Reviewerly/exHarmony huggingface dataset:
The role of CRIS’s in the research life cycle. A case study on implementing a FAIR RDM policy at Radboud University, the Netherlands

In 2015, Radboud University (Nijmegen, the Netherlands) started a project to extend its CRIS (Metis) with functionalities that allow researchers to register (metadata) and archive (uploading files) their research data, while at the same time making the data available for reuse in a FAIR way (via national Dutch data archive DANS). The new functionality was integrated with already existing functions in the CRIS, thus offering a one-stop-shop interface to researchers in which registration and archiving of data is combined with registration of publications, the uploading of full text to the university's repository, the linking of datasets and publications and the creation of researcher's profile (CV) pages. Next to the functional extension of the CRIS, the project also included an organizational element: the establishment of support and management structures and workflows, including data curation processes, in order to assure the quality of the data registration process and to foster the FAIRness of the research data. In the period up to now, we continued to transform the university's CRIS, by bringing it in line with the research life cycle perspective and policy changes in Research Data Management (RDM), including a Data Management Plan (DMP) module and FAIR data. In this paper, it will be argued and demonstrated that both for researchers and research institutes, a CRIS oriented approach to RDM brings added value. We also point to future use cases that put a central role for CRIS's even earlier within the research life cycle, e.g. at pre-registration of research questions and informed consent/ethics approval procedures. We further envision our CRIS to play a linking pin function between storage and service locations of data during research and at publication. The paper will use Radboud University as a good practice of past, present and future use of CRIS's in the research life cycle that universities and research institutes as well as researchers and research support desks are currently dealing with in the FAIR data era.
Privacy-preserving incentive and rewarding scheme for crowd computing in social media

Internet-based social media helps one user to communicate with hundreds of or even thousands of other users. By making use of the advantages of social media, the third party can finish some special jobs more efficiently. Thus, it is important to encourage more users to make contribution to the jobs. Some necessary security problems must be solved in order to ravel out the users’ concerns. They include privacy-preservation, incentive and rewarding for crowd computing in social media. In the paper, we propose the novel concept of privacy-preserving incentive and rewarding (PPIR) for crowd computing in social media. We give the formal definition of PPIR. Then, we formalize its system model and security model. Finally, based on the bilinear pairings and group-oriented cryptography technique, we design a concrete PPIR scheme. The proposed PPIR scheme’s security is proved in the random model. From the communication cost and computation cost, we analyze its efficiency. Security analysis and efficiency analysis show that our concrete PPIR scheme is provably secure and efficient.
ARSAC: Efficient model estimation via adaptively ranked sample consensus

RANSAC is a popular robust model estimation algorithm in various computer vision applications. However, the speed of RANSAC declines dramatically as the inlier rate of the measurements decreases. In this paper, a novel Adaptively Ranked Sample Consensus(ARSAC) algorithm is presented to boost the speed and robustness of RANSAC. The algorithm adopts non-uniform sampling based on the ranked measurements to speed up the sampling process. Instead of a fixed measurement ranking, we design an adaptive scheme which updates the ranking of the measurements, to incorporate high quality measurements into sample at high priority. At the same time, a geometric constraint is proposed during sampling process to select measurements with scattered distribution in images, which could alleviate degenerate cases in epipolar geometry estimation. Experiments on both synthetic and real-world data demonstrate the superiority in efficiency and robustness of the proposed algorithm compared to the state-of-the-art methods.
Provably secure certificateless aggregate signature scheme with designated verifier in an improved security model

An aggregate signature (AS) scheme combines multiple signatures which is generated by many different users into a single one. This feature is very beneficial for diminishing storage cost, bandwidth and verification cost. Many previous attempts have been made for designing AS schemes, while the former security models have not clearly addressed coalition attacks, and most of the existing AS schemes cannot resist these kinds of attacks. In this study, the authors provide a modified security model of certificateless AS (CLAS) schemes and then give a new CLAS scheme. The security of their present scheme can be rigorously proved based on the computational Diffie–Hellman assumption in the random oracle model. Furthermore, their scheme can resist such coalition attacks, i.e. an AS in their scheme is valid iff all single signatures used to generate the AS are valid.
A Mechanics-Based Similarity Measure for Text Classification in Machine Learning Paradigm

