OpenRaiser/Intern-Atlas huggingface dataset:
There are 174 Subdivisions of the Hexahedron into Tetrahedra
Venue: arXiv cs.CG 2018
Year: 2018

This article answers an important theoretical question: How many different subdivisions of the hexahedron into tetrahedra are there? It is well known that the cube has five subdivisions into 6 tetrahedra and one subdivision into 5 tetrahedra. However, all hexahedra are not cubes and moving the vertex positions increases the number of subdivisions. Recent hexahedral dominant meshing methods try to take these configurations into account for combining tetrahedra into hexahedra, but fail to enumerate them all: they use only a set of 10 subdivisions among the 174 we found in this article. The enumeration of these 174 subdivisions of the hexahedron into tetrahedra is our combinatorial result. Each of the 174 subdivisions has between 5 and 15 tetrahedra and is actually a class of 2 to 48 equivalent instances which are identical up to vertex relabeling. We further show that exactly 171 of these subdivisions have a geometrical realization, i.e. there exist coordinates of the eight hexahedron vertices in a three-dimensional space such that the geometrical tetrahedral mesh is valid. We exhibit the tetrahedral meshes for these configurations and show in particular subdivisions of hexahedra with 15 tetrahedra that have a strictly positive Jacobian.
Generalized Network Dismantling
Venue: arXiv cs.SI 2018
Year: 2018

Finding the set of nodes, which removed or (de)activated can stop the spread of (dis)information, contain an epidemic or disrupt the functioning of a corrupt/criminal organization is still one of the key challenges in network science. In this paper, we introduce the generalized network dismantling problem, which aims to find the set of nodes that, when removed from a network, results in a network fragmentation into subcritical network components at minimum cost. For unit costs, our formulation becomes equivalent to the standard network dismantling problem. Our non-unit cost generalization allows for the inclusion of topological cost functions related to node centrality and non-topological features such as the price, protection level or even social value of a node. In order to solve this optimization problem, we propose a method, which is based on the spectral properties of a novel node-weighted Laplacian operator. The proposed method is applicable to large-scale networks with millions of nodes. It outperforms current state-of-the-art methods and opens new directions in understanding the vulnerability and robustness of complex systems.
A fully automated framework for lung tumour detection, segmentation and analysis
Venue: arXiv cs.CV 2018
Year: 2018

Early and correct diagnosis is a very important aspect of cancer treatment. Detection of tumour in Computed Tomography scan is a tedious and tricky task which requires expert knowledge and a lot of human working hours. As small human error is present in any work he does, it is possible that a CT scan could be misdiagnosed causing the patient to become terminal. This paper introduces a novel fully automated framework which helps to detect and segment tumour, if present in a lung CT scan series. It also provides useful analysis of the detected tumour such as its approximate volume, centre location and more. The framework provides a single click solution which analyses all CT images of a single patient series in one go. It helps to reduce the work of manually going through each CT slice and provides quicker and more accurate tumour diagnosis. It makes use of customized image processing and image segmentation methods, to detect and segment the prospective tumour region from the CT scan. It then uses a trained ensemble classifier to correctly classify the segmented region as being tumour or not. Tumour analysis further computed can then be used to determine malignity of the tumour. With an accuracy of 98.14%, the implemented framework can be used in various practical scenarios, capable of eliminating need of any expert pathologist intervention.
String Periods in the Order-Preserving Model
Venue: arXiv cs.DS 2018
Year: 2018

The order-preserving model (op-model, in short) was introduced quite recently but has already attracted significant attention because of its applications in data analysis. We introduce several types of periods in this setting (op-periods). Then we give algorithms to compute these periods in time $O(n)$, $O(n\log\log n)$, $O(n \log^2 \log n/\log \log \log n)$, $O(n\log n)$ depending on the type of periodicity. In the most general variant the number of different periods can be as big as $Ω(n^2)$, and a compact representation is needed. Our algorithms require novel combinatorial insight into the properties of such periods.
Towards Application Portability on Blockchains
Venue: arXiv cs.DC 2018
Year: 2018

We discuss the issue of what we call {\em incentive mismatch}, a fundamental problem with public blockchains supported by economic incentives. This is an open problem, but one potential solution is to make application portable. Portability is desirable for applications on private blockchains. Then, we present examples of middleware designs that enable application portability and, in particular, support migration between blockchains.
Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning
Venue: arXiv cs.RO 2018
Year: 2018

