OpenRaiser/Intern-Atlas huggingface dataset:
Balancedness and coboundaries in symbolic systems
Venue: arXiv cs.DM 2018
Year: 2018

This paper studies balancedness for infinite words and subshifts, both for letters and factors. Balancedness is a measure of disorder that amounts to strong convergence properties for frequencies. It measures the difference between the numbers of occurrences of a given word in factors of the same length. We focus on two families of words, namely dendric words and words generated by substitutions. The family of dendric words includes Sturmian and Arnoux-Rauzy words, as well as codings of regular interval exchanges. We prove that dendric words are balanced on letters if and only if they are balanced on words. In the substitutive case, we stress the role played by the existence of coboundaries taking rational values and show simple criteria when frequencies take rational values for exhibiting imbalancedness.
What might matter in autonomous cars adoption: first person versus third person scenarios
Venue: arXiv cs.CY 2018
Year: 2018

The discussion between the automotive industry, governments, ethicists, policy makers and general public about autonomous cars' moral agency is widening, and therefore we see the need to bring more insight into what meta-factors might actually influence the outcomes of such discussions, surveys and plebiscites. In our study, we focus on the psychological (personality traits), practical (active driving experience), gender and rhetoric/framing factors that might impact and even determine respondents' a priori preferences of autonomous cars' operation. We conducted an online survey (N=430) to collect data that show that the third person scenario is less biased than the first person scenario when presenting ethical dilemma related to autonomous cars. According to our analysis, gender bias should be explored in more extensive future studies as well. We recommend any participatory technology assessment discourse to use the third person scenario and to direct attention to the way any autonomous car related debate is introduced, especially in terms of linguistic and communication aspects and gender.
Axiomatising Infinitary Probabilistic Weak Bisimilarity of Finite-State Behaviours
Venue: arXiv cs.LO 2018
Year: 2018

In concurrency theory, weak bisimilarity is often used to relate processes exhibiting the same observable behaviour. The probabilistic environment gives rise to several generalisations; we study the infinitary semantics, which abstracts from a potentially unbounded number of internal actions being performed when something observable happens. Arguing that this notion yields the most desirable properties, we provide a sound and complete axiomatisation capturing its essence. Previous research has failed to achieve completeness in the presence of unguarded recursion, as only the finitary variant has been axiomatised, yet.
Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences
Venue: arXiv cs.CV 2018
Year: 2018

Statistical models of the human body surface are generally learned from thousands of high-quality 3D scans in predefined poses to cover the wide variety of human body shapes and articulations. Acquisition of such data requires expensive equipment, calibration procedures, and is limited to cooperative subjects who can understand and follow instructions, such as adults. We present a method for learning a statistical 3D Skinned Multi-Infant Linear body model (SMIL) from incomplete, low-quality RGB-D sequences of freely moving infants. Quantitative experiments show that SMIL faithfully represents the RGB-D data and properly factorizes the shape and pose of the infants. To demonstrate the applicability of SMIL, we fit the model to RGB-D sequences of freely moving infants and show, with a case study, that our method captures enough motion detail for General Movements Assessment (GMA), a method used in clinical practice for early detection of neurodevelopmental disorders in infants. SMIL provides a new tool for analyzing infant shape and movement and is a step towards an automated system for GMA.
Algorithms and Fundamental Limits for Unlabeled Detection using Types
Venue: arXiv cs.IT 2018
Year: 2018

Emerging applications of sensor networks for detection sometimes suggest that classical problems ought be revisited under new assumptions. This is the case of binary hypothesis testing with independent - but not necessarily identically distributed - observations under the two hypotheses, a formalism so orthodox that it is used as an opening example in many detection classes. However, let us insert a new element, and address an issue perhaps with impact on strategies to deal with "big data" applications: What would happen if the structure were streamlined such that data flowed freely throughout the system without provenance? How much information (for detection) is contained in the sample values, and how much in their labels? How should decision-making proceed in this case? The theoretical contribution of this work is to answer these questions by establishing the fundamental limits, in terms of error exponents, of the aforementioned binary hypothesis test with unlabeled observations drawn from a finite alphabet. Then, we focus on practical algorithms. A low-complexity detector - called ULR - solves the detection problem without attempting to estimate the labels. A modified version of the auction algorithm is then considered, and two new greedy algorithms with ${\cal O}(n^2)$ worst-case complexity are presented, where $n$ is the number of observations. The detection operational characteristics of these detectors are investigated by computer experiments.
Payment Network Design with Fees
Venue: arXiv cs.DS 2018
Year: 2018

