OpenRaiser/Intern-Atlas huggingface dataset:
TruSDN: Bootstrapping Trust in Cloud Network Infrastructure
Venue: arXiv cs.NI 2017
Year: 2017

Software-Defined Networking (SDN) is a novel architectural model for cloud network infrastructure, improving resource utilization, scalability and administration. SDN deployments increasingly rely on virtual switches executing on commodity operating systems with large code bases, which are prime targets for adversaries attacking the net- work infrastructure. We describe and implement TruSDN, a framework for bootstrapping trust in SDN infrastructure using Intel Software Guard Extensions (SGX), allowing to securely deploy SDN components and protect communication between network endpoints. We introduce ephemeral flow-specific pre-shared keys and propose a novel defense against cuckoo attacks on SGX enclaves. TruSDN is secure under a powerful adversary model, with a minor performance overhead.
Optimal Longest Paths by Dynamic Programming
Venue: arXiv cs.DS 2017
Year: 2017

We propose an optimal algorithm for solving the longest path problem in undirected weighted graphs. By using graph partitioning and dynamic programming, we obtain an algorithm that is significantly faster than other state-of-the-art methods. This enables us to solve instances that have previously been unsolved.
Canonical Completeness in Lattice-Based Languages for Attribute-Based Access Control
Venue: arXiv cs.CR 2017
Year: 2017

The study of canonically complete attribute-based access control (ABAC) languages is relatively new. A canonically complete language is useful as it is functionally complete and provides a "normal form" for policies. However, previous work on canonically complete ABAC languages requires that the set of authorization decisions is totally ordered, which does not accurately reflect the intuition behind the use of the allow, deny and not-applicable decisions in access control. A number of recent ABAC languages use a fourth value and the set of authorization decisions is partially ordered. In this paper, we show how canonical completeness in multi-valued logics can be extended to the case where the set of truth values forms a lattice. This enables us to investigate the canonical completeness of logics having a partially ordered set of truth values, such as Belnap logic, and show that ABAC languages based on Belnap logic, such as PBel, are not canonically complete. We then construct a canonically complete four-valued logic using connections between the generators of the symmetric group (defined over the set of decisions) and unary operators in a canonically suitable logic. Finally, we propose a new authorization language $\text{PTaCL}_{\sf 4}^{\leqslant}$, an extension of PTaCL, which incorporates a lattice-ordered decision set and is canonically complete. We then discuss how the advantages of $\text{PTaCL}_{\sf 4}^{\leqslant}$ can be leveraged within the framework of XACML.
LAMMPS' PPPM Long-Range Solver for the Second Generation Xeon Phi
Venue: arXiv cs.CE 2017
Year: 2017

Molecular Dynamics is an important tool for computational biologists, chemists, and materials scientists, consuming a sizable amount of supercomputing resources. Many of the investigated systems contain charged particles, which can only be simulated accurately using a long-range solver, such as PPPM. We extend the popular LAMMPS molecular dynamics code with an implementation of PPPM particularly suitable for the second generation Intel Xeon Phi. Our main target is the optimization of computational kernels by means of vectorization, and we observe speedups in these kernels of up to 12x. These improvements carry over to LAMMPS users, with overall speedups ranging between 2-3x, without requiring users to retune input parameters. Furthermore, our optimizations make it easier for users to determine optimal input parameters for attaining top performance.
What's in a Name? Understanding Profile Name Reuse on Twitter
Venue: arXiv cs.SI 2017
Year: 2017

