OpenRaiser/Intern-Atlas huggingface dataset:
What indicators matter? The Analysis of Perception toward Research Assessment Indicators and Leiden Manifesto- The Case Study of Taiwan
Venue: arXiv cs.DL 2018
Year: 2018

This study aims to investigate the Taiwanese researchers' awareness toward bibliometric indicators and the principles from Leiden Manifesto. The online survey was conducted and obtained a total of 417 valid responses. The results show that evoking the right concept of use of bibliometric indicators and research evaluation has a long way to go. The lack of recognition of bibliometric indicators exists in Taiwanese academia. Generally speaking, researchers may hear of the certain indicator, but they are not familiar with its definition and calculation process. Only JIF and h-index are considered as well-known indicators. The results also suggest that the ten principles from Leiden Manifesto can be considered as the universal guideline in research evaluation since most of Taiwanese researchers agree the contents. Especially for the principle 6 "Account for variation by field in publication and citation practices" has highest degree of agreement. However, it is interesting to compare the result of recognition of relative citation ratio, only few researchers have fully understood the definition. This result indicates the scientometricians should make more effort to disseminate the concept of field-normalization in bibliometric indicators. The researchers do have understanding about the importance of comparison on the same basis, at the meantime, they may use the inappropriate indicators just because of lacking of enough knowledge on the variety of indicators. Hence, it is important to initiate the education of informetrics to all of the stakeholders in research evaluation so that the misuse and abuse of bibliometric indicators may possibly not happen again, and the bibliometric analysis is able to turn to contextualization-based analysis in the future.
Development of a Social Network for Research Support and Individual Well-being Improvement
Venue: arXiv cs.SI 2018
Year: 2018

The ways of communication and social interactions are changing. Web users are becoming increasingly engaged with Online Social Networks (OSN), which has a significant impact on the relationship mechanisms between individuals and communities. Most OSN platforms have strict policies regarding data access, harming its usage in psychological and social phenomena studies, It is also impacting the development of computational methods to evaluate and improve social and individual well-being via the web. Aiming to fill this gap, we propose a platform that brings together social networks dynamics with forum features, altogether with gamification elements, targeting researchers interested in obtaining access to user's data to study psychological and social phenomena.
TextContourNet: a Flexible and Effective Framework for Improving Scene Text Detection Architecture with a Multi-task Cascade
Venue: arXiv cs.CV 2018
Year: 2018

We study the problem of extracting text instance contour information from images and use it to assist scene text detection. We propose a novel and effective framework for this and experimentally demonstrate that: (1) A CNN that can be effectively used to extract instance-level text contour from natural images. (2) The extracted contour information can be used for better scene text detection. We propose two ways for learning the contour task together with the scene text detection: (1) as an auxiliary task and (2) as multi-task cascade. Extensive experiments with different benchmark datasets demonstrate that both designs improve the performance of a state-of-the-art scene text detector and that a multi-task cascade design achieves the best performance.
The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure
Venue: arXiv cs.LG 2018
Year: 2018

Many modern machine learning classifiers are shown to be vulnerable to adversarial perturbations of the instances. Despite a massive amount of work focusing on making classifiers robust, the task seems quite challenging. In this work, through a theoretical study, we investigate the adversarial risk and robustness of classifiers and draw a connection to the well-known phenomenon of concentration of measure in metric measure spaces. We show that if the metric probability space of the test instance is concentrated, any classifier with some initial constant error is inherently vulnerable to adversarial perturbations. One class of concentrated metric probability spaces are the so-called Levy families that include many natural distributions. In this special case, our attacks only need to perturb the test instance by at most $O(\sqrt n)$ to make it misclassified, where $n$ is the data dimension. Using our general result about Levy instance spaces, we first recover as special case some of the previously proved results about the existence of adversarial examples. However, many more Levy families are known (e.g., product distribution under the Hamming distance) for which we immediately obtain new attacks that find adversarial examples of distance $O(\sqrt n)$. Finally, we show that concentration of measure for product spaces implies the existence of forms of "poisoning" attacks in which the adversary tampers with the training data with the goal of degrading the classifier. In particular, we show that for any learning algorithm that uses $m$ training examples, there is an adversary who can increase the probability of any "bad property" (e.g., failing on a particular test instance) that initially happens with non-negligible probability to $\approx 1$ by substituting only $\tilde{O}(\sqrt m)$ of the examples with other (still correctly labeled) examples.
Classical Proofs as Parallel Programs
Venue: arXiv cs.LO 2018
Year: 2018

