OpenRaiser/Intern-Atlas huggingface dataset:
Classification of Cervical Cancer Dataset
Venue: arXiv cs.CY 2018
Year: 2018

Cervical cancer is the leading gynecological malignancy worldwide. This paper presents diverse classification techniques and shows the advantage of feature selection approaches to the best predicting of cervical cancer disease. There are thirty-two attributes with eight hundred and fifty-eight samples. Besides, this data suffers from missing values and imbalance data. Therefore, over-sampling, under-sampling and embedded over and under sampling have been used. Furthermore, dimensionality reduction techniques are required for improving the accuracy of the classifier. Therefore, feature selection methods have been studied as they divided into two distinct categories, filters and wrappers. The results show that age, first sexual intercourse, number of pregnancies, smokes, hormonal contraceptives, and STDs: genital herpes are the main predictive features with high accuracy with 97.5%. Decision Tree classifier is shown to be advantageous in handling classification assignment with excellent performance.
Data Sharing, Distribution and Updating Using Social Coding Community Github and LaTaX Packages in Graduate Research
Venue: arXiv cs.CY 2018
Year: 2018

Current paper reports the advantages of the application of GitHub and LaTeX for the MSc thesis writing. The existing code-based program implemented in GitHub portal provides a great tool for scientists and students for data sharing and notification of the co-workers, tutors and supervisors involved in research about actual updates. It enables to connect collaborators to share around current results, release datasets and updates and more. Using standard command-line interface GitHub allows registered users to push repositories on the website. The availability of both public and private repositories enables to share current data updates with target audience: e.g. unpublished research work only for co-authors or supervisors, or vice versa. Therefore, there is a need in academic centres and universities to strongly popularize and increase the use of GitHub for student works. The case study is given on the graduate study: an MSc work written and maintained using open source GitHub service at the University of Twente, Faculty of Geo-Information Science and Earth Observation (Netherlands). It reports my successful experience of writing MSc thesis based on the effective combination of LaTeX and GitHub.
Word Embedding based on Low-Rank Doubly Stochastic Matrix Decomposition
Venue: arXiv cs.CL 2018
Year: 2018

Word embedding, which encodes words into vectors, is an important starting point in natural language processing and commonly used in many text-based machine learning tasks. However, in most current word embedding approaches, the similarity in embedding space is not optimized in the learning. In this paper we propose a novel neighbor embedding method which directly learns an embedding simplex where the similarities between the mapped words are optimal in terms of minimal discrepancy to the input neighborhoods. Our method is built upon two-step random walks between words via topics and thus able to better reveal the topics among the words. Experiment results indicate that our method, compared with another existing word embedding approach, is more favorable for various queries.
Applying Predicate Detection to the Constrained Optimization Problems
Venue: arXiv cs.DS 2018
Year: 2018

We present a method to design parallel algorithms for constrained combinatorial optimization problems. Our method solves and generalizes many classical combinatorial optimization problems including the stable marriage problem, the shortest path problem and the market clearing price problem. These three problems are solved in the literature using Gale-Shapley algorithm, Dijkstra's algorithm, and Demange, Gale, Sotomayor algorithm. Our method solves all these problems by casting them as searching for an element that satisfies an appropriate predicate in a distributive lattice. Moreover, it solves generalizations of all these problems - namely finding the optimal solution satisfying additional constraints called {\em lattice-linear} predicates. For stable marriage problems, an example of such a constraint is that Peter's regret is less than that of Paul. For shortest path problems, an example of such a constraint is that cost of reaching vertex $v_1$ is at least the cost of reaching vertex $v_2$. For the market clearing price problem, an example of such a constraint is that $item_1$ is priced at least as much as $item_2$. In addition to finding the optimal solution, our method is useful in enumerating all constrained stable matchings, and all constrained market clearing price vectors.
Finger-GAN: Generating Realistic Fingerprint Images Using Connectivity Imposed GAN
Venue: arXiv cs.CV 2018
Year: 2018

Generating realistic biometric images has been an interesting and, at the same time, challenging problem. Classical statistical models fail to generate realistic-looking fingerprint images, as they are not powerful enough to capture the complicated texture representation in fingerprint images. In this work, we present a machine learning framework based on generative adversarial networks (GAN), which is able to generate fingerprint images sampled from a prior distribution (learned from a set of training images). We also add a suitable regularization term to the loss function, to impose the connectivity of generated fingerprint images. This is highly desirable for fingerprints, as the lines in each finger are usually connected. We apply this framework to two popular fingerprint databases, and generate images which look very realistic, and similar to the samples in those databases. Through experimental results, we show that the generated fingerprint images have a good diversity, and are able to capture different parts of the prior distribution. We also evaluate the Frechet Inception distance (FID) of our proposed model, and show that our model is able to achieve good quantitative performance in terms of this score.
An approximate dynamic programming approach to food security of communities following hazards
Venue: arXiv cs.CY 2018
Year: 2018

