OpenRaiser/Intern-Atlas huggingface dataset:
Super Interaction Neural Network
Venue: arXiv cs.CV 2019
Year: 2019

Recent studies have demonstrated that the convolutional networks heavily rely on the quality and quantity of generated features. However, in lightweight networks, there are limited available feature information because these networks tend to be shallower and thinner due to the efficiency consideration. For farther improving the performance and accuracy of lightweight networks, we develop Super Interaction Neural Networks (SINet) model from a novel point of view: enhancing the information interaction in neural networks. In order to achieve information interaction along the width of the deep network, we propose Exchange Shortcut Connection, which can integrate the information from different convolution groups without any extra computation cost. And then, in order to achieve information interaction along the depth of the network, we proposed Dense Funnel Layer and Attention based Hierarchical Joint Decision, which are able to make full use of middle layer features. Our experiments show that the superior performance of SINet over other state-of-the-art lightweight models in ImageNet dataset. Furthermore, we also exhibit the effectiveness and universality of our proposed components by ablation studies.
Resolution Lower Bounds for Refutation Statements
Venue: arXiv cs.CC 2019
Year: 2019

For any unsatisfiable CNF formula we give an exponential lower bound on the size of resolution refutations of a propositional statement that the formula has a resolution refutation. We describe three applications. (1) An open question in (Atserias, Müller 2019) asks whether a certain natural propositional encoding of the above statement is hard for Resolution. We answer by giving an exponential size lower bound. (2) We show exponential resolution size lower bounds for reflection principles, thereby improving a result in (Atserias, Bonet 2004). (3) We provide new examples of CNFs that exponentially separate Res(2) from Resolution (an exponential separation of these two proof systems was originally proved in (Segerlind, Buss, Impagliazzo 2004)).
Misleading Authorship Attribution of Source Code using Adversarial Learning
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we present a novel attack against authorship attribution of source code. We exploit that recent attribution methods rest on machine learning and thus can be deceived by adversarial examples of source code. Our attack performs a series of semantics-preserving code transformations that mislead learning-based attribution but appear plausible to a developer. The attack is guided by Monte-Carlo tree search that enables us to operate in the discrete domain of source code. In an empirical evaluation with source code from 204 programmers, we demonstrate that our attack has a substantial effect on two recent attribution methods, whose accuracy drops from over 88% to 1% under attack. Furthermore, we show that our attack can imitate the coding style of developers with high accuracy and thereby induce false attributions. We conclude that current approaches for authorship attribution are inappropriate for practical application and there is a need for resilient analysis techniques.
Instance-Aware Representation Learning and Association for Online Multi-Person Tracking
Venue: arXiv cs.CV 2019
Year: 2019

Multi-Person Tracking (MPT) is often addressed within the detection-to-association paradigm. In such approaches, human detections are first extracted in every frame and person trajectories are then recovered by a procedure of data association (usually offline). However, their performances usually degenerate in presence of detection errors, mutual interactions and occlusions. In this paper, we present a deep learning based MPT approach that learns instance-aware representations of tracked persons and robustly online infers states of the tracked persons. Specifically, we design a multi-branch neural network (MBN), which predicts the classification confidences and locations of all targets by taking a batch of candidate regions as input. In our MBN architecture, each branch (instance-subnet) corresponds to an individual to be tracked and new branches can be dynamically created for handling newly appearing persons. Then based on the output of MBN, we construct a joint association matrix that represents meaningful states of tracked persons (e.g., being tracked or disappearing from the scene) and solve it by using the efficient Hungarian algorithm. Moreover, we allow the instance-subnets to be updated during tracking by online mining hard examples, accounting to person appearance variations over time. We comprehensively evaluate our framework on a popular MPT benchmark, demonstrating its excellent performance in comparison with recent online MPT methods.
Generative Parameter Sampler For Scalable Uncertainty Quantification
Venue: arXiv cs.LG 2019
Year: 2019

Uncertainty quantification has been a core of the statistical machine learning, but its computational bottleneck has been a serious challenge for both Bayesians and frequentists. We propose a model-based framework in quantifying uncertainty, called predictive-matching Generative Parameter Sampler (GPS). This procedure considers an Uncertainty Quantification (UQ) distribution on the targeted parameter, which matches the corresponding predictive distribution to the observed data. This framework adopts a hierarchical modeling perspective such that each observation is modeled by an individual parameter. This individual parameterization permits the resulting inference to be computationally scalable and robust to outliers. Our approach is illustrated for linear models, Poisson processes, and deep neural networks for classification. The results show that the GPS is successful in providing uncertainty quantification as well as additional flexibility beyond what is allowed by classical statistical procedures under the postulated statistical models.
Data Breach e-Crime, A Case Study and Legal Analysis
Venue: arXiv cs.CY 2019
Year: 2019