Users on Twitter are commonly identified by their profile names. These names are used when directly addressing users on Twitter, are part of their profile page URLs, and can become a trademark for popular accounts, with people referring to celebrities by their real name and their profile name, interchangeably. Twitter, however, has chosen to not permanently link profile names to their corresponding user accounts. In fact, Twitter allows users to change their profile name, and afterwards makes the old profile names available for other users to take. In this paper, we provide a large-scale study of the phenomenon of profile name reuse on Twitter. We show that this phenomenon is not uncommon, investigate the dynamics of profile name reuse, and characterize the accounts that are involved in it. We find that many of these accounts adopt abandoned profile names for questionable purposes, such as spreading malicious content, and using the profile name's popularity for search engine optimization. Finally, we show that this problem is not unique to Twitter (as other popular online social networks also release profile names) and argue that the risks involved with profile-name reuse outnumber the advantages provided by this feature.
Adding educational funcionalities to classic board games
Venue: arXiv cs.CY 2017
Year: 2017

In this paper we revisit some classic board games like Pachisi or the Game of Gosse. The main contribution of the paper is to design and add some functionalities to the games in order to transform them in serious games, that is, in games with learning and educational purposes. To do that, at the beginning of the game, players choose one or several topics and during the game, players have to anwers questions on these topics in order to move their markers. We choose classic board games because a lot of people are familiar with them so it is very easy to start to play without wasting time learning game rules and, we think that this is an important element to make the game more attractive to people. To enlarge the number of potential users we have implement the games just using html and javascript and the games can be used in any web browser, in any computer (including tablets) , in any computer arquitecture (Windows, Mac, Linux) and no internet/server conexion is required. Associated software is distributed under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 licence and can be obtained at http://www.ctim.es/SeriousGames
DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network
Venue: arXiv cs.CV 2017
Year: 2017

This paper describes the details of Sighthound's fully automated age, gender and emotion recognition system. The backbone of our system consists of several deep convolutional neural networks that are not only computationally inexpensive, but also provide state-of-the-art results on several competitive benchmarks. To power our novel deep networks, we collected large labeled datasets through a semi-supervised pipeline to reduce the annotation effort/time. We tested our system on several public benchmarks and report outstanding results. Our age, gender and emotion recognition models are available to developers through the Sighthound Cloud API at https://www.sighthound.com/products/cloud
T-SKIRT: Online Estimation of Student Proficiency in an Adaptive Learning System
Venue: arXiv cs.AI 2017
Year: 2017

We develop T-SKIRT: a temporal, structured-knowledge, IRT-based method for predicting student responses online. By explicitly accounting for student learning and employing a structured, multidimensional representation of student proficiencies, the model outperforms standard IRT-based methods on an online response prediction task when applied to real responses collected from students interacting with diverse pools of educational content.
Solving Orienteering with Category Constraints Using Prioritized Search
Venue: arXiv cs.DS 2017
Year: 2017

We develop an approach for solving rooted orienteering problems with category constraints as found in tourist trip planning and logistics. It is based on expanding partial solutions in a systematic way, prioritizing promising ones, which reduces the search space we have to traverse during the search. The category constraints help in reducing the space we have to explore even further. We implement an algorithm that computes the optimal solution and also illustrate how our approach can be turned into an approximation algorithm, yielding much faster run times and guaranteeing lower bounds on the quality of the solution found. We demonstrate the effectiveness of our algorithms by comparing them to the state-of-the-art approach and an optimal algorithm based on dynamic programming, showing that our technique clearly outperforms these methods.
A storm is Coming: A Modern Probabilistic Model Checker
Venue: arXiv cs.SE 2017
Year: 2017

We launch the new probabilistic model checker storm. It features the analysis of discrete- and continuous-time variants of both Markov chains and MDPs. It supports the PRISM and JANI modeling languages, probabilistic programs, dynamic fault trees and generalized stochastic Petri nets. It has a modular set-up in which solvers and symbolic engines can easily be exchanged. It offers a Python API for rapid prototyping by encapsulating storm's fast and scalable algorithms. Experiments on a variety of benchmarks show its competitive performance.
Entropy Non-increasing Games for the Improvement of Dataflow Programming
Venue: arXiv cs.AI 2017
Year: 2017

