OpenRaiser/Intern-Atlas huggingface dataset:
Who benefits from a country's scientific research?
Venue: arXiv cs.DL 2018
Year: 2018

When a publication is cited it generates a benefit. Through the country affiliations of the citing authors, it is possible to work upwards, tracing the countries that benefit from results produced in a national research system. In this work we take the knowledge flow from Italy as an example. We develop a methodology for examination of how the knowledge flows vary across fields, in each beneficiary country. We also measure the field comparative advantage of countries in benefiting from Italian research. The results from this method can inform bilateral research collaboration policies.
The relationship among research productivity, research collaboration, and their determinants
Venue: arXiv cs.DL 2018
Year: 2018

This work provides an in-depth analysis of the relation between the different types of collaboration and research productivity, showing how both are influenced by some personal and organizational variables. By applying different cross-lagged panel models, we are able to analyze the relationship among research productivity, collaboration and their determinants. In particular, we show that only collaboration at intramural and domestic level has a positive effect on research productivity. Differently, all the forms of collaboration are positively affected by research productivity. The results can favor the reexamination of the theories related to these issues, and inform policies that would be more suited to their management.
Does your surname affect the citability of your publications?
Venue: arXiv cs.DL 2018
Year: 2018

Prior investigations have offered contrasting results on a troubling question: whether the alphabetical ordering of bylines confers citation advantages on those authors whose surnames put them first in the list. The previous studies analyzed the surname effect at publication level, i.e. whether papers with the first author early in the alphabet trigger more citations than papers with a first author late in the alphabet. We adopt instead a different approach, by analyzing the surname effect on citability at the individual level, i.e. whether authors with alphabetically earlier surnames result as being more cited. Examining the question at both the overall and discipline levels, the analysis finds no evidence whatsoever that alphabetically earlier surnames gain advantage. The same lack of evidence occurs for the subpopulation of scientists with very high publication rates, where alphabetical advantage might gain more ground. The field of observation consists of 14,467 scientists in the sciences.
A farewell to the MNCS and like size-independent indicators
Venue: arXiv cs.DL 2018
Year: 2018

The arguments presented demonstrate that the Mean Normalized Citation Score (MNCS) and other size-independent indicators based on the ratio to publications are not indicators of research performance. The article provides examples of the distortions when rankings by MNCS are compared to those based on indicators of productivity. The authors propose recommendations for the scientometric community to switch to ranking by research efficiency, instead of MNCS and other size-independent indicators.
Visibly Pushdown Languages and Free Profinite Algebras
Venue: arXiv cs.FL 2018
Year: 2018

We build a notion of algebraic recognition for visibly pushdown languages by finite algebraic objects. These come with a typical Eilenberg relationship, now between classes of visibly pushdown languages and classes of finite algebras. Building on that algebraic foundation, we further construct a topological object with one purpose being the possibility to derive a notion of equations, through which it is possible to prove that some given visibly pushdown language is not part of a certain class (or to even show decidability of the membership-problem of the class in some cases). In particular, we obtain a special instance of Reiterman's theorem for pseudo-varieties. These findings are then employed on two subclasses of the visibly pushdown languages, for which we derive concrete sets of equations. For some showcase languages, these equations are utilised to prove non-membership to the previously described classes.
DeepHTTP: Semantics-Structure Model with Attention for Anomalous HTTP Traffic Detection and Pattern Mining
Venue: arXiv cs.CR 2018
Year: 2018

In the Internet age, cyber-attacks occur frequently with complex types. Traffic generated by access activities can record website status and user request information, which brings a great opportunity for network attack detection. Among diverse network protocols, Hypertext Transfer Protocol (HTTP) is widely used in government, organizations and enterprises. In this work, we propose DeepHTTP, a semantics structure integration model utilizing Bidirectional Long Short-Term Memory (Bi-LSTM) with attention mechanism to model HTTP traffic as a natural language sequence. In addition to extracting traffic content information, we integrate structural information to enhance the generalization capabilities of the model. Moreover, the application of attention mechanism can assist in discovering critical parts of anomalous traffic and further mining attack patterns. Additionally, we demonstrate how to incrementally update the data set and retrain model so that it can be adapted to new anomalous traffic. Extensive experimental evaluations over large traffic data have illustrated that DeepHTTP has outstanding performance in traffic detection and pattern discovery.
Early Routability Assessment in VLSI Floorplans: A Generalized Routing Model
Venue: arXiv cs.OH 2018
Year: 2018

