OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Parallel Connected Components Labeling with a Coarse-to-fine Strategy
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes a new parallel approach to solve connected components on a 2D binary image implemented with CUDA. We employ the following strategies to accelerate neighborhood exploration after dividing an input image into independent blocks. In the local labeling stage, a coarse-labeling algorithm, including row-column connection and label-equivalence list unification, is applied first to sort out the mess of an initialized local label map; a refinement algorithm is then introduced to merge separated sub-regions from a single component. In the block merge stage, we scan the pixels located on the boundary of each block instead of solving the connectivity of all the pixels. With the proposed method, the length of label-equivalence lists is compressed, and the number of memory accesses is reduced. Thus, the efficiency of connected components labeling is improved. Experimental results show that our method outperforms the other approaches between $29\%$ and $80\%$ on average.
Deterministic Computations on a PRAM with Static Processor and Memory Faults
Venue: arXiv cs.DC 2018
Year: 2018

We consider Parallel Random Access Machine (PRAM) which has some processors and memory cells faulty. The faults considered are static, i.e., once the machine starts to operate, the operational/faulty status of PRAM components does not change. We develop a deterministic simulation of a fully operational PRAM on a similar faulty machine which has constant fractions of faults among processors and memory cells. The simulating PRAM has $n$ processors and $m$ memory cells, and simulates a PRAM with $n$ processors and a constant fraction of $m$ memory cells. The simulation is in two phases: it starts with preprocessing, which is followed by the simulation proper performed in a step-by-step fashion. Preprocessing is performed in time $O((\frac{m}{n}+ \log n)\log n)$. The slowdown of a step-by-step part of the simulation is $O(\log m)$.
Throughput Enhancement by Concurrent Transmission in WPAN with Multiple Antennas
Venue: arXiv cs.NI 2018
Year: 2018

To achieve high rate of Multi-Giga-bits-per-second for multimedia applications at personal area level, 60 GHz communication technologies are most potential candidates. Due to some special characteristics of 60 GHz band of frequencies and use of multiple directional antennas,the network level and user level throughput can be increased tremendously by identifying and scheduling the non interfering transmission requests for concurrent transmissions. Instead of direct communication between source and destination, by traversing the traffic flow on optimum path (consists of light weight multiple-relying-hops), can further increase the throughput and balances the load condition across the network. In this paper we present a scheduling scheme for concurrent transmission of non interfering transmission requests on instantaneous optimum path. Performance of scheme is investigated for different path loss exponents.
A Comparative Study of LOWESS and RBF Approximations for Visualization
Venue: arXiv cs.GR 2018
Year: 2018

Approximation methods are widely used in many fields and many techniques have been published already. This comparative study presents a comparison of LOWESS (Locally weighted scatterplot smoothing) and RBF (Radial Basis Functions) approximation methods on noisy data as they use different approaches. The RBF approach is generally convenient for high dimensional scattered data sets. The LOWESS method needs finding a subset of nearest points if data are scattered. The experiments proved that LOWESS approximation gives slightly better results than RBF in the case of lower dimension, while in the higher dimensional case
Facial emotion recognition using min-max similarity classifier
Venue: arXiv cs.CV 2018
Year: 2018

Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem. In this paper, we propose an efficient and straightforward facial emotion recognition algorithm to reduce the problem of inter-class pixel mismatch during classification. The proposed method includes the application of pixel normalization to remove intensity offsets followed-up with a Min-Max metric in a nearest neighbor classifier that is capable of suppressing feature outliers. The results indicate an improvement of recognition performance from 92.85% to 98.57% for the proposed Min-Max classification method when tested on JAFFE database. The proposed emotion recognition technique outperforms the existing template matching methods.
Quality assessment metrics for edge detection and edge-aware filtering: A tutorial review
Venue: arXiv cs.CV 2018
Year: 2018

The quality assessment of edges in an image is an important topic as it helps to benchmark the performance of edge detectors, and edge-aware filters that are used in a wide range of image processing tasks. The most popular image quality metrics such as Mean squared error (MSE), Peak signal-to-noise ratio (PSNR) and Structural similarity (SSIM) metrics for assessing and justifying the quality of edges. However, they do not address the structural and functional accuracy of edges in images with a wide range of natural variabilities. In this review, we provide an overview of all the most relevant performance metrics that can be used to benchmark the quality performance of edges in images. We identify four major groups of metrics and also provide a critical insight into the evaluation protocol and governing equations.
TWAM: A Certifying Abstract Machine for Logic Programs
Venue: arXiv cs.PL 2018
Year: 2018

