OpenRaiser/Intern-Atlas huggingface dataset:
A Scheme for Maximal Resource Utilization in Peer-to-Peer Live Streaming
Venue: arXiv cs.NI 2015
Year: 2015

Peer-to-Peer streaming technology has become one of the major Internet applications as it offers the opportunity of broadcasting high quality video content to a large number of peers with low costs. It is widely accepted that with the efficient utilization of peers and server's upload capacities, peers can enjoy watching a high bit rate video with minimal end-to-end delay. In this paper, we present a practical scheduling algorithm that works in the challenging condition where no spare capacity is available, i.e., it maximally utilizes the resources and broadcasts the maximum streaming rate. Each peer contacts with only a small number of neighbours in the overlay network and autonomously subscribes to sub-streams according to a budget-model in such a way that the number of peers forwarding exactly one sub-stream will be maximized. The hop-count delay is also taken into account to construct a short depth trees. Finally, we show through simulation that peers dynamically converge to an efficient overlay structure with a short hop-count delay. Moreover, the proposed scheme gives nice features in the homogeneous case and overcomes SplitStream in all simulated scenarios.
Performance Analysis of an Astrophysical Simulation Code on the Intel Xeon Phi Architecture
Venue: arXiv cs.DC 2015
Year: 2015

We have developed the astrophysical simulation code XFLAT to study neutrino oscillations in supernovae. XFLAT is designed to utilize multiple levels of parallelism through MPI, OpenMP, and SIMD instructions (vectorization). It can run on both CPU and Xeon Phi co-processors based on the Intel Many Integrated Core Architecture (MIC). We analyze the performance of XFLAT on configurations with CPU only, Xeon Phi only and both CPU and Xeon Phi. We also investigate the impact of I/O and the multi-node performance of XFLAT on the Xeon Phi-equipped Stampede supercomputer at the Texas Advanced Computing Center (TACC).
On Summarizing Graph Streams
Venue: arXiv cs.DB 2015
Year: 2015

Graph streams, which refer to the graph with edges being updated sequentially in a form of a stream, have wide applications such as cyber security, social networks and transportation networks. This paper studies the problem of summarizing graph streams. Specifically, given a graph stream G, directed or undirected, the objective is to summarize G as S with much smaller (sublinear) space, linear construction time and constant maintenance cost for each edge update, such that S allows many queries over G to be approximately conducted efficiently. Due to the sheer volume and highly dynamic nature of graph streams, summarizing them remains a notoriously hard, if not impossible, problem. The widely used practice of summarizing data streams is to treat each element independently by e.g., hash- or sampling-based method, without keeping track of the connections between elements in a data stream, which gives these summaries limited power in supporting complicated queries over graph streams. This paper discusses a fundamentally different philosophy for summarizing graph streams. We present gLava, a probabilistic graph model that, instead of treating an edge (a stream element) as the operating unit, uses the finer grained node in an element. This will naturally form a new graph sketch where edges capture the connections inside elements, and nodes maintain relationships across elements. We discuss a wide range of supported graph queries and establish theoretical error bounds for basic queries.
Combining behavioural types with security analysis
Venue: arXiv cs.PL 2015
Year: 2015

Today's software systems are highly distributed and interconnected, and they increasingly rely on communication to achieve their goals; due to their societal importance, security and trustworthiness are crucial aspects for the correctness of these systems. Behavioural types, which extend data types by describing also the structured behaviour of programs, are a widely studied approach to the enforcement of correctness properties in communicating systems. This paper offers a unified overview of proposals based on behavioural types which are aimed at the analysis of security properties.
Notes on Information-Theoretic Privacy
Venue: arXiv cs.IT 2015
Year: 2015

We investigate the tradeoff between privacy and utility in a situation where both privacy and utility are measured in terms of mutual information. For the binary case, we fully characterize this tradeoff in case of perfect privacy and also give an upper-bound for the case where some privacy leakage is allowed. We then introduce a new quantity which quantifies the amount of private information contained in the observable data and then connect it to the optimal tradeoff between privacy and utility.
Bipartite Network Model for Inferring Hidden Ties in Crime Data
Venue: arXiv cs.SI 2015
Year: 2015

Certain crimes are hardly committed by individuals but carefully organised by group of associates and affiliates loosely connected to each other with a single or small group of individuals coordinating the overall actions. A common starting point in understanding the structural organisation of criminal groups is to identify the criminals and their associates. Situations arise in many criminal datasets where there is no direct connection among the criminals. In this paper, we investigate ties and community structure in crime data in order to understand the operations of both traditional and cyber criminals, as well as to predict the existence of organised criminal networks. Our contributions are twofold: we propose a bipartite network model for inferring hidden ties between actors who initiated an illegal interaction and objects affected by the interaction, we then validate the method in two case studies on pharmaceutical crime and underground forum data using standard network algorithms for structural and community analysis. The vertex level metrics and community analysis results obtained indicate the significance of our work in understanding the operations and structure of organised criminal networks which were not immediately obvious in the data. Identifying these groups and mapping their relationship to one another is essential in making more effective disruption strategies in the future.
Multitasking Programming of OBDH Satellite Based On PC-104
Venue: arXiv cs.OS 2015
Year: 2015

