OpenRaiser/Intern-Atlas huggingface dataset:
Secure Symmetric Private Information Retrieval from Colluding Databases with Adversaries
Venue: arXiv cs.IT 2017
Year: 2017

The problem of symmetric private information retrieval (SPIR) from replicated databases with colluding servers and adversaries is studied. Specifically, the database comprises $K$ files, which are replicatively stored among $N$ servers. A user wants to retrieve one file from the database by communicating with the $N$ servers, without revealing the identity of the desired file to any server. Furthermore, the user shall learn nothing about the other $K-1$ files. Any $T$ out of $N$ servers may collude, that is, they may communicate their interactions with the user to guess the identity of the requested file. An adversary in the system can tap in on or even try to corrupt the communication. Three types of adversaries are considered: a Byzantine adversary who can overwrite the transmission of any $B$ servers to the user; a passive eavesdropper who can tap in on the incoming and outgoing transmissions of any $E$ servers; and a combination of both -- an adversary who can tap in on a set of any $E$ nodes, and overwrite the transmission of a set of any $B$ nodes. The problems of SPIR with colluding servers and the three types of adversaries are named T-BSPIR, T-ESPIR and T-BESPIR respectively. The capacity of the problem is defined as the maximum number of information bits of the desired file retrieved per downloaded bit. We show that the information-theoretical capacity of T-BSPIR equals $1-\frac{2B+T}{N}$, if the servers share common randomness (unavailable at the user) with amount at least $\frac{2B+T}{N-2B-T}$ times the file size. Otherwise, the capacity equals zero. The capacity of T-ESPIR is proved to equal $1-\frac{\max(T,E)}{N}$, with common randomness at least $\frac{\max(T,E)}{N-\max(T,E)}$ times the file size. Finally, the capacity of T-BESPIR is proved to be $1-\frac{2B+\max(T,E)}{N}$, with common randomness at least $\frac{2B+\max(T,E)}{N-2B-\max(T,E)}$ times the file size.
A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising
Venue: arXiv cs.CV 2017
Year: 2017

A learning-based framework for representation of domain-specific images is proposed where joint compression and denoising can be done using a VQ-based multi-layer network. While it learns to compress the images from a training set, the compression performance is very well generalized on images from a test set. Moreover, when fed with noisy versions of the test set, since it has priors from clean images, the network also efficiently denoises the test images during the reconstruction. The proposed framework is a regularized version of the Residual Quantization (RQ) where at each stage, the quantization error from the previous stage is further quantized. Instead of codebook learning from the k-means which over-trains for high-dimensional vectors, we show that only generating the codewords from a random, but properly regularized distribution suffices to compress the images globally and without the need to resort to patch-based division of images. The experiments are done on the \textit{CroppedYale-B} set of facial images and the method is compared with the JPEG-2000 codec for compression and BM3D for denoising, showing promising results.
The structural role of the core literature in history
Venue: arXiv cs.DL 2017
Year: 2017

The intellectual landscapes of the humanities are mostly uncharted territory. Little is known on the ways published research of humanist scholars defines areas of intellectual activity. An open question relates to the structural role of core literature: highly cited sources, naturally playing a disproportionate role in the definition of intellectual landscapes. We introduce four indicators in order to map the structural role played by core sources into connecting different areas of the intellectual landscape of citing publications (i.e. communities in the bibliographic coupling network). All indicators factor out the influence of degree distributions by internalizing a null configuration model. By considering several datasets focused on history, we show that two distinct structural actions are performed by the core literature: a global one, by connecting otherwise separated communities in the landscape, or a local one, by rising connectivity within communities. In our study, the global action is mainly performed by small sets of scholarly monographs, reference works and primary sources, while the rest of the core, and especially most journal articles, acts mostly locally.
A parallel corpus of Python functions and documentation strings for automated code documentation and code generation
Venue: arXiv cs.CL 2017
Year: 2017

Automated documentation of programming source code and automated code generation from natural language are challenging tasks of both practical and scientific interest. Progress in these areas has been limited by the low availability of parallel corpora of code and natural language descriptions, which tend to be small and constrained to specific domains. In this work we introduce a large and diverse parallel corpus of a hundred thousands Python functions with their documentation strings ("docstrings") generated by scraping open source repositories on GitHub. We describe baseline results for the code documentation and code generation tasks obtained by neural machine translation. We also experiment with data augmentation techniques to further increase the amount of training data. We release our datasets and processing scripts in order to stimulate research in these areas.
Skin Temperature Measurement
Venue: arXiv cs.OH 2017
Year: 2017

