OpenRaiser/Intern-Atlas huggingface dataset:
The Maestro Attack: Orchestrating Malicious Flows with BGP
Venue: arXiv cs.CR 2019
Year: 2019

We present the Maestro attack, a novel Link Flooding Attack (LFA) that leverages control-plane traffic engineering techniques to concentrate botnet-sourced Distributed Denial of Service flows on transit links. Executed from a compromised or malicious Autonomous System (AS), Maestro advertises specific-prefix routes poisoned for selected ASes to collapse inbound traffic paths onto a single target link. A greedy heuristic fed by publicly available AS relationship data iteratively builds the set of ASes to poison. Given a compromised BGP speaker with advantageous positioning relative to the target link in the Internet topology, an adversary can expect to enhance total flow density by more than 30%. For a large botnet (e.g., Mirai), that translates to augmenting a DDoS by more than a million additional infected hosts. Interestingly, the size of the adversary-controlled AS plays little role in this amplification effect. Devastating attacks on core links can be executed by small, resource-limited ASes. To understand the scope of the attack, we evaluate widespread Internet link vulnerability across several metrics, including BGP betweenness and botnet flow density. We then assess where an adversary must be positioned to execute the attack most successfully. Finally, we present effective mitigations for network operators seeking to insulate themselves from this attack.
Locally Self-Adjusting Hypercubic Networks
Venue: arXiv cs.DC 2019
Year: 2019

In a prior work (ICDCS 2017), we presented a distributed self-adjusting algorithm DSG for skip graphs. DSG performs topological adaption to communication pattern to minimize the average routing costs between communicating nodes. In this work, we present a distributed self-adjusting algorithm (referred to as DyHypes) for topological adaption in hypercubic networks. One of the major differences between hypercubes and skip graphs is that hypercubes are more rigid in structure compared skip graphs. This property makes self-adjustment significantly different in hypercubic networks than skip graphs. Upon a communication between an arbitrary pair of nodes, DyHypes transforms the network to place frequently communicating nodes closer to each other to maximize communication efficiency, and uses randomization in the transformation process to speed up the transformation and reduce message complexity. We show that, as compared to DSG, DyHypes reduces the transformation cost by a factor of $O(\log n)$, where $n$ is the number of nodes involved in the transformation. Moreover, despite achieving faster transformation with lower message complexity, the combined cost (routing and transformation) of DyHypes is at most a $\log \log n$ factor more than that of any algorithm that conforms to the computational model adopted for this work. Similar to DSG, DyHypes is fully decentralized, conforms to the $\mathcal{CONGEST}$ model, and requires $O(\log n)$ bits of memory for each node, where $n$ is the total number of nodes.
Broadcast Congested Clique: Planted Cliques and Pseudorandom Generators
Venue: arXiv cs.DC 2019
Year: 2019

We develop techniques to prove lower bounds for the BCAST(log n) Broadcast Congested Clique model (a distributed message passing model where in each round, each processor can broadcast an O(log n)-sized message to all other processors). Our techniques are built to prove bounds for natural input distributions. So far, all lower bounds for problems in the model relied on constructing specifically tailored graph families for the specific problem at hand, resulting in lower bounds for artificially constructed inputs, instead of natural input distributions. One of our results is a lower bound for the directed planted clique problem. In this problem, an input graph is either a random directed graph (each directed edge is included with probability 1/2), or a random graph with a planted clique of size k. That is, k randomly chosen vertices have all of the edges between them included, and all other edges in the graph appear with probability 1/2. The goal is to determine whether a clique exists. We show that when k = n^(1/4 - eps), this problem requires a number of rounds polynomial in n. Additionally, we construct a pseudo-random generator which fools the Broadcast Congested Clique. This allows us to show that every k round randomized algorithm in which each processor uses up to n random bits can be efficiently transformed into an O(k)-round randomized algorithm in which each processor uses only up to O(k log n) random bits, while maintaining a high success probability. The pseudo-random generator is simple to describe, computationally very cheap, and its seed size is optimal up to constant factors. However, the analysis is quite involved, and is based on the new technique for proving lower bounds in the model. The technique also allows us to prove the first average case lower bound for the Broadcast Congested Clique, as well as an average-case time hierarchy.
Boundary Loss for Remote Sensing Imagery Semantic Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