The physical design of a robot and the policy that controls its motion are inherently coupled, and should be determined according to the task and environment. In an increasing number of applications, data-driven and learning-based approaches, such as deep reinforcement learning, have proven effective at designing control policies. For most tasks, the only way to evaluate a physical design with respect to such control policies is empirical--i.e., by picking a design and training a control policy for it. Since training these policies is time-consuming, it is computationally infeasible to train separate policies for all possible designs as a means to identify the best one. In this work, we address this limitation by introducing a method that performs simultaneous joint optimization of the physical design and control network. Our approach maintains a distribution over designs and uses reinforcement learning to optimize a control policy to maximize expected reward over the design distribution. We give the controller access to design parameters to allow it to tailor its policy to each design in the distribution. Throughout training, we shift the distribution towards higher-performing designs, eventually converging to a design and control policy that are jointly optimal. We evaluate our approach in the context of legged locomotion, and demonstrate that it discovers novel designs and walking gaits, outperforming baselines in both performance and efficiency.
The Impact of Mobility Model in the Optimal placement of Sensor Nodes in Wireless Body Sensor Network
Venue: arXiv cs.NI 2018
Year: 2018

Power and energy consumption is a fundamental issue in Body Sensor Networks (BSNs) since nodes must operate properly and autonomously for a certain period of time without battery replacement or change. This is due to the fact that the sensors in BSNs are either implanted in the body or are in very near position to the body. Thus, the duration of replacing the batteries should be of utmost importance. Most of the existing researches suggested the development of a more improved battery cells or developing an energy aware routing protocol to tackle the energy consumption in WBSN. But this is not the case as most energy consumption in WBSN occur as a result of mobility in routing and sensor node placement. Therefore, improving the battery cells might not solve the energy consumption in WBSN.
Integrated NFV/SDN Architectures: A Systematic Literature Review
Venue: arXiv cs.NI 2018
Year: 2018

Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) are new paradigms in the move towards open software and network hardware. While NFV aims to virtualize network functions and deploy them into general purpose hardware, SDN makes networks programmable by separating the control and data planes. NFV and SDN are complementary technologies capable of providing one network solution. SDN can provide connectivity between Virtual Network Functions (VNFs) in a flexible and automated way, whereas NFV can use SDN as part of a service function chain. There are many studies designing NFV/SDN architectures in different environments. Researchers have been trying to address reliability, performance, and scalability problems using different architectural designs. This Systematic Literature Review (SLR) focuses on integrated NFV/SDN architectures, with the following goals: i) to investigate and provide an in-depth review of the state-of-the-art of NFV/SDN architectures, ii) to synthesize their architectural designs, and iii) to identify areas for further improvements. Broadly, this SLR will encourage researchers to advance the current stage of development (i.e., the state-of-the-practice) of integrated NFV/SDN architectures, and shed some light on future research efforts and the challenges faced.
Correlation between clustering and degree in affiliation networks
Venue: arXiv cs.SI 2018
Year: 2018

We are interested in the probability that two randomly selected neighbors of a random vertex of degree (at least) $k$ are adjacent. We evaluate this probability for a power law random intersection graph, where each vertex is prescribed a collection of attributes and two vertices are adjacent whenever they share a common attribute. We show that the probability obeys the scaling $k^{-δ}$ as $k\to+\infty$. Our results are mathematically rigorous. The parameter $0\le δ\le 1$ is determined by the tail indices of power law random weights defining the links between vertices and attributes.
A deep learning approach for detecting traffic accidents from social media data
Venue: arXiv cs.SI 2018
Year: 2018

This paper employs deep learning in detecting the traffic accident from social media data. First, we thoroughly investigate the 1-year over 3 million tweet contents in two metropolitan areas: Northern Virginia and New York City. Our results show that paired tokens can capture the association rules inherent in the accident-related tweets and further increase the accuracy of the traffic accident detection. Second, two deep learning methods: Deep Belief Network (DBN) and Long Short-Term Memory (LSTM) are investigated and implemented on the extracted token. Results show that DBN can obtain an overall accuracy of 85% with about 44 individual token features and 17 paired token features. The classification results from DBN outperform those of Support Vector Machines (SVMs) and supervised Latent Dirichlet allocation (sLDA). Finally, to validate this study, we compare the accident-related tweets with both the traffic accident log on freeways and traffic data on local roads from 15,000 loop detectors. It is found that nearly 66% of the accident-related tweets can be located by the accident log and more than 80% of them can be tied to nearby abnormal traffic data. Several important issues of using Twitter to detect traffic accidents have been brought up by the comparison including the location and time bias, as well as the characteristics of influential users and hashtags.
Testing Optimality of Sequential Decision-Making
Venue: arXiv cs.IT 2018
Year: 2018