Multiple design iterations are inevitable in nanometer Integrated Circuit (IC) design flow until desired printability and performance metrics are achieved. This starts with placement optimization aimed at improving routability, wirelength, congestion and timing in the design. Contrarily, no such practice exists on a floorplanned layout, during the early stage of the design flow. Recently, STAIRoute \cite{karb2} aimed to address that by identifying the shortest routing path of a net through a set of routing regions in the floorplan in multiple metal layers. Since the blocks in hierarchical ASIC/SoC designs do not use all the permissible routing layers for the internal routing corresponding to standard cell connectivity, the proposed STAIRoute framework is not an effective for early global routability assessment. This leads to improper utilization of routing area, specifically in higher routing layers with fewer routing blockages, as the lack of placement of standard cells does not facilitates any routing of their interconnections. This paper presents a generalized model for early global routability assessment, HGR, by utilizing the free regions over the blocks beyond certain metal layers. The proposed (hybrid) routing model comprises of (a) the junction graph model in STAIRoute routing through the block boundary regions in lower routing layers, and (ii) the grid graph model for routing in higher layers over the free regions of the blocks. Experiment with the latest floorplanning benchmarks exhibit an average reduction of $4\%$, $54\%$ and $70\%$ in netlength, via count, and congestion respectively when HGR is used over STAIRoute. Further, we conducted another experiment on an industrial design flow targeted for $45nm$ process, and the results are encouraging with $~3$X runtime boost when early global routing is used in conjunction with the existing physical design flow.
Mean-square Stabilizability via Output Feedback for Non-minimum Phase Networked Feedback Systems
Venue: arXiv cs.SY 2018
Year: 2018

This work studies mean-square stabilizability via output feedback for a networked linear time invariant (LTI) feedback system with a non-minimum phase plant. In the feedback system, the control signals are transmitted to the plant over a set of parallel communication channels with possible packet dropout. Our goal is to analytically describe intrinsic constraints among channel packet dropout probabilities and the plant's characteristics, such as unstable poles, non-minimum phase zeros in the mean-square stabilizability of the system. It turns out that this is a very hard problem. Here, we focus on the case in which the plant has relative degree one and each non-minimum zero of the plant is only associated with one of control input channels. Then, the admissible region of packet dropout probabilities in the mean-square stabilizability of the system is obtained. Moreover, a set of hyper-rectangles in this region is presented in terms of the plant's non-minimum phase zeros, unstable poles and Wonham decomposition forms which is related to the structure of controllable subspace of the plant. When the non-minimum phase zeros are void, it is found that the supremum of packet dropout probabilities' product in the admissible region is determined by the product of plant's unstable poles only. A numerical example is presented to illustrate the fundamental constraints in the mean-square stabilizability of the networked system.
Stress-Testing Neural Models of Natural Language Inference with Multiply-Quantified Sentences
Venue: arXiv cs.CL 2018
Year: 2018

Standard evaluations of deep learning models for semantics using naturalistic corpora are limited in what they can tell us about the fidelity of the learned representations, because the corpora rarely come with good measures of semantic complexity. To overcome this limitation, we present a method for generating data sets of multiply-quantified natural language inference (NLI) examples in which semantic complexity can be precisely characterized, and we use this method to show that a variety of common architectures for NLI inevitably fail to encode crucial information; only a model with forced lexical alignments avoids this damaging information loss.
A Large-scale Study of Social Media Sources in News Articles
Venue: arXiv cs.SI 2018
Year: 2018

In this study, we closely look at the use of social media contents as source or reference in the U.S. news media. Specifically, we examine about 60 thousand news articles published within the 5 years period of 2013-2017 by 153 U.S. media outlets and analyze use of social media content as source compared to other sources. We designed a social media source extraction algorithm and investigated the extent and nature of social media source usage across different news topics. Our results show that uses of social media content in news almost doubled in five years. Unreliable media outlets rely on social media more than the mainstream media. Both mainstream and unreliable sites prefer Twitter to Facebook as a source of information.
Multirobot Coordination with Counting Temporal Logics
Venue: arXiv cs.RO 2018
Year: 2018