In response to the growing importance of geospatial data, its analysis including semantic segmentation becomes an increasingly popular task in computer vision today. Convolutional neural networks are powerful visual models that yield hierarchies of features and practitioners widely use them to process remote sensing data. When performing remote sensing image segmentation, multiple instances of one class with precisely defined boundaries are often the case, and it is crucial to extract those boundaries accurately. The accuracy of segments boundaries delineation influences the quality of the whole segmented areas explicitly. However, widely-used segmentation loss functions such as BCE, IoU loss or Dice loss do not penalize misalignment of boundaries sufficiently. In this paper, we propose a novel loss function, namely a differentiable surrogate of a metric accounting accuracy of boundary detection. We can use the loss function with any neural network for binary segmentation. We performed validation of our loss function with various modifications of UNet on a synthetic dataset, as well as using real-world data (ISPRS Potsdam, INRIA AIL). Trained with the proposed loss function, models outperform baseline methods in terms of IoU score.
Learning Ensembles of Anomaly Detectors on Synthetic Data
Venue: arXiv cs.LG 2019
Year: 2019

The main aim of this work is to develop and implement an automatic anomaly detection algorithm for meteorological time-series. To achieve this goal we develop an approach to constructing an ensemble of anomaly detectors in combination with adaptive threshold selection based on artificially generated anomalies. We demonstrate the efficiency of the proposed method by integrating the corresponding implementation into ``Minimax-94'' road weather information system.
Learning to Count Objects with Few Exemplar Annotations
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we study the problem of object counting with incomplete annotations. Based on the observation that in many object counting problems the target objects are normally repeated and highly similar to each other, we are particularly interested in the setting when only a few exemplar annotations are provided. Directly applying object detection with incomplete annotations will result in severe accuracy degradation due to its improper handling of unlabeled object instances. To address the problem, we propose a positiveness-focused object detector (PFOD) to progressively propagate the incomplete labels before applying the general object detection algorithm. The PFOD focuses on the positive samples and ignore the negative instances at most of the learning time. This strategy, though simple, dramatically boosts the object counting accuracy. On the CARPK dataset for parking lot car counting, we improved mAP@0.5 from 4.58% to 72.44% using only 5 training images each with 5 bounding boxes. On the Drink35 dataset for shelf product counting, the mAP@0.5 is improved from 14.16% to 53.73% using 10 training images each with 5 bounding boxes.
Robust Online Algorithms for Dynamic Problems
Venue: arXiv cs.DS 2019
Year: 2019

Online algorithms that allow a small amount of migration or recourse have been intensively studied in the last years. They are essential in the design of competitive algorithms for dynamic problems, where objects can also depart from the instance. In this work, we give a general framework to obtain so called robust online algorithms for these dynamic problems: these online algorithm achieve an asymptotic competitive ratio of $γ+ε$ with migration $O(1/ε)$, where $γ$ is the best known offline asymptotic approximation ratio. In order to use our framework, one only needs to construct a suitable online algorithm for the static online case, where items never depart. To show the usefulness of our approach, we improve upon the best known robust algorithms for the dynamic versions of generalizations of Strip Packing and Bin Packing, including the first robust algorithm for general $d$-dimensional Bin Packing and Vector Packing.
Catastrophic forgetting: still a problem for DNNs
Venue: arXiv cs.LG 2019
Year: 2019

We investigate the performance of DNNs when trained on class-incremental visual problems consisting of initial training, followed by retraining with added visual classes. Catastrophic forgetting (CF) behavior is measured using a new evaluation procedure that aims at an application-oriented view of incremental learning. In particular, it imposes that model selection must be performed on the initial dataset alone, as well as demanding that retraining control be performed only using the retraining dataset, as initial dataset is usually too large to be kept. Experiments are conducted on class-incremental problems derived from MNIST, using a variety of different DNN models, some of them recently proposed to avoid catastrophic forgetting. When comparing our new evaluation procedure to previous approaches for assessing CF, we find their findings are completely negated, and that none of the tested methods can avoid CF in all experiments. This stresses the importance of a realistic empirical measurement procedure for catastrophic forgetting, and the need for further research in incremental learning for DNNs.
Toward Operator-to-Waveform 5G Radio Access Network Slicing
Venue: arXiv cs.NI 2019
Year: 2019

Radio access network (RAN) slicing realizes a vision where physical network resources that belong to a specific infrastructure provider can be shared among multiple mobile network operators (MNOs). Existing work in this area has addressed RAN slicing at different levels of network abstractions, but has often neglected the multitude of tightly intertwined inter-level operations involved in real-world slicing systems. For this reason, this article discusses a novel framework for operator-to-waveform 5G RAN slicing. In the proposed framework, slicing operations are treated holistically, including MNO's selection of base stations (BSs) and maximum number of users, down to the waveform-level scheduling of resource blocks. Experimental results show that the proposed framework provides up to 150% improvement in terms of number of resource blocks that can be used to enable 5G transmission technologies that require coordination and synchronization among BSs.
Pretty Modular Symmetric Encryption (PMSE), compact algorithm for ''embedded cryptography'' with quite low computational cost
Venue: arXiv cs.CR 2019
Year: 2019

