OpenRaiser/Intern-Atlas huggingface dataset:
An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning
Venue: arXiv cs.CV 2016
Year: 2016

The incidence of malignant melanoma continues to increase worldwide. This cancer can strike at any age; it is one of the leading causes of loss of life in young persons. Since this cancer is visible on the skin, it is potentially detectable at a very early stage when it is curable. New developments have converged to make fully automatic early melanoma detection a real possibility. First, the advent of dermoscopy has enabled a dramatic boost in clinical diagnostic ability to the point that melanoma can be detected in the clinic at the very earliest stages. The global adoption of this technology has allowed accumulation of large collections of dermoscopy images of melanomas and benign lesions validated by histopathology. The development of advanced technologies in the areas of image processing and machine learning have given us the ability to allow distinction of malignant melanoma from the many benign mimics that require no biopsy. These new technologies should allow not only earlier detection of melanoma, but also reduction of the large number of needless and costly biopsy procedures. Although some of the new systems reported for these technologies have shown promise in preliminary trials, widespread implementation must await further technical progress in accuracy and reproducibility. In this paper, we provide an overview of computerized detection of melanoma in dermoscopy images. First, we discuss the various aspects of lesion segmentation. Then, we provide a brief overview of clinical feature segmentation. Finally, we discuss the classification stage where machine learning algorithms are applied to the attributes generated from the segmented features to predict the existence of melanoma.
Blind Interference Alignment for Private Information Retrieval
Venue: arXiv cs.IT 2016
Year: 2016

Blind interference alignment (BIA) refers to interference alignment schemes that are designed only based on channel coherence pattern knowledge at the transmitters (the "blind" transmitters do not know the exact channel values). Private information retrieval (PIR) refers to the problem where a user retrieves one out of K messages from N non-communicating databases (each holds all K messages) without revealing anything about the identity of the desired message index to any individual database. In this paper, we identify an intriguing connection between PIR and BIA. Inspired by this connection, we characterize the information theoretic optimal download cost of PIR, when we have K = 2 messages and the number of databases, N, is arbitrary.
TrAD: Traffic Adaptive Data Dissemination Protocol for Both Urban and Highway VANETs
Venue: arXiv cs.NI 2016
Year: 2016

Vehicular Ad hoc Networks (VANETs) aim to improve transportation activities that include traffic safety, transport efficiency and even infotainment on the wheels, in which a great number of traffic event-driven messages are needed to disseminate in a region of interest timely. However, due to the nature of VANETs, highly dynamic mobility and frequent disconnection, data dissemination faces great challenges. Inter-Vehicle Communication (IVC) protocols are the key technology to mitigate this issue. Therefore, we propose an infrastructure-less Traffic Adaptive data Dissemination (TrAD) protocol that considers road traffic and network traffic status for both highway and urban scenarios. TrAD is flexible to fit the irregular road topology and owns double broadcast suppression techniques. Three state-of-the-art IVC protocols have been compared with TrAD by means of realistic simulations. The performance of all protocols is quantitatively evaluated with different real city maps and traffic routes. Finally, TrAD gets an outstanding overall performance in terms of several metrics, even though under the worse condition of GPS drift.
Relaxed Byzantine Vector Consensus
Venue: arXiv cs.DC 2016
Year: 2016

Exact Byzantine consensus problem requires that non-faulty processes reach agreement on a decision (or output) that is in the convex hull of the inputs at the non-faulty processes. It is well-known that exact consensus is impossible in an asynchronous system in presence of faults, and in a synchronous system, n>=3f+1 is tight on the number of processes to achieve exact Byzantine consensus with scalar inputs, in presence of up to f Byzantine faulty processes. Recent work has shown that when the inputs are d-dimensional vectors of reals, n>=max(3f+1,(d+1)f+1) is tight to achieve exact Byzantine consensus in synchronous systems, and n>= (d+2)f+1 for approximate Byzantine consensus in asynchronous systems. Due to the dependence of the lower bound on vector dimension d, the number of processes necessary becomes large when the vector dimension is large. With the hope of reducing the lower bound on n, we consider two relaxed versions of Byzantine vector consensus: k-Relaxed Byzantine vector consensus and (delta,p)-Relaxed Byzantine vector consensus. In k-relaxed consensus, the validity condition requires that the output must be in the convex hull of projection of the inputs onto any subset of k-dimensions of the vectors. For (delta,p)-consensus the validity condition requires that the output must be within distance delta of the convex hull of the inputs of the non-faulty processes, where L_p norm is used as the distance metric. For (delta,p)-consensus, we consider two versions: in one version, delta is a constant, and in the second version, delta is a function of the inputs themselves. We show that for k-relaxed consensus and (delta,p)-consensus with constant delta>=0, the bound on n is identical to the bound stated above for the original vector consensus problem. On the other hand, when delta depends on the inputs, we show that the bound on n is smaller when d>=3.
Interference Management in Heterogeneous Networks with Blind Transmitters
Venue: arXiv cs.IT 2016
Year: 2016