This paper provides a statistical method to test whether a system that performs a binary sequential hypothesis test is optimal in the sense of minimizing the average decision times while taking decisions with given reliabilities. The proposed method requires samples of the decision times, the decision outcomes, and the true hypotheses, but does not require knowledge on the statistics of the observations or the properties of the decision-making system. The method is based on fluctuation relations for decision time distributions which are proved for sequential probability ratio tests. These relations follow from the martingale property of probability ratios and hold under fairly general conditions. We illustrate these tests with numerical experiments and discuss potential applications.
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines
Venue: arXiv cs.LG 2018
Year: 2018

Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model. The amount of these variables is also important, since performance tends to decline as the input dimensionality increases, hence the interest in using feature fusion techniques, able to produce feature sets that are more compact and higher level. A plethora of procedures to fuse original variables for producing new ones has been developed in the past decades. The most basic ones use linear combinations of the original variables, such as PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis), while others find manifold embeddings of lower dimensionality based on non-linear combinations, such as Isomap or LLE (Linear Locally Embedding) techniques. More recently, autoencoders (AEs) have emerged as an alternative to manifold learning for conducting nonlinear feature fusion. Dozens of AE models have been proposed lately, each with its own specific traits. Although many of them can be used to generate reduced feature sets through the fusion of the original ones, there also AEs designed with other applications in mind. The goal of this paper is to provide the reader with a broad view of what an AE is, how they are used for feature fusion, a taxonomy gathering a broad range of models, and how they relate to other classical techniques. In addition, a set of didactic guidelines on how to choose the proper AE for a given task is supplied, together with a discussion of the software tools available. Finally, two case studies illustrate the usage of AEs with datasets of handwritten digits and breast cancer.
Intelligence Graph
Venue: arXiv cs.AI 2018
Year: 2018

In fact, there exist three genres of intelligence architectures: logics (e.g. \textit{Random Forest, A$^*$ Searching}), neurons (e.g. \textit{CNN, LSTM}) and probabilities (e.g. \textit{Naive Bayes, HMM}), all of which are incompatible to each other. However, to construct powerful intelligence systems with various methods, we propose the intelligence graph (short as \textbf{\textit{iGraph}}), which is composed by both of neural and probabilistic graph, under the framework of forward-backward propagation. By the paradigm of iGraph, we design a recommendation model with semantic principle. First, the probabilistic distributions of categories are generated from the embedding representations of users/items, in the manner of neurons. Second, the probabilistic graph infers the distributions of features, in the manner of probabilities. Last, for the recommendation diversity, we perform an expectation computation then conduct a logic judgment, in the manner of logics. Experimentally, we beat the state-of-the-art baselines and verify our conclusions.
High Throughput Low Delay Wireless Multicast via Multi-Channel Moving Window Codes
Venue: arXiv cs.IT 2018
Year: 2018

A fundamental challenge in wireless multicast has been how to simultaneously achieve high-throughput and low-delay for reliably serving a large number of users. In this paper, we show how to harness substantial throughput and delay gains by exploiting multi-channel resources. We develop a new scheme called Multi-Channel Moving Window Codes (MC-MWC) for multi-channel multi-session wireless multicast. The salient features of MC-MWC are three-fold. (i) High throughput: we show that MC-MWC achieves order-optimal throughput in the many-user many-channel asymptotic regime. Moreover, the number of channels required by a conventional channel-allocation based scheme is shown to be doubly-exponentially larger than that required by MC-MWC. (ii) Low delay: using large deviations theory, we show that the delay of MC-MWC decreases linearly with the number of channels, while the delay reduction of conventional schemes is no more than a finite constant. (iii) Low feedback overhead: the feedback overhead of MC-MWC is a constant that is independent of both the number of receivers in each session and the number of sessions in the network. Finally, our trace-driven simulation and numerical results validate the analytical results and show that the implementation complexity of MC-MWC is low.
Enabling Strong Database Integrity using Trusted Execution Environments
Venue: arXiv cs.DB 2018
Year: 2018