We introduce a first proofs-as-parallel-programs correspondence for classical logic. We define a parallel and more powerful extension of the simply typed lambda calculus corresponding to an analytic natural deduction based on the excluded middle law. The resulting functional language features a natural higher-order communication mechanism between processes, which also supports broadcasting. The normalization procedure makes use of reductions that implement novel techniques for handling and transmitting process closures.
A Simplicial Complex Model for Dynamic Epistemic Logic to study Distributed Task Computability
Venue: arXiv cs.LO 2018
Year: 2018

The usual epistemic model S5n for a multi-agent system is based on a Kripke frame, which is a graph whose edges are labeled with agents that do not distinguish between two states. We propose to uncover the higher dimensional information implicit in this structure, by considering a dual, simplicial complex model. We use dynamic epistemic logic (DEL) to study how an epistemic simplicial complex model changes after a set of agents communicate with each other. We concentrate on an action model that represents the so called immediate snapshot communication patterns of asynchronous agents, because it is central to distributed computability (but our setting works for other communication patterns). There are topological invariants preserved from the initial epistemic complex to the one after the action model is applied, which determine the knowledge that the agents gain after communication. Finally, we describe how a distributed task specification can be modeled as a DEL action model, and show that the topological invariants determine whether the task is solvable. We thus provide a bridge between DEL and the topological theory of distributed computability, which studies task solvability in a shared memory or message passing architecture.
Temporal Logic and Model Checking for Operator Precedence Languages
Venue: arXiv cs.LO 2018
Year: 2018

In the last decades much research effort has been devoted to extending the success of model checking from the traditional field of finite state machines and various versions of temporal logics to suitable subclasses of context-free languages and appropriate extensions of temporal logics. To the best of our knowledge such attempts only covered structured languages, i.e. languages whose structure is immediately "visible" in their sentences, such as tree-languages or visibly pushdown ones. In this paper we present a new temporal logic suitable to express and automatically verify properties of operator precedence languages. This "historical" language family has been recently proved to enjoy fundamental algebraic and logic properties that make it suitable for model checking applications yet breaking the barrier of visible-structure languages (in fact the original motivation of its inventor Floyd was just to support efficient parsing, i.e. building the "hidden syntax tree" of language sentences). We prove that our logic is at least as expressive as analogous logics defined for visible pushdown languages yet covering a much more powerful family; we design a procedure that, given a formula in our logic builds an automaton recognizing the sentences satisfying the formula, whose size is at most exponential in the length of the formula.
Mixed-ADC/DAC Multipair Massive MIMO Relaying Systems: Performance Analysis and Power Optimization
Venue: arXiv cs.IT 2018
Year: 2018

High power consumption and expensive hardware are two bottlenecks for practical massive multiple-input multiple-output (mMIMO) systems. One promising solution is to employ low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). In this paper, we consider a general multipair mMIMO relaying system with a mixed-ADC/DAC architecture, in which some antennas are connected to low-resolution ADCs/DACs, while the rest of the antennas are connected to high-resolution ADCs/DACs. Leveraging on the additive quantization noise model, both exact and approximate closed-form expressions for the achievable rate are derived. It is shown that the achievable rate can approach the unquantized one by using only 2-3 bits of resolutions. Moreover, a power scaling law is presented to reveal that the transmit power can be scaled down inversely proportional to the number of antennas at the relay. We further propose an efficient power allocation scheme by solving a complementary geometric programming problem. In addition, a trade-off between the achievable rate and power consumption for different numbers of low-resolution ADCs/DACs is investigated by deriving the energy efficiency. Our results reveal that the large antenna array can be exploited to enable the mixed-ADC/DAC architecture, which significantly reduces the power consumption and hardware cost for practical mMIMO systems.
Towards one-shot learning for rare-word translation with external experts
Venue: arXiv cs.CL 2018
Year: 2018

Neural machine translation (NMT) has significantly improved the quality of automatic translation models. One of the main challenges in current systems is the translation of rare words. We present a generic approach to address this weakness by having external models annotate the training data as Experts, and control the model-expert interaction with a pointer network and reinforcement learning. Our experiments using phrase-based models to simulate Experts to complement neural machine translation models show that the model can be trained to copy the annotations into the output consistently. We demonstrate the benefit of our proposed framework in outof-domain translation scenarios with only lexical resources, improving more than 1.0 BLEU point in both translation directions English to Spanish and German to English
Intelligent flat-and-textureless object manipulation in Service Robots
Venue: arXiv cs.RO 2018
Year: 2018

