OpenRaiser/Intern-Atlas huggingface dataset:
Set Containment Join Revisited
Venue: arXiv cs.DB 2016
Year: 2016

Given two collections of set objects $R$ and $S$, the $R \bowtie_{\subseteq} S$ set containment join returns all object pairs $(r, s) \in R \times S$ such that $r \subseteq s$. Besides being a basic operator in all modern data management systems with a wide range of applications, the join can be used to evaluate complex SQL queries based on relational division and as a module of data mining algorithms. The state-of-the-art algorithm for set containment joins (PRETTI) builds an inverted index on the right-hand collection $S$ and a prefix tree on the left-hand collection $R$ that groups set objects with common prefixes and thus, avoids redundant processing. In this paper, we present a framework which improves PRETTI in two directions. First, we limit the prefix tree construction by proposing an adaptive methodology based on a cost model; this way, we can greatly reduce the space and time cost of the join. Second, we partition the objects of each collection based on their first contained item, assuming that the set objects are internally sorted. We show that we can process the partitions and evaluate the join while building the prefix tree and the inverted index progressively. This allows us to significantly reduce not only the join cost, but also the maximum memory requirements during the join. An experimental evaluation using both real and synthetic datasets shows that our framework outperforms PRETTI by a wide margin.
A Short Note on Infinite Union/Intersection of Omega Regular Languages
Venue: arXiv cs.FL 2016
Year: 2016

We in this paper show that omega regular languages are not closed under infinite union and intersection. As an attempt, we propose to add step variables and quantifiers to temporal logics to enhance the expressiveness of the underlying logic. We also show that doing this would cause undecidability in satisfiability, even if for a rather limited fragment of temporal logic.
Extracting Wyner's Common Information Using Polar Codes and Polar Lattices
Venue: arXiv cs.IT 2016
Year: 2016

Explicit constructions of polar codes and polar lattices for both lossless and lossy Gray-Wyner problems are studied. Polar codes are employed to extract Wyner's common information of doubly symmetric binary source; polar lattices are then extended to extract that of a pair of Gaussian sources or multiple Gaussian sources. With regard to the discrete sources, the entire best-known region of the lossless Gray-Wyner problem are achieved by specifying the test channels to construct polar codes without time-sharing. As a result, we are able to give an interpretation that the Wyner's common information remains the same to the lossy case when the distortion is small [1]. Finally, the entire best-known lossy Gray-Wyner region for discrete sources can also be achieved using polar codes. With regard to the Gaussian sources, the best-known lossy Gray-Wyner region for bivariate Gaussian sources with a specific covariance matrix [1] can be achieved by using polar lattices. Moreover, we prove that extracting Wyner's common information of a pair of Gaussian sources is equivalent to implementing the lossy compression for a single Gaussian source, which implies that the common information can be extracted by a polar lattice for quantization. Furthermore, we extend this result to the case of multiple Gaussian sources.
Bank distress in the news: Describing events through deep learning
Venue: arXiv cs.CL 2016
Year: 2016

While many models are purposed for detecting the occurrence of significant events in financial systems, the task of providing qualitative detail on the developments is not usually as well automated. We present a deep learning approach for detecting relevant discussion in text and extracting natural language descriptions of events. Supervised by only a small set of event information, comprising entity names and dates, the model is leveraged by unsupervised learning of semantic vector representations on extensive text data. We demonstrate applicability to the study of financial risk based on news (6.6M articles), particularly bank distress and government interventions (243 events), where indices can signal the level of bank-stress-related reporting at the entity level, or aggregated at national or European level, while being coupled with explanations. Thus, we exemplify how text, as timely, widely available and descriptive data, can serve as a useful complementary source of information for financial and systemic risk analytics.
Online Networks, Social Interaction and Segregation: An Evolutionary Approach
Venue: arXiv cs.SI 2016
Year: 2016

We have developed an evolutionary game model, where agents can choose between two forms of social participation: interaction via online social networks and interaction by exclusive means of face-to-face encounters. We illustrate the societal dynamics that the model predicts, in light of the empirical evidence provided by previous literature. We then assess their welfare implications. We show that dynamics, starting from a world in which online social interaction is less gratifying than offline encounters, will lead to the extinction of the sub-population of online networks users, thereby making Facebook and alike disappear in the long run. Furthermore, we show that the higher the propensity for discrimination between the two sub-populations of socially active individuals, the greater the probability that individuals will ultimately segregate themselves, making society fall into a social poverty trap.
Geometric Hypergraph Learning for Visual Tracking
Venue: arXiv cs.CV 2016
Year: 2016