On Board Data Handling (OBDH) has functions to monitor, control, acquire, analyze, take a decision, and execute the command. OBDH should organize the task between sub system. OBDH like a heart which has a vital function. Because the function is seriously important therefore designing and implementing the OBDH should be carefully, in order to have a good reliability. Many OBDHs have been made to support the satellite mission using primitive programming. In handling the data from various input, OBDH should always be available to all sub systems, when the tasks are many, it is not easy to program using primitive programming. Sometimes the data become corrupt because the data which come to the OBDH is in the same time. Therefore it is required to have a way to handle the data safely and also easy in programming perspective. In this research, OBDH is programmed using multi tasking programming perspective has been created. The Operating System (OS) has been implemented so that can run the tasks simultaneously. The OS is prepared by configuring the Linux Kernel for the specific processor, creating Root File System (RFS), installing the BusyBox. In order to do the above method, preparing the environment in our machine has been done, they are installing the Cross Tool Chain, U-Boot, GNU-Linux Kernel Source etc. After that, programming using c code with multitasking programming can be implemented. By using above method, it is found that programming is easier and the corruption data because of reentrancy can be minimized. Keywords- Operating System, PC-104, Kernel, C Programming
Community Detection Using Slow Mixing Markov Models
Venue: arXiv cs.SI 2015
Year: 2015

The task of \emph{community detection} in a graph formalizes the intuitive task of grouping together subsets of vertices such that vertices within clusters are connected tighter than those in disparate clusters. This paper approaches community detection in graphs by constructing Markov random walks on the graphs. The mixing properties of the random walk are then used to identify communities. We use coupling from the past as an algorithmic primitive to translate the mixing properties of the walk into revealing the community structure of the graph. We analyze the performance of our algorithms on specific graph structures, including the stochastic block models (SBM) and LFR random graphs.
Controlled Experiments for Word Embeddings
Venue: arXiv cs.CL 2015
Year: 2015

An experimental approach to studying the properties of word embeddings is proposed. Controlled experiments, achieved through modifications of the training corpus, permit the demonstration of direct relations between word properties and word vector direction and length. The approach is demonstrated using the word2vec CBOW model with experiments that independently vary word frequency and word co-occurrence noise. The experiments reveal that word vector length depends more or less linearly on both word frequency and the level of noise in the co-occurrence distribution of the word. The coefficients of linearity depend upon the word. The special point in feature space, defined by the (artificial) word with pure noise in its co-occurrence distribution, is found to be small but non-zero.
Large-scale Artificial Neural Network: MapReduce-based Deep Learning
Venue: arXiv cs.DC 2015
Year: 2015

Faced with continuously increasing scale of data, original back-propagation neural network based machine learning algorithm presents two non-trivial challenges: huge amount of data makes it difficult to maintain both efficiency and accuracy; redundant data aggravates the system workload. This project is mainly focused on the solution to the issues above, combining deep learning algorithm with cloud computing platform to deal with large-scale data. A MapReduce-based handwriting character recognizer will be designed in this project to verify the efficiency improvement this mechanism will achieve on training and practical large-scale data. Careful discussion and experiment will be developed to illustrate how deep learning algorithm works to train handwritten digits data, how MapReduce is implemented on deep learning neural network, and why this combination accelerates computation. Besides performance, the scalability and robustness will be mentioned in this report as well. Our system comes with two demonstration software that visually illustrates our handwritten digit recognition/encoding application.
Procams-Based Cybernetics
Venue: arXiv cs.CV 2015
Year: 2015

Procams-based cybernetics is a unique, emerging research field, which aims at enhancing and supporting our activities by naturally connecting human and computers/machines as a cooperative integrated system via projector-camera systems (procams). It rests on various research domains such as virtual/augmented reality, computer vision, computer graphics, projection display, human computer interface, human robot interaction and so on. This laboratory presentation provides a brief history including recent achievements of our procams-based cybernetics project.
A Novel Approach to Document Classification using WordNet
Venue: arXiv cs.IR 2015
Year: 2015