In this article, we introduce a new conception of a family of esport games called Samu Entropy to try to improve dataflow program graphs like the ones that are based on Google's TensorFlow. Currently, the Samu Entropy project specifies only requirements for new esport games to be developed with particular attention to the investigation of the relationship between esport and artificial intelligence. It is quite obvious that there is a very close and natural relationship between esport games and artificial intelligence. Furthermore, the project Samu Entropy focuses not only on using artificial intelligence, but on creating AI in a new way. We present a reference game called Face Battle that implements the Samu Entropy requirements.
A Concurrency-Optimal Binary Search Tree
Venue: arXiv cs.DC 2017
Year: 2017

The paper presents the first \emph{concurrency-optimal} implementation of a binary search tree (BST). The implementation, based on a standard sequential implementation of an internal tree, ensures that every \emph{schedule} is accepted, i.e., interleaving of steps of the sequential code, unless linearizability is violated. To ensure this property, we use a novel read-write locking scheme that protects tree \emph{edges} in addition to nodes. Our implementation outperforms the state-of-the art BSTs on most basic workloads, which suggests that optimizing the set of accepted schedules of the sequential code can be an adequate design principle for efficient concurrent data structures.
Guess & Check Codes for Deletions and Synchronization
Venue: arXiv cs.IT 2017
Year: 2017

We consider the problem of constructing codes that can correct $δ$ deletions occurring in an arbitrary binary string of length $n$ bits. Varshamov-Tenengolts (VT) codes can correct all possible single deletions $(δ=1)$ with an asymptotically optimal redundancy. Finding similar codes for $δ\geq 2$ deletions is an open problem. We propose a new family of codes, that we call Guess & Check (GC) codes, that can correct, with high probability, a constant number of deletions $δ$ occurring at uniformly random positions within an arbitrary string. The GC codes are based on MDS codes and have an asymptotically optimal redundancy that is $Θ(δ\log n)$. We provide deterministic polynomial time encoding and decoding schemes for these codes. We also describe the applications of GC codes to file synchronization.
A Time Localization System in Smart Home Using Hierarchical Structure and Dynamic Frequency
Venue: arXiv cs.NI 2017
Year: 2017

Both GPS and WiFi based localization have been exploited in recent years, yet most researches focus on localizing at home without environment context. Besides, the near home or workplace area is complex and has little attention in smart home or IOT. Therefore, after exploring the realistic route in and out of building, we conducted a time localization system (TLS) based on off-the-shelf smart phones with WiFi identification. TLS can identify the received signal strength indication (RSSI) of home and construct radio map of users' time route without site survey. As designed to service the smart devices in home, TLS applies the time interval as the distance of positions and as the variables of WiFi environment to mark time points. Experimental results with real users show that TLS as a service system for timeline localization achieves a median accuracy of 70 seconds and is more robust compared with nearest neighbor localization approach.
Leveraging cloud based big data analytics in knowledge management for enhanced decision making in organizations
Venue: arXiv cs.DC 2017
Year: 2017

In recent past, big data opportunities have gained much momentum to enhance knowledge management in organizations. However, big data due to its various properties like high volume, variety, and velocity can no longer be effectively stored and analyzed with traditional data management techniques to generate values for knowledge development. Hence, new technologies and architectures are required to store and analyze this big data through advanced data analytics and in turn generate vital real-time knowledge for effective decision making by organizations. More specifically, it is necessary to have a single infrastructure which provides common functionality of knowledge management, and flexible enough to handle different types of big data and big data analysis tasks. Cloud computing infrastructures capable of storing and processing large volume of data can be used for efficient big data processing because it minimizes the initial cost for the large-scale computing infrastructure demanded by big data analytics. This paper aims to explore the impact of big data analytics on knowledge management and proposes a cloud-based conceptual framework that can analyze big data in real time to facilitate enhanced decision making intended for competitive advantage. Thus, this framework will pave the way for organizations to explore the relationship between big data analytics and knowledge management which are mostly deemed as two distinct entities.
Normalized Total Gradient: A New Measure for Multispectral Image Registration
Venue: arXiv cs.CV 2017
Year: 2017