Graph based representation is widely used in visual tracking field by finding correct correspondences between target parts in consecutive frames. However, most graph based trackers consider pairwise geometric relations between local parts. They do not make full use of the target's intrinsic structure, thereby making the representation easily disturbed by errors in pairwise affinities when large deformation and occlusion occur. In this paper, we propose a geometric hypergraph learning based tracking method, which fully exploits high-order geometric relations among multiple correspondences of parts in consecutive frames. Then visual tracking is formulated as the mode-seeking problem on the hypergraph in which vertices represent correspondence hypotheses and hyperedges describe high-order geometric relations. Besides, a confidence-aware sampling method is developed to select representative vertices and hyperedges to construct the geometric hypergraph for more robustness and scalability. The experiments are carried out on two challenging datasets (VOT2014 and Deform-SOT) to demonstrate that the proposed method performs favorable against other existing trackers.
Report on the Poblacion Flotante of Bogota (D.C.)
Venue: arXiv cs.SI 2016
Year: 2016

In this document we describe the size of the Poblacion Flotante of Bogota (D.C.). The Poblacion Flotante is composed by people who live outside Bogota (D.C.), but who rely on the city for performing their job. We estimate the Poblacion Flotante impact relying on a new data source provided by telecommunications operators in Colombia, which enables us to estimate how many people commute daily from every municipality of Colombia to a specific area of Bogota (D.C.). We estimate that the size of the Poblacion Flotante could represent a 5.4% increase of Bogota (D.C.)'s population. During weekdays, the commuters tend to visit the city center more.
Transferring Learned Microcalcification Group Detection from 2D Mammography to 3D Digital Breast Tomosynthesis Using a Hierarchical Model and Scope-based Normalization Features
Venue: arXiv cs.CV 2016
Year: 2016

A novel hierarchical model is introduced to solve a general problem of detecting groups of similar objects. Under this model, detection of groups is performed in hierarchically organized layers while each layer represents a scope for target objects. The processing of these layers involves sequential extraction of appearance features for an individual object, consistency measurement features for nearby objects, and finally the distribution features for all objects within the group. Using the concept of scope-based normalization, the extracted features not only enhance local contrast of an individual object, but also provide consistent characterization for all related objects. As an example, a microcalcification group detection system for 2D mammography was developed, and then the learned model was transferred to 3D digital breast tomosynthesis without any retraining or fine-tuning. The detection system demonstrated state-of-the-art performance and detected 96% of cancerous lesions at the rate of 1.2 false positives per volume as measured on an independent tomosynthesis test set.
A Light-Weight Forwarding Plane for Content-Centric Networks
Venue: arXiv cs.NI 2016
Year: 2016

We present CCN-DART, a more efficient forwarding approach for content-centric networking (CCN) than named data networking (NDN) that substitutes Pending Interest Tables (PIT) with Data Answer Routing Tables (DART) and uses a novel approach to eliminate forwarding loops. The forwarding state required at each router using CCN-DART consists of segments of the routes between consumers and content providers that traverse a content router, rather than the Interests that the router forwards towards content providers. Accordingly, the size of a DART is proportional to the number of routes used by Interests traversing a router, rather than the number of Interests traversing a router. We show that CCN-DART avoids forwarding loops by comparing distances to name prefixes reported by neighbors, even when routing loops exist. Results of simulation experiments comparing CCN-DART with NDN using the ndnSIM simulation tool show that CCN-DART incurs 10 to 20 times less storage overhead.
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
Venue: arXiv cs.NE 2016
Year: 2016

As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning---pipeline design. We implement an open source Tree-based Pipeline Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work represents an important step toward fully automating machine learning pipeline design.
Flow of Information in Feed-Forward Deep Neural Networks
Venue: arXiv cs.IT 2016
Year: 2016

Feed-forward deep neural networks have been used extensively in various machine learning applications. Developing a precise understanding of the underling behavior of neural networks is crucial for their efficient deployment. In this paper, we use an information theoretic approach to study the flow of information in a neural network and to determine how entropy of information changes between consecutive layers. Moreover, using the Information Bottleneck principle, we develop a constrained optimization problem that can be used in the training process of a deep neural network. Furthermore, we determine a lower bound for the level of data representation that can be achieved in a deep neural network with an acceptable level of distortion.
Beyond Binary Computers: How To Implement Multi-Switch Computer Hardware and Software and; The Advantage of a Multi-Switched Computer
Venue: arXiv cs.OH 2016
Year: 2016