In many multirobot applications, planning trajectories in a way to guarantee that the collective behavior of the robots satisfies a certain high-level specification is crucial. Motivated by this problem, we introduce counting temporal logics---formal languages that enable concise expression of multirobot task specifications over possibly infinite horizons. We first introduce a general logic called counting linear temporal logic plus (cLTL+), and propose an optimization-based method that generates individual trajectories such that satisfaction of a given cLTL+ formula is guaranteed when these trajectories are synchronously executed. We then introduce a fragment of cLTL+, called counting linear temporal logic (cLTL), and show that a solution to planning problem with cLTL constraints can be obtained more efficiently if all robots have identical dynamics. In the second part of the paper, we relax the synchrony assumption and discuss how to generate trajectories that can be asynchronously executed, while preserving the satisfaction of the desired cLTL+ specification. In particular, we show that when the asynchrony between robots is bounded, the method presented in this paper can be modified to generate robust trajectories. We demonstrate these ideas with an experiment and provide numerical results that showcase the scalability of the method.
Attention-based sequence-to-sequence model for speech recognition: development of state-of-the-art system on LibriSpeech and its application to non-native English
Venue: arXiv cs.CL 2018
Year: 2018

Recent research has shown that attention-based sequence-to-sequence models such as Listen, Attend, and Spell (LAS) yield comparable results to state-of-the-art ASR systems on various tasks. In this paper, we describe the development of such a system and demonstrate its performance on two tasks: first we achieve a new state-of-the-art word error rate of 3.43% on the test clean subset of LibriSpeech English data; second on non-native English speech, including both read speech and spontaneous speech, we obtain very competitive results compared to a conventional system built with the most updated Kaldi recipe.
Query Adaptive Late Fusion for Image Retrieval
Venue: arXiv cs.IR 2018
Year: 2018

Feature fusion is a commonly used strategy in image retrieval tasks, which aggregates the matching responses of multiple visual features. Feasible sets of features can be either descriptors (SIFT, HSV) for an entire image or the same descriptor for different local parts (face, body). Ideally, the to-be-fused heterogeneous features are pre-assumed to be discriminative and complementary to each other. However, the effectiveness of different features varies dramatically according to different queries. That is to say, for some queries, a feature may be neither discriminative nor complementary to existing ones, while for other queries, the feature suffices. As a result, it is important to estimate the effectiveness of features in a query-adaptive manner. To this end, this article proposes a new late fusion scheme at the score level. We base our method on the observation that the sorted score curves contain patterns that describe their effectiveness. For example, an "L"-shaped curve indicates that the feature is discriminative while a gradually descending curve suggests a bad feature. As such, this paper introduces a query-adaptive late fusion pipeline. In the hand-crafted version, it can be an unsupervised approach to tasks like particular object retrieval. In the learning version, it can also be applied to supervised tasks like person recognition and pedestrian retrieval, based on a trainable neural module. Extensive experiments are conducted on two object retrieval datasets and one person recognition dataset. We show that our method is able to highlight the good features and suppress the bad ones, is resilient to distractor features, and achieves very competitive retrieval accuracy compared with the state of the art. In an additional person re-identification dataset, the application scope and limitation of the proposed method are studied.
Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule
Venue: arXiv cs.DL 2018
Year: 2018

In this paper, a likelihood based evidence acquisition approach is proposed to acquire evidence from experts'assessments as recorded in historical datasets. Then a data-driven evidential reasoning rule based model is introduced to R&D project selection process by combining multiple pieces of evidence with different weights and reliabilities. As a result, the total belief degrees and the overall performance can be generated for ranking and selecting projects. Finally, a case study on the R&D project selection for the National Science Foundation of China is conducted to show the effectiveness of the proposed model. The data-driven evidential reasoning rule based model for project evaluation and selection (1) utilizes experimental data to represent experts' assessments by using belief distributions over the set of final funding outcomes, and through this historic statistics it helps experts and applicants to understand the funding probability to a given assessment grade, (2) implies the mapping relationships between the evaluation grades and the final funding outcomes by using historical data, and (3) provides a way to make fair decisions by taking experts' reliabilities into account. In the data-driven evidential reasoning rule based model, experts play different roles in accordance with their reliabilities which are determined by their previous review track records, and the selection process is made interpretable and fairer. The newly proposed model reduces the time-consuming panel review work for both managers and experts, and significantly improves the efficiency and quality of project selection process. Although the model is demonstrated for project selection in the NSFC, it can be generalized to other funding agencies or industries.
Tilt estimator for 3D non-rigid pendulum based on a tri-axial accelerometer and gyrometer
Venue: arXiv cs.RO 2018
Year: 2018

