OpenRaiser/Intern-Atlas huggingface dataset:
Securing Microservices and Microservice Architectures: A Systematic Mapping Study
Venue: arXiv cs.CR 2020
Year: 2020

Microservice architectures (MSA) are becoming trending alternatives to existing software development paradigms notably for developing complex and distributed applications. Microservices emerged as an architectural design pattern aiming to address the scalability and ease the maintenance of online services. However, security breaches have increased threatening availability, integrity and confidentiality of microservice-based systems. A growing body of literature is found addressing security threats and security mechanisms to individual microservices and microservice architectures. The aim of this study is to provide a helpful guide to developers about already recognized threats on microservices and how they can be detected, mitigated or prevented; we also aim to identify potential research gaps on securing MSA. In this paper, we conduct a systematic mapping in order to categorize threats on MSA with their security proposals. Therefore, we extracted threats and details of proposed solutions reported in selected studies. Obtained results are used to design a lightweight ontology for security patterns of MSA. The ontology can be queried to identify source of threats, security mechanisms used to prevent each threat, applicability layer and validation techniques used for each mechanism. The systematic search yielded 1067 studies of which 46 are selected as primary studies. The results of the mapping revealed an unbalanced research focus in favor of external attacks; auditing and enforcing access control are the most investigated techniques compared with prevention and mitigation. Additionally, we found that most proposed solutions are soft-infrastructure applicable layer compared with other layers such as communication and deployment. We also found that performance analysis and case studies are the most used validation techniques of security proposals.
On the Performance of Cooperative NOMA Using MRC at Road Intersections in the Presence of Interference
Venue: arXiv cs.IT 2020
Year: 2020

As the traffic safety has become of utmost importance, much attention is given to intelligent transportation systems (ITSs), and more particularly to vehicular communications (VCs). Moreover, 50 % of all crashes happen at road intersections, which makes theme a critical areas. In this paper, we investigate the improvement when implementing maximum ratio combining (MRC) in cooperative VCs transmission schemes using non-orthogonal multiple access scheme (NOMA) at road intersections. We consider that a source transmits a message to two destinations with a aid of a relay. The transmission undergoes interference generated from a set of vehicles on the roads. We obtained closed form outage probability expressions, and we extend the derivation for a scenario involving K destination nodes and several road lanes. The performance of MRC cooperative NOMA is compared with the standard cooperative NOMA, and we show that implementing MRC with NOMA offers a significant improvement over the standard cooperative NOMA. Also, we compare the performance of MRC using NOMA with MRC cooperative orthogonal multiple access (OMA), and demonstrate that NOMA significantly outperforms OMA. We conclude that it is always beneficial to use MRC and NOMA even at the cost of implementation complexity. Finally, we demonstrate that the outage probability increases drasticallyen the vehicles are closer to the road intersection, and that using MRC with NOMA improves significantly the performance in this context. To verify the correctness of our analysis, extensive Monte-Carlo simulations are carried out.
On the left primeness of some polynomial matrices with applications to convolutional codes
Venue: arXiv cs.IT 2020
Year: 2020

Maximum distance profile (MDP) convolutional codes have the property that their column distances are as large as possible for given rate and degree. There exists a well-known criterion to check whether a code is MDP using the generator or the parity-check matrix of the code. In this paper, we show that under the assumption that $n-k$ divides $δ$ or $k$ divides $δ$, a polynomial matrix that fulfills the MDP criterion is actually always left prime. In particular, when $k$ divides $δ$, this implies that each MDP convolutional code is noncatastrophic. Moreover, when $n-k$ and $k$ do not divide $δ$, we show that the MDP criterion is in general not enough to ensure left primeness. In this case, with one more assumption, we still can guarantee the result.
Tuning Ranking in Co-occurrence Networks with General Biased Exchange-based Diffusion on Hyper-bag-graphs
Venue: arXiv cs.SI 2020
Year: 2020

Co-occurence networks can be adequately modeled by hyper-bag-graphs (hb-graphs for short). A hb-graph is a family of multisets having same universe, called the vertex set. An efficient exchange-based diffusion scheme has been previously proposed that allows the ranking of both vertices and hb-edges. In this article, we extend this scheme to allow biases of different kinds and explore their effect on the different rankings obtained. The biases enhance the emphasize on some particular aspects of the network.
Developing a Recommendation Benchmark for MLPerf Training and Inference
Venue: arXiv cs.LG 2020
Year: 2020

