OpenRaiser/Intern-Atlas huggingface dataset:
Ergodic Capacity Analysis of Free-Space Optical Links With Nonzero Boresight Pointing Errors
Venue: arXiv cs.IT 2018
Year: 2018

A unified capacity analysis of a free-space optical (FSO) link that accounts for nonzero boresight pointing errors and both types of detection techniques (i.e. intensity modulation/direct detection as well as heterodyne detection) is addressed in this work. More specifically, an exact closed-form expression for the moments of the end-to-end signal-to-noise ratio (SNR) of a single link FSO transmission system is presented in terms of well-known elementary functions. Capitalizing on these new moments expressions, we present approximate and simple closed-form results for the ergodic capacity at high and low SNR regimes. All the presented results are verified via computer-based Monte-Carlo simulations.
The Parallel Persistent Memory Model
Venue: arXiv cs.DC 2018
Year: 2018

We consider a parallel computational model that consists of $P$ processors, each with a fast local ephemeral memory of limited size, and sharing a large persistent memory. The model allows for each processor to fault with bounded probability, and possibly restart. On faulting all processor state and local ephemeral memory are lost, but the persistent memory remains. This model is motivated by upcoming non-volatile memories that are as fast as existing random access memory, are accessible at the granularity of cache lines, and have the capability of surviving power outages. It is further motivated by the observation that in large parallel systems, failure of processors and their caches is not unusual. Within the model we develop a framework for developing locality efficient parallel algorithms that are resilient to failures. There are several challenges, including the need to recover from failures, the desire to do this in an asynchronous setting (i.e., not blocking other processors when one fails), and the need for synchronization primitives that are robust to failures. We describe approaches to solve these challenges based on breaking computations into what we call capsules, which have certain properties, and developing a work-stealing scheduler that functions properly within the context of failures. The scheduler guarantees a time bound of $O(W/P_A + D(P/P_A) \lceil\log_{1/f} W\rceil)$ in expectation, where $W$ and $D$ are the work and depth of the computation (in the absence of failures), $P_A$ is the average number of processors available during the computation, and $f \le 1/2$ is the probability that a capsule fails. Within the model and using the proposed methods, we develop efficient algorithms for parallel sorting and other primitives.
Stories for Images-in-Sequence by using Visual and Narrative Components
Venue: arXiv cs.AI 2018
Year: 2018

Recent research in AI is focusing towards generating narrative stories about visual scenes. It has the potential to achieve more human-like understanding than just basic description generation of images- in-sequence. In this work, we propose a solution for generating stories for images-in-sequence that is based on the Sequence to Sequence model. As a novelty, our encoder model is composed of two separate encoders, one that models the behaviour of the image sequence and other that models the sentence-story generated for the previous image in the sequence of images. By using the image sequence encoder we capture the temporal dependencies between the image sequence and the sentence-story and by using the previous sentence-story encoder we achieve a better story flow. Our solution generates long human-like stories that not only describe the visual context of the image sequence but also contains narrative and evaluative language. The obtained results were confirmed by manual human evaluation.
Complexity Reduction in the Negotiation of New Lexical Conventions
Venue: arXiv cs.MA 2018
Year: 2018

In the process of collectively inventing new words for new concepts in a population, conflicts can quickly become numerous, in the form of synonymy and homonymy. Remembering all of them could cost too much memory, and remembering too few may slow down the overall process. Is there an efficient behavior that could help balance the two? The Naming Game is a multi-agent computational model for the emergence of language, focusing on the negotiation of new lexical conventions, where a common lexicon self-organizes but going through a phase of high complexity. Previous work has been done on the control of complexity growth in this particular model, by allowing agents to actively choose what they talk about. However, those strategies were relying on ad hoc heuristics highly dependent on fine-tuning of parameters. We define here a new principled measure and a new strategy, based on the beliefs of each agent on the global state of the population. The measure does not rely on heavy computation, and is cognitively plausible. The new strategy yields an efficient control of complexity growth, along with a faster agreement process. Also, we show that short-term memory is enough to build relevant beliefs about the global lexicon.
Accelerated Model Checking of Parametric Markov Chains
Venue: arXiv cs.LO 2018
Year: 2018