The Bonafede V. EE data breach is a reported data breach e-Crime in the media, also published by the BBC on 8th February 2019 in the United Kingdom which has not yet come to Court. Three laws and regulations of the United Kingdom that have been breached and references to relevant case laws are highlighted in this paper. Further, Facts of the Case; Issues observed; Decision made; Reasonings to the case; Opinions; and Analysis are discussed. The discussions include legal points raised in the case, with the relevant laws to draw attention to the keywords or phrases that are in dispute. There are sufficient details of the organization for the reader to understand both the scale and its activity. Discussions refer to case law in support with the reasonings outlined, point by point in numbered paragraphs. At the analysis stage, the significance of the case, its relationship to other referenced cases, its place in history and the value of the current laws in place are evaluated while making references to current law. KEYWORDS: Data breach, e-Crime, Cyberlaw, Cybercrime, I.T and Internet law
Food for thought: Ethical considerations of user trust in computer vision
Venue: arXiv cs.CY 2019
Year: 2019

In computer vision research, especially when novel applications of tools are developed, ethical implications around user perceptions of trust in the underlying technology should be considered and supported. Here, we describe an example of the incorporation of such considerations within the long-term care sector for tracking resident food and fluid intake. We highlight our recent user study conducted to develop a Goldilocks quality horizontal prototype designed to support trust cues in which perceived trust in our horizontal prototype was higher than the existing system in place. We discuss the importance and need for user engagement as part of ongoing computer vision-driven technology development and describe several important factors related to trust that are relevant to developing decision-making tools.
Smooth Shells: Multi-Scale Shape Registration with Functional Maps
Venue: arXiv cs.CV 2019
Year: 2019

We propose a novel 3D shape correspondence method based on the iterative alignment of so-called smooth shells. Smooth shells define a series of coarse-to-fine shape approximations designed to work well with multiscale algorithms. The main idea is to first align rough approximations of the geometry and then add more and more details to refine the correspondence. We fuse classical shape registration with Functional Maps by embedding the input shapes into an intrinsic-extrinsic product space. Moreover, we disambiguate intrinsic symmetries by applying a surrogate based Markov chain Monte Carlo initialization. Our method naturally handles various types of noise that commonly occur in real scans, like non-isometry or incompatible meshing. Finally, we demonstrate state-of-the-art quantitative results on several datasets and show that our pipeline produces smoother, more realistic results than other automatic matching methods in real world applications.
Digital Availability of Product Information for Collaborative Engineering of Spacecraft
Venue: arXiv cs.OH 2019
Year: 2019

In this paper, we introduce a system to collect product information from manufacturers and make it available in tools that are used for concurrent design of spacecraft. The planning of a spacecraft needs experts from different disciplines, like propulsion, power, and thermal. Since these different disciplines rely on each other there is a high need for communication between them, which is often realized by a Model-Based Systems Engineering (MBSE) process and corresponding tools. We show by comparison that the product information provided by manufacturers often does not match the information needed by MBSE tools on a syntactic or semantic level. The information from manufacturers is also currently not available in machine-readable formats. Afterwards, we present a prototype of a system that makes product information from manufacturers directly available in MBSE tools, in a machine-readable way.
Use of Artificial Intelligence Techniques / Applications in Cyber Defense
Venue: arXiv cs.AI 2019
Year: 2019

Nowadays, considering the speed of the processes and the amount of data used in cyber defense, it cannot be expected to have an effective defense by using only human power without the help of automation systems. However, for the effective defense against dynamically evolving attacks on networks, it is difficult to develop software with conventional fixed algorithms. This can be achieved by using artificial intelligence methods that provide flexibility and learning capability. The likelihood of developing cyber defense capabilities through increased intelligence of defense systems is quite high. Given the problems associated with cyber defense in real life, it is clear that many cyber defense problems can be successfully solved only when artificial intelligence methods are used. In this article, the current artificial intelligence practices and techniques are reviewed and the use and importance of artificial intelligence in cyber defense systems is mentioned. The aim of this article is to be able to explain the use of these methods in the field of cyber defense with current examples by considering and analyzing the artificial intelligence technologies and methodologies that are currently being developed and integrating them with the role and adaptation of the technology and methodology in the defense of cyberspace.
MementoMap Framework for Flexible and Adaptive Web Archive Profiling
Venue: arXiv cs.DL 2019
Year: 2019

