OpenRaiser/Intern-Atlas huggingface dataset:
Joint Learning for Pulmonary Nodule Segmentation, Attributes and Malignancy Prediction
Venue: arXiv cs.CV 2018
Year: 2018

Refer to the literature of lung nodule classification, many studies adopt Convolutional Neural Networks (CNN) to directly predict the malignancy of lung nodules with original thoracic Computed Tomography (CT) and nodule location. However, these studies cannot tell how the CNN works in terms of predicting the malignancy of the given nodule, e.g., it's hard to conclude that whether the region within the nodule or the contextual information matters according to the output of the CNN. In this paper, we propose an interpretable and multi-task learning CNN -- Joint learning for \textbf{P}ulmonary \textbf{N}odule \textbf{S}egmentation \textbf{A}ttributes and \textbf{M}alignancy \textbf{P}rediction (PN-SAMP). It is able to not only accurately predict the malignancy of lung nodules, but also provide semantic high-level attributes as well as the areas of detected nodules. Moreover, the combination of nodule segmentation, attributes and malignancy prediction is helpful to improve the performance of each single task. In addition, inspired by the fact that radiologists often change window widths and window centers to help to make decision on uncertain nodules, PN-SAMP mixes multiple WW/WC together to gain information for the raw CT input images. To verify the effectiveness of the proposed method, the evaluation is implemented on the public LIDC-IDRI dataset, which is one of the largest dataset for lung nodule malignancy prediction. Experiments indicate that the proposed PN-SAMP achieves significant improvement with respect to lung nodule classification, and promising performance on lung nodule segmentation and attribute learning, compared with the-state-of-the-art methods.
Running genetic algorithms on Hadoop for solving high dimensional optimization problems
Venue: arXiv cs.DC 2018
Year: 2018

Hadoop is a popular MapReduce framework for developing parallel applications in distributed environments. Several advantages of MapReduce such as programming ease and ability to use commodity hardware make the applicability of soft computing methods for parallel and distributed systems easier than before. In this paper, we present the results of an experimental study on running soft computing algorithms using Hadoop. This study shows how a simple genetic algorithm running on Hadoop can be used to produce solutions for high dimensional optimization problems. In addition, a simple but effective technique, which did not need MapReduce chains, has been proposed.
Feature-Distributed SVRG for High-Dimensional Linear Classification
Venue: arXiv cs.LG 2018
Year: 2018

Linear classification has been widely used in many high-dimensional applications like text classification. To perform linear classification for large-scale tasks, we often need to design distributed learning methods on a cluster of multiple machines. In this paper, we propose a new distributed learning method, called feature-distributed stochastic variance reduced gradient (FD-SVRG) for high-dimensional linear classification. Unlike most existing distributed learning methods which are instance-distributed, FD-SVRG is feature-distributed. FD-SVRG has lower communication cost than other instance-distributed methods when the data dimensionality is larger than the number of data instances. Experimental results on real data demonstrate that FD-SVRG can outperform other state-of-the-art distributed methods for high-dimensional linear classification in terms of both communication cost and wall-clock time, when the dimensionality is larger than the number of instances in training data.
Learning Correlation Space for Time Series
Venue: arXiv cs.LG 2018
Year: 2018

We propose an approximation algorithm for efficient correlation search in time series data. In our method, we use Fourier transform and neural network to embed time series into a low-dimensional Euclidean space. The given space is learned such that time series correlation can be effectively approximated from Euclidean distance between corresponding embedded vectors. Therefore, search for correlated time series can be done using an index in the embedding space for efficient nearest neighbor search. Our theoretical analysis illustrates that our method's accuracy can be guaranteed under certain regularity conditions. We further conduct experiments on real-world datasets and the results show that our method indeed outperforms the baseline solution. In particular, for approximation of correlation, our method reduces the approximation loss by a half in most test cases compared to the baseline solution. For top-$k$ highest correlation search, our method improves the precision from 5\% to 20\% while the query time is similar to the baseline approach query time.
On the Tractability of (k,i)-Coloring
Venue: arXiv cs.DS 2018
Year: 2018