Payment channels are the most prominent solution to the blockchain scalability problem. We introduce the problem of network design with fees for payment channels from the perspective of a Payment Service Provider (PSP). Given a set of transactions, we examine the optimal graph structure and fee assignment to maximize the PSP's profit. A customer prefers to route transactions through the PSP's network if the cheapest path from sender to receiver is financially interesting, i.e., if the path costs less than the blockchain fee. When the graph structure is a tree, and the PSP facilitates all transactions, the problem can be formulated as a linear program. For a path graph, we present a polynomial time algorithm to assign optimal fees. We also show that the star network, where the center is an additional node acting as an intermediary, is a near-optimal solution to the network design problem.
Algorithmic Blockchain Channel Design
Venue: arXiv cs.DS 2018
Year: 2018

Payment networks, also known as channels, are a most promising solution to the throughput problem of cryptocurrencies. In this paper we study the design of capital-efficient payment networks, offline as well as online variants. We want to know how to compute an efficient payment network topology, how capital should be assigned to the individual edges, and how to decide which transactions to accept. Towards this end, we present a flurry of interesting results, basic but generally applicable insights on the one hand, and hardness results and approximation algorithms on the other hand.
Deep Diabetologist: Learning to Prescribe Hyperglycemia Medications with Hierarchical Recurrent Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

In healthcare, applying deep learning models to electronic health records (EHRs) has drawn considerable attention. EHR data consist of a sequence of medical visits, i.e. a multivariate time series of diagnosis, medications, physical examinations, lab tests, etc. This sequential nature makes EHR well matching the power of Recurrent Neural Network (RNN). In this paper, we propose "Deep Diabetologist" - using RNNs for EHR sequential data modelling, to provide the personalized hyperglycemia medication prediction for diabetic patients. Particularly, we develop a hierarchical RNN to capture the heterogeneous sequential information in the EHR data. Our experimental results demonstrate the improved performance, compared with a baseline classifier using logistic regression. Moreover, hierarchical RNN models outperform basic ones, providing deeper data insights for clinical decision support.
A Bi-layered Parallel Training Architecture for Large-scale Convolutional Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

Benefitting from large-scale training datasets and the complex training network, Convolutional Neural Networks (CNNs) are widely applied in various fields with high accuracy. However, the training process of CNNs is very time-consuming, where large amounts of training samples and iterative operations are required to obtain high-quality weight parameters. In this paper, we focus on the time-consuming training process of large-scale CNNs and propose a Bi-layered Parallel Training (BPT-CNN) architecture in distributed computing environments. BPT-CNN consists of two main components: (a) an outer-layer parallel training for multiple CNN subnetworks on separate data subsets, and (b) an inner-layer parallel training for each subnetwork. In the outer-layer parallelism, we address critical issues of distributed and parallel computing, including data communication, synchronization, and workload balance. A heterogeneous-aware Incremental Data Partitioning and Allocation (IDPA) strategy is proposed, where large-scale training datasets are partitioned and allocated to the computing nodes in batches according to their computing power. To minimize the synchronization waiting during the global weight update process, an Asynchronous Global Weight Update (AGWU) strategy is proposed. In the inner-layer parallelism, we further accelerate the training process for each CNN subnetwork on each computer, where computation steps of convolutional layer and the local weight training are parallelized based on task-parallelism. We introduce task decomposition and scheduling strategies with the objectives of thread-level load balancing and minimum waiting time for critical paths. Extensive experimental results indicate that the proposed BPT-CNN effectively improves the training performance of CNNs while maintaining the accuracy.
A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment
Venue: arXiv cs.DC 2018
Year: 2018

With the emergence of the big data age, the issue of how to obtain valuable knowledge from a dataset efficiently and accurately has attracted increasingly attention from both academia and industry. This paper presents a Parallel Random Forest (PRF) algorithm for big data on the Apache Spark platform. The PRF algorithm is optimized based on a hybrid approach combining data-parallel and task-parallel optimization. From the perspective of data-parallel optimization, a vertical data-partitioning method is performed to reduce the data communication cost effectively, and a data-multiplexing method is performed is performed to allow the training dataset to be reused and diminish the volume of data. From the perspective of task-parallel optimization, a dual parallel approach is carried out in the training process of RF, and a task Directed Acyclic Graph (DAG) is created according to the parallel training process of PRF and the dependence of the Resilient Distributed Datasets (RDD) objects. Then, different task schedulers are invoked for the tasks in the DAG. Moreover, to improve the algorithm's accuracy for large, high-dimensional, and noisy data, we perform a dimension-reduction approach in the training process and a weighted voting approach in the prediction process prior to parallelization. Extensive experimental results indicate the superiority and notable advantages of the PRF algorithm over the relevant algorithms implemented by Spark MLlib and other studies in terms of the classification accuracy, performance, and scalability.
MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities
Venue: arXiv cs.CY 2018
Year: 2018

