OpenRaiser/Intern-Atlas huggingface dataset:
The EcoLexicon English Corpus as an open corpus in Sketch Engine
Venue: arXiv cs.CL 2018
Year: 2018

The EcoLexicon English Corpus (EEC) is a 23.1-million-word corpus of contemporary environmental texts. It was compiled by the LexiCon research group for the development of EcoLexicon (Faber, Leon-Arauz & Reimerink 2016; San Martin et al. 2017), a terminological knowledge base on the environment. It is available as an open corpus in the well-known corpus query system Sketch Engine (Kilgarriff et al. 2014), which means that any user, even without a subscription, can freely access and query the corpus. In this paper, the EEC is introduced by de- scribing how it was built and compiled and how it can be queried and exploited, based both on the functionalities provided by Sketch Engine and on the parameters in which the texts in the EEC are classified.
Human Perception of Surprise: A User Study
Venue: arXiv cs.IR 2018
Year: 2018

Understanding how to engage users is a critical question in many applications. Previous research has shown that unexpected or astonishing events can attract user attention, leading to positive outcomes such as engagement and learning. In this work, we investigate the similarity and differences in how people and algorithms rank the surprisingness of facts. Our crowdsourcing study, involving 106 participants, shows that computational models of surprise can be used to artificially induce surprise in humans.
Siamese Survival Analysis with Competing Risks
Venue: arXiv cs.LG 2018
Year: 2018

Survival analysis in the presence of multiple possible adverse events, i.e., competing risks, is a pervasive problem in many industries (healthcare, finance, etc.). Since only one event is typically observed, the incidence of an event of interest is often obscured by other related competing events. This nonidentifiability, or inability to estimate true cause-specific survival curves from empirical data, further complicates competing risk survival analysis. We introduce Siamese Survival Prognosis Network (SSPN), a novel deep learning architecture for estimating personalized risk scores in the presence of competing risks. SSPN circumvents the nonidentifiability problem by avoiding the estimation of cause-specific survival curves and instead determines pairwise concordant time-dependent risks, where longer event times are assigned lower risks. Furthermore, SSPN is able to directly optimize an approximation to the C-discrimination index, rather than relying on well-known metrics which are unable to capture the unique requirements of survival analysis with competing risks.
Evolving Differentiable Gene Regulatory Networks
Venue: arXiv cs.NE 2018
Year: 2018

Over the past twenty years, artificial Gene Regulatory Networks (GRNs) have shown their capacity to solve real-world problems in various domains such as agent control, signal processing and artificial life experiments. They have also benefited from new evolutionary approaches and improvements to dynamic which have increased their optimization efficiency. In this paper, we present an additional step toward their usability in machine learning applications. We detail an GPU-based implementation of differentiable GRNs, allowing for local optimization of GRN architectures with stochastic gradient descent (SGD). Using a standard machine learning dataset, we evaluate the ways in which evolution and SGD can be combined to further GRN optimization. We compare these approaches with neural network models trained by SGD and with support vector machines.
Zap: Making Predictions Based on Online User Behavior
Venue: arXiv cs.LG 2018
Year: 2018

This paper introduces Zap, a generic machine learning pipeline for making predictions based on online user behavior. Zap combines well known techniques for processing sequential data with more obscure techniques such as Bloom filters, bucketing, and model calibration into an end-to-end solution. The pipeline creates website- and task-specific models without knowing anything about the structure of the website. It is designed to minimize the amount of website-specific code, which is realized by factoring all website-specific logic into example generators. New example generators can typically be written up in a few lines of code.
On the Information Theoretic Distance Measures and Bidirectional Helmholtz Machines
Venue: arXiv cs.LG 2018
Year: 2018

By establishing a connection between bi-directional Helmholtz machines and information theory, we propose a generalized Helmholtz machine. Theoretical and experimental results show that given \textit{shallow} architectures, the generalized model outperforms the previous ones substantially.
Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays
Venue: arXiv cs.CV 2018
Year: 2018

Chest X-rays is one of the most commonly available and affordable radiological examinations in clinical practice. While detecting thoracic diseases on chest X-rays is still a challenging task for machine intelligence, due to 1) the highly varied appearance of lesion areas on X-rays from patients of different thoracic disease and 2) the shortage of accurate pixel-level annotations by radiologists for model training. Existing machine learning methods are unable to deal with the challenge that thoracic diseases usually happen in localized disease-specific areas. In this article, we propose a weakly supervised deep learning framework equipped with squeeze-and-excitation blocks, multi-map transfer, and max-min pooling for classifying thoracic diseases as well as localizing suspicious lesion regions. The comprehensive experiments and discussions are performed on the ChestX-ray14 dataset. Both numerical and visual results have demonstrated the effectiveness of the proposed model and its better performance against the state-of-the-art pipelines.
Performance Optimization of MapReduce-based Apriori Algorithm on Hadoop Cluster
Venue: arXiv cs.DC 2018
Year: 2018