Type-preserving (or typed) compilation uses typing derivations to certify correctness properties of compilation. We have designed and implemented a type-preserving compiler for a simply-typed dialect of Prolog we call T-Prolog. The crux of our approach is a new certifying abstract machine which we call the Typed Warren Abstract Machine (TWAM). The TWAM has a dependent type system strong enough to specify the semantics of a logic program in the logical framework LF. We present a soundness metatheorem which constitutes a partial correctness guarantee: well-typed programs implement the logic program specified by their type. This metatheorem justifies our design and implementation of a certifying compiler from T-Prolog to TWAM.
Accelerating Deep Learning with Memcomputing
Venue: arXiv cs.LG 2018
Year: 2018

Restricted Boltzmann machines (RBMs) and their extensions, called 'deep-belief networks', are powerful neural networks that have found applications in the fields of machine learning and artificial intelligence. The standard way to training these models resorts to an iterative unsupervised procedure based on Gibbs sampling, called 'contrastive divergence' (CD), and additional supervised tuning via back-propagation. However, this procedure has been shown not to follow any gradient and can lead to suboptimal solutions. In this paper, we show an efficient alternative to CD by means of simulations of digital memcomputing machines (DMMs). We test our approach on pattern recognition using a modified version of the MNIST data set. DMMs sample effectively the vast phase space given by the model distribution of the RBM, and provide a very good approximation close to the optimum. This efficient search significantly reduces the number of pretraining iterations necessary to achieve a given level of accuracy, as well as a total performance gain over CD. In fact, the acceleration of pretraining achieved by simulating DMMs is comparable to, in number of iterations, the recently reported hardware application of the quantum annealing method on the same network and data set. Notably, however, DMMs perform far better than the reported quantum annealing results in terms of quality of the training. We also compare our method to advances in supervised training, like batch-normalization and rectifiers, that work to reduce the advantage of pretraining. We find that the memcomputing method still maintains a quality advantage ($>1\%$ in accuracy, and a $20\%$ reduction in error rate) over these approaches. Furthermore, our method is agnostic about the connectivity of the network. Therefore, it can be extended to train full Boltzmann machines, and even deep networks at once.
A Fair Adaptive Data Rate Algorithm for LoRaWAN
Venue: arXiv cs.NI 2018
Year: 2018

LoRaWAN exhibits several characteristics that can lead to an unfair distribution of the Data Extracted Rate (DER) among nodes. Firstly, the capture effect leads to a strong signal suppressing a weaker signal at the gateway and secondly, the spreading codes used are not perfectly orthogonal, causing packet loss if an interfering signal is strong enough. In these conditions, nodes experiencing higher attenuation are less likely to see their packets received correctly. We develop FADR, a Fair Adaptive Data Rate algorithm for LoRaWAN that exploits the different Spreading Factors (SFs) and Transmission Powers (TPs) settings available in LoRa to achieve a fair Data Extraction Rate among all nodes while at the same time avoiding excessively high TPs. Simulations show that FADR, in highly congested cells, achieves 300% higher fairness than the minimum airtime allocation approach and 22% higher fairness than Brechts approach, while consuming almost 22% lower energy.
M2: Malleable Metal as a Service
Venue: arXiv cs.DC 2018
Year: 2018

Existing bare-metal cloud services that provide users with physical nodes have a number of serious disadvantage over their virtual alternatives, including slow provisioning times, difficulty for users to release nodes and then reuse them to handle changes in demand, and poor tolerance to failures. We introduce M2, a bare-metal cloud service that uses network-mounted boot drives to overcome these disadvantages. We describe the architecture and implementation of M2 and compare its agility, scalability, and performance to existing systems. We show that M2 can reduce provisioning time by over 50% while offering richer functionality, and comparable run-time performance with respect to tools that provision images into local disks. M2 is open source and available at https://github.com/CCI-MOC/ims.
Unsupervised Object-Level Video Summarization with Online Motion Auto-Encoder
Venue: arXiv cs.CV 2018
Year: 2018

Unsupervised video summarization plays an important role on digesting, browsing, and searching the ever-growing videos every day, and the underlying fine-grained semantic and motion information (i.e., objects of interest and their key motions) in online videos has been barely touched. In this paper, we investigate a pioneer research direction towards the fine-grained unsupervised object-level video summarization. It can be distinguished from existing pipelines in two aspects: extracting key motions of participated objects, and learning to summarize in an unsupervised and online manner. To achieve this goal, we propose a novel online motion Auto-Encoder (online motion-AE) framework that functions on the super-segmented object motion clips. Comprehensive experiments on a newly-collected surveillance dataset and public datasets have demonstrated the effectiveness of our proposed method.
DeepMind Control Suite
Venue: arXiv cs.AI 2018
Year: 2018