Future multi-tier communication networks will require enhanced network capacity and reduced overhead. In the absence of Channel State Information (CSI) at the transmitters, Blind Interference Alignment (BIA) and Topological Interference Management (TIM) can achieve optimal Degrees of Freedom (DoF), minimising network's overhead. In addition, Non-Orthogonal Multiple Access (NOMA) can increase the sum rate of the network, compared to orthogonal radio access techniques currently adopted by 4G networks. Our contribution is two interference management schemes, BIA and a hybrid TIM-NOMA scheme, employed in heterogeneous networks by applying user-pairing and Kronecker Product representation. BIA manages inter- and intra-cell interference by antenna selection and appropriate message scheduling. The hybrid scheme manages intra-cell interference based on NOMA and inter-cell interference based on TIM. We show that both schemes achieve at least double the rate of TDMA. The hybrid scheme always outperforms TDMA and BIA in terms of Degrees of Freedom (DoF). Comparing the two proposed schemes, BIA achieves more DoF than TDMA under certain restrictions, and provides better Bit-Error-Rate (BER) and sum rate performance to macrocell users, whereas the hybrid scheme improves the performance of femtocell users.
Semantic Localization in the PCL library
Venue: arXiv cs.RO 2016
Year: 2016

The semantic localization problem in robotics consists in determining the place where a robot is located by means of semantic categories. The problem is usually addressed as a supervised classification process, where input data correspond to robot perceptions while classes to semantic categories, like kitchen or corridor. In this paper we propose a framework, implemented in the PCL library, which provides a set of valuable tools to easily develop and evaluate semantic localization systems. The implementation includes the generation of 3D global descriptors following a Bag-of-Words approach. This allows the generation of dimensionality-fixed descriptors from any type of keypoint detector and feature extractor combinations. The framework has been designed, structured and implemented in order to be easily extended with different keypoint detectors, feature extractors as well as classification models. The proposed framework has also been used to evaluate the performance of a set of already implemented descriptors, when used as input for a specific semantic localization system. The results obtained are discussed paying special attention to the internal parameters of the BoW descriptor generation process. Moreover, we also review the combination of some keypoint detectors with different 3D descriptor generation techniques.
A State-of-the-art Integrated Transportation Simulation Platform
Venue: arXiv cs.MA 2016
Year: 2016

Nowadays, universities and companies have a huge need for simulation and modelling methodologies. In the particular case of traffic and transportation, making physical modifications to the real traffic networks could be highly expensive, dependent on political decisions and could be highly disruptive to the environment. However, while studying a specific domain or problem, analysing a problem through simulation may not be trivial and may need several simulation tools, hence raising interoperability issues. To overcome these problems, we propose an agent-directed transportation simulation platform, through the cloud, by means of services. We intend to use the IEEE standard HLA (High Level Architecture) for simulators interoperability and agents for controlling and coordination. Our motivations are to allow multiresolution analysis of complex domains, to allow experts to collaborate on the analysis of a common problem and to allow co-simulation and synergy of different application domains. This paper will start by presenting some preliminary background concepts to help better understand the scope of this work. After that, the results of a literature review is shown. Finally, the general architecture of a transportation simulation platform is proposed.
WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases
Venue: arXiv cs.DB 2016
Year: 2016

Workload management for cloud databases must deal with the tasks of resource provisioning, query placement and query scheduling in a manner that meets the application's performance goals while minimizing the cost of using cloud resources. Existing solutions have approached these three challenges in isolation, and with only a particular type of performance goal in mind. In this paper, we introduce WiSeDB, a learning-based framework for generating holistic workload management solutions customized to application-defined performance metrics and workload characteristics. Our approach relies on supervised learning to train cost-effective decision tree models for guiding query placement, scheduling, and resource provisioning decisions. Applications can use these models for both batch and online scheduling of incoming workloads. A unique feature of our system is that it can adapt its offline model to stricter/looser performance goals with minimal re-training. This allows us to present alternative workload management strategies that address the typical performance vs. cost trade-off of cloud services. Experimental results show that our approach has very low training overhead while offering low cost strategies for a variety of performance goals and workload characteristics.
Dynamic Virtual Machine Management via Approximate Markov Decision Process
Venue: arXiv cs.NI 2016
Year: 2016