In this work we propose MementoMap, a flexible and adaptive framework to efficiently summarize holdings of a web archive. We described a simple, yet extensible, file format suitable for MementoMap. We used the complete index of the Arquivo.pt comprising 5B mementos (archived web pages/files) to understand the nature and shape of its holdings. We generated MementoMaps with varying amount of detail from its HTML pages that have an HTTP status code of 200 OK. Additionally, we designed a single-pass, memory-efficient, and parallelization-friendly algorithm to compact a large MementoMap into a small one and an in-file binary search method for efficient lookup. We analyzed more than three years of MemGator (a Memento aggregator) logs to understand the response behavior of 14 public web archives. We evaluated MementoMaps by measuring their Accuracy using 3.3M unique URIs from MemGator logs. We found that a MementoMap of less than 1.5% Relative Cost (as compared to the comprehensive listing of all the unique original URIs) can correctly identify the presence or absence of 60% of the lookup URIs in the corresponding archive while maintaining 100% Recall (i.e., zero false negatives).
Learning latent state representation for speeding up exploration
Venue: arXiv cs.LG 2019
Year: 2019

Exploration is an extremely challenging problem in reinforcement learning, especially in high dimensional state and action spaces and when only sparse rewards are available. Effective representations can indicate which components of the state are task relevant and thus reduce the dimensionality of the space to explore. In this work, we take a representation learning viewpoint on exploration, utilizing prior experience to learn effective latent representations, which can subsequently indicate which regions to explore. Prior experience on separate but related tasks help learn representations of the state which are effective at predicting instantaneous rewards. These learned representations can then be used with an entropy-based exploration method to effectively perform exploration in high dimensional spaces by effectively lowering the dimensionality of the search space. We show the benefits of this representation for meta-exploration in a simulated object pushing environment.
Cognitively-inspired Agent-based Service Composition for Mobile & Pervasive Computing
Venue: arXiv cs.SE 2019
Year: 2019

Automatic service composition in mobile and pervasive computing faces many challenges due to the complex and highly dynamic nature of the environment. Common approaches consider service composition as a decision problem whose solution is usually addressed from optimization perspectives which are not feasible in practice due to the intractability of the problem, limited computational resources of smart devices, service host's mobility, and time constraints to tailor composition plans. Thus, our main contribution is the development of a cognitively-inspired agent-based service composition model focused on bounded rationality rather than optimality, which allows the system to compensate for limited resources by selectively filtering out continuous streams of data. Our approach exhibits features such as distributedness, modularity, emergent global functionality, and robustness, which endow it with capabilities to perform decentralized service composition by orchestrating manifold service providers and conflicting goals from multiple users. The evaluation of our approach shows promising results when compared against state-of-the-art service composition models.
Graph Learning Network: A Structure Learning Algorithm
Venue: arXiv cs.LG 2019
Year: 2019

Recently, graph neural networks (GNNs) have proved to be suitable in tasks on unstructured data. Particularly in tasks as community detection, node classification, and link prediction. However, most GNN models still operate with static relationships. We propose the Graph Learning Network (GLN), a simple yet effective process to learn node embeddings and structure prediction functions. Our model uses graph convolutions to propose expected node features, and predict the best structure based on them. We repeat these steps recursively to enhance the prediction and the embeddings.
Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks
Venue: arXiv cs.CV 2019
Year: 2019

In this work, we examine the feasibility of applying Deep Convolutional Generative Adversarial Networks (DCGANs) with Single Shot Detector (SSD) as data-processing technique to handle with the challenge of pedestrian detection in the wild. Specifically, we attempted to use in-fill completion (where a portion of the image is masked) to generate random transformations of images with portions missing to expand existing labelled datasets. In our work, GAN has been trained intensively on low resolution images, in order to neutralize the challenges of the pedestrian detection in the wild, and considered humans, and few other classes for detection in smart cities. The object detector experiment performed by training GAN model along with SSD provided a substantial improvement in the results. This approach presents a very interesting overview in the current state of art on GAN networks for object detection. We used Canadian Institute for Advanced Research (CIFAR), Caltech, KITTI data set for training and testing the network under different resolutions and the experimental results with comparison been showedbetween DCGAN cascaded with SSD and SSD itself.
iVAMS 1.0: Polynomial-Metamodel-Integrated Intelligent Verilog-AMS for Fast, Accurate Mixed-Signal Design Optimization
Venue: arXiv cs.AR 2019
Year: 2019