Many techniques have been proposed to implement the Apriori algorithm on MapReduce framework but only a few have focused on performance improvement. FPC (Fixed Passes Combined-counting) and DPC (Dynamic Passes Combined-counting) algorithms combine multiple passes of Apriori in a single MapReduce phase to reduce the execution time. In this paper, we propose improved MapReduce based Apriori algorithms VFPC (Variable Size based Fixed Passes Combined-counting) and ETDPC (Elapsed Time based Dynamic Passes Combined-counting) over FPC and DPC. Further, we optimize the multi-pass phases of these algorithms by skipping pruning step in some passes, and propose Optimized-VFPC and Optimized-ETDPC algorithms. Quantitative analysis reveals that counting cost of additional un-pruned candidates produced due to skipped-pruning is less significant than reduction in computation cost due to the same. Experimental results show that VFPC and ETDPC are more robust and flexible than FPC and DPC whereas their optimized versions are more efficient in terms of execution time.
Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation
Venue: arXiv cs.LG 2018
Year: 2018

Training 3D object detectors for autonomous driving has been limited to small datasets due to the effort required to generate annotations. Reducing both task complexity and the amount of task switching done by annotators is key to reducing the effort and time required to generate 3D bounding box annotations. This paper introduces a novel ground truth generation method that combines human supervision with pretrained neural networks to generate per-instance 3D point cloud segmentation, 3D bounding boxes, and class annotations. The annotators provide object anchor clicks which behave as a seed to generate instance segmentation results in 3D. The points belonging to each instance are then used to regress object centroids, bounding box dimensions, and object orientation. Our proposed annotation scheme requires 30x lower human annotation time. We use the KITTI 3D object detection dataset to evaluate the efficiency and the quality of our annotation scheme. We also test the the proposed scheme on previously unseen data from the Autonomoose self-driving vehicle to demonstrate generalization capabilities of the network.
Dynamic Visual Analytics for Elicitation Meetings with ELICA
Venue: arXiv cs.CY 2018
Year: 2018

Requirements elicitation can be very challenging in projects that require deep domain knowledge about the system at hand. As analysts have the full control over the elicitation process, their lack of knowledge about the system under study inhibits them from asking related questions and reduces the accuracy of requirements provided by stakeholders. We present ELICA, a generic interactive visual analytics tool to assist analysts during requirements elicitation process. ELICA uses a novel information extraction algorithm based on a combination of Weighted Finite State Transducers (WFSTs) (generative model) and SVMs (discriminative model). ELICA presents the extracted relevant information in an interactive GUI (including zooming, panning, and pinching) that allows analysts to explore which parts of the ongoing conversation (or specification document) match with the extracted information. In this demonstration, we show that ELICA is usable and effective in practice, and is able to extract the related information in real-time. We also demonstrate how carefully designed features in ELICA facilitate the interactive and dynamic process of information extraction.
Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency
Venue: arXiv cs.CY 2018
Year: 2018

Due to its significant contribution to global energy usage and the associated greenhouse gas emissions, existing building stock's energy efficiency must improve. Predictive building control promises to contribute to that by increasing the efficiency of building operations. Predictive control complements other means to increase performance such as refurbishments as well as modernizations of systems. This survey reviews recent works and contextualizes these with the current state of the art of interrelated topics in data handling, building automation, distributed control, and semantics. The comprehensive overview leads to seven research questions guiding future research directions.
Experimental Resilience Assessment of An Open-Source Driving Agent
Venue: arXiv cs.SY 2018
Year: 2018

Autonomous vehicles (AV) depend on the sensors like RADAR and camera for the perception of the environment, path planning, and control. With the increasing autonomy and interactions with the complex environment, there have been growing concerns regarding the safety and reliability of AVs. This paper presents a Systems-Theoretic Process Analysis (STPA) based fault injection framework to assess the resilience of an open-source driving agent, called openpilot, under different environmental conditions and faults affecting sensor data. To increase the coverage of unsafe scenarios during testing, we use a strategic software fault-injection approach where the triggers for injecting the faults are derived from the unsafe scenarios identified during the high-level hazard analysis of the system. The experimental results show that the proposed strategic fault injection approach increases the hazard coverage compared to random fault injection and, thus, can help with more effective simulation of safety-critical faults and testing of AVs. In addition, the paper provides insights on the performance of openpilot safety mechanisms and its ability in timely detection and recovery from faulty inputs.
Container-based Cluster Orchestration Systems: A Taxonomy and Future Directions
Venue: arXiv cs.DC 2018
Year: 2018