Efficient virtual machine (VM) management can dramatically reduce energy consumption in data centers. Existing VM management algorithms fall into two categories based on whether the VMs' resource demands are assumed to be static or dynamic. The former category fails to maximize the resource utilization as they cannot adapt to the dynamic nature of VMs' resource demands. Most approaches in the latter category are heuristical and lack theoretical performance guarantees. In this work, we formulate dynamic VM management as a large-scale Markov Decision Process (MDP) problem and derive an optimal solution. Our analysis of real-world data traces supports our choice of the modeling approach. However, solving the large-scale MDP problem suffers from the curse of dimensionality. Therefore, we further exploit the special structure of the problem and propose an approximate MDP-based dynamic VM management method, called MadVM. We prove the convergence of MadVM and analyze the bound of its approximation error. Moreover, MadVM can be implemented in a distributed system, which should suit the needs of real data centers. Extensive simulations based on two real-world workload traces show that MadVM achieves significant performance gains over two existing baseline approaches in power consumption, resource shortage and the number of VM migrations. Specifically, the more intensely the resource demands fluctuate, the more MadVM outperforms.
A Proximal Stochastic Quasi-Newton Algorithm
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we discuss the problem of minimizing the sum of two convex functions: a smooth function plus a non-smooth function. Further, the smooth part can be expressed by the average of a large number of smooth component functions, and the non-smooth part is equipped with a simple proximal mapping. We propose a proximal stochastic second-order method, which is efficient and scalable. It incorporates the Hessian in the smooth part of the function and exploits multistage scheme to reduce the variance of the stochastic gradient. We prove that our method can achieve linear rate of convergence.
Transmitter Optimization in Slow Fading MISO Wiretap Channel
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we consider the transmitter optimization problem in slow fading multiple-input-single-output (MISO) wiretap channel. The source transmits a secret message intended for $K$ users in the presence of $J$ non-colluding eavesdroppers, and operates under a total power constraint. The channels between the source and all users and eavesdroppers are assumed to be slow fading, and only statistical channel state information (CSI) is known at the source. For a given code rate and secrecy rate pair of the wiretap code, denoted by $(R_{D}, R_{s})$, we define the non-outage event as the joint event of the link information rates to $K$ users be greater than or equal to $R_{D}$ and the link information rates to $J$ eavesdroppers be less than or equal to $(R_{D} - R_{s})$. We minimize the transmit power subject to the total power constraint and satisfying the probability of the non-outage event to be greater than or equal to a desired threshold $(1-ε)$.
Modelling the transmission dynamics of online social contagion
Venue: arXiv cs.SI 2016
Year: 2016

During 2014-15, there were several outbreaks of nominated-based online social contagion. These infections, which were transmitted from one individual to another via posts on social media, included games such as 'neknomination', 'ice bucket challenge', 'no make up selfies', and Facebook users re-posting their first profile pictures. Fitting a mathematical model of infectious disease transmission to outbreaks of these four games in the United Kingdom, I estimated the basic reproduction number, $R_0$, and generation time of each infection. Median estimates for $R_0$ ranged from 1.9-2.5 across the four outbreaks, and the estimated generation times were between 1.0 and 2.0 days. Tests using out-of-sample data from Australia suggested that the model had reasonable predictive power, with $R^2$ values between 0.52-0.70 across the four Australian datasets. Further, the relatively low basic reproduction numbers for the infections suggests that only 48-60% of index cases in nomination-based games may subsequently generate major outbreaks.
WASSUP? LOL : Characterizing Out-of-Vocabulary Words in Twitter
Venue: arXiv cs.CL 2016
Year: 2016

Language in social media is mostly driven by new words and spellings that are constantly entering the lexicon thereby polluting it and resulting in high deviation from the formal written version. The primary entities of such language are the out-of-vocabulary (OOV) words. In this paper, we study various sociolinguistic properties of the OOV words and propose a classification model to categorize them into at least six categories. We achieve 81.26% accuracy with high precision and recall. We observe that the content features are the most discriminative ones followed by lexical and context features.
Novel Views of Objects from a Single Image
Venue: arXiv cs.CV 2016
Year: 2016

Taking an image of an object is at its core a lossy process. The rich information about the three-dimensional structure of the world is flattened to an image plane and decisions such as viewpoint and camera parameters are final and not easily revertible. As a consequence, possibilities of changing viewpoint are limited. Given a single image depicting an object, novel-view synthesis is the task of generating new images that render the object from a different viewpoint than the one given. The main difficulty is to synthesize the parts that are disoccluded; disocclusion occurs when parts of an object are hidden by the object itself under a specific viewpoint. In this work, we show how to improve novel-view synthesis by making use of the correlations observed in 3D models and applying them to new image instances. We propose a technique to use the structural information extracted from a 3D model that matches the image object in terms of viewpoint and shape. For the latter part, we propose an efficient 2D-to-3D alignment method that associates precisely the image appearance with the 3D model geometry with minimal user interaction. Our technique is able to simulate plausible viewpoint changes for a variety of object classes within seconds. Additionally, we show that our synthesized images can be used as additional training data that improves the performance of standard object detectors.
Hadoop on HPC: Integrating Hadoop and Pilot-based Dynamic Resource Management
Venue: arXiv cs.DC 2016
Year: 2016