Food security can be threatened by extreme natural hazard events for households of all social classes within a community. To address food security issues following a natural disaster, the recovery of several elements of the built environment within a community, including its building portfolio, must be considered. Building portfolio restoration is one of the most challenging elements of recovery owing to the complexity and dimensionality of the problem. This study introduces a stochastic scheduling algorithm for the identification of optimal building portfolio recovery strategies. The proposed approach provides a computationally tractable formulation to manage multi-state, large-scale infrastructure systems. A testbed community modeled after Gilroy, California, is used to illustrate how the proposed approach can be implemented efficiently and accurately to find the near-optimal decisions related to building recovery following a severe earthquake.
Hierarchical Clustering for Euclidean Data
Venue: arXiv cs.DS 2018
Year: 2018

Recent works on Hierarchical Clustering (HC), a well-studied problem in exploratory data analysis, have focused on optimizing various objective functions for this problem under arbitrary similarity measures. In this paper we take the first step and give novel scalable algorithms for this problem tailored to Euclidean data in R^d and under vector-based similarity measures, a prevalent model in several typical machine learning applications. We focus primarily on the popular Gaussian kernel and other related measures, presenting our results through the lens of the objective introduced recently by Moseley and Wang [2017]. We show that the approximation factor in Moseley and Wang [2017] can be improved for Euclidean data. We further demonstrate both theoretically and experimentally that our algorithms scale to very high dimension d, while outperforming average-linkage and showing competitive results against other less scalable approaches.
Malicious Software Detection and Classification utilizing Temporal-Graphs of System-call Group Relations
Venue: arXiv cs.CR 2018
Year: 2018

In this work we propose a graph-based model that, utilizing relations between groups of System-calls, distinguishes malicious from benign software samples and classifies the detected malicious samples to one of a set of known malware families. More precisely, given a System-call Dependency Graph (ScDG) that depicts the malware's behavior, we first transform it to a more abstract representation, utilizing the indexing of System-calls to a set of groups of similar functionality, constructing thus an abstract and mutation-tolerant graph that we call Group Relation Graph (GrG); then, we construct another graph representation, which we call Coverage Graph (CvG), that depicts the dominating relations between the nodes of a GrG graph. Based on the research so far in the field, we pointed out that behavior-based graph representations had not leveraged the aspect of the temporal evolution of the graph. Hence, the novelty of our work is that, preserving the initial representations of GrG and CvG graphs, we focus on augmenting the potentials of theses graphs by adding further features that enhance its abilities on detecting and further classifying to a known malware family an unknown malware sample. To that end, we construct periodical instances of the graph that represent its temporal evolution concerning its structural modifications, creating another graph representation that we call Temporal Graphs. In this paper, we present the theoretical background behind our approach, discuss the current technological status on malware detection and classification and demonstrate the overall architecture of our proposed detection and classification model alongside with its underlying main principles and its structural key-components.
Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize
Venue: arXiv cs.CL 2018
Year: 2018

Building open domain conversational systems that allow users to have engaging conversations on topics of their choice is a challenging task. Alexa Prize was launched in 2016 to tackle the problem of achieving natural, sustained, coherent and engaging open-domain dialogs. In the second iteration of the competition in 2018, university teams advanced the state of the art by using context in dialog models, leveraging knowledge graphs for language understanding, handling complex utterances, building statistical and hierarchical dialog managers, and leveraging model-driven signals from user responses. The 2018 competition also included the provision of a suite of tools and models to the competitors including the CoBot (conversational bot) toolkit, topic and dialog act detection models, conversation evaluators, and a sensitive content detection model so that the competing teams could focus on building knowledge-rich, coherent and engaging multi-turn dialog systems. This paper outlines the advances developed by the university teams as well as the Alexa Prize team to achieve the common goal of advancing the science of Conversational AI. We address several key open-ended problems such as conversational speech recognition, open domain natural language understanding, commonsense reasoning, statistical dialog management, and dialog evaluation. These collaborative efforts have driven improved experiences by Alexa users to an average rating of 3.61, the median duration of 2 mins 18 seconds, and average turns to 14.6, increases of 14%, 92%, 54% respectively since the launch of the 2018 competition. For conversational speech recognition, we have improved our relative Word Error Rate by 55% and our relative Entity Error Rate by 34% since the launch of the Alexa Prize. Socialbots improved in quality significantly more rapidly in 2018, in part due to the release of the CoBot toolkit.
Complex Semidefinite Programming and Max-k-Cut
Venue: arXiv cs.DS 2018
Year: 2018