Content based Document Classification is one of the biggest challenges in the context of free text mining. Current algorithms on document classifications mostly rely on cluster analysis based on bag-of-words approach. However that method is still being applied to many modern scientific dilemmas. It has established a strong presence in fields like economics and social science to merit serious attention from the researchers. In this paper we would like to propose and explore an alternative grounded more securely on the dictionary classification and correlatedness of words and phrases. It is expected that application of our existing knowledge about the underlying classification structure may lead to improvement of the classifier's performance.
Realizing the Tactile Internet: Haptic Communications over Next Generation 5G Cellular Networks
Venue: arXiv cs.NI 2015
Year: 2015

Prior Internet designs encompassed the fixed, mobile and lately the things Internet. In a natural evolution to these, the notion of the Tactile Internet is emerging which allows one to transmit touch and actuation in real-time. With voice and data communications driving the designs of the current Internets, the Tactile Internet will enable haptic communications, which in turn will be a paradigm shift in how skills and labor are digitally delivered globally. Design efforts for both the Tactile Internet and the underlying haptic communications are in its infancy. The aim of this article is thus to review some of the most stringent design challenges, as well as proposing first avenues for specific solutions to enable the Tactile Internet revolution.
Sponges and Engines: An introduction to Keccak and Keyak
Venue: arXiv cs.CR 2015
Year: 2015

In this document we present an introductory overview of the algorithms and design components underlying the Keccac cryptographic primitive and the Keyak encryption scheme for authenticated (session-supporting) encryption. This document aims to familiarize readers with the basic principles of authenticated encryption, the Sponge and Duplex constructions (full-state, keyed as well as regular versions), the permutation functions underlying Keccak and Keyak as well as Keyak v2's Motorist mode of operation.
Assessing the Value of Peer-Produced Information for Exploratory Search
Venue: arXiv cs.SI 2015
Year: 2015

Tagging is a popular feature that supports several collaborative tasks, including search, as tags produced by one user can help others finding relevant content. However, task performance depends on the existence of 'good' tags. A first step towards creating incentives for users to produce 'good' tags is the quantification of their value in the first place. This work fills this gap by combining qualitative and quantitative research methods. In particular, using contextual interviews, we first determine aspects that influence users' perception of tags' value for exploratory search. Next, we formalize some of the identified aspects and propose an information-theoretical method with provable properties that quantifies the two most important aspects (according to the qualitative analysis) that influence the perception of tag value: the ability of a tag to reduce the search space while retrieving relevant items to the user. The evaluation on real data shows that our method is accurate: tags that users consider more important have higher value than tags users have not expressed interest.
ParallelPC: an R package for efficient constraint based causal exploration
Venue: arXiv cs.AI 2015
Year: 2015

Discovering causal relationships from data is the ultimate goal of many research areas. Constraint based causal exploration algorithms, such as PC, FCI, RFCI, PC-simple, IDA and Joint-IDA have achieved significant progress and have many applications. A common problem with these methods is the high computational complexity, which hinders their applications in real world high dimensional datasets, e.g gene expression datasets. In this paper, we present an R package, ParallelPC, that includes the parallelised versions of these causal exploration algorithms. The parallelised algorithms help speed up the procedure of experimenting big datasets and reduce the memory used when running the algorithms. The package is not only suitable for super-computers or clusters, but also convenient for researchers using personal computers with multi core CPUs. Our experiment results on real world datasets show that using the parallelised algorithms it is now practical to explore causal relationships in high dimensional datasets with thousands of variables in a single multicore computer. ParallelPC is available in CRAN repository at https://cran.rproject.org/web/packages/ParallelPC/index.html.
Inferring disease correlation from healthcare data
Venue: arXiv cs.IR 2015
Year: 2015

Electronic Health Records maintained in health care settings are a potential source of substantial clinical knowledge. The massive volume of data, unstructured nature of records and obligatory requirement of domain acquaintance together pose a challenge in knowledge extraction from it. The aim of this study is to overcome this challenge with a methodical analysis, abstraction and summarization of such data. This is an attempt to explain clinical observations through bio-medical and genomic data. Discharge summaries of obesity patients were processed to extract coherent patterns. This was supported by Machine Learning and Natural Language Processing based technologies and concept mapping tool along with biomedical, clinical and genomic knowledge bases. Semantic relations between diseases were extracted and filtered through Chi square test to remove spurious relations. The remaining relations were validated against biomedical literature and gene interaction networks. A collection of binary relations of diseases was derived from the data. One set implied co-morbidity while the other set contained diseases which are risk factors of others. Validation against bio-medical literature increased the prospect of correlation between diseases. Gene interaction network revealed that the diseases are related and their corresponding genes are in close proximity. Conclusion: This study focuses on deducing meaningful relations between diseases from discharge summaries. For analytical purpose, the scope has been limited to a few common, well-researched diseases. It can be extended to incorporate relatively unknown, complex diseases and discover new traits to help in clinical assessments.
Statistical Channel Model with Multi-Frequency and Arbitrary Antenna Beamwidth for Millimeter-Wave Outdoor Communications
Venue: arXiv cs.IT 2015
Year: 2015