Nowadays, the dataflux shared between IOT systems must be secured from 8-bits to 64-bits processors systems. Several symmetric cryptographic algorithm already exist such as AES (Advanced Encryption Standard), RC4, Blowfish, etc. In this work, we propose an 8-bits encryption algorithm, combining several ideas of standard symmetric encryption algorithms. The aim is to provide a efficient, modular and compact algorithm able to tend to truly random one-time-pad encryption quality even for large data flux. The algorithm combines the implementation of a divergent polynomial with variable coefficients for pseudo-random keys generation, variable bit swapping and bitwise operations on data, and the use of one or two passwords. The encryption has been evaluated statistically, tested for image encryption and compared with One-Time-Pad encryption on the same data. Three implementations have been tested respectively in C, Javascript and GNU Octave. Encryption time performances are compared with AES on a 8-bits architecture: the Arduino Uno (ATmega328) microcontroller. A new concept of self-decryptionable web encrypted object, called Blocksnet is also presented and tested as a potential application of the compact and embedded PMSE algorithm.
An Intra-Cluster Model with Diffuse Scattering for mmWave Communications: RT-ICM
Venue: arXiv cs.IT 2019
Year: 2019

In millimeter-wave (mmWave) channels, to overcome the high path loss, beamforming is required. Hence, the spatial representation of the channel is essential. Further, for accurate beam alignment and minimizing the outages, inter-beam interferences, etc., cluster-level spatial modeling is also necessary. Since, statistical channel models fail to reproduce the intra-cluster parameters due to the site-specific nature of the mmWave channel, in this paper, we propose a ray tracing intra-cluster model (RT-ICM) for mmWave channels. The model considers only the first-order reflection; thereby reducing the computation load while capturing most of the energy in a large number of important cases. The model accounts for diffuse scattering as it contributes significantly to the received power. Finally, since the clusters are spatially well-separated due to the sparsity of first-order reflectors, we generalize the intra-cluster model to the mmWave channel model via replication. Since narrow beamwidth increases the number of single-order clusters, we show that the proposed model suits well to MIMO and massive MIMO applications. We illustrate that the model gives matching results with published measurements made in a classroom at 60 GHz. For this specific implementation, while the maximum cluster angle of arrival (AoA) error is 1 degree, mean angle spread error is 9 degrees. The RMS error for the cluster peak power is found to be 2.2 dB.
Ingesting High-Velocity Streaming Graphs from Social Media Sources
Venue: arXiv cs.DB 2019
Year: 2019

Many data science applications like social network analysis use graphs as their primary form of data. However, acquiring graph-structured data from social media presents some interesting challenges. The first challenge is the high data velocity and bursty nature of the social media data. The second challenge is that the complex nature of the data makes the ingestion process expensive. If we want to store the streaming graph data in a graph database, we face a third challenge -- the database is very often unable to sustain the ingestion of high-velocity, high-burst data. We have developed an adaptive buffering mechanism and a graph compression technique that effectively mitigates the problem. A novel aspect of our method is that the adaptive buffering algorithm uses the data rate, the data content as well as the CPU resources of the database machine to determine an optimal data ingestion mechanism. We further show that an ingestion-time graph-compression strategy improves the efficiency of the data ingestion into the database. We have verified the efficacy of our ingestion optimization strategy through extensive experiments.
Robust sound event detection in bioacoustic sensor networks
Venue: arXiv cs.SD 2019
Year: 2019

Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60 milliseconds) and long-term (30 minutes) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer. Combining them yields state-of-the-art results that are unmatched by artificial data augmentation alone. We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings.
SmartNight: Turning Off the Lights on Android
Venue: arXiv cs.HC 2019
Year: 2019

Smartphone users benefit from content with dark color schemes: increasingly common OLED displays are more power efficient the darker the display, and many users prefer a dark display for night time use. Despite these benefits, many applications and the majority of web content are drawn with white backgrounds. There are many partial solutions to darken the displayed content, but none work in all situations. Enter SmartNight, a content-aware solution to dynamically darken content on Android. By trading off content fidelity, Android with SmartNight displays content with nearly 90% lower average picture level. It is implemented in the Android framework, and requires no external support. It seamlessly incorporates existing solutions, making it a bridge between the state-of-the-art and future solutions.
Scylla: A Mesos Framework for Container Based MPI Jobs
Venue: arXiv cs.PF 2019
Year: 2019