In a second seminal paper on the application of semidefinite programming to graph partitioning problems, Goemans and Williamson showed how to formulate and round a complex semidefinite program to give what is to date still the best-known approximation guarantee of .836008 for Max-$3$-Cut. (This approximation ratio was also achieved independently by De Klerk et al.) Goemans and Williamson left open the problem of how to apply their techniques to Max-$k$-Cut for general $k$. They point out that it does not seem straightforward or even possible to formulate a good quality complex semidefinite program for the general Max-$k$-Cut problem, which presents a barrier for the further application of their techniques. We present a simple rounding algorithm for the standard semidefinite programmming relaxation of Max-$k$-Cut and show that it is equivalent to the rounding of Goemans and Williamson in the case of Max-$3$-Cut. This allows us to transfer the elegant analysis of Goemans and Williamson for Max-3-Cut to Max-$k$-Cut. For $k \geq 4$, the resulting approximation ratios are about $.01$ worse than the best known guarantees. Finally, we present a generalization of our rounding algorithm and conjecture (based on computational observations) that it matches the best-known guarantees of De Klerk et al.
Do we have the time for IRM?: Service denial attacks and SDN-based defences
Venue: arXiv cs.CR 2018
Year: 2018

Distributed sensor networks such as IoT deployments generate large quantities of measurement data. Often, the analytics that runs on this data is available as a web service which can be purchased for a fee. A major concern in the analytics ecosystem is ensuring the security of the data. Often, companies offer Information Rights Management (IRM) as a solution to the problem of managing usage and access rights of the data that transits administrative boundaries. IRM enables individuals and corporations to create restricted IoT data, which can have its flow from organisation to individual control -- disabling copying, forwarding, and allowing timed expiry. We describe our investigations into this functionality and uncover a weak-spot in the architecture -- its dependence upon the accurate global availability of \emph{time}. We present an amplified denial-of-service attack which attacks time synchronisation and could prevent all the users in an organisation from reading any sort of restricted data until their software has been re-installed and re-configured. We argue that IRM systems built on current technology will be too fragile for businesses to risk widespread use. We also present defences that leverage the capabilities of Software-Defined Networks to apply a simple filter-based approach to detect and isolate attack traffic.
Open-endedness in AI systems, cellular evolution and intellectual discussions
Venue: arXiv cs.AI 2018
Year: 2018

One of the biggest challenges that artificial intelligence (AI) research is facing in recent times is to develop algorithms and systems that are not only good at performing a specific intelligent task but also good at learning a very diverse of skills somewhat like humans do. In other words, the goal is to be able to mimic biological evolution which has produced all the living species on this planet and which seems to have no end to its creativity. The process of intellectual discussions is also somewhat similar to biological evolution in this regard and is responsible for many of the innovative discoveries and inventions that scientists and engineers have made in the past. In this paper, we present an information theoretic analogy between the process of discussions and the molecular dynamics within a cell, showing that there is a common process of information exchange at the heart of these two seemingly different processes, which can perhaps help us in building AI systems capable of open-ended innovation. We also discuss the role of consciousness in this process and present a framework for the development of open-ended AI systems.
Wikibook-Bot - Automatic Generation of a Wikipedia Book
Venue: arXiv cs.DL 2018
Year: 2018

A Wikipedia book (known as Wikibook) is a collection of Wikipedia articles on a particular theme that is organized as a book. We propose Wikibook-Bot, a machine-learning based technique for automatically generating high quality Wikibooks based on a concept provided by the user. In order to create the Wikibook we apply machine learning algorithms to the different steps of the proposed technique. Firs, we need to decide whether an article belongs to a specific Wikibook - a classification task. Then, we need to divide the chosen articles into chapters - a clustering task - and finally, we deal with the ordering task which includes two subtasks: order articles within each chapter and order the chapters themselves. We propose a set of structural, text-based and unique Wikipedia features, and we show that by using these features, a machine learning classifier can successfully address the above challenges. The predictive performance of the proposed method is evaluated by comparing the auto-generated books to existing 407 Wikibooks which were manually generated by humans. For all the tasks we were able to obtain high and statistically significant results when comparing the Wikibook-bot books to books that were manually generated by Wikipedia contributors
Generalization of the Ball-Collision Algorithm
Venue: arXiv cs.IT 2018
Year: 2018

In this paper we generalize the Ball-Collision Algorithm by Bernstein, Lange, Peters from the binary field to a general finite field. We also provide a complexity analysis and compare the asymptotic complexity to other generalized information set decoding algorithms.
TROVE Feature Detection for Online Pose Recovery by Binocular Cameras
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes a new and efficient method to estimate 6-DoF ego-states: attitudes and positions in real time. The proposed method extract information of ego-states by observing a feature called "TROVE" (Three Rays and One VErtex). TROVE features are projected from structures that are ubiquitous on man-made constructions and objects. The proposed method does not search for conventional corner-type features nor use Perspective-n-Point (PnP) methods, and it achieves a real-time estimation of attitudes and positions up to 60 Hz. The accuracy of attitude estimates can reach 0.3 degrees and that of position estimates can reach 2 cm in an indoor environment. The result shows a promising approach for unmanned robots to localize in an environment that is rich in man-made structures.
Compact and Efficient Representation of General Graph Databases
Venue: arXiv cs.DS 2018
Year: 2018