The paper presents a new observer for tilt estimation of a 3-D non-rigid pendulum. The system can be seen as a multibody robot attached to the environment with a ball joint. There is no sensor for the joint position of the sensor. The estimation of tilt, i.e. roll and pitch angles, is mandatory for balance control for a humanoid robot and all tasks requiring verticality. Our method obtains tilt estimations using encoders on other joints and inertial measurements given by an IMU equipped with tri-axial accelerometer and gyrometer mounted in any body of the robot. The estimator takes profit from the kinematic coupling resulting from the pivot constraint and uses the entire signal of accelerometer including linear accelerations. Almost Global Asymptotic convergence of the estimation errors is proven together with local exponential stability. The performance of the proposed observer is illustrated by simulations.
Face Presentation Attack Detection in Learned Color-liked Space
Venue: arXiv cs.CV 2018
Year: 2018

Face presentation attack detection (PAD) has become a thorny problem for biometric systems and numerous countermeasures have been proposed to address it. However, majority of them directly extract feature descriptors and distinguish fake faces from the real ones in existing color spaces (e.g. RGB, HSV and YCbCr). Unfortunately, it is unknown for us which color space is the best or how to combine different spaces together. To make matters worse, the real and fake faces are overlapped in existing color spaces. So, in this paper, a learned distinguishable color-liked space is generated to deal with the problem of face PAD. More specifically, we present an end-to-end deep learning network that can map existing color spaces to a new learned color-liked space. Inspired by the generator of generative adversarial network (GAN), the proposed network consists of a space generator and a feature extractor. When training the color-liked space, a new triplet combination mechanism of points-to-center is explored to maximize interclass distance and minimize intraclass distance, and also keep a safe margin between the real and presented fake faces. Extensive experiments on two standard face PAD databases, i.e., Relay-Attack and OULU-NPU, indicate that our proposed color-liked space analysis based countermeasure significantly outperforms the state-of-the-art methods and show excellent generalization capability.
Nearly-tight bounds on linear regions of piecewise linear neural networks
Venue: arXiv cs.LG 2018
Year: 2018

The developments of deep neural networks (DNN) in recent years have ushered a brand new era of artificial intelligence. DNNs are proved to be excellent in solving very complex problems, e.g., visual recognition and text understanding, to the extent of competing with or even surpassing people. Despite inspiring and encouraging success of DNNs, thorough theoretical analyses still lack to unravel the mystery of their magics. The design of DNN structure is dominated by empirical results in terms of network depth, number of neurons and activations. A few of remarkable works published recently in an attempt to interpret DNNs have established the first glimpses of their internal mechanisms. Nevertheless, research on exploring how DNNs operate is still at the initial stage with plenty of room for refinement. In this paper, we extend precedent research on neural networks with piecewise linear activations (PLNN) concerning linear regions bounds. We present (i) the exact maximal number of linear regions for single layer PLNNs; (ii) a upper bound for multi-layer PLNNs; and (iii) a tighter upper bound for the maximal number of liner regions on rectifier networks. The derived bounds also indirectly explain why deep models are more powerful than shallow counterparts, and how non-linearity of activation functions impacts on expressiveness of networks.
Multi-Task Learning for Left Atrial Segmentation on GE-MRI
Venue: arXiv cs.CV 2018
Year: 2018

Segmentation of the left atrium (LA) is crucial for assessing its anatomy in both pre-operative atrial fibrillation (AF) ablation planning and post-operative follow-up studies. In this paper, we present a fully automated framework for left atrial segmentation in gadolinium-enhanced magnetic resonance images (GE-MRI) based on deep learning. We propose a fully convolutional neural network and explore the benefits of multi-task learning for performing both atrial segmentation and pre/post ablation classification. Our results show that, by sharing features between related tasks, the network can gain additional anatomical information and achieve more accurate atrial segmentation, leading to a mean Dice score of 0.901 on a test set of 20 3D MRI images. Code of our proposed algorithm is available at https://github.com/cherise215/atria_segmentation_2018/.
Methods for Segmentation and Classification of Digital Microscopy Tissue Images
Venue: arXiv cs.CV 2018
Year: 2018

High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help cancer researchers develop a better understanding of cancer biology. Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis. Development of accurate and efficient algorithms for these tasks is a challenging problem because of the complexity of tissue morphology and tumor heterogeneity. In this paper we present two computer algorithms; one designed for segmentation of nuclei and the other for classification of whole slide tissue images. The segmentation algorithm implements a multiscale deep residual aggregation network to accurately segment nuclear material and then separate clumped nuclei into individual nuclei. The classification algorithm initially carries out patch-level classification via a deep learning method, then patch-level statistical and morphological features are used as input to a random forest regression model for whole slide image classification. The segmentation and classification algorithms were evaluated in the MICCAI 2017 Digital Pathology challenge. The segmentation algorithm achieved an accuracy score of 0.78. The classification algorithm achieved an accuracy score of 0.81.