This report represents the design and implementation of a skin temperature measurement system. The system aims to measure the skin temperature from a sensor and send it to the PC using a USB cable to display on screen. The data needs to be updated every second. The PIC18F4550 microcontroller has been used in this project to obtain data from the sensor and send it to the PC using USB 2.0 that has been built into the microcontroller. The microcontroller has a 10-bit Analog Digital Converting accuracy that is one of the important criteria for this design as it is going to be used for medical purposes. As the project concentrates more on designing software than hardware, the EasyPIC4 development board was used which comes with all hardware required for this project. The Jackson diagram method was used to design and implement the coding program for the microcontroller software part of the system. The MikroC IDE has been used to compile and load the program into PIC18F4550 microcontroller. The program for the microcontroller uses C language that aims to keep the USB link alive by using interrupt function. A sensor collects data from sensor as 4 bits and send it to the PC every second using a USB cable. The data received from sensor, will be sent by microcontroller to the PC. The Visual Basic software was used in the PC side of device to catch and output the data on the screen. A template file for the Visual Basic program was generated by Easy HID wizard to make software programming part easier for designer. The USBTrace analyzer has been used in the scenario any problems occur during or after the design and construction of the software. The software enables a user to monitor the data on the USB bus that sends the data to the PC from microcontroller.
Evaluating race and sex diversity in the world's largest companies using deep neural networks
Venue: arXiv cs.CY 2017
Year: 2017

Diversity is one of the fundamental properties for the survival of species, populations, and organizations. Recent advances in deep learning allow for the rapid and automatic assessment of organizational diversity and possible discrimination by race, sex, age and other parameters. Automating the process of assessing the organizational diversity using the deep neural networks and eliminating the human factor may provide a set of real-time unbiased reports to all stakeholders. In this pilot study we applied the deep-learned predictors of race and sex to the executive management and board member profiles of the 500 largest companies from the 2016 Forbes Global 2000 list and compared the predicted ratios to the ratios within each company's country of origin and ranked them by the sex-, age- and race- diversity index (DI). While the study has many limitations and no claims are being made concerning the individual companies, it demonstrates a method for the rapid and impartial assessment of organizational diversity using deep neural networks.
Fast Heuristic Algorithm for Multi-scale Hierarchical Community Detection
Venue: arXiv cs.SI 2017
Year: 2017

Complex networks constitute the backbones of many complex systems such as social networks. Detecting the community structure in a complex network is both a challenging and a computationally expensive task. In this paper, we present the HAMUHI-CODE, a novel fast heuristic algorithm for multi-scale hierarchical community detection inspired on an agglomerative hierarchical clustering technique. We define a new structural similarity of vertices based on the classical cosine similarity by removing some vertices in order to increase the probability of identifying inter-cluster edges. Then we use the proposed structural similarity in a new agglomerative hierarchical algorithm that does not merge only clusters with maximal similarity as in the classical approach, but merges any cluster that does not meet a parameterized community definition with its most similar adjacent cluster. The algorithm computes all the similar clusters at the same time is checking if each cluster meets the parameterized community definition. It is done in linear time complexity in terms of the number of cluster in the iteration. Since a complex network is a sparse graph, our approach HAMUHI-CODE has a super-linear time complexity with respect to the size of the input in the worst-case scenario (if the clusters merge in pairs), making it suitable to be applied on large-scale complex networks. To test the properties and the efficiency of our algorithm we have conducted extensive experiments on real world and synthetic benchmark networks by comparing it to several baseline state-of-the-art algorithms.
Enhancing PHY Security of Cooperative Cognitive Radio Multicast Communications
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we propose a cooperative approach to improve the security of both primary and secondary systems in cognitive radio multicast communications. During their access to the frequency spectrum licensed to the primary users, the secondary unlicensed users assist the primary system in fortifying security by sending a jamming noise to the eavesdroppers, while simultaneously protect themselves from eavesdropping. The main objective of this work is to maximize the secrecy rate of the secondary system, while adhering to all individual primary users' secrecy rate constraints. In the case of active eavesdroppers and perfect channel state information (CSI) at the transceivers, the utility function of interest is nonconcave and the involved constraints are nonconvex, and thus, the optimal solutions are troublesome. To solve this problem, we propose an iterative algorithm to arrive at least to a local optimum of the original nonconvex problem. This algorithm is guaranteed to achieve a Karush-Kuhn-Tucker solution. Then, we extend the optimization approach to the case of passive eavesdroppers and imperfect CSI knowledge at the transceivers, where the constraints are transformed into a linear matrix inequality and convex constraints, in order to facilitate the optimal solution.
Fast Asymmetric Fronts Propagation for Image Segmentation
Venue: arXiv cs.CG 2017
Year: 2017