In this paper, we propose a compact data structure to store labeled attributed graphs based on the k2-tree, which is a very compact data structure designed to represent a simple directed graph. The idea we propose can be seen as an extension of the k2-tree to support property graphs. In addition to the static approach, we also propose a dynamic version of the storage representation, which allows exible schemas and insertion or deletion of data. We provide an implementation of a basic set of operations, which can be combined to form complex queries over these graphs with attributes. We evaluate the performance of our proposal with existing graph database systems and prove that our compact attributed graph representation obtains also competitive time results.
A Multi-Agent Approach to Optimal Sizing of a Combined Heating and Power Microgrid
Venue: arXiv cs.SY 2018
Year: 2018

In this paper, a novel multi-agent based method is applied to the problem of optimal sizing of the components of an islanded combined heating and power residential microgrid such that the residential power and heat demands and hydrogen refilling demands of the fuel cell electric vehicles (FCEVs) are met. It is assumed that the proposed multi agent-based architecture consists of five different agents, namely generation agent (GA), electrical and thermal loads agent (LA), FCEV refilling station agent (SA), control agent (CA), and design agent (DA) which are organized in three levels. Design agent (DA) is the main agent of the proposed multi-agent system (MAS) that according to its interactions with CA and by minimizing the total costs of the system through particle swarm optimization (PSO), finds the optimal sizes of the system's components. This study is performed for a typical stand-alone combined heating and power microgrid.
Enhanced Touchable Projector-depth System with Deep Hand Pose Estimation
Venue: arXiv cs.HC 2018
Year: 2018

Touchable projection with structured light range cameras is a prolific medium for large interaction surfaces, affording multiple simultaneous users and simple, cheap setup. However robust touch detection in such projector-depth systems is difficult to achieve due to measurement noise. We propose a novel combination of surface touch detection and a deep network for hand pose estimation, which aids in detecting both on- and above-surface hand gestures, disambiguating multiple touch fingers, as well as recovering fingertip positions in face of noisy input. We present the details of our GPU-accelerated system and an evaluation of its performance, as well as applications such as an enhanced virtual keyboard that utilizes the added features.
Scalable GAM using sparse variational Gaussian processes
Venue: arXiv cs.LG 2018
Year: 2018

Generalized additive models (GAMs) are a widely used class of models of interest to statisticians as they provide a flexible way to design interpretable models of data beyond linear models. We here propose a scalable and well-calibrated Bayesian treatment of GAMs using Gaussian processes (GPs) and leveraging recent advances in variational inference. We use sparse GPs to represent each component and exploit the additive structure of the model to efficiently represent a Gaussian a posteriori coupling between the components.
Online Decentralized Receding Horizon Trajectory Optimization for Multi-Robot systems
Venue: arXiv cs.RO 2018
Year: 2018

A novel decentralised trajectory generation algorithm for Multi Agent systems is presented. Multi-robot systems have the capacity to transform lives in a variety of fields. But, trajectory generation for multi-robot systems is still in its nascent stage and limited to heavily controlled environments. To overcome that, an online trajectory optimization algorithm that generates collision-free trajectories for robots, when given initial state and desired end pose, is proposed. It utilizes a simple method for obstacle detection, local shape based maps for obstacles and communication of robots' current states. Using the local maps, safe regions are formulated. Based upon the communicated data, trajectories are predicted for other robots and incorporated for collision-avoidance by resizing the regions of free space that the robot can be in without colliding. A trajectory is then optimized constraining the robot to remain within the safe region with the trajectories represented by piecewise polynomials parameterized by time. The algorithm is implemented using a receding horizon principle. The proposed algorithm is extensively tested in simulations on Gazebo using ROS with fourth order differentially flat aerial robots and non-holonomic second order wheeled robots in structured and unstructured environments.
CFA Bayer image sequence denoising and demosaicking chain
Venue: arXiv cs.CV 2018
Year: 2018

The demosaicking provokes the spatial and color correlation of noise, which is afterwards enhanced by the imaging pipeline. The correct removal previous or simultaneously with the demosaicking process is not usually considered in the literature. We present a novel imaging chain including a denoising of the Bayer CFA and a demosaicking method for image sequences. The proposed algorithm uses a spatio-temporal patch method for the noise removal and demosaicking of the CFA. The experimentation, including real examples, illustrates the superior performance of the proposed chain, avoiding the creation of artifacts and colored spots in the final image.