Document classification and clustering is emerging as a new challenge in the Big Data era where terabytes of data are generated every second through billions of mobile phones, desktops, servers, and mobile devices such as cameras and watches. The effectiveness of classification and clustering algorithms depends on the similarity measure used between two text documents in the corpus. We have applied Maxwell-Boltzmann distribution to find the similarity between the two documents within a document corpus. In this paper, the document corpus is treated as a large system, individual documents as containers, attributes as subcontainers, and each term as a particle. The proposed similarity measure is named Maxwell-Boltzmann Similarity Measure (MBSM). MBSM is derived from the overall distribution of feature values and total number of nonzero features among the documents. We demonstrate that MBSM satisfies all properties of a document similarity measure. The MBSM is incorporated in single label K-nearest neighbors classification (SLKNN), multi label K-nearest neighbors classification (MLKNN) and K-means clustering. We benchmark MBSM against other similarity measures like Euclidian, Cosine, Jaccard, Pairwise, ITSim, and SMTP. The comparative performance shows that MBSM outperformed all existing similarity measures and increased classification accuracy of SLKNN and MLKNN and clustering accuracy and entropy of K-means algorithm while making them more robust. The highest accuracy obtained from tenfold cross validation for SLKNN is 0.9531 and MLKNN is 0.9373. The MBSM achieved maximum accuracy of 0.6592 and minimum entropy of 0.2426 amongst all similarity measures in the scale of unity for K-means clustering.
Recent advances in virtual reality therapy for anxiety and related disorders: Introduction to the special issue

Neurocognitive impairments in patients with depression compromise everyday functioning. Thus, should neuropsychological therapy be designed as real-life-like as possible to maximize transfer effects? We investigated whether ecological validity of computerized cognitive training could be increased by a comprehensive everyday-life-simulating training device combining virtual reality, 360°-all-around visibility and autonomous navigation motions. In an eight days training program, patients exercised the learning and purchasing of shopping list products in a virtual supermarket using either the novel training device (n = 21) or a corresponding desktop application (n = 17). In a pre-post-design, effects of the two training conditions were compared regarding several outcome measures. Altogether, results did not prove a benefit of the more naturalistic training setting regarding different training performances (recognition, performance speed, spatial orientation), self-perceived daily cognitive impairments, a real-life shopping task as well as various neuropsychological capabilities. Findings are discussed in the context of general challenges in striving after ecological validity in neuropsychology.
A Metahierarchical Rule Decision System to Design Robust Fuzzy Classifiers Based on Data Complexity

There is a wide variety of studies that propose different classifiers to solve a large amount of problems in distinct classification scenarios. The no free lunch theorem states that if we use a big enough set of varied problems, all classifiers would be equivalent in performance. From another point of view, the performance of the classifiers is dependant of the scope and properties of the datasets. In this sense, new proposals on the topic often focus on a given context, aiming at improving the related state-of-the-art approaches. Data complexity metrics have been traditionally used to determine the inner characteristics of datasets. This way, researchers are able to categorize the problems in different scenarios. Then, this taxonomy can be applied to determine inner characteristics of the datasets in order to determine intervals of good and bad behavior for a given classifier. In this paper, we will take advantage of the data complexity metrics in order to design a fuzzy metaclassifier. The final goal is to create decision rules based on the inner characteristics of the data to apply a different version of the fuzzy classifier for a given problem. To do so, we will make use of the FARC-HD classifier, an evolutionary fuzzy system that has led to different extensions in the specialized literature. Experimental results show the goodness of this novel approach as it is able to outperform all versions of FARC-HD on a wide set of problems, and obtain competitive results (in terms of performance and interpretability) versus two selected state-of-the-art rule-based classification system, C4.5 and FURIA.
Proofs of Replicated Storage Without Timing Assumptions

In this paper we provide a formal treatment of proof of replicated storage, a novel cryptographic primitive recently proposed in the context of a novel cryptocurrency, namely Filecoin. In a nutshell, proofs of replicated storage is a solution to the following problem: A user stores a file m on n different servers to ensure that the file will be available even if some of the servers fail. Using proof of retrievability, the user could check that every server is indeed storing the file. However, what if the servers collude and, in order to save on resources, decide to only store one copy of the file? A proof of replicated storage guarantees that, unless the (potentially colluding) servers are indeed reserving the space necessary to store n copies of the file, the user will not accept the proofs. While some candidate proofs of replicated storage have already been proposed, their soundness relies on timing assumptions i.e., the user must reject the proof if the prover does not reply within a certain time-bound. In this paper we provide the first construction of a proof of replication which does not rely on any timing assumptions.
On LMVDR Estimators for LDSS Models: Conditions for Existence and Further Applications

For linear discrete state-space models, under certain conditions, the linear least mean squares (LLMS) filter estimate has a recursive format, a.k.a. the Kalman filter (KF). Interestingly, the linear minimum variance distortionless response (LMVDR) filter, when it exists, shares exactly the same recursion as the KF, except for the initialization. If LMVDR estimators are suboptimal in mean-squared error sense, they do not depend on the prior knowledge on the initial state. Thus, the LMVDR estimators may outperform the usual LLMS estimators in case of misspecification of the prior knowledge on the initial state. In this perspective, we establish the general conditions under which existence of the LMVDRF is guaranteed. An immediate benefit is the introduction of LMVDR fixed-point and fixed-lag smoothers (and possibly other smoothers or predictors), which has not been possible so far. Indeed, the LMVDR fixed-point smoother can be used to compute recursively the solution of a generalization of the deterministic least-squares problem.
The influence of media type and length of time delay on user attitude: Effects of product-focused virtual reality