This paper explores the possibilities of using a computing methodology --hardware and software-- that employs technology other than binary. I refer to this as "supra - binary" computing. Software constructs that use more than binary techniques are discussed. The gains in supra - binary software are demonstrated, which includes supra - binary code being RISC. Possible hardware implementations of a computer with other than binary based architecture are demonstrated and considered. The advantages and possible disadvantages of these hardware implementations are discussed. The gain in computing speed is evaluated and demonstrated. Supra - binary processing would streamline parallel processing and make its implementation a built - in feature of the software and hardware. Also, supra - binary would bring an advancement to neural networking. This is discussed and demonstrated. In addition, possible applications of supra - binary computing to database and neural networking are discussed. Also, the possible implementations could be applied to telecommunications with dramatic results.
Homemade assembly and parameterization of a High Performance Cluster using PelicanHPC with Flops testing and controlled temperature thanks to MCUs Arduino project
Venue: arXiv cs.DC 2016
Year: 2016

This article shows a lower cost realization of a compute cluster using Debian distribution such as PelicanHPC. We will explain parameterization and network configuration for master and compute slave nodes. Performance testing will take place using flops.f file given by MPI. The results will be compared between differents clusters. We will explain quickly how the temperature is controlled by a microcontroller unit.
Preprint: Bigdata Oriented Multimedia Mobile Health Applications
Venue: arXiv cs.HC 2016
Year: 2016

This is the preprint version of our paper on JOMS. In this paper, two mHealth applications are introduced, which can be employed as the terminals of bigdata based health service to collect information for electronic medical records (EMRs). The first one is a hybrid system for improving the user experience in the hyperbaric oxygen chamber by 3D stereoscopic virtual reality glasses and immersive perception. Several HMDs have been tested and compared. The second application is a voice interactive serious game as a likely solution for providing assistive rehabilitation tool for therapists. The recorder of the voice of patients could be analysed to evaluate the long-time rehabilitation results and further to predict the rehabilitation process.
Adaptive Path Planning for Depth Constrained Bathymetric Mapping with an Autonomous Surface Vessel
Venue: arXiv cs.RO 2016
Year: 2016

This paper describes the design, implementation and testing of a suite of algorithms to enable depth constrained autonomous bathymetric (underwater topography) mapping by an Autonomous Surface Vessel (ASV). Given a target depth and a bounding polygon, the ASV will find and follow the intersection of the bounding polygon and the depth contour as modeled online with a Gaussian Process (GP). This intersection, once mapped, will then be used as a boundary within which a path will be planned for coverage to build a map of the Bathymetry. Methods for sequential updates to GP's are described allowing online fitting, prediction and hyper-parameter optimisation on a small embedded PC. New algorithms are introduced for the partitioning of convex polygons to allow efficient path planning for coverage. These algorithms are tested both in simulation and in the field with a small twin hull differential thrust vessel built for the task.
Interoperability in the OpenDreamKit Project: The Math-in-the-Middle Approach
Venue: arXiv cs.MS 2016
Year: 2016

OpenDreamKit --- "Open Digital Research Environment Toolkit for the Advancement of Mathematics" --- is an H2020 EU Research Infrastructure project that aims at supporting, over the period 2015--2019, the ecosystem of open-source mathematical software systems. From that, OpenDreamKit will deliver a flexible toolkit enabling research groups to set up Virtual Research Environments, customised to meet the varied needs of research projects in pure mathematics and applications. An important step in the OpenDreamKit endeavor is to foster the interoperability between a variety of systems, ranging from computer algebra systems over mathematical databases to front-ends. This is the mission of the integration work package (WP6). We report on experiments and future plans with the \emph{Math-in-the-Middle} approach. This information architecture consists in a central mathematical ontology that documents the domain and fixes a joint vocabulary, combined with specifications of the functionalities of the various systems. Interaction between systems can then be enriched by pivoting off this information architecture.
Qualitative stability of nonlinear networked systems
Venue: arXiv cs.SY 2016
Year: 2016

In many large systems, such as those encountered in biology or economics, the dynamics are nonlinear and are only known very coarsely. It is often the case, however, that the signs (excitation or inhibition) of individual interactions are known. This paper extends to nonlinear systems the classical criteria of linear sign stability introduced in the 70's, yielding simple sufficient conditions to determine stability using only the sign patterns of the interactions.
Forensic Acquisition of Cloud Drives
Venue: arXiv cs.CR 2016
Year: 2016