In this paper, we introduce a generalized asymmetric fronts propagation model based on the geodesic distance maps and the Eikonal partial differential equations. One of the key ingredients for the computation of the geodesic distance map is the geodesic metric, which can govern the action of the geodesic distance level set propagation. We consider a Finsler metric with the Randers form, through which the asymmetry and anisotropy enhancements can be taken into account to prevent the fronts leaking problem during the fronts propagation. These enhancements can be derived from the image edge-dependent vector field such as the gradient vector flow. The numerical implementations are carried out by the Finsler variant of the fast marching method, leading to very efficient interactive segmentation schemes. We apply the proposed Finsler fronts propagation model to image segmentation applications. Specifically, the foreground and background segmentation is implemented by the Voronoi index map. In addition, for the application of tubularity segmentation, we exploit the level set lines of the geodesic distance map associated to the proposed Finsler metric providing that a thresholding value is given.
Computer Self-efficacy and Its Relationship with Web Portal Usage: Evidence from the University of the East
Venue: arXiv cs.CY 2017
Year: 2017

The University of the East Web Portal is an academic, web based system that provides educational electronic materials and e-learning services. To fully optimize its usage, it is imperative to determine the factors that relate to its usage. Thus, this study, to determine the computer self-efficacy of the faculty members of the University of the East and its relationship with their web portal usage, was conceived. Using a validated questionnaire, the profile of the respondents, their computer self-efficacy, and web portal usage were gathered. Data showed that the respondents were relatively young (M = 40 years old), majority had masters degree (f = 85, 72%), most had been using the web portal for four semesters (f = 60, 51%), and the large part were intermediate web portal users (f = 69, 59%). They were highly skilled in using the computer (M = 4.29) and skilled in using the Internet (M = 4.28). E-learning services (M = 3.29) and online library resources (M = 3.12) were only used occasionally. Pearson correlation revealed that age was positively correlated with online library resources (r = 0.267, p < 0.05) and a negative relationship existed between perceived skill level in using the portal and online library resources usage (r = -0.206, p < 0.05). A 2x2 chi square revealed that the highest educational attainment had a significant relationship with online library resources (chi square = 5.489, df = 1, p < 0.05). Basic computer (r = 0.196, p < 0.05) and Internet skills (r = 0.303, p < 0.05) were significantly and positively related with e-learning services usage but not with online library resources usage. Other individual factors such as attitudes towards the web portal and anxiety towards using the web portal can be investigated.
Whittle Indexability in Egalitarian Processor Sharing Systems
Venue: arXiv cs.SY 2017
Year: 2017

The egalitarian processor sharing model is viewed as a restless bandit and its Whittle indexability is established. A numerical scheme for computing the Whittle indices is provided, along with supporting numerical experiments.
Event Stream Processing with Multiple Threads
Venue: arXiv cs.DC 2017
Year: 2017

Current runtime verification tools seldom make use of multi-threading to speed up the evaluation of a property on a large event trace. In this paper, we present an extension to the BeepBeep 3 event stream engine that allows the use of multiple threads during the evaluation of a query. Various parallelization strategies are presented and described on simple examples. The implementation of these strategies is then evaluated empirically on a sample of problems. Compared to the previous, single-threaded version of the BeepBeep engine, the allocation of just a few threads to specific portions of a query provides dramatic improvement in terms of running time.
PELESent: Cross-domain polarity classification using distant supervision
Venue: arXiv cs.CL 2017
Year: 2017

The enormous amount of texts published daily by Internet users has fostered the development of methods to analyze this content in several natural language processing areas, such as sentiment analysis. The main goal of this task is to classify the polarity of a message. Even though many approaches have been proposed for sentiment analysis, some of the most successful ones rely on the availability of large annotated corpus, which is an expensive and time-consuming process. In recent years, distant supervision has been used to obtain larger datasets. So, inspired by these techniques, in this paper we extend such approaches to incorporate popular graphic symbols used in electronic messages, the emojis, in order to create a large sentiment corpus for Portuguese. Trained on almost one million tweets, several models were tested in both same domain and cross-domain corpora. Our methods obtained very competitive results in five annotated corpora from mixed domains (Twitter and product reviews), which proves the domain-independent property of such approach. In addition, our results suggest that the combination of emoticons and emojis is able to properly capture the sentiment of a message.
Feature Joint-State Posterior Estimation in Factorial Speech Processing Models using Deep Neural Networks
Venue: arXiv cs.SD 2017
Year: 2017