Many applications require the immutable and consistent sharing of data across organizational boundaries. Because conventional datastores cannot provide this functionality, blockchains have been proposed as one possible solution. Yet public blockchains are energy inefficient, hard to scale and suffer from limited throughput and high latencies, while permissioned blockchains depend on specially designated nodes, potentially leak meta-information, and also suffer from scale and performance bottlenecks. This paper presents CreDB, a datastore that provides blockchain-like guarantees of integrity using trusted execution environments. CreDB employs four novel mechanisms to support a new class of applications. First, it creates a permanent record of every transaction, known as a witness, that clients can then use not only to audit the database but to prove to third parties that desired actions took place. Second, it associates with every object an inseparable and inviolable policy, which not only performs access control but enables the datastore to implement state machines whose behavior is amenable to analysis. Third, timeline inspection allows authorized parties to inspect and reason about the history of changes made to the data. Finally, CreDB provides a protected function evaluation mechanism that allows integrity-protected computation over private data. The paper describes these mechanisms, and the applications they collectively enable, in detail. We have fully implemented a prototype of CreDB on Intel SGX. Evaluation shows that CreDB can serve as a drop-in replacement for other NoSQL stores, such as MongoDB while providing stronger integrity guarantees.
Dynamic Island Model based on Spectral Clustering in Genetic Algorithm
Venue: arXiv cs.NE 2018
Year: 2018

How to maintain relative high diversity is important to avoid premature convergence in population-based optimization methods. Island model is widely considered as a major approach to achieve this because of its flexibility and high efficiency. The model maintains a group of sub-populations on different islands and allows sub-populations to interact with each other via predefined migration policies. However, current island model has some drawbacks. One is that after a certain number of generations, different islands may retain quite similar, converged sub-populations thereby losing diversity and decreasing efficiency. Another drawback is that determining the number of islands to maintain is also very challenging. Meanwhile initializing many sub-populations increases the randomness of island model. To address these issues, we proposed a dynamic island model~(DIM-SP) which can force each island to maintain different sub-populations, control the number of islands dynamically and starts with one sub-population. The proposed island model outperforms the other three state-of-the-art island models in three baseline optimization problems including job shop scheduler problem, travelling salesmen problem and quadratic multiple knapsack problem.
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
Venue: arXiv cs.CR 2018
Year: 2018

The automatic detection of software vulnerabilities is an important research problem. However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negative rate). In this paper, we initiate the study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features. Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection. In particular, we need to find representations of software programs that are suitable for deep learning. For this purpose, we propose using code gadgets to represent programs and then transform them into vectors, where a code gadget is a number of (not necessarily consecutive) lines of code that are semantically related to each other. This leads to the design and implementation of a deep learning-based vulnerability detection system, called Vulnerability Deep Pecker (VulDeePecker). In order to evaluate VulDeePecker, we present the first vulnerability dataset for deep learning approaches. Experimental results show that VulDeePecker can achieve much fewer false negatives (with reasonable false positives) than other approaches. We further apply VulDeePecker to 3 software products (namely Xen, Seamonkey, and Libav) and detect 4 vulnerabilities, which are not reported in the National Vulnerability Database but were "silently" patched by the vendors when releasing later versions of these products; in contrast, these vulnerabilities are almost entirely missed by the other vulnerability detection systems we experimented with.
Variable-Length Intrinsic Randomness Allowing Positive Value of the Average Variational Distance
Venue: arXiv cs.IT 2018
Year: 2018

This paper considers the problem of variable-length intrinsic randomness. We propose the average variational distance as the performance criterion from the viewpoint of a dual relationship with the problem formulation of variable-length resolvability. Previous study has derived the general formula of the $ε$-variable-length resolvability. We derive the general formula of the $ε$-variable-length intrinsic randomness. Namely, we characterize the supremum of the mean length under the constraint that the value of the average variational distance is smaller than or equal to a constant $ε$. Our result clarifies a dual relationship between the general formula of $ε$-variable-length resolvability and that of $ε$-variable-length intrinsic randomness. We also derive a lower bound of the quantity characterizing our general formula.