The DeepMind Control Suite is a set of continuous control tasks with a standardised structure and interpretable rewards, intended to serve as performance benchmarks for reinforcement learning agents. The tasks are written in Python and powered by the MuJoCo physics engine, making them easy to use and modify. We include benchmarks for several learning algorithms. The Control Suite is publicly available at https://www.github.com/deepmind/dm_control . A video summary of all tasks is available at http://youtu.be/rAai4QzcYbs .
Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data
Venue: arXiv cs.CV 2018
Year: 2018

We propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but labelled data is in limited supply. We consider the specific case of classifying skin lesions as either malignant or benign. In this setting, the proposed approach -- the semi-supervised, denoising adversarial autoencoder -- is able to utilise vast amounts of unlabelled data to learn a representation for skin lesions, and small amounts of labelled data to assign class labels based on the learned representation. We analyse the contributions of both the adversarial and denoising components of the model and find that the combination yields superior classification performance in the setting of limited labelled training data.
Computing Hitting Set Kernels By AC^0-Circuits
Venue: arXiv cs.CC 2018
Year: 2018

Given a hypergraph $H = (V,E)$, what is the smallest subset $X \subseteq V$ such that $e \cap X \neq \emptyset$ holds for all $e \in E$? This problem, known as the hitting set problem, is a basic problem in parameterized complexity theory. There are well-known kernelization algorithms for it, which get a hypergraph $H$ and a number $k$ as input and output a hypergraph $H'$ such that (1) $H$ has a hitting set of size $k$ if, and only if, $H'$ has such a hitting set and (2) the size of $H'$ depends only on $k$ and on the maximum cardinality $d$ of edges in $H$. The algorithms run in polynomial time, but are highly sequential. Recently, it has been shown that one of them can be parallelized to a certain degree: one can compute hitting set kernels in parallel time $O(d)$ -- but it was conjectured that this is the best parallel algorithm possible. We refute this conjecture and show how hitting set kernels can be computed in constant parallel time. For our proof, we introduce a new, generalized notion of hypergraph sunflowers and show how iterated applications of the color coding technique can sometimes be collapsed into a single application.
Semantic Modeling with Foundries
Venue: arXiv cs.DL 2018
Year: 2018

We analyze challenges for the development of the Human Activities and Infrastructures Foundry. We explore a rich semantic modeling approach to describe two Korean ceramic water droppers used to mix ink for calligraphy, how they were produced and the reasons for their differing aesthetic. Our modeling supports schema and allows for transitions of Entities based on the relationships to other Entities with which they are associated. We explore the similarity of our approach to object-oriented analysis and modeling.
Sorting Real Numbers in $O(n\sqrt{\log n})$ Time and Linear Space
Venue: arXiv cs.DS 2018
Year: 2018

We present an $O(n\sqrt{\log n})$ time and linear space algorithm for sorting real numbers. This breaks the long time illusion that real numbers have to be sorted by comparison sorting and take $Ω(n\log n)$ time to be sorted.
Identifying emergency stages in Facebook posts of police departments with convolutional and recurrent neural networks and support vector machines
Venue: arXiv cs.CL 2018
Year: 2018

Classification of social media posts in emergency response is an important practical problem: accurate classification can help automate processing of such messages and help other responders and the public react to emergencies in a timely fashion. This research focused on classifying Facebook messages of US police departments. Randomly selected 5,000 messages were used to train classifiers that distinguished between four categories of messages: emergency preparedness, response and recovery, as well as general engagement messages. Features were represented with bag-of-words and word2vec, and models were constructed using support vector machines (SVMs) and convolutional (CNNs) and recurrent neural networks (RNNs). The best performing classifier was an RNN with a custom-trained word2vec model to represent features, which achieved the F1 measure of 0.839.
MVG Mechanism: Differential Privacy under Matrix-Valued Query
Venue: arXiv cs.CR 2018
Year: 2018

Differential privacy mechanism design has traditionally been tailored for a scalar-valued query function. Although many mechanisms such as the Laplace and Gaussian mechanisms can be extended to a matrix-valued query function by adding i.i.d. noise to each element of the matrix, this method is often suboptimal as it forfeits an opportunity to exploit the structural characteristics typically associated with matrix analysis. To address this challenge, we propose a novel differential privacy mechanism called the Matrix-Variate Gaussian (MVG) mechanism, which adds a matrix-valued noise drawn from a matrix-variate Gaussian distribution, and we rigorously prove that the MVG mechanism preserves $(ε,δ)$-differential privacy. Furthermore, we introduce the concept of directional noise made possible by the design of the MVG mechanism. Directional noise allows the impact of the noise on the utility of the matrix-valued query function to be moderated. Finally, we experimentally demonstrate the performance of our mechanism using three matrix-valued queries on three privacy-sensitive datasets. We find that the MVG mechanism notably outperforms four previous state-of-the-art approaches, and provides comparable utility to the non-private baseline.
Optimizing the Number of Fog Nodes for Cloud-Fog-Thing Networks
Venue: arXiv cs.NI 2018
Year: 2018