Image registration is a fundamental issue in multispectral image processing. In filter wheel based multispectral imaging systems, the non-coplanar placement of the filters always causes the misalignment of multiple channel images. The selective characteristic of spectral response in multispectral imaging raises two challenges to image registration. First, the intensity levels of a local region may be different in individual channel images. Second, the local intensity may vary rapidly in some channel images while keeps stationary in others. Conventional multimodal measures, such as mutual information, correlation coefficient, and correlation ratio, can register images with different regional intensity levels, but will fail in the circumstance of severe local intensity variation. In this paper, a new measure, namely normalized total gradient (NTG), is proposed for multispectral image registration. The NTG is applied on the difference between two channel images. This measure is based on the key assumption (observation) that the gradient of difference image between two aligned channel images is sparser than that between two misaligned ones. A registration framework, which incorporates image pyramid and global/local optimization, is further introduced for rigid transform. Experimental results validate that the proposed method is effective for multispectral image registration and performs better than conventional methods.
The Accuracy-Privacy Tradeoff of Mobile Crowdsensing
Venue: arXiv cs.NI 2017
Year: 2017

Mobile crowdsensing has emerged as an efficient sensing paradigm which combines the crowd intelligence and the sensing power of mobile devices, e.g.,~mobile phones and Internet of Things (IoT) gadgets. This article addresses the contradicting incentives of privacy preservation by crowdsensing users and accuracy maximization and collection of true data by service providers. We firstly define the individual contributions of crowdsensing users based on the accuracy in data analytics achieved by the service provider from buying their data. We then propose a truthful mechanism for achieving high service accuracy while protecting the privacy based on the user preferences. The users are incentivized to provide true data by being paid based on their individual contribution to the overall service accuracy. Moreover, we propose a coalition strategy which allows users to cooperate in providing their data under one identity, increasing their anonymity privacy protection, and sharing the resulting payoff. Finally, we outline important open research directions in mobile and people-centric crowdsensing.
Developing an ontology for the access to the contents of an archival fonds: the case of the Catasto Gregoriano
Venue: arXiv cs.AI 2017
Year: 2017

The research was proposed to exploit and extend the relational and contextual nature of the information assets of the Catasto Gregoriano, kept at the Archivio di Stato in Rome. Developed within the MODEUS project (Making Open Data Effectively Usable), this study originates from the following key ideas of MODEUS: to require Open Data to be expressed in terms of an ontology, and to include such an ontology as a documentation of the data themselves. Thus, Open Data are naturally linked by means of the ontology, which meets the requirements of the Linked Open Data vision.
Automated Identification of Drug-Drug Interactions in Pediatric Congestive Heart Failure Patients
Venue: arXiv cs.CL 2017
Year: 2017

Congestive Heart Failure, or CHF, is a serious medical condition that can result in fluid buildup in the body as a result of a weak heart. When the heart can't pump enough blood to efficiently deliver nutrients and oxygen to the body, kidney function may be impaired, resulting in fluid retention. CHF patients require a broad drug regimen to maintain the delicate system balance, particularly between their heart and kidneys. These drugs include ACE inhibitors and Beta Blockers to control blood pressure, anticoagulants to prevent blood clots, and diuretics to reduce fluid overload. Many of these drugs may interact, and potential effects of these interactions must be weighed against their benefits. For this project, we consider a set of 44 drugs identified as specifically relevant for treating CHF by pediatric cardiologists at Lucile Packard Children's Hospital. This list was generated as part of our current work at the LPCH Heart Center. The goal of this project is to identify and evaluate potentially harmful drug-drug interactions (DDIs) within pediatric patients with Congestive Heart Failure. This identification will be done autonomously, so that it may continuously update by evaluating newly published literature.
Filling missing data in point clouds by merging structured and unstructured point clouds
Venue: arXiv cs.CG 2017
Year: 2017