Parametric Markov chains occur quite naturally in various applications: they can be used for a conservative analysis of probabilistic systems (no matter how the parameter is chosen, the system works to specification); they can be used to find optimal settings for a parameter; they can be used to visualise the influence of system parameters; and they can be used to make it easy to adjust the analysis for the case that parameters change. Unfortunately, these advancements come at a cost: parametric model checking is---or rather was---often slow. To make the analysis of parametric Markov models scale, we need three ingredients: clever algorithms, the right data structure, and good engineering. Clever algorithms are often the main (or sole) selling point; and we face the trouble that this paper focuses on -- the latter ingredients to efficient model checking. Consequently, our easiest claim to fame is in the speed-up we have often realised when comparing to the state of the art.
Approximate Edge Analytics for the IoT Ecosystem
Venue: arXiv cs.DC 2018
Year: 2018

IoT-enabled devices continue to generate a massive amount of data. Transforming this continuously arriving raw data into timely insights is critical for many modern online services. For such settings, the traditional form of data analytics over the entire dataset would be prohibitively limiting and expensive for supporting real-time stream analytics. In this work, we make a case for approximate computing for data analytics in IoT settings. Approximate computing aims for efficient execution of workflows where an approximate output is sufficient instead of the exact output. The idea behind approximate computing is to compute over a representative sample instead of the entire input dataset. Thus, approximate computing - based on the chosen sample size - can make a systematic trade-off between the output accuracy and computation efficiency. This motivated the design of APPROXIOT - a data analytics system for approximate computing in IoT. To realize this idea, we designed an online hierarchical stratified reservoir sampling algorithm that uses edge computing resources to produce approximate output with rigorous error bounds. To showcase the effectiveness of our algorithm, we implemented APPROXIOT based on Apache Kafka and evaluated its effectiveness using a set of microbenchmarks and real-world case studies. Our results show that APPROXIOT achieves a speedup 1.3X-9.9X with varying sampling fraction of 80% to 10% compared to simple random sampling.
Distribution-based Label Space Transformation for Multi-label Learning
Venue: arXiv cs.CV 2018
Year: 2018

Multi-label learning problems have manifested themselves in various machine learning applications. The key to successful multi-label learning algorithms lies in the exploration of inter-label correlations, which usually incur great computational cost. Another notable factor in multi-label learning is that the label vectors are usually extremely sparse, especially when the candidate label vocabulary is very large and only a few instances are assigned to each category. Recently, a label space transformation (LST) framework has been proposed targeting these challenges. However, current methods based on LST usually suffer from information loss in the label space dimension reduction process and fail to address the sparsity problem effectively. In this paper, we propose a distribution-based label space transformation (DLST) model. By defining the distribution based on the similarity of label vectors, a more comprehensive label structure can be captured. Then, by minimizing KL-divergence of two distributions, the information of the original label space can be approximately preserved in the latent space. Consequently, multi-label classifier trained using the dense latent codes yields better performance. The leverage of distribution enables DLST to fill out additional information about the label correlations. This endows DLST the capability to handle label set sparsity and training data sparsity in multi-label learning problems. With the optimal latent code, a kernel logistic regression function is learned for the mapping from feature space to the latent space. Then ML-KNN is employed to recover the original label vector from the transformed latent code. Extensive experiments on several benchmark datasets demonstrate that DLST not only achieves high classification performance but also is computationally more efficient.
2sRanking-CNN: A 2-stage ranking-CNN for diagnosis of glaucoma from fundus images using CAM-extracted ROI as an intermediate input
Venue: arXiv cs.CV 2018
Year: 2018

Glaucoma is a disease in which the optic nerve is chronically damaged by the elevation of the intra-ocular pressure, resulting in visual field defect. Therefore, it is important to monitor and treat suspected patients before they are confirmed with glaucoma. In this paper, we propose a 2-stage ranking-CNN that classifies fundus images as normal, suspicious, and glaucoma. Furthermore, we propose a method of using the class activation map as a mask filter and combining it with the original fundus image as an intermediate input. Our results have improved the average accuracy by about 10% over the existing 3-class CNN and ranking-CNN, and especially improved the sensitivity of suspicious class by more than 20% over 3-class CNN. In addition, the extracted ROI was also found to overlap with the diagnostic criteria of the physician. The method we propose is expected to be efficiently applied to any medical data where there is a suspicious condition between normal and disease.
Multi-label Classification of Surgical Tools with Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Automatic tool detection from surgical imagery has a multitude of useful applications, such as real-time computer assistance for the surgeon. Using the successful residual network architecture, a system that can distinguish 21 different tools in cataract surgery videos is created. The videos are provided as part of the 2017 CATARACTS challenge and pose difficulties found in many real-world datasets, for example a strong class imbalance. The construction of the detection system is guided by a wide array of experiments that explore different design decisions.
Active Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration
Venue: arXiv cs.LG 2018
Year: 2018

