OpenRaiser/Intern-Atlas huggingface dataset:
Identification of multidisciplinary research based upon dissimilarity analysis of journals included in reference lists of Wageningen University & Research articles
Venue: arXiv cs.DL 2018
Year: 2018

This paper discusses the identification of journal articles that probably report on multidisciplinary research. Identification of these articles may be important for strategic purposes for the institution where the research is performed or for the evaluation of researchers or groups. In order to identify possibly multidisciplinary research, this paper describes an analysis on the journals from which articles have been cited in the journal articles published by Wageningen University & Research (WUR) staff in 2006-2015. The journals with cited articles are inventoried from the reference lists of the WUR journal articles. For each WUR article a mean dissimilarity is calculated between the journal in which it has been published and the journals that contain the cited articles. Dissimilarities are derived from a large matrix with similarity values between journals, calculated from co-citations to these journals from the same WUR articles published in 2006-2015. For 21,191 WUR articles published in 2,535 journals mean dissimilarities have been calculated. For WUR articles with high mean dissimilarities this paper shows that they often are published in multidisciplinary journals. WUR articles with high mean dissimilarities also are found in non-multidisciplinary (research field specific) journals. For these articles (with high mean dissimilarities) this paper shows that citations are often made to more various research fields than for articles with lower mean dissimilarities. The mean dissimilarities, calculated per WUR article, also can be aggregated for the journals in which they have been published. This results in a listing of journals than can be ordered on the mean dissimilarity of the WUR articles that have been published in them. The analysis described in this paper shows that journals for which a high mean dissimilarity is calculated tend to have a multidisciplinary scope.
Batch Sparse Recovery, or How to Leverage the Average Sparsity
Venue: arXiv cs.DS 2018
Year: 2018

We introduce a \emph{batch} version of sparse recovery, where the goal is to report a sequence of vectors $A_1',\ldots,A_m' \in \mathbb{R}^n$ that estimate unknown signals $A_1,\ldots,A_m \in \mathbb{R}^n$ using a few linear measurements, each involving exactly one signal vector, under an assumption of \emph{average sparsity}. More precisely, we want to have \newline $(1) \;\;\; \sum_{j \in [m]}{\|A_j- A_j'\|_p^p} \le C \cdot \min \Big\{ \sum_{j \in [m]}{\|A_j - A_j^*\|_p^p} \Big\}$ for predetermined constants $C \ge 1$ and $p$, where the minimum is over all $A_1^*,\ldots,A_m^*\in\mathbb{R}^n$ that are $k$-sparse on average. We assume $k$ is given as input, and ask for the minimal number of measurements required to satisfy $(1)$. The special case $m=1$ is known as stable sparse recovery and has been studied extensively. We resolve the question for $p =1$ up to polylogarithmic factors, by presenting a randomized adaptive scheme that performs $\tilde{O}(km)$ measurements and with high probability has output satisfying $(1)$, for arbitrarily small $C > 1$. Finally, we show that adaptivity is necessary for every non-trivial scheme.
Domain Generalization via Conditional Invariant Representation
Venue: arXiv cs.LG 2018
Year: 2018

Domain generalization aims to apply knowledge gained from multiple labeled source domains to unseen target domains. The main difficulty comes from the dataset bias: training data and test data have different distributions, and the training set contains heterogeneous samples from different distributions. Let $X$ denote the features, and $Y$ be the class labels. Existing domain generalization methods address the dataset bias problem by learning a domain-invariant representation $h(X)$ that has the same marginal distribution $\mathbb{P}(h(X))$ across multiple source domains. The functional relationship encoded in $\mathbb{P}(Y|X)$ is usually assumed to be stable across domains such that $\mathbb{P}(Y|h(X))$ is also invariant. However, it is unclear whether this assumption holds in practical problems. In this paper, we consider the general situation where both $\mathbb{P}(X)$ and $\mathbb{P}(Y|X)$ can change across all domains. We propose to learn a feature representation which has domain-invariant class conditional distributions $\mathbb{P}(h(X)|Y)$. With the conditional invariant representation, the invariance of the joint distribution $\mathbb{P}(h(X),Y)$ can be guaranteed if the class prior $\mathbb{P}(Y)$ does not change across training and test domains. Extensive experiments on both synthetic and real data demonstrate the effectiveness of the proposed method.
Conformal Mesh Parameterization Using Discrete Calabi Flow
Venue: arXiv cs.GR 2018
Year: 2018

In this paper, we introduce discrete Calabi flow to the graphics research community and present a novel conformal mesh parameterization algorithm. Calabi energy has a succinct and explicit format. Its corresponding flow is conformal and convergent under certain conditions. Our method is based on the Calabi energy and Calabi flow with solid theoretical and mathematical base. We demonstrate our approach on dozens of models and compare it with other related flow based methods, such as the well-known Ricci flow and CETM. Our experiments show that the performance of our algorithm is comparably the same with other methods. The discrete Calabi flow in our method provides another perspective on conformal flow and conformal parameterization.
The Concept, Principles of Design and Implementation of the University Cloud-based Learning and Research Environment
Venue: arXiv cs.CY 2018
Year: 2018