This paper presents a 3-dimensional millimeter-wave statistical channel impulse response model from 28 GHz and 73 GHz ultrawideband propagation measurements. An accurate 3GPP-like channel model that supports arbitrary carrier frequency, RF bandwidth, and antenna beamwidth (for both omnidirectional and arbitrary directional antennas), is provided. Time cluster and spatial lobe model parameters are extracted from empirical distributions from field measurements. A step-by-step modeling procedure for generating channel coefficients is shown to agree with statistics from the field measurements, thus confirming that the statistical channel model faithfully recreates spatial and temporal channel impulse responses for use in millimeter-wave 5G air interface designs.
An Extension of Interactive Scores for Multimedia Scenarios with Temporal Relations for Micro and Macro Controls
Venue: arXiv cs.MM 2015
Year: 2015

Software to design multimedia scenarios is usually based either on a fixed timeline or on cue lists, but both models are unrelated temporally. On the contrary, the formalism of interactive scores can describe multimedia scenarios with flexible and fixed temporal relations among the objects of the scenario, but cannot express neither temporal relations for micro controls nor signal processing. We extend interactive scores with such relations and with sound processing. We show some applications and we describe how they can be implemented in Pure Data. Our implementation has low average relative jitter even under high cpu load.
A Robust Generalized Chinese Remainder Theorem for Two Integers
Venue: arXiv cs.IT 2015
Year: 2015

A generalized Chinese remainder theorem (CRT) for multiple integers from residue sets has been studied recently, where the correspondence between the remainders and the integers in each residue set modulo several moduli is not known. A robust CRT has also been proposed lately for robustly reconstruct a single integer from its erroneous remainders. In this paper, we consider the reconstruction problem of two integers from their residue sets, where the remainders are not only out of order but also may have errors. We prove that two integers can be robustly reconstructed if their remainder errors are less than $M/8$, where $M$ is the greatest common divisor (gcd) of all the moduli. We also propose an efficient reconstruction algorithm. Finally, we present some simulations to verify the efficiency of the proposed algorithm. The study is motivated and has applications in the determination of multiple frequencies from multiple undersampled waveforms.
Ordered Tree-Pushdown Systems
Venue: arXiv cs.FL 2015
Year: 2015

We define a new class of pushdown systems where the pushdown is a tree instead of a word. We allow a limited form of lookahead on the pushdown conforming to a certain ordering restriction, and we show that the resulting class enjoys a decidable reachability problem. This follows from a preservation of recognizability result for the backward reachability relation of such systems. As an application, we show that our simple model can encode several formalisms generalizing pushdown systems, such as ordered multi-pushdown systems, annotated higher-order pushdown systems, the Krivine machine, and ordered annotated multi-pushdown systems. In each case, our procedure yields tight complexity.
OptImatch: Semantic Web System with Knowledge Base for Query Performance Problem Determination
Venue: arXiv cs.DB 2015
Year: 2015

Database query performance problem determination is often performed by analyzing query execution plans (QEPs) in addition to other performance data. As the query workloads that organizations run, have become larger and more complex, analyzing QEPs manually even by experts has become a very time consuming. Most performance diagnostic tools help with identifying problematic queries and most query tuning tools address a limited number of known problems and recommendations. We present the OptImatch system that offers a way to (a) look for varied user defined problem patterns in QEPs and (b) automatically get recommendations from an expert provided and user customizable knowledge base. Existing approaches do not provide the ability to perform workload analysis with flexible user defined patterns, as they lack the ability to impose a proper structure on QEPs. We introduce a novel semantic web system that allows a relatively naive user to search for arbitrary patterns and to get recommendations stored in a knowledge base either by experts or added by the user tailored to the environment in which they operate. Our methodology includes transforming a QEP into an RDF graph and transforming a GUI based user-defined pattern into a SPARQL query through handlers. The SPARQL query is matched against the abstracted RDF graph, and any matched portion of the abstracted RDF graph is relayed back to the user. With the knowledge base, the OptImatch system automatically scans and matches interesting stored patterns in a statistical way as appropriate and returns the corresponding recommendations. Although the knowledge base patterns and solution recommendations are not in the context of the user supplied QEPs, the context is adapted automatically through the handler tagging interface.
Inverse Privacy
Venue: arXiv cs.CY 2015
Year: 2015

An item of your personal information is inversely private if some party has access to it but you do not. We analyze the provenance of inversely private information and its rise to dominance over other kinds of personal information. In a nutshell, the inverse privacy problem is unjustified inaccessibility to you of your inversely private information. We argue that the inverse privacy problem has a market-based solution.