This paper proposes a new method for calculating joint-state posteriors of mixed-audio features using deep neural networks to be used in factorial speech processing models. The joint-state posterior information is required in factorial models to perform joint-decoding. The novelty of this work is its architecture which enables the network to infer joint-state posteriors from the pairs of state posteriors of stereo features. This paper defines an objective function to solve an underdetermined system of equations, which is used by the network for extracting joint-state posteriors. It develops the required expressions for fine-tuning the network in a unified way. The experiments compare the proposed network decoding results to those of the vector Taylor series method and show 2.3% absolute performance improvement in the monaural speech separation and recognition challenge. This achievement is substantial when we consider the simplicity of joint-state posterior extraction provided by deep neural networks.
Ten simple rules for collaborative lesson development
Venue: arXiv cs.CY 2017
Year: 2017

The collaborative development methods pioneered by the open source software community offer a way to create lessons that are open, accessible, and sustainable. This paper presents ten simple rules for doing this drawn from our experience with several successful projects.
Power domination on triangular grids
Venue: arXiv cs.DM 2017
Year: 2017

The concept of power domination emerged from the problem of monitoring electrical systems. Given a graph G and a set S $\subseteq$ V (G), a set M of monitored vertices is built as follows: at first, M contains only the vertices of S and their direct neighbors, and then each time a vertex in M has exactly one neighbor not in M, this neighbor is added to M. The power domination number of a graph G is the minimum size of a set S such that this process ends up with the set M containing every vertex of G. We here show that the power domination number of a triangular grid T\_k with hexagonal-shape border of length k -- 1 is exactly $\lceil k/3 \rceil.
Exploiting Parallelism in Optical Network Systems: A Case Study of Random Linear Network Coding (RLNC) in Ethernet-over-Optical Networks
Venue: arXiv cs.PF 2017
Year: 2017

As parallelism becomes critically important in the semiconductor technology, high-performance computing, and cloud applications, parallel network systems will increasingly follow suit. Today, parallelism is an essential architectural feature of 40/100/400 Gigabit Ethernet standards, whereby high speed Ethernet systems are equipped with multiple parallel network interfaces. This creates new network topology abstractions and new technology requirements: instead of a single high capacity network link, multiple Ethernet end-points and interfaces need to be considered together with multiple links in form of discrete parallel paths. This new paradigm is enabling implementations of various new features to improve overall system performance. In this paper, we analyze the performance of parallel network systems with network coding. In particular, by using random LNC (RLNC), - a code without the need for decoding, we can make use of the fact that we have codes that are both distributed (removing the need for coordination or optimization of resources) and composable (without the need to exchange code information), leading to a fully stateless operation. We propose a novel theoretical modeling framework, including derivation of the upper and lower bounds as well as an expected value of the differential delay of parallel paths, and the resulting queue size at the receiver. The results show a great promise of network system parallelism in combination with RLNC: with a proper set of design parameters, the differential delay and the buffer size at the Ethernet receiver can be reduced significantly, while the cross-layer design and routing can be greatly simplified.
Delay Properties of Energy Efficient Ethernet Networks
Venue: arXiv cs.NI 2017
Year: 2017

Networking operational costs and environmental concerns have lately driven the quest for energy efficient equipment. In wired networks, energy efficient Ethernet (EEE) interfaces can greatly reduce power demands when compared to regular Ethernet interfaces. Their power saving capabilities have been studied and modeled in many research articles in the last few years, together with their effects on traffic delay. However, to this date, all articles have considered them in isolation instead of as part of a network of EEE interfaces. In this paper we develop a model for the traffic delay on a network of EEE interfaces. We prove that, whatever the network topology, the per interface delay increment due to the power savings capabilities is bounded and, in most scenarios, negligible. This confirms that EEE interfaces can be used in all but the most delay constrained scenarios to save considerable amounts of power.
Towards Crafting Text Adversarial Samples
Venue: arXiv cs.LG 2017
Year: 2017

Adversarial samples are strategically modified samples, which are crafted with the purpose of fooling a classifier at hand. An attacker introduces specially crafted adversarial samples to a deployed classifier, which are being mis-classified by the classifier. However, the samples are perceived to be drawn from entirely different classes and thus it becomes hard to detect the adversarial samples. Most of the prior works have been focused on synthesizing adversarial samples in the image domain. In this paper, we propose a new method of crafting adversarial text samples by modification of the original samples. Modifications of the original text samples are done by deleting or replacing the important or salient words in the text or by introducing new words in the text sample. Our algorithm works best for the datasets which have sub-categories within each of the classes of examples. While crafting adversarial samples, one of the key constraint is to generate meaningful sentences which can at pass off as legitimate from language (English) viewpoint. Experimental results on IMDB movie review dataset for sentiment analysis and Twitter dataset for gender detection show the efficiency of our proposed method.