In an undirected graph, a proper (k,i)-coloring is an assignment of a set of k colors to each vertex such that any two adjacent vertices have at most i common colors. The (k,i)-coloring problem is to compute the minimum number of colors required for a proper (k,i)-coloring. This is a generalization of the classic graph coloring problem. We show a parameterized algorithm for the (k,i)-coloring problem with the size of the feedback vertex set as a parameter. Our algorithm does not use tree-width machinery, thus answering a question of Majumdar, Neogi, Raman and Tale [CALDAM 2017]. We also give a faster and simpler exact algorithm for (k, k-1)-coloring. From the hardness perspective, we show that the (k,i)-coloring problem is NP-complete for any fixed values i, k, whenever i<k, thereby settling a conjecture of Mendez-Diaz and Zabala [1999] and again asked by Majumdar, Neogi, Raman and Tale. The NP-completeness result improves the partial NP-completeness shown in the preliminary version of this paper published in CALDAM 2018.
Stochastic Non-preemptive Co-flow Scheduling with Time-Indexed Relaxation
Venue: arXiv cs.DC 2018
Year: 2018

Co-flows model a modern scheduling setting that is commonly found in a variety of applications in distributed and cloud computing. A stochastic co-flow task contains a set of parallel flows with randomly distributed sizes. Further, many applications require non-preemptive scheduling of co-flow tasks. This paper gives an approximation algorithm for stochastic non-preemptive co-flow scheduling. The proposed approach uses a time-indexed linear relaxation, and uses its solution to come up with a feasible schedule. This algorithm is shown to achieve a competitive ratio of $(2\log{m}+1)(1+\sqrt{m}Δ)(1+mΔ){(3+Δ)}/{2}$ for zero-release times, and $(2\log{m}+1)(1+\sqrt{m}Δ)(1+mΔ)(2+Δ)$ for general release times, where $Δ$ represents the upper bound of squared coefficient of variation of processing times, and $m$ is the number of servers.
FDM-Structured Preamble Optimization for Channel Estimation in MIMO-OQAM/FBMC Systems
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we consider the problem of preamble design in multiple-input multiple-output (MIMO) systems employing offset quadrature amplitude modulation based filter bank multicarrier (OQAM/FBMC) and propose a preamble optimization method for the frequency division multiplexing (FDM)-structured preamble. Specifically, we formulate an optimization problem to determine the frequency division multiplexed preambles, where the objective is to minimize the mean square error (MSE) of the channel estimation, subject to the constraint on the transmit energy. For two transmit antennas, we find the relationship between preambles and the intrinsic interference from neighboring symbols to achieve the minimum channel estimation MSE, and derive the optimal closed-form solution. For more than two transmit antennas, the constrained preamble optimization problem is nonconvex quadratic. Therefore, we convert the original optimization problem into a quadratically constrained quadratic program (QCQP) and obtain the suboptimal solution by relaxing the nonconvex constraint. Simulation results demonstrate that, in terms of MSE and bit error rate (BER) performances, the proposed method outperforms the conventional FDM preamble design method at all signal-to-noise ratio (SNR) regimes and outperforms the interference approximation method-complex (IAM-C) preamble design method at low to medium SNR regimes with lower preamble overhead.
A Generative Model for Dynamic Networks with Applications
Venue: arXiv cs.SI 2018
Year: 2018

Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our model allows the number of communities in the network to be different at different time steps. We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version. Experiments done on synthetic and real world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches.
Event Structures for Petri nets with Persistence
Venue: arXiv cs.LO 2018
Year: 2018