Single-trial classification of event-related potentials in electroencephalogram (EEG) signals is a very important paradigm of brain-computer interface (BCI). Because of individual differences, usually some subject-specific calibration data are required to tailor the classifier for each subject. Transfer learning has been extensively used to reduce such calibration data requirement, by making use of auxiliary data from similar/relevant subjects/tasks. However, all previous research assumes that all auxiliary data have been labeled. This paper considers a more general scenario, in which part of the auxiliary data could be unlabeled. We propose active semi-supervised transfer learning (ASTL) for offline BCI calibration, which integrates active learning, semi-supervised learning, and transfer learning. Using a visual evoked potential oddball task and three different EEG headsets, we demonstrate that ASTL can achieve consistently good performance across subjects and headsets, and it outperforms some state-of-the-art approaches in the literature.
Passivity based distributed tracking control of networked Euler-Lagrange systems
Venue: arXiv cs.SY 2018
Year: 2018

In this paper we present three distributed control laws for the coordination of networked Euler-Lagrange (EL) systems. We first reformulate the passivity-based control design method in \cite{Arcak} by considering that each edge is associated with an \emph{artificial spring system} instead of the usual diffusive coupling among the communicating agents. With this configuration, the networked EL system possesses a "symmetric" feedback structure which together with the strict passivity of both agents' and edges' dynamics lead to a strictly passive network dynamics. Subsequently we present the networked version of two different passivity-based tracking controllers %local controllers that are particular cases of our method and the one in \cite{Arcak}. Numerical simulation is presented to show the performance of the proposed methods.
CLINIQA: A Machine Intelligence Based Clinical Question Answering System
Venue: arXiv cs.CL 2018
Year: 2018

The recent developments in the field of biomedicine have made large volumes of biomedical literature available to the medical practitioners. Due to the large size and lack of efficient searching strategies, medical practitioners struggle to obtain necessary information available in the biomedical literature. Moreover, the most sophisticated search engines of age are not intelligent enough to interpret the clinicians' questions. These facts reflect the urgent need of an information retrieval system that accepts the queries from medical practitioners' in natural language and returns the answers quickly and efficiently. In this paper, we present an implementation of a machine intelligence based CLINIcal Question Answering system (CLINIQA) to answer medical practitioner's questions. The system was rigorously evaluated on different text mining algorithms and the best components for the system were selected. The system makes use of Unified Medical Language System for semantic analysis of both questions and medical documents. In addition, the system employs supervised machine learning algorithms for classification of the documents, identifying the focus of the question and answer selection. Effective domain-specific heuristics are designed for answer ranking. The performance evaluation on hundred clinical questions shows the effectiveness of our approach.
Towards a Design Philosophy for Interoperable Blockchain Systems
Venue: arXiv cs.CR 2018
Year: 2018

In this paper we discuss a design philosophy for interoperable blockchain systems, using the design philosophy of the Internet architecture as the basis to identify key design principles. Several interoperability challenges are discussed in the context of cross-domain transactions. We illustrate how these principles are informing the interoperability architecture of the MIT Tradecoin system.
Agent Based Rumor Spreading in a scale-free network
Venue: arXiv cs.MA 2018
Year: 2018

In the last years, the study of rumor spreading on social networks produced a lot of interest among the scientific community, expecially due to the role of social networks in the last political events. The goal of this work is to reproduce real-like diffusions of information and misinformation in a scale-free network using a multi-agent-based model. The data concerning the virtual spreading are easily obtainable, in particular the diffusion of information during the announcement for the discovery of the Higgs Boson on Twitter was recorded and investigated in detail. We made some assumptions on the micro behavior of our agents and registered the effects in a statistical analysis replying the real data diffusion. Then, we studied an hypotetical response to a misinformation diffusion adding debunking agents and trying to model a critic response from the agents using real data from a hoax regarding the Occupy Wall Street movement. After tuning our model to reproduce these results, we measured some network properties and proved the emergence of substantially separated structures like echochambers, independently from the network size scale, i.e. with one hundred, one thousand and ten thousand agents.
Topological Eulerian Synthesis of Slow Motion Periodic Videos
Venue: arXiv cs.MM 2018
Year: 2018