The present study explores the effects of product-focused virtual reality (VR) media type (termed virtualgraph) such as still and motion pictures, on product attitudes in terms of user attitudes, and telepresence. Two experiments were conducted. In experiment 1, all 51 participants were shown product images produced by each type of media type, and the users' attitudes and telepresence were measured. Virtualgraph had the highest recorded telepresence and was associated with the most positive attitudes toward the products compared to still pictures and motion pictures. In experiment 2, which was designed to assess the delayed effects, participants were contacted via cellular phone four weeks following experiment 1. The results demonstrated that participants in the virtualgraph group maintained a more stable attitude toward the products over time compared to those in the still picture and motion picture groups.
Integrating Language Model and Reading Control Gate in BLSTM-CRF for Biomedical Named Entity Recognition

Biomedical named entity recognition (Bio-NER) is an important preliminary step for many biomedical text mining tasks. The current mainstream methods for NER are based on the neural networks to avoid the complex hand-designed features derived from various linguistic analyses. However, these methods ignore some potential sentence-level semantic information and general features of semantic and syntactic. Therefore, we propose a novel Long Short Term Memory (LSTM) Networks model integrating language model and sentence-level reading control gate (LS-BLSTM-CRF) for Bio-NER. In our model, a sentence-level reading control gate (SC) is inserted into the networks to integrate the implicit meaning of an entire sentence and the language model is integrated to our model to learn richer potential features. Besides, character-level embeddings are introduced as the input to deal with out-of-vocabulary words. The experimental results conducted on the BioCreative II GM corpus show that our method can achieve an F-score of 89.94 percent, which outperforms all state-of-the-art systems and is 1.33 percent higher than the best performing neural networks.
Hierarchical viewpoint discovery from tweets using Bayesian modelling

• A novel Bayesian model for hierarchical viewpoint discovery from tweets. • Phrases with arbitrary length are modelled by a hierarchical Pitman-Yor process. • Approaches for utilizing prior information like lexicons and hashtags are explored. • Experiments are conducted against probabilistic models to show the effectiveness. When users express their stances towards a topic in social media, they might elaborate their viewpoints or reasoning. Oftentimes, viewpoints expressed by different users exhibit a hierarchical structure. Therefore, detecting this kind of hierarchical viewpoints offers a better insight to understand the public opinion. In this paper, we propose a novel Bayesian model for hierarchical viewpoint discovery from tweets. Driven by the motivation that a viewpoint expressed in a tweet can be regarded as a path from the root to a leaf of a hierarchical viewpoint tree, the assignment of the relevant viewpoint topics is assumed to follow two nested Chinese restaurant processes. Moreover, opinions in text are often expressed in un-semantically decomposable multi-terms or phrases, such as ‘ economic recession ’. Hence, a hierarchical Pitman–Yor process is employed as a prior for modelling the generation of phrases with arbitrary length. Experimental results on two Twitter corpora demonstrate the effectiveness of the proposed Bayesian model for hierarchical viewpoint discovery.
Label Denoising with Large Ensembles of Heterogeneous Neural Networks

Despite recent advances in computer vision based on various convolutional architectures, video understanding remains an important challenge. In this work, we present and discuss a top solution for the large-scale video classification (labeling) problem introduced as a Kaggle competition based on the YouTube-8M dataset. We show and compare different approaches to preprocessing, data augmentation, model architectures, and model combination. Our final model is based on a large ensemble of video- and frame-level models but fits into rather limiting hardware constraints. We apply an approach based on knowledge distillation to deal with noisy labels in the original dataset and the recently developed mixup technique to improve the basic models.
Identification of Temporally Varying Areas of Interest in Long-Duration Eye-Tracking Data Sets

Eye-tracking has become an invaluable tool for the analysis of working practices in many technological fields of activity. Typically studies focus on short tasks and use static expected areas of interest (AoI) in the display to explore subjects' behaviour, making the analyst's task quite straightforward. In long-duration studies, where the observations may last several hours over a complete work session, the AoIs may change over time in response to altering workload, emergencies or other variables making the analysis more difficult. This work puts forward a novel method to automatically identify spatial AoIs changing over time through a combination of clustering and cluster merging in the temporal domain. A visual analysis system based on the proposed methods is also presented. Finally, we illustrate our approach within the domain of air traffic control, a complex task sensitive to prevailing conditions over long durations, though it is applicable to other domains such as monitoring of complex systems.
Selected trace-elements in alluvium and rocks, western Mojave Desert, southern California