Event structures are a well-accepted model of concurrency. In a seminal paper by Nielsen, Plotkin and Winskel, they are used to establish a bridge between the theory of domains and the approach to concurrency proposed by Petri. A basic role is played by an unfolding construction that maps (safe) Petri nets into a subclass of event structures, called prime event structures, where each event has a uniquely determined set of causes. Prime event structures, in turn, can be identified with their domain of configurations. At a categorical level, this is nicely formalised by Winskel as a chain of coreflections. Contrary to prime event structures, general event structures allow for the presence of disjunctive causes, i.e., events can be enabled by distinct minimal sets of events. In this paper, we extend the connection between Petri nets and event structures in order to include disjunctive causes. In particular, we show that, at the level of nets, disjunctive causes are well accounted for by persistent places. These are places where tokens, once generated, can be used several times without being consumed and where multiple tokens are interpreted collectively, i.e., their histories are inessential. Generalising the work on ordinary nets, Petri nets with persistence are related to a new subclass of general event structures, called locally connected, by means of a chain of coreflections relying on an unfolding construction.
FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategy
Venue: arXiv cs.CV 2018
Year: 2018

We present Fast-Downsampling MobileNet (FD-MobileNet), an efficient and accurate network for very limited computational budgets (e.g., 10-140 MFLOPs). Our key idea is applying an aggressive downsampling strategy to MobileNet framework. In FD-MobileNet, we perform 32$\times$ downsampling within 12 layers, only half the layers in the original MobileNet. This design brings three advantages: (i) It remarkably reduces the computational cost. (ii) It increases the information capacity and achieves significant performance improvements. (iii) It is engineering-friendly and provides fast actual inference speed. Experiments on ILSVRC 2012 and PASCAL VOC 2007 datasets demonstrate that FD-MobileNet consistently outperforms MobileNet and achieves comparable results with ShuffleNet under different computational budgets, for instance, surpassing MobileNet by 5.5% on the ILSVRC 2012 top-1 accuracy and 3.6% on the VOC 2007 mAP under a complexity of 12 MFLOPs. On an ARM-based device, FD-MobileNet achieves 1.11$\times$ inference speedup over MobileNet and 1.82$\times$ over ShuffleNet under the same complexity.
Machine Learning and Evolutionary Computing for GUI-based Regression Testing
Venue: arXiv cs.SE 2018
Year: 2018

ReTest is a novel testing tool for Java applications with a graphical user interface (GUI), combining monkey testing and difference testing. Since this combination sidesteps the oracle problem, it enables the generation of GUI-based regression tests. ReTest makes use of evolutionary computing (EC), particularly a genetic algorithm (GA), to optimize these tests towards code coverage. While this is indeed a desirable goal in terms of software testing and potentially finds many bugs, it lacks one major ingredient: human behavior. Consequently, human testers often find the results less reasonable and difficult to interpret. This thesis proposes a new approach to improve the initial population of the GA with the aid of machine learning (ML), forming an ML-technique enhanced-EC (MLEC) algorithm. In order to do so, existing tests are exploited to extract information on how human testers use the given GUI. The obtained data is then utilized to train an artificial neural network (ANN), which ranks the available GUI actions respectively their underlying GUI components at runtime---reducing the gap between manually created and automatically generated regression tests. Although the approach is implemented on top of ReTest, it can be easily used to guide any form of monkey testing. The results show that with only little training data, the ANN is able to reach an accuracy of 82% and the resulting tests represent an improvement without reducing the overall code coverage and performance significantly.
An Algebraic Framework for Runtime Verification
Venue: arXiv cs.LO 2018
Year: 2018