Containers, enabling lightweight environment and performance isolation, fast and flexible deployment, and fine-grained resource sharing, have gained popularity in better application management and deployment in addition to hardware virtualization. They are being widely used by organizations to deploy their increasingly diverse workloads derived from modern-day applications such as web services, big data, and IoT in either proprietary clusters or private and public cloud data centers. This has led to the emergence of container orchestration platforms, which are designed to manage the deployment of containerized applications in large-scale clusters. These systems are capable of running hundreds of thousands of jobs across thousands of machines. To do so efficiently, they must address several important challenges including scalability, fault-tolerance and availability, efficient resource utilization, and request throughput maximization among others. This paper studies these management systems and proposes a taxonomy that identifies different mechanisms that can be used to meet the aforementioned challenges. The proposed classification is then applied to various state-of-the-art systems leading to the identification of open research challenges and gaps in the literature intended as future directions for researchers.
Photo-unrealistic Image Enhancement for Subject Placement in Outdoor Photography
Venue: arXiv cs.CV 2018
Year: 2018

Camera display reflections are an issue in bright light situations, as they may prevent users from correctly positioning the subject in the picture. We propose a software solution to this problem, which consists in modifying the image in the viewer, in real time. In our solution, the user is seeing a posterized image which roughly represents the contour of the objects. Five enhancement methods are compared in a user study. Our results indicate that the problem considered is a valid one, as users had problems locating landmarks nearly 37% of the time under sunny conditions, and that our proposed enhancement method using contrasting colors is a practical solution to that problem.
Bench-Marking Information Extraction in Semi-Structured Historical Handwritten Records
Venue: arXiv cs.CV 2018
Year: 2018

In this report, we present our findings from benchmarking experiments for information extraction on historical handwritten marriage records Esposalles from IEHHR - ICDAR 2017 robust reading competition. The information extraction is modeled as semantic labeling of the sequence across 2 set of labels. This can be achieved by sequentially or jointly applying handwritten text recognition (HTR) and named entity recognition (NER). We deploy a pipeline approach where first we use state-of-the-art HTR and use its output as input for NER. We show that given low resource setup and simple structure of the records, high performance of HTR ensures overall high performance. We explore the various configurations of conditional random fields and neural networks to benchmark NER on given certain noisy input. The best model on 10-fold cross-validation as well as blind test data uses n-gram features with bidirectional long short-term memory.
Generation of planar tensegrity structures through cellular multiplication
Venue: arXiv cs.CE 2018
Year: 2018

Tensegrity structures are frameworks in a stable self-equilibrated prestress state that have been applied in various fields in science and engineering. Research into tensegrity structures has resulted in reliable techniques for their form finding and analysis. However, most techniques address topology and form separately. This paper presents a bio-inspired approach for the combined topology identification and form finding of planar tensegrity structures. Tensegrity structures are generated using tensegrity cells (elementary stable self-stressed units that have been proven to compose any tensegrity structure) according to two multiplication mechanisms: cellular adhesion and fusion. Changes in the dimension of the self-stress space of the structure are found to depend on the number of adhesion and fusion steps conducted as well as on the interaction among the cells composing the system. A methodology for defining a basis of the self-stress space is also provided. Through the definition of the equilibrium shape, the number of nodes and members as well as the number of self-stress states, the cellular multiplication method can integrate design considerations, providing great flexibility and control over the tensegrity structure designed and opening the door to the development of a whole new realm of planar tensegrity systems with controllable characteristics.
Linear Algebra and Number of Spanning Trees
Venue: arXiv cs.DM 2018
Year: 2018

A network-theoretic approach for determining the complexity of a graph is proposed. This approach is based on the relationship between the linear algebra (theory of determinants) and the graph theory. In this paper we contribute a new algebraic method to derive simple formulas of the complexity of some new networks using linear algebra. We apply this method to derive the explicit formulas for the friendship network and the subdivision of friendship graph . We also calculate their spanning trees entropy and compare it between them. Finally, we introduce an open problem "Any improvement for calculating of the determinant in linear algebra, we can investigate this improvement as a new method to determine the number of spanning tree for a given graph.
DLA: Compiler and FPGA Overlay for Neural Network Inference Acceleration
Venue: arXiv cs.DC 2018
Year: 2018

