automated manner without requiring manual design of feature detectors, using the approach of Deep Learning. In this study, the effects of various factors and hyper-parameters of deep neural networks are investigated for an optimal network configuration that can accurately recognize semantic facial features like emotions, age, gender, ethnicity etc. Furthermore, the relation between the effect of high-level concepts on low level features is explored through the analysis of the similarities in low-level descriptors of different semantic features. This paper also demonstrates a novel idea of using a deep network to generate 3-D Active Appearance Models of faces from real-world 2-D images. For a more detailed report on this work, please see .
Information Theory and an Extension of the Maximum Likelihood Principle by Hirotogu Akaike

1.  The problem of estimating the dimensionality of a model occurs in various forms in applied statistics. There is estimating the number of factor in factor analysis, estimating the degree of a polynomial describing the data, selecting the variables to be introduced in a multiple regression equation, estimating the order of an AR or MA time series model, and so on. In factor analysis this problem was traditionally solved by eyeballing residual eigen-values, or by applying some other kind of heuristic procedure. When maximum likelihood factor analysis became computationally feasible the likelihoods for diierent dimensionalities could be compared. Most statisticians were aware of the fact that comparison of successive chi squares was not optimal in any well deened decision theoretic sense. With the advent of the electronic computer the forward and backward stepwise selection procedures in multiple regression also became quite popular, but again there were plenty of examples around showing that the procedures were not optimal and could easily lead one astray. When even more computational power became available one could solve the best subset selection problem for up to 20 or 30 variables, but choosing an appropriate criterion on the basis of which to compare the many models remains a problem. But exactly because of these advances in computation, nding a solution of the problem became more and more urgent. In the linear regression situation the C p criterion of Mallows (1973), which had already been around much longer, and the PRESS criterion of Allen (1971) were suggested. Although they seemed to work quite well, they were too limited in scope. The structural covariance models of Joreskog and others, and the log linear models of Goodman and others, made search over a much more complicated set of models necessary, and the model choice problems in those contexts could not be attacked by inherently linear methods. Three major closely related developments occurred around 1974. Akaike (1973) introduced the information criterion for model selection, generalizing his earlier work on time series analysis and factor analysis. Stone (1974) reintroduced and systematized cross
A Fast Spectral Method for Active 3D Shape Reconstruction

Variational energy minimization techniques for surface reconstruction are implemented by evolving an active surface according to the solutions of a sequence of elliptic partial differential equations (PDE's). For these techniques, most current approaches to solving the elliptic PDE are iterative involving the implementation of costly finite element methods (FEM) or finite difference methods (FDM). The heavy computational cost of these methods makes practical application to 3D surface reconstruction burdensome. In this paper, we develop a fast spectral method which is applied to 3D active surface reconstruction of star-shaped surfaces parameterized in polar coordinates. For this parameterization the Euler-Lagrange equation is a Helmholtz-type PDE governing a diffusion on the unit sphere. After linearization, we implement a spectral non-iterative solution of the Helmholtz equation by representing the active surface as a double Fourier series over angles in spherical coordinates. We show how this approach can be extended to include region-based penalization. A number of 3D examples and simulation results are presented to illustrate the performance of our fast spectral active surface algorithms.
Building Privacy-Preserving Cryptographic Credentials from Federated Online Identities

Federated identity providers, e.g., Facebook and PayPal, offer a convenient means for authenticating users to third-party applications. Unfortunately such cross-site authentications carry privacy and tracking risks. For example, federated identity providers can learn what applications users are accessing; meanwhile, the applications can know the users' identities in reality.
 This paper presents Crypto-Book, an anonymizing layer enabling federated identity authentications while preventing these risks. Crypto-Book uses a set of independently managed servers that employ a (t,n)-threshold cryptosystem to collectively assign credentials to each federated identity (in the form of either a public/private keypair or blinded signed messages). With the credentials in hand, clients can then leverage anonymous authentication techniques such as linkable ring signatures or partially blind signatures to log into third-party applications in an anonymous yet accountable way.
 We have implemented a prototype of Crypto-Book and demonstrated its use with three applications: a Wiki system, an anonymous group communication system, and a whistleblower submission system. Crypto-Book is practical and has low overhead: in a deployment within our research group, Crypto-Book group authentication took 1.607s end-to-end, an overhead of 1.2s compared to traditional non-privacy-preserving federated authentication.
An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea

Reference phylogenies are crucial for providing a taxonomic framework for interpretation of marker gene and metagenomic surveys, which continue to reveal novel species at a remarkable rate. Greengenes is a dedicated full-length 16S rRNA gene database that provides users with a curated taxonomy based on de novo tree inference. We developed a ‘taxonomy to tree’ approach for transferring group names from an existing taxonomy to a tree topology, and used it to apply the Greengenes, National Center for Biotechnology Information (NCBI) and cyanoDB (Cyanobacteria only) taxonomies to a de novo tree comprising 408 315 sequences. We also incorporated explicit rank information provided by the NCBI taxonomy to group names (by prefixing rank designations) for better user orientation and classification consistency. The resulting merged taxonomy improved the classification of 75% of the sequences by one or more ranks relative to the original NCBI taxonomy with the most pronounced improvements occurring in under-classified environmental sequences. We also assessed candidate phyla (divisions) currently defined by NCBI and present recommendations for consolidation of 34 redundantly named groups. All intermediate results from the pipeline, which includes tree inference, jackknifing and transfer of a donor taxonomy to a recipient tree (tax2tree) are available for download. The improved Greengenes taxonomy should provide important infrastructure for a wide range of megasequencing projects studying ecosystems on scales ranging from our own bodies (the Human Microbiome Project) to the entire planet (the Earth Microbiome Project). The implementation of the software can be obtained from http://sourceforge.net/projects/tax2tree/.
Multi-Query Optimization for Subgraph Isomorphism Search

Existing work on subgraph isomorphism search mainly focuses on a-query-at-a-time approaches: optimizing and answering each query separately. When multiple queries arrive at the same time, sequential processing is not always the most efficient. In this paper, we study multi-query optimization for subgraph isomorphism search. We first propose a novel method for efficiently detecting useful common subgraphs and a data structure to organize them. Then we propose a heuristic algorithm based on the data structure to compute a query execution order so that cached intermediate results can be effectively utilized. To balance memory usage and the time for cached results retrieval, we present a novel structure for caching the intermediate results. We provide strategies to revise existing single-query subgraph isomorphism algorithms to seamlessly utilize the cached results, which leads to significant performance improvement. Extensive experiments verified the effectiveness of our solution.
Artificial evolution using neuroevolution of augmenting topologies (NEAT) for kinetics study in diverse viscous mediums

This paper proposes a novel approach for the evolution of artificial creatures which moves in a 3D virtual environment based on the neuroevolution of augmenting topologies (NEAT) algorithm. The NEAT algorithm is used to evolve neural networks that observe the virtual environment and respond to it, by controlling the muscle force of the creature. The genetic algorithm is used to emerge the architecture of creature based on the distance metrics for fitness evaluation. The damaged morphologies of creature are elaborated, and a crossover algorithm is used to control it. Creatures with similar morphological traits are grouped into the same species to limit the complexity of the search space. The motion of virtual creature having 2–3 limbs is recorded at three different angles to check their performance in different types of viscous mediums. The qualitative demonstration of motion of virtual creature represents that improved swimming of virtual creatures is achieved in simulating mediums with viscous drag 1–10 arbitrary unit.
Evaluation of sampling techniques in software fault prediction using metrics and code smells

The highly imbalanced nature of software fault datasets results in poor performance of machine leaning techniques used for software fault prediction. The objective of this paper is to evaluate sampling techniques and Meta-Cost learning in software fault prediction to alleviate problem of imbalanced data. We evaluate four sampling techniques in metrics as well as code smells based fault prediction on fault data sets of two open source systems ANT and POI. Our results indicate that Resample technique is best for metrics based fault prediction whereas Synthetic Minority Oversampling is best suited for code smells based fault prediction. The results are presented in terms of accuracy measures like G-Mean, F-measure and area under ROC curve. We also evaluate Meta-Cost learning and found that all sampling techniques outperform Meta-Cost learning. Our results also indicate that software metrics are better predictor of software faults than code smells.