This work introduces our approach to the flat and textureless object grasping problem. In particular, we address the tableware and cutlery manipulation problem where a service robot has to clean up a table. Our solution integrates colour and 2D and 3D geometry information to describe objects, and this information is given to the robot action planner to find the best grasping trajectory depending on the object class. Furthermore, we use visual feedback as a verification step to determine if the grasping process has successfully occurred. We evaluate our approach in both an open and a standard service robot platform following the RoboCup@Home international tournament regulations.
Multi-Context Deep Network for Angle-Closure Glaucoma Screening in Anterior Segment OCT
Venue: arXiv cs.CV 2018
Year: 2018

A major cause of irreversible visual impairment is angle-closure glaucoma, which can be screened through imagery from Anterior Segment Optical Coherence Tomography (AS-OCT). Previous computational diagnostic techniques address this screening problem by extracting specific clinical measurements or handcrafted visual features from the images for classification. In this paper, we instead propose to learn from training data a discriminative representation that may capture subtle visual cues not modeled by predefined features. Based on clinical priors, we formulate this learning with a presented Multi-Context Deep Network (MCDN) architecture, in which parallel Convolutional Neural Networks are applied to particular image regions and at corresponding scales known to be informative for clinically diagnosing angle-closure glaucoma. The output feature maps of the parallel streams are merged into a classification layer to produce the deep screening result. Moreover, we incorporate estimated clinical parameters to further enhance performance. On a clinical AS-OCT dataset, our system is validated through comparisons to previous screening methods.
Finding Better Topologies for Deep Convolutional Neural Networks by Evolution
Venue: arXiv cs.NE 2018
Year: 2018

Due to the nonlinearity of artificial neural networks, designing topologies for deep convolutional neural networks (CNN) is a challenging task and often only heuristic approach, such as trial and error, can be applied. An evolutionary algorithm can solve optimization problems where the fitness landscape is unknown. However, evolutionary algorithms are computing resource intensive, which makes it difficult for problems when deep CNNs are involved. In this paper, we propose an evolutionary strategy to find better topologies for deep CNNs. Incorporating the concept of knowledge inheritance and knowledge learning, our evolutionary algorithm can be executed with limited computing resources. We applied the proposed algorithm in finding effective topologies of deep CNNs for the image classification task using CIFAR-10 dataset. After the evolution, we analyzed the topologies that performed well for this task. Our studies verify the techniques that have been commonly used in human designed deep CNNs. We also discovered that some of the graph properties greatly affect the system performance. We applied the guidelines learned from the evolution and designed new network topologies that outperform Residual Net with less layers on CIFAR-10, CIFAR-100, and SVHN dataset.
Action-conditional Sequence Modeling for Recommendation
Venue: arXiv cs.IR 2018
Year: 2018

In many online applications interactions between a user and a web-service are organized in a sequential way, e.g., user browsing an e-commerce website. In this setting, recommendation system acts throughout user navigation by showing items. Previous works have addressed this recommendation setup through the task of predicting the next item user will interact with. In particular, Recurrent Neural Networks (RNNs) has been shown to achieve substantial improvements over collaborative filtering baselines. In this paper, we consider interactions triggered by the recommendations of deployed recommender system in addition to browsing behavior. Indeed, it is reported that in online services interactions with recommendations represent up to 30\% of total interactions. Moreover, in practice, recommender system can greatly influence user behavior by promoting specific items. In this paper, we extend the RNN modeling framework by taking into account user interaction with recommended items. We propose and evaluate RNN architectures that consist of the recommendation action module and the state-action fusion module. Using real-world large-scale datasets we demonstrate improved performance on the next item prediction task compared to the baselines.
Landslide Monitoring based on Terrestrial Laser Scanning: A Novel Semi-automatic Workflow
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a workflow that uses Terrestrial Laser Scanning(TLS) to semi-automatically monitor landslide and then test it in practice. Firstly, several groups of TLS stations are set on different time to collect the raw point cloud of the object mountain. Next, Hierarchical Merging Based Multi-view (HMMR) registration algorithm is adapted to accomplish single-phase multi-view registration.In order to analyze deformation between multiple periods, Iterative Global Similarity Point (IGSP) algorithm is applied to accomplish multiple-phase registration, which outperforms ICP in experiments. Then the cloth simulation filtering (CSF) algorithm was used together with manual post-processing to remove vegetation on the slope. After that, the mountain slope's digital terrain model (DTM) is generated for each period, and the distance between adjacent DTMs are calculated as the landslide deformation mass. Furthermore, average deformation rate of the landslide surface is calculated and analyzed.To validate the effectiveness of proposed workflow, we uses the TLS data of five periods of the landslide in the Shanhou village of northern Changshan Island from 2013 to 2015. The results indicate that the method can obtain centimeter-level deformation monitoring accuracy which can effectively monitor and analyze long-term landslide morphology and trend as well as position the significant deformation area and determine the type of landslide. In addition, the process can be automated to provide end-to-end TLS based long-term landslide monitoring applications, providing reference for monitoring and early warning of potential landslides.
Wideband Hybrid Precoding for Next-Generation Backhaul/Fronthaul Based on mmWave FD-MIMO
Venue: arXiv cs.IT 2018
Year: 2018