The scientific and methodological background of creation and development of the university cloud-based learning and research environment is substantiated. The conceptual and terminology body of the cloud-based environment investigation is defined, the main features of such environment are revealed. The main methodological principles of the environment design and development are considered among them there are the principles of open education, open science and also the specific principles inherent to the cloud-based systems. The general model of the university cloud-based learning and research environment formation is substantiated and six main stages of this environment formation are distinguished in the model. The cloud-based environment functions, content and tools are revealed in accordance with the proposed methodological principles, the criteria for the estimation of this environment efficiency are elaborated. The results of implementation and experimental research of the cloud-based environment formation at the National University of Life and Environmental Sciences of Ukraine are described. The influence of different environment components use on students' success is explored.
Exploratory and Live, Programming and Coding: A Literature Study Comparing Perspectives on Liveness
Venue: arXiv cs.PL 2018
Year: 2018

Various programming tools, languages, and environments give programmers the impression of changing a program while it is running. This experience of liveness has been discussed for over two decades and a broad spectrum of research on this topic exists. Amongst others, this work has been carried out in the communities around three major ideas which incorporate liveness as an important aspect: live programming, exploratory programming, and live coding. While there have been publications on the focus of each particular community, the overall spectrum of liveness across these three communities has not been investigated yet. Thus, we want to delineate the variety of research on liveness. At the same time, we want to investigate overlaps and differences in the values and contributions between the three communities. Therefore, we conducted a literature study with a sample of 212 publications on the terms retrieved from three major indexing services. On this sample, we conducted a thematic analysis regarding the following aspects: motivation for liveness, application domains, intended outcomes of running a system, and types of contributions. We also gathered bibliographic information such as related keywords and prominent publications. Besides other characteristics the results show that the field of exploratory programming is mostly about technical designs and empirical studies on tools for general-purpose programming. In contrast, publications on live coding have the most variety in their motivations and methodologies with a majority being empirical studies with users. As expected, most publications on live coding are applied to performance art. Finally, research on live programming is mostly motivated by making programming more accessible and easier to understand, evaluating their tool designs through empirical studies with users. In delineating the spectrum of work on liveness, we hope to make the individual communities more aware of the work of the others. Further, by giving an overview of the values and methods of the individual communities, we hope to provide researchers new to the field of liveness with an initial overview.
A Queuing Model for CPU Functional Unit and Issue Queue Configuration
Venue: arXiv cs.PF 2018
Year: 2018

In a superscalar processor, instructions of various types flow through an execution pipeline, traversing hardware resources which are mostly shared among many different instruction types. A notable exception to shared pipeline resources is the collection of functional units, the hardware that performs specific computations. In a trade-off of cost versus performance, a pipeline designer must decide how many of each type of functional unit to place in a processor's pipeline. In this paper, we model a superscalar processor's issue queue and functional units as a novel queuing network. We treat the issue queue as a finite-sized waiting area and the functional units as servers. In addition to common queuing problems, customers of the network share the queue but wait for specific servers to become ready (e.g., addition instructions wait for adders). Furthermore, the customers in this queue are not necessary ready for service, since instructions may be waiting for operands. In this paper we model a novel queuing network that provides a solution to the expected queue length of each type of instruction. This network and its solution can also be generalized to other problems, notably other resource-allocation issues that arise in superscalar pipelines.
Multi-View Fuzzy Logic System with the Cooperation between Visible and Hidden Views
Venue: arXiv cs.AI 2018
Year: 2018

Multi-view datasets are frequently encountered in learning tasks, such as web data mining and multimedia information analysis. Given a multi-view dataset, traditional learning algorithms usually decompose it into several single-view datasets, from each of which a single-view model is learned. In contrast, a multi-view learning algorithm can achieve better performance by cooperative learning on the multi-view data. However, existing multi-view approaches mainly focus on the views that are visible and ignore the hidden information behind the visible views, which usually contains some intrinsic information of the multi-view data, or vice versa. To address this problem, this paper proposes a multi-view fuzzy logic system, which utilizes both the hidden information shared by the multiple visible views and the information of each visible view. Extensive experiments were conducted to validate its effectiveness.
Computational and applied topology, tutorial
Venue: arXiv cs.MS 2018
Year: 2018

This is a tutorial in applied and computational topology and topological data analysis. It is illustrated with numerous computational examples that utilize Gudhi library. It is under constant development, so please do not consider this version as final.
Measuring the Impact of Spectre and Meltdown
Venue: arXiv cs.DC 2018
Year: 2018