Open source cloud technologies provide a wide range of support for creating customized compute node clusters to schedule tasks and managing resources. In cloud infrastructures such as Jetstream and Chameleon, which are used for scientific research, users receive complete control of the Virtual Machines (VM) that are allocated to them. Importantly, users get root access to the VMs. This provides an opportunity for HPC users to experiment with new resource management technologies such as Apache Mesos that have proven scalability, flexibility, and fault tolerance. To ease the development and deployment of HPC tools on the cloud, the containerization technology has matured and is gaining interest in the scientific community. In particular, several well known scientific code bases now have publicly available Docker containers. While Mesos provides support for Docker containers to execute individually, it does not provide support for container inter-communication or orchestration of the containers for a parallel or distributed application. In this paper, we present the design, implementation, and performance analysis of a Mesos framework, Scylla, which integrates Mesos with Docker Swarm to enable orchestration of MPI jobs on a cluster of VMs acquired from the Chameleon cloud [1]. Scylla uses Docker Swarm for communication between containerized tasks (MPI processes) and Apache Mesos for resource pooling and allocation. Scylla allows a policy-driven approach to determine how the containers should be distributed across the nodes depending on the CPU, memory, and network throughput requirement for each application.
Exploring the Fairness and Resource Distribution in an Apache Mesos Environment
Venue: arXiv cs.PF 2019
Year: 2019

Apache Mesos, a cluster-wide resource manager, is widely deployed in massive scale at several Clouds and Data Centers. Mesos aims to provide high cluster utilization via fine grained resource co-scheduling and resource fairness among multiple users through Dominant Resource Fairness (DRF) based allocation. DRF takes into account different resource types (CPU, Memory, Disk I/O) requested by each application and determines the share of each cluster resource that could be allocated to the applications. Mesos has adopted a two-level scheduling policy: (1) DRF to allocate resources to competing frameworks and (2) task level scheduling by each framework for the resources allocated during the previous step. We have conducted experiments in a local Mesos cluster when used with frameworks such as Apache Aurora, Marathon, and our own framework Scylla, to study resource fairness and cluster utilization. Experimental results show how informed decision regarding second level scheduling policy of frameworks and attributes like offer holding period, offer refusal cycle and task arrival rate can reduce unfair resource distribution. Bin-Packing scheduling policy on Scylla with Marathon can reduce unfair allocation from 38\% to 3\%. By reducing unused free resources in offers we bring down the unfairness from to 90\% to 28\%. We also show the effect of task arrival rate to reduce the unfairness from 23\% to 7\%.
A Causality-Guided Prediction of the TED Talk Ratings from the Speech-Transcripts using Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

Automated prediction of public speaking performance enables novel systems for tutoring public speaking skills. We use the largest open repository---TED Talks---to predict the ratings provided by the online viewers. The dataset contains over 2200 talk transcripts and the associated meta information including over 5.5 million ratings from spontaneous visitors to the website. We carefully removed the bias present in the dataset (e.g., the speakers' reputations, popularity gained by publicity, etc.) by modeling the data generating process using a causal diagram. We use a word sequence based recurrent architecture and a dependency tree based recursive architecture as the neural networks for predicting the TED talk ratings. Our neural network models can predict the ratings with an average F-score of 0.77 which largely outperforms the competitive baseline method.
Dual-branch residual network for lung nodule segmentation
Venue: arXiv cs.CV 2019
Year: 2019

An accurate segmentation of lung nodules in computed tomography (CT) images is critical to lung cancer analysis and diagnosis. However, due to the variety of lung nodules and the similarity of visual characteristics between nodules and their surroundings, a robust segmentation of nodules becomes a challenging problem. In this study, we propose the Dual-branch Residual Network (DB-ResNet) which is a data-driven model. Our approach integrates two new schemes to improve the generalization capability of the model: 1) the proposed model can simultaneously capture multi-view and multi-scale features of different nodules in CT images; 2) we combine the features of the intensity and the convolution neural networks (CNN). We propose a pooling method, called the central intensity-pooling layer (CIP), to extract the intensity features of the center voxel of the block, and then use the CNN to obtain the convolutional features of the center voxel of the block. In addition, we designed a weighted sampling strategy based on the boundary of nodules for the selection of those voxels using the weighting score, to increase the accuracy of the model. The proposed method has been extensively evaluated on the LIDC dataset containing 986 nodules. Experimental results show that the DB-ResNet achieves superior segmentation performance with an average dice score of 82.74% on the dataset. Moreover, we compared our results with those of four radiologists on the same dataset. The comparison showed that our average dice score was 0.49% higher than that of human experts. This proves that our proposed method is as good as the experienced radiologist.