Runtime verification (RV) is a pragmatic and scalable, yet rigorous technique, to assess the correctness of complex systems, including cyber-physical systems (CPS). By measuring how robustly a CPS run satisfies a specification, RV allows in addition, to quantify the resiliency of a CPS to perturbations. In this paper we propose Algebraic Runtime Verification (ARV), a general, semantic framework for RV, which takes advantage of the monoidal structure of runs (w.r.t. concatenation) and the semiring structure of a specification automaton (w.r.t. choice and concatenation), to compute in an incremental and application specific fashion the resiliency measure. This allows us to expose the core aspects of RV, by developing an abstract monitoring algorithm, and to strengthen and unify the various qualitative and quantitative approaches to RV, by instantiating choice and concatenation with real-valued functions as dictated by the application. We demonstrate the power and effectiveness of our framework on two case studies from the automotive domain.
Large-Scale Validation of Hypothesis Generation Systems via Candidate Ranking
Venue: arXiv cs.IR 2018
Year: 2018

The first step of many research projects is to define and rank a short list of candidates for study. In the modern rapidity of scientific progress, some turn to automated hypothesis generation (HG) systems to aid this process. These systems can identify implicit or overlooked connections within a large scientific corpus, and while their importance grows alongside the pace of science, they lack thorough validation. Without any standard numerical evaluation method, many validate general-purpose HG systems by rediscovering a handful of historical findings, and some wishing to be more thorough may run laboratory experiments based on automatic suggestions. These methods are expensive, time consuming, and cannot scale. Thus, we present a numerical evaluation framework for the purpose of validating HG systems that leverages thousands of validation hypotheses. This method evaluates a HG system by its ability to rank hypotheses by plausibility; a process reminiscent of human candidate selection. Because HG systems do not produce a ranking criteria, specifically those that produce topic models, we additionally present novel metrics to quantify the plausibility of hypotheses given topic model system output. Finally, we demonstrate that our proposed validation method aligns with real-world research goals by deploying our method within Moliere, our recent topic-driven HG system, in order to automatically generate a set of candidate genes related to HIV-associated neurodegenerative disease (HAND). By performing laboratory experiments based on this candidate set, we discover a new connection between HAND and Dead Box RNA Helicase 3 (DDX3). Reproducibility: code, validation data, and results can be found at sybrandt.com/2018/validation.
MeltdownPrime and SpectrePrime: Automatically-Synthesized Attacks Exploiting Invalidation-Based Coherence Protocols
Venue: arXiv cs.CR 2018
Year: 2018

The recent Meltdown and Spectre attacks highlight the importance of automated verification techniques for identifying hardware security vulnerabilities. We have developed a tool for synthesizing microarchitecture-specific programs capable of producing any user-specified hardware execution pattern of interest. Our tool takes two inputs: a formal description of (i) a microarchitecture in a domain-specific language, and (ii) a microarchitectural execution pattern of interest, e.g. a threat pattern. All programs synthesized by our tool are capable of producing the specified execution pattern on the supplied microarchitecture. We used our tool to specify a hardware execution pattern common to Flush+Reload attacks and automatically synthesized security litmus tests representative of those that have been publicly disclosed for conducting Meltdown and Spectre attacks. We also formulated a Prime+Probe threat pattern, enabling our tool to synthesize a new variant of each---MeltdownPrime and SpectrePrime. Both of these new exploits use Prime+Probe approaches to conduct the timing attack. They are both also novel in that they are 2-core attacks which leverage the cache line invalidation mechanism in modern cache coherence protocols. These are the first proposed Prime+Probe variants of Meltdown and Spectre. But more importantly, both Prime attacks exploit invalidation-based coherence protocols to achieve the same level of precision as a Flush+Reload attack. While mitigation techniques in software (e.g., barriers that prevent speculation) will likely be the same for our Prime variants as for original Spectre and Meltdown, we believe that hardware protection against them will be distinct. As a proof of concept, we implemented SpectrePrime as a C program and ran it on an Intel x86 processor, averaging about the same accuracy as Spectre over 100 runs---97.9% for Spectre and 99.95% for SpectrePrime.
Machine Learning-based Variability Handling in IoT Agents
Venue: arXiv cs.MA 2018
Year: 2018