The Spectre and Meltdown flaws in modern microprocessors represent a new class of attacks that have been difficult to mitigate. The mitigations that have been proposed have known performance impacts. The reported magnitude of these impacts varies depending on the industry sector and expected workload characteristics. In this paper, we measure the performance impact on several workloads relevant to HPC systems. We show that the impact can be significant on both synthetic and realistic workloads. We also show that the performance penalties are difficult to avoid even in dedicated systems where security is a lesser concern.
Fault Localization for Declarative Models in Alloy
Venue: arXiv cs.SE 2018
Year: 2018

Fault localization is a popular research topic and many techniques have been proposed to locate faults in imperative code, e.g. C and Java. In this paper, we focus on the problem of fault localization for declarative models in Alloy -- a first order relational logic with transitive closure. We introduce AlloyFL, the first set of fault localization techniques for faulty Alloy models which leverages multiple test formulas. AlloyFL is also the first set of fault localization techniques at the AST node granularity. We implements in AlloyFL both spectrum-based and mutation-based fault localization techniques, as well as techniques that are based on Alloy's built-in unsat core. We introduce new metrics to measure the accuracy of AlloyFL and systematically evaluate AlloyFL on 38 real faulty models and 9000 mutant models. The results show that the mutation-based fault localization techniques are significantly more accurate than other types of techniques.
Data Science with Vadalog: Bridging Machine Learning and Reasoning
Venue: arXiv cs.DB 2018
Year: 2018

Following the recent successful examples of large technology companies, many modern enterprises seek to build knowledge graphs to provide a unified view of corporate knowledge and to draw deep insights using machine learning and logical reasoning. There is currently a perceived disconnect between the traditional approaches for data science, typically based on machine learning and statistical modelling, and systems for reasoning with domain knowledge. In this paper we present a state-of-the-art Knowledge Graph Management System, Vadalog, which delivers highly expressive and efficient logical reasoning and provides seamless integration with modern data science toolkits, such as the Jupyter platform. We demonstrate how to use Vadalog to perform traditional data wrangling tasks, as well as complex logical and probabilistic reasoning. We argue that this is a significant step forward towards combining machine learning and reasoning in data science.
Round Compression for Parallel Graph Algorithms in Strongly Sublinear Space
Venue: arXiv cs.DS 2018
Year: 2018

The Massive Parallel Computation (MPC) model is a theoretical framework for popular parallel and distributed platforms such as MapReduce, Hadoop, or Spark. We consider the task of computing a large matching or small vertex cover in this model when the space per machine is $n^δ$ for $δ\in (0,1)$, where $n$ is the number of vertices in the input graph. A direct simulation of classic PRAM and distributed algorithms from the 1980s results in algorithms that require at least a logarithmic number of MPC rounds. We give the first algorithm that breaks this logarithmic barrier and runs in $\tilde O(\sqrt{\log n})$ rounds, as long as the total space is at least slightly superlinear in the number of vertices. The result is obtained by repeatedly compressing several rounds of a natural peeling algorithm to a logarithmically smaller number of MPC rounds. Each time we show that it suffices to consider a low-degree subgraph, in which local neighborhoods can be explored with exponential speedup. Our techniques are relatively simple and can also be used to accelerate the simulation of distributed algorithms for bounded-degree graphs and finding a maximal independent set in bounded-arboricity graphs.
Panchromatic Sharpening of Remote Sensing Images Using a Multi-scale Approach
Venue: arXiv cs.CV 2018
Year: 2018

An ideal fusion method preserves the Spectral information in fused image and adds spatial information to it with no spectral distortion. Recently wavelet kalman filter method is proposed which uses ARSIS concept to fuses MS and PAN images. This method is applied in a multiscale version, i.e. the variable index is scale instead of time. With the aim of fusion we present a more detailed study on this model and discuss about rationality of its assumptions such as first order markov model and Gaussian distribution of the posterior density. Finally, we propose a method using wavelet Kalman Particle filter to improve the spectral and spatial quality of the fused image. We show that our model is more consistent with natural MS and PAN images. Visual and statistical analyzes show that the proposed algorithm clearly improves the fusion quality in terms of: correlation coefficient, ERGAS, UIQI, and Q4; compared to other methods including IHS, HMP, PCA, A`trous, udWI, udWPC, Adaptive IHS, Improved Adaptive PCA and wavelet kalman filter.
Competitive Inner-Imaging Squeeze and Excitation for Residual Network
Venue: arXiv cs.CV 2018
Year: 2018