Deep learning-based recommendation models are used pervasively and broadly, for example, to recommend movies, products, or other information most relevant to users, in order to enhance the user experience. Among various application domains which have received significant industry and academia research attention, such as image classification, object detection, language and speech translation, the performance of deep learning-based recommendation models is less well explored, even though recommendation tasks unarguably represent significant AI inference cycles at large-scale datacenter fleets. To advance the state of understanding and enable machine learning system development and optimization for the commerce domain, we aim to define an industry-relevant recommendation benchmark for the MLPerf Training andInference Suites. The paper synthesizes the desirable modeling strategies for personalized recommendation systems. We lay out desirable characteristics of recommendation model architectures and data sets. We then summarize the discussions and advice from the MLPerf Recommendation Advisory Board.
Deep Adaptive Semantic Logic (DASL): Compiling Declarative Knowledge into Deep Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

We introduce Deep Adaptive Semantic Logic (DASL), a novel framework for automating the generation of deep neural networks that incorporates user-provided formal knowledge to improve learning from data. We provide formal semantics that demonstrate that our knowledge representation captures all of first order logic and that finite sampling from infinite domains converges to correct truth values. DASL's representation improves on prior neural-symbolic work by avoiding vanishing gradients, allowing deeper logical structure, and enabling richer interactions between the knowledge and learning components. We illustrate DASL through a toy problem in which we add structure to an image classification problem and demonstrate that knowledge of that structure reduces data requirements by a factor of $1000$. We then evaluate DASL on a visual relationship detection task and demonstrate that the addition of commonsense knowledge improves performance by $10.7\%$ in a data scarce setting.
Harnessing Explanations to Bridge AI and Humans
Venue: arXiv cs.HC 2020
Year: 2020

Machine learning models are increasingly integrated into societally critical applications such as recidivism prediction and medical diagnosis, thanks to their superior predictive power. In these applications, however, full automation is often not desired due to ethical and legal concerns. The research community has thus ventured into developing interpretable methods that explain machine predictions. While these explanations are meant to assist humans in understanding machine predictions and thereby allowing humans to make better decisions, this hypothesis is not supported in many recent studies. To improve human decision-making with AI assistance, we propose future directions for closing the gap between the efficacy of explanations and improvement in human performance.
System to Identify and Elide Superfluous JavaScript Code for Faster Webpage Loads
Venue: arXiv cs.NI 2020
Year: 2020

Many websites import large JavaScript (JS) libraries to customize and enhance user experiences. Our data shows that many JS libraries are only partially utilized during a page load, and therefore, contain superfluous code that is never executed. Many top-ranked websites contain up to hundreds of kilobytes of compressed superfluous code and a JS resource on a median page contains 31% superfluous code. Superfluous JS code inflates the page weight, and thereby, the time to download, parse, and compile a JS resource. It is therefore important to monitor the usage and optimize the payload of JS resources to improve Web performance. However, given that the webpage design and functionality could depend on a user's preferences or device, among many other factors, actively loading webpages in controlled environments cannot cover all possible conditions in which webpage content and functionality changes. In this paper, we show that passive measurement techniques, such as real user monitoring systems (RUM), that monitor the performance of real user page loads under different conditions can be leveraged to identify superfluous code. Using a custom man-in-the-middle proxy (similar to a content delivery network's proxy server), we designed a systematic approach for website developers that relies on pages loaded by real users to passively identify superfluous code on JS resources. We then elide any superfluous code from JS resources before subsequent page load requests. Our data shows that eliding superfluous JS code improves the median page load time by 5% and by at least 10% for pages in the long tail. Through results presented in this paper, we motivate for the need for rigorous monitoring of the usage of JS resources under different real world conditions, with the goal to improve Web performance.
ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition
Venue: arXiv cs.LG 2020
Year: 2020

Sensor-based human activity recognition has become a critical component of many emerging applications ranging from behavioral medicine to gaming. However, an unprecedented increase in the diversity of sensor devices in the Internet-of-Things era has limited the adoption of activity recognition models for use across different domains. We propose ActiLabel a combinatorial framework that learns structural similarities among the events in an arbitrary domain and those of a different domain. The structural similarities are captured through a graph model, referred to as the it dependency graph, which abstracts details of activity patterns in low-level signal and feature space. The activity labels are then autonomously learned by finding an optimal tiered mapping between the dependency graphs. Extensive experiments based on three public datasets demonstrate the superiority of ActiLabel over state-of-the-art transfer learning and deep learning methods.
Weak and Strong Gradient Directions: Explaining Memorization, Generalization, and Hardness of Examples at Scale
Venue: arXiv cs.LG 2020
Year: 2020