Cloud computing and cloud storage services, in particular, pose a new challenge to digital forensic investigations. Currently, evidence acquisition for such services still follows the traditional method of collecting artifacts on a client device. This approach requires labor-intensive reverse engineering efforts, and ultimately results in an acquisition that is inherently incomplete. Specifically, it makes the incorrect assumption that all storage content for an account is fully replicated on the client; further, there are no means to acquire historical data in the form of document revisions, nor is there a way to acquire cloud-native artifacts, such as Google Docs. In this work, we introduce the concept of API-based evidence acquisition for cloud services, which addresses these concerns by utilizing the officially supported API of the service. To demonstrate the utility of this approach, we present a proof-of-concept acquisition tool, kumodd, which can acquire evidence from four major cloud drive providers: Google Drive, Microsoft OneDrive, Dropbox, and Box. The implementation provides both command-line and web user interfaces, and can be readily incorporated into established forensic processes.
Modelling Temporal Information Using Discrete Fourier Transform for Recognizing Emotions in User-generated Videos
Venue: arXiv cs.CV 2016
Year: 2016

With the widespread of user-generated Internet videos, emotion recognition in those videos attracts increasing research efforts. However, most existing works are based on framelevel visual features and/or audio features, which might fail to model the temporal information, e.g. characteristics accumulated along time. In order to capture video temporal information, in this paper, we propose to analyse features in frequency domain transformed by discrete Fourier transform (DFT features). Frame-level features are firstly extract by a pre-trained deep convolutional neural network (CNN). Then, time domain features are transferred and interpolated into DFT features. CNN and DFT features are further encoded and fused for emotion classification. By this way, static image features extracted from a pre-trained deep CNN and temporal information represented by DFT features are jointly considered for video emotion recognition. Experimental results demonstrate that combining DFT features can effectively capture temporal information and therefore improve emotion recognition performance. Our approach has achieved a state-of-the-art performance on the largest video emotion dataset (VideoEmotion-8 dataset), improving accuracy from 51.1% to 62.6%.
Evaluating the Security of a DNS Query Obfuscation Scheme for Private Web Surfing
Venue: arXiv cs.CR 2016
Year: 2016

The Domain Name System (DNS) does not provide query privacy. Query obfuscation schemes have been proposed to overcome this limitation, but, so far, they have not been evaluated in a realistic setting. In this paper we evaluate the security of a random set range query scheme in a real-world web surfing scenario. We demonstrate that the scheme does not sufficiently obfuscate characteristic query patterns, which can be used by an adversary to determine the visited websites. We also illustrate how to thwart the attack and discuss practical challenges. Our results suggest that previously published evaluations of range queries may give a false sense of the attainable security, because they do not account for any interdependencies between queries.
Input Aggregated Network for Face Video Representation
Venue: arXiv cs.CV 2016
Year: 2016

Recently, deep neural network has shown promising performance in face image recognition. The inputs of most networks are face images, and there is hardly any work reported in literature on network with face videos as input. To sufficiently discover the useful information contained in face videos, we present a novel network architecture called input aggregated network which is able to learn fixed-length representations for variable-length face videos. To accomplish this goal, an aggregation unit is designed to model a face video with various frames as a point on a Riemannian manifold, and the mapping unit aims at mapping the point into high-dimensional space where face videos belonging to the same subject are close-by and others are distant. These two units together with the frame representation unit build an end-to-end learning system which can learn representations of face videos for the specific tasks. Experiments on two public face video datasets demonstrate the effectiveness of the proposed network.
Side Channel Attacks on STTRAM and Low-Overhead Countermeasures
Venue: arXiv cs.CR 2016
Year: 2016

Spin Transfer Torque RAM (STTRAM) is a promising candidate for Last Level Cache (LLC) due to high endurance, high density and low leakage. One of the major disadvantages of STTRAM is high write latency and write current. Additionally, the latency and current depends on the polarity of the data being written. These features introduce major security vulnerabilities and expose the cache memory to side channel attacks. In this paper we propose a novel side channel attack model where the adversary can monitor the supply current of the memory array to partially identify the sensitive cache data that is being read or written. We propose several low cost solutions such as short retention STTRAM, 1-bit parity, multi-bit random write and constant current write driver to mitigate the attack. 1-bit parity reduces the number of distinct write current states by 30% for 32-bit word and the current signature is further obfuscated by multi-bit random writes. The constant current write makes it more challenging for the attacker to extract the entire word using a single supply current signature.