Overlays have shown significant promise for field-programmable gate-arrays (FPGAs) as they allow for fast development cycles and remove many of the challenges of the traditional FPGA hardware design flow. However, this often comes with a significant performance burden resulting in very little adoption of overlays for practical applications. In this paper, we tailor an overlay to a specific application domain, and we show how we maintain its full programmability without paying for the performance overhead traditionally associated with overlays. Specifically, we introduce an overlay targeted for deep neural network inference with only ~1% overhead to support the control and reprogramming logic using a lightweight very-long instruction word (VLIW) network. Additionally, we implement a sophisticated domain specific graph compiler that compiles deep learning languages such as Caffe or Tensorflow to easily target our overlay. We show how our graph compiler performs architecture-driven software optimizations to significantly boost performance of both convolutional and recurrent neural networks (CNNs/RNNs) - we demonstrate a 3x improvement on ResNet-101 and a 12x improvement for long short-term memory (LSTM) cells, compared to naive implementations. Finally, we describe how we can tailor our hardware overlay, and use our graph compiler to achieve ~900 fps on GoogLeNet on an Intel Arria 10 1150 - the fastest ever reported on comparable FPGAs.
Resource Allocation for Secure Gaussian Parallel Relay Channels with Finite-Length Coding and Discrete Constellations
Venue: arXiv cs.IT 2018
Year: 2018

We investigate the transmission of a secret message from Alice to Bob in the presence of an eavesdropper (Eve) and many of decode-and-forward relay nodes. Each link comprises a set of parallel channels, modeling for example an orthogonal frequency division multiplexing transmission. We consider the impact of discrete constellations and finite-length coding, defining an achievable secrecy rate under a constraint on the equivocation rate at Eve. Then we propose a power and channel allocation algorithm that maximizes the achievable secrecy rate by resorting to two coupled Gale-Shapley algorithms for stable matching problem. We consider the scenarios of both full and partial channel state information at Alice. In the latter case, we only guarantee an outage secrecy rate, i.e., the rate of a message that remains secret with a given probability. Numerical results are provided for Rayleigh fading channels in terms of average outage secrecy rate, showing that practical schemes achieve a performance quite close to that of ideal ones.
Global sensitivity analysis of frequency band gaps in one-dimensional phononic crystals
Venue: arXiv cs.CE 2018
Year: 2018

Phononic crystals have been widely employed in many engineering fields, which is due to their unique feature of frequency band gaps. For example, their capability to filter out the incoming elastic waves, which include seismic waves, will have a significant impact on the seismic safety of nuclear infrastructure. In order to accurately design the desired frequency band gaps, one must pay attention on how the input parameters and the interaction of the parameters can affect the frequency band gaps. Global sensitivity analysis can decompose the dispersion relationship of the phononic crystals and screen the variance attributed to each of the parameters and the interaction between them. Prior to the application in one-dimensional (1D) phononic crystals, this paper will first review the theory of global sensitivity analysis using variance decomposition (Sobol sensitivity analysis). Afterwards, the sensitivity analysis is applied to study a simple mathematical model with three input variables for better understanding of the concept. Then, the sensitivity analysis is utilized to study the characteristic of the first frequency band gap in 1D phononic crystals with respect to the input parameters. This study reveals the quantified influence of the parameters and their correlation in determining the first frequency band gap. In addition, simple straight-forward design equations based on reduced Sobol functions are proposed to easily estimate the first frequency band gap. Finally, the error associated with the proposed design equations is also addressed.
Is Uncertainty Always Bad?: Effect of Topic Competence on Uncertain Opinions
Venue: arXiv cs.SI 2018
Year: 2018

The proliferation of information disseminated by public/social media has made decision-making highly challenging due to the wide availability of noisy, uncertain, or unverified information. Although the issue of uncertainty in information has been studied for several decades, little work has investigated how noisy (or uncertain) or valuable (or credible) information can be formulated into people's opinions, modeling uncertainty both in the quantity and quality of evidence leading to a specific opinion. In this work, we model and analyze an opinion and information model by using Subjective Logic where the initial set of evidence is mixed with different types of evidence (i.e., pro vs. con or noisy vs. valuable) which is incorporated into the opinions of original propagators, who propagate information over a network. With the help of an extensive simulation study, we examine how the different ratios of information types or agents' prior belief or topic competence affect the overall information diffusion. Based on our findings, agents' high uncertainty is not necessarily always bad in making a right decision as long as they are competent enough not to be at least biased towards false information (e.g., neutral between two extremes).
Icing on the Cake: An Easy and Quick Post-Learnig Method You Can Try After Deep Learning
Venue: arXiv cs.LG 2018
Year: 2018

We found an easy and quick post-learning method named "Icing on the Cake" to enhance a classification performance in deep learning. The method is that we train only the final classifier again after an ordinary training is done.