Coherent Gradients (CGH) is a recently proposed hypothesis to explain why over-parameterized neural networks trained with gradient descent generalize well even though they have sufficient capacity to memorize the training set. The key insight of CGH is that, since the overall gradient for a single step of SGD is the sum of the per-example gradients, it is strongest in directions that reduce the loss on multiple examples if such directions exist. In this paper, we validate CGH on ResNet, Inception, and VGG models on ImageNet. Since the techniques presented in the original paper do not scale beyond toy models and datasets, we propose new methods. By posing the problem of suppressing weak gradient directions as a problem of robust mean estimation, we develop a coordinate-based median of means approach. We present two versions of this algorithm, M3, which partitions a mini-batch into 3 groups and computes the median, and a more efficient version RM3, which reuses gradients from previous two time steps to compute the median. Since they suppress weak gradient directions without requiring per-example gradients, they can be used to train models at scale. Experimentally, we find that they indeed greatly reduce overfitting (and memorization) and thus provide the first convincing evidence that CGH holds at scale. We also propose a new test of CGH that does not depend on adding noise to training labels or on suppressing weak gradient directions. Using the intuition behind CGH, we posit that the examples learned early in the training process (i.e., "easy" examples) are precisely those that have more in common with other training examples. Therefore, as per CGH, the easy examples should generalize better amongst themselves than the hard examples amongst themselves. We validate this hypothesis with detailed experiments, and believe that it provides further orthogonal evidence for CGH.
Real Time Multi-Class Object Detection and Recognition Using Vision Augmentation Algorithm
Venue: arXiv cs.CV 2020
Year: 2020

The aim of this research is to detect small objects with low resolution and noise. The existing real time object detection algorithm is based on the deep neural network of convolution need to perform multilevel convolution and pooling operations on the entire image to extract a deep semantic characteristic of the image. The detection models perform better for large objects. The features of existing models do not fully represent the essential features of small objects after repeated convolution operations. We have introduced a novel real time detection algorithm which employs upsampling and skip connection to extract multiscale features at different convolution levels in a learning task resulting a remarkable performance in detecting small objects. The detection precision of the model is shown to be higher and faster than that of the state-of-the-art models.
A Label Proportions Estimation Technique for Adversarial Domain Adaptation in Text Classification
Venue: arXiv cs.CL 2020
Year: 2020

Many text classification tasks are domain-dependent, and various domain adaptation approaches have been proposed to predict unlabeled data in a new domain. Domain-adversarial neural networks (DANN) and their variants have been used widely recently and have achieved promising results for this problem. However, most of these approaches assume that the label proportions of the source and target domains are similar, which rarely holds in most real-world scenarios. Sometimes the label shift can be large and the DANN fails to learn domain-invariant features. In this study, we focus on unsupervised domain adaptation of text classification with label shift and introduce a domain adversarial network with label proportions estimation (DAN-LPE) framework. The DAN-LPE simultaneously trains a domain adversarial net and processes label proportions estimation by the confusion of the source domain and the predictions of the target domain. Experiments show the DAN-LPE achieves a good estimate of the target label distributions and reduces the label shift to improve the classification performance.
Object-Centric Image Generation from Layouts
Venue: arXiv cs.CV 2020
Year: 2020

Despite recent impressive results on single-object and single-domain image generation, the generation of complex scenes with multiple objects remains challenging. In this paper, we start with the idea that a model must be able to understand individual objects and relationships between objects in order to generate complex scenes well. Our layout-to-image-generation method, which we call Object-Centric Generative Adversarial Network (or OC-GAN), relies on a novel Scene-Graph Similarity Module (SGSM). The SGSM learns representations of the spatial relationships between objects in the scene, which lead to our model's improved layout-fidelity. We also propose changes to the conditioning mechanism of the generator that enhance its object instance-awareness. Apart from improving image quality, our contributions mitigate two failure modes in previous approaches: (1) spurious objects being generated without corresponding bounding boxes in the layout, and (2) overlapping bounding boxes in the layout leading to merged objects in images. Extensive quantitative evaluation and ablation studies demonstrate the impact of our contributions, with our model outperforming previous state-of-the-art approaches on both the COCO-Stuff and Visual Genome datasets. Finally, we address an important limitation of evaluation metrics used in previous works by introducing SceneFID -- an object-centric adaptation of the popular Fr{é}chet Inception Distance metric, that is better suited for multi-object images.
Heat and Blur: An Effective and Fast Defense Against Adversarial Examples
Venue: arXiv cs.CV 2020
Year: 2020