Urbanism is no longer planned on paper thanks to powerful models and 3D simulation platforms. However, current work is not open to the public and lacks an optimisation agent that could help in decision making. This paper describes the creation of an open-source simulation based on an existing Dutch liveability score with a built-in AI module. Features are selected using feature engineering and Random Forests. Then, a modified scoring function is built based on the former liveability classes. The score is predicted using Random Forest for regression and achieved a recall of 0.83 with 10-fold cross-validation. Afterwards, Exploratory Factor Analysis is applied to select the actions present in the model. The resulting indicators are divided into 5 groups, and 12 actions are generated. The performance of four optimisation algorithms is compared, namely NSGA-II, PAES, SPEA2 and eps-MOEA, on three established criteria of quality: cardinality, the spread of the solutions, spacing, and the resulting score and number of turns. Although all four algorithms show different strengths, eps-MOEA is selected to be the most suitable for this problem. Ultimately, the simulation incorporates the model and the selected AI module in a GUI written in the Kivy framework for Python. Tests performed on users show positive responses and encourage further initiatives towards joining technology and public applications.
Using Intuitionistic Fuzzy Set for Anomaly Detection of Network Traffic from Flow Interaction
Venue: arXiv cs.NI 2018
Year: 2018

We present a method to detect anomalies in a time series of flow interaction patterns. There are many existing methods for anomaly detection in network traffic, such as number of packets. However, there is non established method detecting anomalies in a time series of flow interaction patterns that can be represented as complex network. Firstly, based on proposed multivariate flow similarity method on temporal locality, a complex network model (MFS-TL) is constructed to describe the interactive behaviors of traffic flows. Having analyzed the relationships between MFS-TL characteristics, temporal locality window and multivariate flow similarity critical threshold, an approach for parameter determination is established. Having observed the evolution of MFS-TL characteristics, three non-deterministic correlations are defined for network states (i.e. normal or abnormal). Furthermore, intuitionistic fuzzy set (IFS) is introduced to quantify three non-deterministic correlations, and then a anomaly detection method is put forward for single characteristic sequence. To build an objective IFS, we design a Gaussian distribution-based membership function with a variable hesitation degree. To determine the mapping of IFS's clustering intervals to network states, a distinction index is developed. Then, an IFS ensemble method (IFSE-AD) is proposed to eliminate the impacts of the inconsistent about MFS-TL characteristic to network state and improve detection performance. Finally, we carried out extensive experiments on several network traffic datasets for anomaly detection, and the results demonstrate the superiority of IFSE-AD to state-of-the-art approaches, validating the effectiveness of our method.
Openness and Impact of Leading Scientific Countries
Venue: arXiv cs.DL 2018
Year: 2018

The rapid rise of international collaboration over the past three decades, demonstrated in coauthorship of scientific articles, raises the question of whether countries benefit from cooperative science and how this might be measured. We develop and compare measures to ask this question. For all source publications in 2013, we obtained from Elsevier national level full and fractional paper counts as well as accompanying field-weighted citation counts. Then we collected information from Elsevier on the percent of all internationally coauthored papers for each country, as well as Organization for Economic Cooperation and Development measures of the international mobility of the scientific workforce in 2013, and conducted a principle component analysis that produced an openness index. We added data from the OECD on government budget allocation on research and development for 2011 to tie in the public spending that contributed to the 2013 output. We found that openness among advanced science systems is strongly correlated with impact: the more internationally engaged a nation is in terms of coauthorships and researcher mobility, the higher the impact of scientific work. The results have important implications for policy making around investment, as well as the flows of students, researchers, and technical workers.
A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation
Venue: arXiv cs.CV 2018
Year: 2018

We propose a generalized focal loss function based on the Tversky index to address the issue of data imbalance in medical image segmentation. Compared to the commonly used Dice loss, our loss function achieves a better trade off between precision and recall when training on small structures such as lesions. To evaluate our loss function, we improve the attention U-Net model by incorporating an image pyramid to preserve contextual features. We experiment on the BUS 2017 dataset and ISIC 2018 dataset where lesions occupy 4.84% and 21.4% of the images area and improve segmentation accuracy when compared to the standard U-Net by 25.7% and 3.6%, respectively.
Optimum Overflow Thresholds in Variable-Length Source Coding Allowing Non-Vanishing Error Probability
Venue: arXiv cs.IT 2018
Year: 2018