Electronic circuit behavioral models built with hardware description/modeling languages such as Verilog-AMS for system-level simulations are typically functional models. They do not capture the physical design (layout) information of the target design. Numerous iterations of post-layout design adjustments are usually required to ensure that design specifications are met with the presence of layout parasitics. In this paper a paradigm shift of the current trend is presented that integrates layout-level information in Verilog-AMS through metamodels such that system-level simulation of a mixed-signal circuit/system is realistic and as accurate as true parasitic netlist simulation. The simulations performed with these parasitic-aware models can be used to estimate system performance without layout iterations. We call this new form of Verilog-AMS as iVAMS (i.e. Intelligent Verilog-AMS). We call this iVAMS 1.0 as it is simple polynomial-metamodel integrated Intelligent Verilog-AMS. As a specific case study, a voltage-controlled oscillator (VCO) Verilog-AMS behavioral model and design flow are proposed to assist fast PLL design space exploration. The PLL simulation employing quadratic metamodels achieves approximately 10X speedup compared to that employing the layout extracted, parasitic netlist. The simulations using this behavioral model attain high accuracy. The observed error for the simulated lock time and average power dissipation are 0.7% and 3%, respectively. This behavioral metamodel approach bridges the gap between layout-accurate but fast simulation and design space exploration. The proposed method also allows much shorter design verification and optimization to meet stringent time-to-market requirements. Compared to the optimization using the layout netlist, the runtime using the behavioral model is reduced by 88.9%.
Wasserstein Style Transfer
Venue: arXiv cs.LG 2019
Year: 2019

We propose Gaussian optimal transport for Image style transfer in an Encoder/Decoder framework. Optimal transport for Gaussian measures has closed forms Monge mappings from source to target distributions. Moreover interpolates between a content and a style image can be seen as geodesics in the Wasserstein Geometry. Using this insight, we show how to mix different target styles , using Wasserstein barycenter of Gaussian measures. Since Gaussians are closed under Wasserstein barycenter, this allows us a simple style transfer and style mixing and interpolation. Moreover we show how mixing different styles can be achieved using other geodesic metrics between gaussians such as the Fisher Rao metric, while the transport of the content to the new interpolate style is still performed with Gaussian OT maps. Our simple methodology allows to generate new stylized content interpolating between many artistic styles. The metric used in the interpolation results in different stylizations.
A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension
Venue: arXiv cs.CL 2019
Year: 2019

Conversational machine comprehension (CMC) requires understanding the context of multi-turn dialogue. Using BERT, a pre-training language model, has been successful for single-turn machine comprehension, while modeling multiple turns of question answering with BERT has not been established because BERT has a limit on the number and the length of input sequences. In this paper, we propose a simple but effective method with BERT for CMC. Our method uses BERT to encode a paragraph independently conditioned with each question and each answer in a multi-turn context. Then, the method predicts an answer on the basis of the paragraph representations encoded with BERT. The experiments with representative CMC datasets, QuAC and CoQA, show that our method outperformed recently published methods (+0.8 F1 on QuAC and +2.1 F1 on CoQA). In addition, we conducted a detailed analysis of the effects of the number and types of dialogue history on the accuracy of CMC, and we found that the gold answer history, which may not be given in an actual conversation, contributed to the model performance most on both datasets.
Information theoretic learning of robust deep representations
Venue: arXiv cs.LG 2019
Year: 2019

We propose a novel objective function for learning robust deep representations of data based on information theory. Data is projected into a feature-vector space such that the mutual information of all subsets of features relative to the supervising signal is maximized. This objective function gives rise to robust representations by conserving available information relative to supervision in the face of noisy or unavailable features. Although the objective function is not directly tractable, we are able to derive a surrogate objective function. Minimizing this surrogate loss encourages features to be non-redundant and conditionally independent relative to the supervising signal. To evaluate the quality of obtained solutions, we have performed a set of preliminary experiments that show promising results.
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
Venue: arXiv cs.LG 2019
Year: 2019

Given datasets from multiple domains, a key challenge is to efficiently exploit these data sources for modeling a target domain. Variants of this problem have been studied in many contexts, such as cross-domain translation and domain adaptation. We propose AlignFlow, a generative modeling framework that models each domain via a normalizing flow. The use of normalizing flows allows for a) flexibility in specifying learning objectives via adversarial training, maximum likelihood estimation, or a hybrid of the two methods; and b) learning and exact inference of a shared representation in the latent space of the generative model. We derive a uniform set of conditions under which AlignFlow is marginally-consistent for the different learning objectives. Furthermore, we show that AlignFlow guarantees exact cycle consistency in mapping datapoints from a source domain to target and back to the source domain. Empirically, AlignFlow outperforms relevant baselines on image-to-image translation and unsupervised domain adaptation and can be used to simultaneously interpolate across the various domains using the learned representation.