The growing incorporation of artificial neural networks (NNs) into many fields, and especially into life-critical systems, is restrained by their vulnerability to adversarial examples (AEs). Some existing defense methods can increase NNs' robustness, but they often require special architecture or training procedures and are irrelevant to already trained models. In this paper, we propose a simple defense that combines feature visualization with input modification, and can, therefore, be applicable to various pre-trained networks. By reviewing several interpretability methods, we gain new insights regarding the influence of AEs on NNs' computation. Based on that, we hypothesize that information about the "true" object is preserved within the NN's activity, even when the input is adversarial, and present a feature visualization version that can extract that information in the form of relevance heatmaps. We then use these heatmaps as a basis for our defense, in which the adversarial effects are corrupted by massive blurring. We also provide a new evaluation metric that can capture the effects of both attacks and defenses more thoroughly and descriptively, and demonstrate the effectiveness of the defense and the utility of the suggested evaluation measurement with VGG19 results on the ImageNet dataset.
Efficient Bitwidth Search for Practical Mixed Precision Neural Network
Venue: arXiv cs.LG 2020
Year: 2020

Network quantization has rapidly become one of the most widely used methods to compress and accelerate deep neural networks. Recent efforts propose to quantize weights and activations from different layers with different precision to improve the overall performance. However, it is challenging to find the optimal bitwidth (i.e., precision) for weights and activations of each layer efficiently. Meanwhile, it is yet unclear how to perform convolution for weights and activations of different precision efficiently on generic hardware platforms. To resolve these two issues, in this paper, we first propose an Efficient Bitwidth Search (EBS) algorithm, which reuses the meta weights for different quantization bitwidth and thus the strength for each candidate precision can be optimized directly w.r.t the objective without superfluous copies, reducing both the memory and computational cost significantly. Second, we propose a binary decomposition algorithm that converts weights and activations of different precision into binary matrices to make the mixed precision convolution efficient and practical. Experiment results on CIFAR10 and ImageNet datasets demonstrate our mixed precision QNN outperforms the handcrafted uniform bitwidth counterparts and other mixed precision techniques.
Reinforcement Learning Driven Adaptive VR Streaming with Optical Flow Based QoE
Venue: arXiv cs.MM 2020
Year: 2020

With the merit of containing full panoramic content in one camera, Virtual Reality (VR) and 360-degree videos have attracted more and more attention in the field of industrial cloud manufacturing and training. Industrial Internet of Things (IoT), where many VR terminals needed to be online at the same time, can hardly guarantee VR's bandwidth requirement. However, by making use of users' quality of experience (QoE) awareness factors, including the relative moving speed and depth difference between the viewpoint and other content, bandwidth consumption can be reduced. In this paper, we propose OFB-VR (Optical Flow Based VR), an interactive method of VR streaming that can make use of VR users' QoE awareness to ease the bandwidth pressure. The Just-Noticeable Difference through Optical Flow Estimation (JND-OFE) is explored to quantify users' awareness of quality distortion in 360-degree videos. Accordingly, a novel 360-degree videos QoE metric based on PSNR and JND-OFE (PSNR-OF) is proposed. With the help of PSNR-OF, OFB-VR proposes a versatile-size tiling scheme to lessen the tiling overhead. A Reinforcement Learning(RL) method is implemented to make use of historical data to perform Adaptive BitRate(ABR). For evaluation, we take two prior VR streaming schemes, Pano and Plato, as baselines. Vast evaluations show that our system can increase the mean PSNR-OF score by 9.5-15.8% while maintaining the same rebuffer ratio compared with Pano and Plato in a fluctuate LTE bandwidth dataset. Evaluation results show that OFB-VR is a promising prototype for actual interactive industrial VR. A prototype of OFB-VR can be found in https://github.com/buptexplorers/OFB-VR.
A Scaling Algorithm for Weighted $f$-Factors in General Graphs
Venue: arXiv cs.DS 2020
Year: 2020

We study the maximum weight perfect $f$-factor problem on any general simple graph $G=(V,E,w)$ with positive integral edge weights $w$, and $n=|V|$, $m=|E|$. When we have a function $f:V\rightarrow \mathbb{N}_+$ on vertices, a perfect $f$-factor is a generalized matching so that every vertex $u$ is matched to $f(u)$ different edges. The previous best algorithms on this problem have running time $O(m f(V))$ [Gabow 2018] or $\tilde{O}(W(f(V))^{2.373}))$ [Gabow and Sankowski 2013], where $W$ is the maximum edge weight, and $f(V)=\sum_{u\in V}f(u)$. In this paper, we present a scaling algorithm for this problem with running time $\tilde{O}(mn^{2/3}\log W)$. Previously this bound is only known for bipartite graphs [Gabow and Tarjan 1989]. The running time of our algorithm is independent of $f(V)$, and consequently it first breaks the $Ω(mn)$ barrier for large $f(V)$ even for the unweighted $f$-factor problem in general graphs.