Concentrations of twenty-seven elements, including naturally-occurring water-quality contaminants arsenic, chromium, and uranium, were measured in 217 samples of alluvium and rock from the western Mojave Desert, southern California, using portable (pXRF) and laboratory (LXRF) X-ray fluorescence. Comparison of measurements with NIST-traceable standards was good, although pXRF overestimated iron compared to LXRF. Results suggest pXRF survey data are sufficiently accurate to assess regional geochemical differences in geologic-source terrains. Principal component analysis showed rubidium and potassium were associated with alluvium eroded from felsic terrain, while iron, copper, chromium, and to a lesser extent titanium, manganese, and nickel were associated with alluvium eroded from mafic terrain. Zinc, vanadium, and arsenic were associated with alluvium eroded from hydrothermal terrain. Elemental assemblages associated with different source terrains were traced spatially to identify the source and composition of alluvium composing aquifers pumped for water supply. Changes in geologic source terrain to the Mojave River, associated with movement along the San Andreas Fault over the past one to five million years, reduced the mafic fraction and increased the felsic fraction of alluvium deposited to the regionally important floodplain aquifer along the Mojave River—lowering chromium concentrations in alluvium through geologic time. Comparison of pXRF and sequential extraction data from 40 samples showed arsenic and uranium were more abundant on the surfaces of mineral grains, while chromium and vanadium remained mostly within unweathered mineral grains—suggesting arsenic and uranium may be more readily mobilized into groundwater with changes in pH, redox, or ionic strength than chromium or vanadium.
Entity representation for pairwise collaborative ranking using restricted Boltzmann machine

Pairwise preference data is one of several kinds of feedback data that many modern intelligent systems gather and process in order to help people make better decisions. Recommender systems are of main categories of such intelligent systems. An emerging recommendation approach in this domain is Pairwise Collaborative Ranking (PCR) which seeks to learn pairwise preferences of users over items. Current PCR algorithms generally learn latent factors for users and items that are consistent to the ground truth pairwise preferences, instead of directly extracting latent factors for pairwise preferences. Although this is the typical approach for rating oriented recommendation, it does not seem to be the best option for pairwise collaborative ranking. In this paper RBMRank, a novel model-based PCR approach, is introduced, that is designed for recommendation based on pairwise preference data. RBMRank uses the power of the restricted Boltzmann machines to map the entities of the system into dense feature spaces. Its embedding system is able to generate useful representations even when dealing with sparse data sets, that is a very important issue in recommender systems domain. The other important feature of RBMRank is that it is able to extract latent factors for pairwise preferences, instead of items, that allows it to directly predict the unknown preferences of each user. Experimental results on MovieLens datasets show up to 5.9% improvement of accuracy in terms of [email protected] and [email protected] compared to other state-of-the-art algorithms in the field of collaborative ranking.
Quantum thermometry with trapped ions

We introduce the estimation protocol for detecting the temperature of the transverse vibrational modes of linear ion crystal. We show that thanks to the laser induced laser coupling between the vibrational modes and the collective spin states the estimation of the temperature is carried out by set of measurements of the spin populations. We show that temperature estimation protocol using single ion as a quantum probe is optimal in a sense that the set of state projective measurement saturates the fundamental Cramer-Rao bound. We find a plateau of the maximal temperature sensitivity using ion chain as a quantum probe. Moreover, we show that the non-classical part of the quantum Fisher information could leads to enhancement of the temperature sensitivity compared to the single ion case.
On the Representational Power of Restricted Boltzmann Machines for Symmetric Functions and Boolean Functions

Restricted Boltzmann machines (RBMs) are used to build deep-belief networks that are widely thought to be one of the first effective deep learning neural networks. This paper studies the ability of RBMs to represent distributions over {0,1}n via softplus/hardplus RBM networks. It is shown that any distribution whose density depends on the number of 1's in their input can be approximated with arbitrarily high accuracy by an RBM of size 2n+1 , which improves the result of a previous study by reducing the size from n2 to 2n+1 . A theorem for representing partially symmetric Boolean functions by softplus RBM networks is established. Accordingly, the representational power of RBMs for distributions whose mass represents the Boolean functions is investigated in comparison with that of threshold circuits and polynomial threshold functions. It is shown that a distribution over {0,1}n whose mass represents a Boolean function can be computed with a given margin δ by an RBM of size and parameters bounded by polynomials in n , if and only if it can be computed by a depth-2 threshold circuit with size and parameters bounded by polynomials in n .