Going from theory to practice in fog networking raises the question of the optimum number of fog nodes that will be upgraded from the existing nodes. This paper finds the optimum number of fog nodes for a given total number of ordinary nodes residing in the area of interest for different channel conditions. Determining the optimum number of fog nodes is quite beneficial, because it can strongly affect the SINR, and thus the average data rate and transmission delay. The numerical results indicate that the average data rate increases nearly an order of magnitude for an optimized number of fog nodes in case of shadowing and fading. It is further shown that the optimum number of fog nodes does not increase in direct proportion to the increase in the total number of nodes. Furthermore, the optimum number of fog nodes decreases when channels have high path loss exponents. These findings suggest that the fog nodes must be selected among those that have the highest computation capability for densely deployed networks and high path loss exponents channels.
Neural Networks in Adversarial Setting and Ill-Conditioned Weight Space
Venue: arXiv cs.LG 2018
Year: 2018

Recently, Neural networks have seen a huge surge in its adoption due to their ability to provide high accuracy on various tasks. On the other hand, the existence of adversarial examples have raised suspicions regarding the generalization capabilities of neural networks. In this work, we focus on the weight matrix learnt by the neural networks and hypothesize that ill conditioned weight matrix is one of the contributing factors in neural network's susceptibility towards adversarial examples. For ensuring that the learnt weight matrix's condition number remains sufficiently low, we suggest using orthogonal regularizer. We show that this indeed helps in increasing the adversarial accuracy on MNIST and F-MNIST datasets.
Rapid Information Transfer in Networks with Delayed Self Reinforcement
Venue: arXiv cs.SY 2018
Year: 2018

The cohesiveness of response to external stimuli depends on rapid distortion-free information transfer across the network. Aligning with the information from the network has been used to model such information transfer. Nevertheless, the rate of such diffusion-type, neighbor-based information transfer is limited by the update rate at which each individual can sense and process information. Moreover, models of the diffusion-type information transfer do not predict the superfluid-like information transfer observed in nature. The contribution of this article is to show that self reinforcement, where each individual augments its neighbor-averaged information update using its previous update, can (i) increase the information-transfer rate without requiring an increased, individual update-rate; and (ii) capture the observed superfluid-like information transfer. This improvement in the information-transfer rate without modification of the network structure or increase of the bandwidth of each agent can lead to better understanding and design of networks with fast response.
Power Analysis Based Side Channel Attack
Venue: arXiv cs.CR 2018
Year: 2018

Power analysis is a branch of side channel attacks where power consumption data is used as the side channel to attack the system. First using a device like an oscilloscope power traces are collected when the cryptographic device is doing the cryptographic operation. Then those traces are statistically analysed using methods such as Correlation Power Analysis (CPA) to derive the secret key of the system. Being possible to break Advanced Encryption Standard (AES) in few minutes, power analysis attacks have become a serious security issue for cryptographic devices such as smart card. As the first phase of our project, we build a testbed for doing research on power analysis attacks. As power analysis is a practical type of attack in order to do any research, a testbed is the first requirement. Since building a test bed is a complicated process, having a pre-built testbed would save the time of future researchers. The second phase of our project is to attack the latest cryptographic algorithm called Speck which has been released by National Security Agency (NSA) for use in embedded systems. In spite it has lot of differences to AES making impossible to directly use the power analysis approach used for AES, we introduce novel approaches to break Speck in less than an hour. In the third phase of the project, we select few already introduced countermeasures and practically attack them on our testbed to do a comparative analysis. We show that software countermeasures such as random instruction injection and randomly shuffling S-boxes are good enough for their simplicity and cost. But we identify the possible threat due to the problem of generating a good seed for the pseudo-random algorithm running on the microcontroller. We attempt to address this issue by using a hardware-based true random generator that amplifies a random electrical signal and samples to generate a proper seed.
A Look at the Time Delays in CVSS Vulnerability Scoring
Venue: arXiv cs.CR 2018
Year: 2018

This empirical paper examines the time delays that occur between the publication of Common Vulnerabilities and Exposures (CVEs) in the National Vulnerability Database (NVD) and the Common Vulnerability Scoring System (CVSS) information attached to published CVEs. According to the empirical results based on regularized regression analysis of over eighty thousand archived vulnerabilities, (i) the CVSS content does not statistically influence the time delays, which, however, (ii) are strongly affected by a decreasing annual trend. In addition to these results, the paper contributes to the empirical research tradition of software vulnerabilities by a couple of insights on misuses of statistical methodology.