Residual networks, which use a residual unit to supplement the identity mappings, enable very deep convolutional architecture to operate well, however, the residual architecture has been proved to be diverse and redundant, which may leads to low-efficient modeling. In this work, we propose a competitive squeeze-excitation (SE) mechanism for the residual network. Re-scaling the value for each channel in this structure will be determined by the residual and identity mappings jointly, and this design enables us to expand the meaning of channel relationship modeling in residual blocks. Modeling of the competition between residual and identity mappings cause the identity flow to control the complement of the residual feature maps for itself. Furthermore, we design a novel inner-imaging competitive SE block to shrink the consumption and re-image the global features of intermediate network structure, by using the inner-imaging mechanism, we can model the channel-wise relations with convolution in spatial. We carry out experiments on the CIFAR, SVHN, and ImageNet datasets, and the proposed method can challenge state-of-the-art results.
Hyperspectral Images Classification Using Energy Profiles of Spatial and Spectral Features
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes a spatial feature extraction method based on energy of the features for classification of the hyperspectral data. A proposed orthogonal filter set extracts spatial features with maximum energy from the principal components and then, a profile is constructed based on these features. The important characteristic of the proposed approach is that the filter sets coefficients are extracted from statistical properties of data, thus they are more consistent with the type and texture of the remotely sensed images compared with those of other filters such as Gabor. To assess the performance of the proposed feature extraction method, the extracted features are fed into a support vector machine (SVM) classifier. Experiments on the widely used hyperspectral images namely, Indian Pines, and Salinas data sets reveal that the proposed approach improves the classification results in comparison with some recent spectral spatial classification methods.
Rule Based Metadata Extraction Framework from Academic Articles
Venue: arXiv cs.IR 2018
Year: 2018

Metadata of scientific articles such as title, abstract, keywords or index terms, body text, conclusion, reference and others play a decisive role in collecting, managing and storing academic data in scientific databases, academic journals and digital libraries. An accurate extraction of these kinds of data from scientific papers is crucial to organize and retrieve important scientific information for researchers as well as librarians. Research social network systems and academic digital library systems provide academic data extracting, organizing and retrieving services. Mostly these types of services are not free or open source. They also have some performance problems and extracting limitations in the number of PDF (Portable Document Format) files that you can upload to the extraction systems. In this paper, a completely free and open source Java based high performance metadata extraction framework is proposed. This frameworks extraction speed is 9-10 times faster than existing metadata extraction systems. It is also flexible in that it allows uploading of unlimited number of PDF files. In this approach, titles of papers are extracted using layout features, font and size characteristics of text. Other metadata fields such as abstracts, body text, keywords, conclusions and references are extracted from PDF files using fixed rule sets. Extracted metadata are stored in both Oracle database and XML (Extensible Markup Language) file. This framework can be used to make scientific collections in digital libraries, online journals, online and offline scientific databases, government research agencies and research centers.
Uncertainty Modelling in Deep Networks: Forecasting Short and Noisy Series
Venue: arXiv cs.LG 2018
Year: 2018

Deep Learning is a consolidated, state-of-the-art Machine Learning tool to fit a function when provided with large data sets of examples. However, in regression tasks, the straightforward application of Deep Learning models provides a point estimate of the target. In addition, the model does not take into account the uncertainty of a prediction. This represents a great limitation for tasks where communicating an erroneous prediction carries a risk. In this paper we tackle a real-world problem of forecasting impending financial expenses and incomings of customers, while displaying predictable monetary amounts on a mobile app. In this context, we investigate if we would obtain an advantage by applying Deep Learning models with a Heteroscedastic model of the variance of a network's output. Experimentally, we achieve a higher accuracy than non-trivial baselines. More importantly, we introduce a mechanism to discard low-confidence predictions, which means that they will not be visible to users. This should help enhance the user experience of our product.
360 virtual reality travel media for elderly
Venue: arXiv cs.HC 2018
Year: 2018

The objectives of this qualitative research were to study the model of 360-degree virtual reality travel media, to compare appropriateness of moving 360-degree virtual reality travel media for elderly with both still and moving cameras, and to study satisfaction of elderly in 360-degree virtual reality travel media. The informants are 10 elders with age above and equal to 60 years old who live in Bangkok regardless of genders. Data were collected through documents, detailed interview, and non-participant observation of elders to 360-degree virtual reality travel media with data triangulation. 1. From the literature review 1. The creation must primarily consider the target consumers on their physics 2. must have fluidity on changing the view of the camera by calibrating with the target consumers 3. The image displayed must not move too fast to prevent dizziness and improve the comfort of the target consumers. It is also highly recommended to implement a function to customize the movement rate for the customer. 2. From the in-depth interview with the target consumers, the results found that 1. They are worried and not used to the equipment 2. They have no idea where to look 3. They feel excited 5. They are interested in what is more to see 6. They feel like they did actually travel there 7. They can hear the sound clearly 8. They do not like when the camera is moving and find still camera more comfortable. 3. From the non-participant observation and found that they are always excited, laughed, and smiled when watching the media. They always asked where this is and why they cannot see anything when turning around.