Agent-based IoT applications have recently been proposed in several domains, such as health care, smart cities and agriculture. Deploying these applications in specific settings has been very challenging for many reasons including the complex static and dynamic variability of the physical devices such as sensors and actuators, the software application behavior and the environment in which the application is embedded. In this paper, we propose a self-configurable IoT agent approach based on feedback-evaluative machine-learning. The approach involves: i) a variability model of IoT agents; ii) generation of sets of customized agents; iii) feedback evaluative machine learning; iv) modeling and composition of a group of IoT agents; and v) a feature-selection method based on manual and automatic feedback.
ClosNets: a Priori Sparse Topologies for Faster DNN Training
Venue: arXiv cs.LG 2018
Year: 2018

Fully-connected layers in deep neural networks (DNN) are often the throughput and power bottleneck during training. This is due to their large size and low data reuse. Pruning dense layers can significantly reduce the size of these networks, but this approach can only be applied after training. In this work we propose a novel fully-connected layer that reduces the memory requirements of DNNs without sacrificing accuracy. We replace a dense matrix with products of sparse matrices whose topologies we pick in advance. This allows us to: (1) train significantly smaller networks without a loss in accuracy, and (2) store the network weights without having to store connection indices. We therefore achieve significant training speedups due to the smaller network size, and a reduced amount of computation per epoch. We tested several sparse layer topologies and found that Clos networks perform well due to their high path diversity, shallowness, and high model accuracy. With the ClosNets, we are able to reduce dense layer sizes by as much as an order of magnitude without hurting model accuracy.
Signal Processing for High Throughput Satellite Systems: Challenges in New Interference-Limited Scenarios
Venue: arXiv cs.IT 2018
Year: 2018

The field of satellite communications is enjoying a renewed interest in the global telecom market, and very high throughput satellites (V/HTS), with their multiple spot-beams, are key for delivering the future rate demands. In this article, the state-of-the-art and open research challenges of signal processing techniques for V/HTS systems are presented for the first time, with focus on novel approaches for efficient interference mitigation. The main signal processing topics for the ground, satellite, and user segment are addressed. Also, the critical components for the integration of satellite and terrestrial networks are studied, such as cognitive satellite systems and satellite-terrestrial backhaul for caching. All the reviewed techniques are essential in empowering satellite systems to support the increasing demands of the upcoming generation of communication networks.
Email Classification into Relevant Category Using Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

In the real world, many online shopping websites or service provider have single email-id where customers can send their query, concern etc. At the back-end service provider receive million of emails every week, how they can identify which email is belonged of a particular department? This paper presents an artificial neural network (ANN) model that is used to solve this problem and experiments are carried out on user personal Gmail emails datasets. This problem can be generalised as typical Text Classification or Categorization.
ProofWatch: Watchlist Guidance for Large Theories in E
Venue: arXiv cs.AI 2018
Year: 2018

Watchlist (also hint list) is a mechanism that allows related proofs to guide a proof search for a new conjecture. This mechanism has been used with the Otter and Prover9 theorem provers, both for interactive formalizations and for human-assisted proving of open conjectures in small theories. In this work we explore the use of watchlists in large theories coming from first-order translations of large ITP libraries, aiming at improving hammer-style automation by smarter internal guidance of the ATP systems. In particular, we (i) design watchlist-based clause evaluation heuristics inside the E ATP system, and (ii) develop new proof guiding algorithms that load many previous proofs inside the ATP and focus the proof search using a dynamically updated notion of proof matching. The methods are evaluated on a large set of problems coming from the Mizar library, showing significant improvement of E's standard portfolio of strategies, and also of the previous best set of strategies invented for Mizar by evolutionary methods.
Average Size of Implicational Bases
Venue: arXiv cs.AI 2018
Year: 2018

Implicational bases are objects of interest in formal concept analysis and its applications. Unfortunately, even the smallest base, the Duquenne-Guigues base, has an exponential size in the worst case. In this paper, we use results on the average number of minimal transversals in random hypergraphs to show that the base of proper premises is, on average, of quasi-polynomial size.