Point clouds arising from structured data, mainly as a result of CT scans, provides special properties on the distribution of points and the distances between those. Yet often, the amount of data provided can not compare to unstructured point clouds, i.e. data that arises from 3D light scans or laser scans. This article hereby proposes an approach to extend structured data and enhance the quality by inserting selected points from an unstructured point cloud. The resulting point cloud still has a partial structure that is called "half-structure". In this way, missing data that can not be optimally recovered through other surface reconstruction methods can be completed.
A parallel implementation of the Synchronised Louvain method
Venue: arXiv cs.DS 2017
Year: 2017

Community detection in networks is a very actual and important field of research with applications in many areas. But, given that the amount of processed data increases more and more, existing algorithms need to be adapted for very large graphs. The objective of this project was to parallelise the Synchronised Louvain Method, a community detection algorithm developed by Arnaud Browet, in order to improve its performances in terms of computation time and thus be able to faster detect communities in very large graphs. To reach this goal, we used the API OpenMP to parallelise the algorithm and then carried out performance tests. We studied the computation time and speedup of the parallelised algorithm and were able to bring out some qualitative trends. We obtained a great speedup, compared with the theoretical prediction of Amdahl law. To conclude, using the parallel implementation of the algorithm of Browet on large graphs seems to give good results, both in terms of computation time and speedup. Further tests should be carried out in order to obtain more quantitative results.
Is your software on dope? Formal analysis of surreptitiously "enhanced" programs
Venue: arXiv cs.LO 2017
Year: 2017

Usually, it is the software manufacturer who employs verification or testing to ensure that the software embedded in a device meets its main objectives. However, these days we are confronted with the situation that economical or technological reasons might make a manufacturer become interested in the software slightly deviating from its main objective for dubious reasons. Examples include lock-in strategies and the $\mathrm{NO}_x$ emission scandals in automotive industry. This phenomenon is what we call software doping. It is turning more widespread as software is embedded in ever more devices of daily use. The primary contribution of this article is to provide a hierarchy of simple but solid formal definitions that enable to distinguish whether a program is clean or doped. Moreover, we show that these characterisations provide an immediate framework for analysis by using already existing verification techniques. We exemplify this by applying self-composition on sequential programs and model checking of HyperLTL formulas on reactive models.
A note on complete classification of $(δ+αu^2)$-constacyclic codes of length $p^k$ over $\F_{p^m}+u\F_{p^m}+u^2\F_{p^m}$
Venue: arXiv cs.IT 2017
Year: 2017

For units $δ$ and $α$ in $\F_{p^m}$, the structure of $(δ+αu^2)$-constacyclic codes of length $p^k$ over $\mathbb{F}_{p^m}+u\mathbb{F}_{p^m}+u^2\mathbb{F}_{p^m}$ is studied and self-dual $(δ+αu^2)$-constacyclic codes are analyzed.
Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition
Venue: arXiv cs.CV 2017
Year: 2017

This paper explores multi-task learning (MTL) for face recognition. We answer the questions of how and why MTL can improve the face recognition performance. First, we propose a multi-task Convolutional Neural Network (CNN) for face recognition where identity classification is the main task and pose, illumination, and expression estimations are the side tasks. Second, we develop a dynamic-weighting scheme to automatically assign the loss weight to each side task, which is a crucial problem in MTL. Third, we propose a pose-directed multi-task CNN by grouping different poses to learn pose-specific identity features, simultaneously across all poses. Last but not least, we propose an energy-based weight analysis method to explore how CNN-based MTL works. We observe that the side tasks serve as regularizations to disentangle the variations from the learnt identity features. Extensive experiments on the entire Multi-PIE dataset demonstrate the effectiveness of the proposed approach. To the best of our knowledge, this is the first work using all data in Multi-PIE for face recognition. Our approach is also applicable to in-the-wild datasets for pose-invariant face recognition and achieves comparable or better performance than state of the art on LFW, CFP, and IJB-A datasets.