Millimeter-wave (mmWave) communication is considered as an indispensable technique for the next-generation backhaul/fronthaul network thanks to its large transmission bandwidth. Especially for heterogeneous network (HetNet), the mmWave full-dimension (FD)-MIMO is exploited to establish the backhaul/fronthaul link between phantom-cell base stations (BSs) and macro-cell BSs, where an efficient precoding is prerequisite. Against this background, this paper proposes a principle component analysis (PCA)-based hybrid precoding for wideband mmWave MIMO backhaul/fronthaul channels. We first propose an optimal hybrid precoder by exploiting principal component analysis (PCA), whereby the optimal high dimensional frequency-selective precoder are projected to the low-dimensional frequency-flat precoder. Moreover, the combiner is designed by leveraging the weighted PCA, where the covariance of received signal is taken into account as weight to the optimal minimum mean square error (MMSE) fully-digital combiner for further improved performance. Simulations have confirmed that the proposed scheme outperforms conventional schemes in spectral efficiency (SE) and bit-error-rate (BER) performance.
Identifying Relationships Among Sentences in Court Case Transcripts Using Discourse Relations
Venue: arXiv cs.CL 2018
Year: 2018

Case Law has a significant impact on the proceedings of legal cases. Therefore, the information that can be obtained from previous court cases is valuable to lawyers and other legal officials when performing their duties. This paper describes a methodology of applying discourse relations between sentences when processing text documents related to the legal domain. In this study, we developed a mechanism to classify the relationships that can be observed among sentences in transcripts of United States court cases. First, we defined relationship types that can be observed between sentences in court case transcripts. Then we classified pairs of sentences according to the relationship type by combining a machine learning model and a rule-based approach. The results obtained through our system were evaluated using human judges. To the best of our knowledge, this is the first study where discourse relationships between sentences have been used to determine relationships among sentences in legal court case transcripts.
The HyperKron Graph Model for higher-order features
Venue: arXiv cs.SI 2018
Year: 2018

Graph models have long been used in lieu of real data which can be expensive and hard to come by. A common class of models constructs a matrix of probabilities, and samples an adjacency matrix by flipping a weighted coin for each entry. Examples include the Erdős-Rényi model, Chung-Lu model, and the Kronecker model. Here we present the HyperKron Graph model: an extension of the Kronecker Model, but with a distribution over hyperedges. We prove that we can efficiently generate graphs from this model in order proportional to the number of edges times a small log-factor, and find that in practice the runtime is linear with respect to the number of edges. We illustrate a number of useful features of the HyperKron model including non-trivial clustering and highly skewed degree distributions. Finally, we fit the HyperKron model to real-world networks, and demonstrate the model's flexibility with a complex application of the HyperKron model to networks with coherent feed-forward loops.
Annotating shadows, highlights and faces: the contribution of a 'human in the loop' for digital art history
Venue: arXiv cs.CV 2018
Year: 2018

While automatic computational techniques appear to reveal novel insights in digital art history, a complementary approach seems to get less attention: that of human annotation. We argue and exemplify that a 'human in the loop' can reveal insights that may be difficult to detect automatically. Specifically, we focussed on perceptual aspects within pictorial art. Using rather simple annotation tasks (e.g. delineate human lengths, indicate highlights and classify gaze direction) we could both replicate earlier findings and reveal novel insights into pictorial conventions. We found that Canaletto depicted human figures in rather accurate perspective, varied viewpoint elevation between approximately 3 and 9 meters and highly preferred light directions parallel to the projection plane. Furthermore, we found that taking the averaged images of leftward looking faces reveals a woman, and for rightward looking faces showed a male, confirming earlier accounts on lateral gender bias in pictorial art. Lastly, we confirmed and refined the well-known light-from-the-left bias. Together, the annotations, analyses and results exemplify how human annotation can contribute and complement to technical and digital art history.