High-performance computing platforms such as supercomputers have traditionally been designed to meet the compute demands of scientific applications. Consequently, they have been architected as producers and not consumers of data. The Apache Hadoop ecosystem has evolved to meet the requirements of data processing applications and has addressed many of the limitations of HPC platforms. There exist a class of scientific applications however, that need the collective capabilities of traditional high-performance computing environments and the Apache Hadoop ecosystem. For example, the scientific domains of bio-molecular dynamics, genomics and network science need to couple traditional computing with Hadoop/Spark based analysis. We investigate the critical question of how to present the capabilities of both computing environments to such scientific applications. Whereas this questions needs answers at multiple levels, we focus on the design of resource management middleware that might support the needs of both. We propose extensions to the Pilot-Abstraction to provide a unifying resource management layer. This is an important step that allows applications to integrate HPC stages (e.g. simulations) to data analytics. Many supercomputing centers have started to officially support Hadoop environments, either in a dedicated environment or in hybrid deployments using tools such as myHadoop. This typically involves many intrinsic, environment-specific details that need to be mastered, and often swamp conceptual issues like: How best to couple HPC and Hadoop application stages? How to explore runtime trade-offs (data localities vs. data movement)? This paper provides both conceptual understanding and practical solutions to the integrated use of HPC and Hadoop environments.
Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers
Venue: arXiv cs.CL 2016
Year: 2016

Document classification tasks were primarily tackled at word level. Recent research that works with character-level inputs shows several benefits over word-level approaches such as natural incorporation of morphemes and better handling of rare words. We propose a neural network architecture that utilizes both convolution and recurrent layers to efficiently encode character inputs. We validate the proposed model on eight large scale document classification tasks and compare with character-level convolution-only models. It achieves comparable performances with much less parameters.
Scene Invariant Crowd Segmentation and Counting Using Scale-Normalized Histogram of Moving Gradients (HoMG)
Venue: arXiv cs.CV 2016
Year: 2016

The problem of automated crowd segmentation and counting has garnered significant interest in the field of video surveillance. This paper proposes a novel scene invariant crowd segmentation and counting algorithm designed with high accuracy yet low computational complexity in mind, which is key for widespread industrial adoption. A novel low-complexity, scale-normalized feature called Histogram of Moving Gradients (HoMG) is introduced for highly effective spatiotemporal representation of individuals and crowds within a video. Real-time crowd segmentation is achieved via boosted cascade of weak classifiers based on sliding-window HoMG features, while linear SVM regression of crowd-region HoMG features is employed for real-time crowd counting. Experimental results using multi-camera crowd datasets show that the proposed algorithm significantly outperform state-of-the-art crowd counting algorithms, as well as achieve very promising crowd segmentation results, thus demonstrating the efficacy of the proposed method for highly-accurate, real-time video-driven crowd analysis.
The Price of Order
Venue: arXiv cs.CG 2016
Year: 2016

We present tight bounds on the spanning ratio of a large family of ordered $θ$-graphs. A $θ$-graph partitions the plane around each vertex into $m$ disjoint cones, each having aperture $θ= 2 π/m$. An ordered $θ$-graph is constructed by inserting the vertices one by one and connecting each vertex to the closest previously-inserted vertex in each cone. We show that for any integer $k \geq 1$, ordered $θ$-graphs with $4k + 4$ cones have a tight spanning ratio of $1 + 2 \sin(θ/2) / (\cos(θ/2) - \sin(θ/2))$. We also show that for any integer $k \geq 2$, ordered $θ$-graphs with $4k + 2$ cones have a tight spanning ratio of $1 / (1 - 2 \sin(θ/2))$. We provide lower bounds for ordered $θ$-graphs with $4k + 3$ and $4k + 5$ cones. For ordered $θ$-graphs with $4k + 2$ and $4k + 5$ cones these lower bounds are strictly greater than the worst case spanning ratios of their unordered counterparts. These are the first results showing that ordered $θ$-graphs have worse spanning ratios than unordered $θ$-graphs. Finally, we show that, unlike their unordered counterparts, the ordered $θ$-graphs with 4, 5, and 6 cones are not spanners.