The variable-length source coding problem allowing the error probability up to some constant is considered for general sources. In this problem the optimum mean codeword length of variable-length codes has already been determined. On the other hand, in this paper, we focus on the overflow (or excess codeword length) probability instead of the mean codeword length. The infimum of overflow thresholds under the constraint that both of the error probability and the overflow probability are smaller than or equal to some constant is called the optimum overflow threshold. In this paper, we first derive finite-length upper and lower bounds on these probabilities so as to analyze the optimum overflow thresholds. Then, by using these bounds we determine the general formula of the optimum overflow thresholds in both of the first-order and second-order forms. Next, we consider another expression of the derived general formula so as to reveal the relationship with the optimum coding rate in the fixed-length source coding problem. We also derive general formulas of the optimum overflow thresholds in the optimistic coding scenario. Finally, we apply general formulas derived in this paper to the case of stationary memoryless sources.
Making Double Spectrum Auction Practical: Both Privacy and Efficiency Matter
Venue: arXiv cs.CR 2018
Year: 2018

Truthful spectrum auction is believed to be an effective method for spectrum redistribution. However, privacy concerns have largely hampered the practical applications of truthful spectrum auctions. In this paper, to make the applications of double spectrum auctions practical, we present a privacy-preserving and socially efficient double spectrum auction design, SDSA. Specifically, by combining three security techniques: homomorphic encryption, secret sharing and garbled circuits, we design a secure two-party protocol computing a socially efficient double spectrum auction, TDSA, without leaking any information about sellers' requests or buyers' bids beyond the auction outcome. We give the formal security definition in our context, and theoretically prove the security that our design achieves. Experimental results show that our design is also efficient in performance, even for large-scale double spectrum auctions.
Measuring the Effects of Scalar and Spherical Colormaps on Ensembles of DMRI Tubes
Venue: arXiv cs.GR 2018
Year: 2018

We report empirical study results on the color encoding of ensemble scalar and orientation to visualize diffusion magnetic resonance imaging (DMRI) tubes. The experiment tested six scalar colormaps for average fractional anisotropy (FA) tasks (grayscale, blackbody, diverging, isoluminant-rainbow, extended-blackbody, and coolwarm) and four three-dimensional (3D) directional encodings for tract tracing tasks (uniform gray, absolute, eigenmap, and Boy's surface embedding). We found that extended-blackbody, coolwarm, and blackbody remain the best three approaches for identifying ensemble average in 3D. Isoluminant-rainbow coloring led to the same ensemble mean accuracy as other colormaps. However, more than 50% of the answers consistently had higher estimates of the ensemble average, independent of the mean values. Hue, not luminance, influences ensemble estimates of mean values. For ensemble orientation-tracing tasks, we found that the Boy's surface embedding (greatest spatial resolution and contrast) and absolute color (lowest spatial resolution and contrast) schemes led to more accurate answers than the eigenmaps scheme (medium resolution and contrast), acting as the uncanny-valley phenomenon of visualization design in terms of accuracy.
Profile-Based Ad Hoc Social Networking Using Wi-Fi Direct on the Top of Android
Venue: arXiv cs.SI 2018
Year: 2018

Ad-hoc Social Networks have become popular to support novel applications related to location-based mobile services that are of great importance to users and businesses. Unlike traditional social services using a centralized server to fetch location, ad-hoc social network services support infrastructure less real-time social networking. It allows users to collaborate and share views anytime anywhere. However, current ad-hoc social network applications are either not available without rooting the mobile phones or don't filter the nearby users based on common interests without a centralized server. This paper presents an architecture and implementation of social networks on commercially available mobile devices that allow broadcasting name and a limited number of keywords representing users' interests without any connection in a nearby region to facilitate matching of interests. The broadcasting region creates a digital aura and is limited by WiFi region that is around 200 meters. The application connects users to form a group based on their profile or interests using peer-to-peer communication mode without using any centralized networking or profile matching infrastructure. The peer-to-peer group can be used for private communication when the network is not available.
Decoupling Semantic Context and Color Correlation with multi-class cross branch regularization
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents a novel design methodology for architecting a light-weight and faster DNN architecture for vision applications. The effectiveness of the architecture is demonstrated on Color-Constancy use case an inherent block in camera and imaging pipelines. Specifically, we present a multi-branch architecture that disassembles the contextual features and color properties from an image, and later combines them to predict a global property (e.g. Global Illumination). We also propose an implicit regularization technique by designing cross-branch regularization block that enables the network to retain high generalization accuracy. With a conservative use of best computational operators, the proposed architecture achieves state-of-the-art accuracy with 30X lesser model parameters and 70X faster inference time for color constancy. It is also shown that the proposed architecture is generic and achieves similar efficiency in other vision applications such as Low-Light photography.