We consider the problem of taking a video that is comprised of multiple periods of repetitive motion, and reordering the frames of the video into a single period, producing a detailed, single cycle video of motion. This problem is challenging, as such videos often contain noise, drift due to camera motion and from cycle to cycle, and irrelevant background motion/occlusions, and these factors can confound the relevant periodic motion we seek in the video. To address these issues in a simple and efficient manner, we introduce a tracking free Eulerian approach for synthesizing a single cycle of motion. Our approach is geometric: we treat each frame as a point in high-dimensional Euclidean space, and analyze the sliding window embedding formed by this sequence of points, which yields samples along a topological loop regardless of the type of periodic motion. We combine tools from topological data analysis and spectral geometric analysis to estimate the phase of each window, and we exploit the sliding window structure to robustly reorder frames. We show quantitative results that highlight the robustness of our technique to camera shake, noise, and occlusions, and qualitative results of single-cycle motion synthesis across a variety of scenarios.
String Methods for Stochastic Image and Shape Matching
Venue: arXiv cs.CV 2018
Year: 2018

Matching of images and analysis of shape differences is traditionally pursued by energy minimization of paths of deformations acting to match the shape objects. In the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, iterative gradient descents on the matching functional lead to matching algorithms informally known as Beg algorithms. When stochasticity is introduced to model stochastic variability of shapes and to provide more realistic models of observed shape data, the corresponding matching problem can be solved with a stochastic Beg algorithm, similar to the finite temperature string method used in rare event sampling. In this paper, we apply a stochastic model compatible with the geometry of the LDDMM framework to obtain a stochastic model of images and we derive the stochastic version of the Beg algorithm which we compare with the string method and an expectation-maximization optimization of posterior likelihoods. The algorithm and its use for statistical inference is tested on stochastic LDDMM landmarks and images.
Strategic analysis of implementation assets and threats
Venue: arXiv cs.GT 2018
Year: 2018

The aim of the strategic analysis is to (simply) carry out the game between the implementing body and possible links to the existing market situation. We are therefore playing a strategic game between us and the outside world. This situation is most often associated with existing and potential threats, such as competition, fashion trends, cost situation, marketing effectiveness, market demand, etc. However, this does not exhaust all aspects resulting from the situation, as conditions conducive to the implementation may also be taken into account, such as the possibility of cooperation, favourable location, new forms of reaching the recipient, new legal solutions, etc. Generally speaking, we can divide the set of conditions into assets and threats. Playing a strategic game leads not only to the estimation of the game's value but also points to the equilibrium points (saddle points) and to the conditions of market stabilization. We therefore have two players (one zero-sum two-player game depending on the assumptions made); one side is our assets and the other side is external threats. The strategies of both players will be a combination of implementation, market and marketing parameters. This will be described as an example in the introduction (chapter 1). The next chapters (2 and 3) are proposals for estimating the effects of strategy selection, i.e. creating payoffs for players. The final stage will be playing the game, the analysis of its results (chapters 3) and the summary (chapter 4).
A Survey of Intrusion Detection Systems Leveraging Host Data
Venue: arXiv cs.CR 2018
Year: 2018

This survey focuses on intrusion detection systems (IDS) that leverage host-based data sources for detecting attacks on enterprise network. The host-based IDS (HIDS) literature is organized by the input data source, presenting targeted sub-surveys of HIDS research leveraging system logs, audit data, Windows Registry, file systems, and program analysis. While system calls are generally included in audit data, several publicly available system call datasets have spawned a flurry of IDS research on this topic, which merits a separate section. Similarly, a section surveying algorithmic developments that are applicable to HIDS but tested on network data sets is included, as this is a large and growing area of applicable literature. To accommodate current researchers, a supplementary section giving descriptions of publicly available datasets is included, outlining their characteristics and shortcomings when used for IDS evaluation. Related surveys are organized and described. All sections are accompanied by tables concisely organizing the literature and datasets discussed. Finally, challenges, trends, and broader observations are throughout the survey and in the conclusion along with future directions of IDS research.
An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-Identification
Venue: arXiv cs.CV 2018
Year: 2018

In recent years, a variety of proposed methods based on deep convolutional neural networks (CNNs) have improved the state of the art for large-scale person re-identification (ReID). While a large number of optimizations and network improvements have been proposed, there has been relatively little evaluation of the influence of training data and baseline network architecture. In particular, it is usually assumed either that networks are trained on labeled data from the deployment location (scene-dependent), or else adapted with unlabeled data, both of which complicate system deployment. In this paper, we investigate the feasibility of achieving scene-independent person ReID by forming a large composite dataset for training. We present an in-depth comparison of several CNN baseline architectures for both scene-dependent and scene-independent ReID, across a range of training dataset sizes. We show that scene-independent ReID can produce leading-edge results, competitive with unsupervised domain adaption techniques. Finally, we introduce a new dataset for comparing within-camera and across-camera person ReID.