OpenRaiser/Intern-Atlas huggingface dataset:
Transmission and Scheduling Aspects of Distributed Storage and Their Connections with Index Coding
Venue: arXiv cs.IT 2015
Year: 2015

Index coding is often studied with the assumption that a single source has all the messages requested by the receivers. We refer to this as \emph{centralized} index coding. In contrast, this paper focuses on \emph{distributed} index coding and addresses the following question: How does the availability of messages at distributed sources (storage nodes) affect the solutions and achievable rates of index coding? An extension to the work of Arbabjolfaei et al. in ISIT 2013 is presented when distributed sources communicate via a semi-deterministic multiple access channel (MAC) to simultaneous receivers. A numbers of examples are discussed that show the effect of message distribution and redundancy across the network on achievable rates of index coding and motivate future research on designing practical network storage codes that offer high index coding rates.
Performance of Cloud Radio Networks
Venue: arXiv cs.IT 2015
Year: 2015

Cloud radio networks coordinate transmission among base stations (BSs) to reduce the interference effects, particularly for the cell-edge users. In this paper, we analyze the performance of a cloud network with static clustering where geographically close BSs form a cloud network of cooperating BSs. Because, of finite cooperation, the interference in a practical cloud radio cannot be removed and in this paper, the distance based interference is taken into account in the analysis. In particular, we consider centralized zero forcing equalizer and dirty paper precoding for cancelling the interference. Bounds are developed on the signal-to-interference ratio distribution and achievable rate with full and limited channel feedback from the cluster users. The adverse effect of finite clusters on the achievable rate is quantified. We show that, the number of cooperating BSs is more crucial than the cluster area when full channel state information form the cluster is available for precoding. Also, we study the impact of limiting the channel state information on the achievable rate. We show that even with a practically feasible feedback of about five to six channel states from each user, significant gain in mean rate and cell edge rate compared to conventional cellular systems can be obtained.
Asymptotic Behavior of Mean Partitions in Consensus Clustering
Venue: arXiv cs.LG 2015
Year: 2015

Although consistency is a minimum requirement of any estimator, little is known about consistency of the mean partition approach in consensus clustering. This contribution studies the asymptotic behavior of mean partitions. We show that under normal assumptions, the mean partition approach is consistent and asymptotic normal. To derive both results, we represent partitions as points of some geometric space, called orbit space. Then we draw on results from the theory of Fréchet means and stochastic programming. The asymptotic properties hold for continuous extensions of standard cluster criteria (indices). The results justify consensus clustering using finite but sufficiently large sample sizes. Furthermore, the orbit space framework provides a mathematical foundation for studying further statistical, geometrical, and analytical properties of sets of partitions.
When the extension property does not hold for vector space alphabets
Venue: arXiv cs.IT 2015
Year: 2015

In our recent paper we characterized the extension property for symmetrized weight composition for linear codes over a module alphabet. Several improvements for the case of vector space alphabets are given in this paper. A detailed description of the property of $G$-pseudo-injectivity for vector spaces is made.
Algorithms for Provisioning Queries and Analytics
Venue: arXiv cs.DB 2015
Year: 2015

Provisioning is a technique for avoiding repeated expensive computations in what-if analysis. Given a query, an analyst formulates $k$ hypotheticals, each retaining some of the tuples of a database instance, possibly overlapping, and she wishes to answer the query under scenarios, where a scenario is defined by a subset of the hypotheticals that are "turned on". We say that a query admits compact provisioning if given any database instance and any $k$ hypotheticals, one can create a poly-size (in $k$) sketch that can then be used to answer the query under any of the $2^{k}$ possible scenarios without accessing the original instance. In this paper, we focus on provisioning complex queries that combine relational algebra (the logical component), grouping, and statistics/analytics (the numerical component). We first show that queries that compute quantiles or linear regression (as well as simpler queries that compute count and sum/average of positive values) can be compactly provisioned to provide (multiplicative) approximate answers to an arbitrary precision. In contrast, exact provisioning for each of these statistics requires the sketch size to be exponential in $k$. We then establish that for any complex query whose logical component is a positive relational algebra query, as long as the numerical component can be compactly provisioned, the complex query itself can be compactly provisioned. On the other hand, introducing negation or recursion in the logical component again requires the sketch size to be exponential in $k$. While our positive results use algorithms that do not access the original instance after a scenario is known, we prove our lower bounds even for the case when, knowing the scenario, limited access to the instance is allowed.
A new robust adaptive algorithm for underwater acoustic channel equalization
Venue: arXiv cs.SD 2015
Year: 2015

We introduce a novel family of adaptive robust equalizers for highly challenging underwater acoustic (UWA) channel equalization. Since the underwater environment is highly non-stationary and subjected to impulsive noise, we use adaptive filtering techniques based on a relative logarithmic cost function inspired by the competitive methods from the online learning literature. To improve the convergence performance of the conventional linear equalization methods, while mitigating the stability issues, we intrinsically combine different norms of the error in the cost function, using logarithmic functions. Hence, we achieve a comparable convergence performance to least mean fourth (LMF) equalizer, while significantly enhancing the stability performance in such an adverse communication medium. We demonstrate the performance of our algorithms through highly realistic experiments performed on accurately simulated underwater acoustic channels.
A Survey of Digital Privacy Rights Under CISA
Venue: arXiv cs.CR 2015
Year: 2015

The recent passing of the Cybersecurity Information Sharing Act of 2015 introduces a new framework for information sharing between private and US government entities with the expressed intent to identify cybersecurity threats. This is the latest in a series of similar bills that have been introduced to Congress over the last several years. While each of the previous standalone bills were defeated following widespread public resistance, the latest edition was included as an amendment to the United States' 2016 spending bill. This means that any dissenting congressmen unwilling to pass the spending bill with the CISA rider would be willing to risk another government shutdown due to the inability to come to terms on the budget measures. This paper seeks to explore the potential impacts of the measures introduced or enabled by CISA, and consider the formalization of digital privacy rights in an increasingly online and monitored world.
Revisiting Differentially Private Regression: Lessons From Learning Theory and their Consequences
Venue: arXiv cs.CR 2015
Year: 2015

Private regression has received attention from both database and security communities. Recent work by Fredrikson et al. (USENIX Security 2014) analyzed the functional mechanism (Zhang et al. VLDB 2012) for training linear regression models over medical data. Unfortunately, they found that model accuracy is already unacceptable with differential privacy when $\varepsilon = 5$. We address this issue, presenting an explicit connection between differential privacy and stable learning theory through which a substantially better privacy/utility tradeoff can be obtained. Perhaps more importantly, our theory reveals that the most basic mechanism in differential privacy, output perturbation, can be used to obtain a better tradeoff for all convex-Lipschitz-bounded learning tasks. Since output perturbation is simple to implement, it means that our approach is potentially widely applicable in practice. We go on to apply it on the same medical data as used by Fredrikson et al. Encouragingly, we achieve accurate models even for $\varepsilon = 0.1$. In the last part of this paper, we study the impact of our improved differentially private mechanisms on model inversion attacks, a privacy attack introduced by Fredrikson et al. We observe that the improved tradeoff makes the resulting differentially private model more susceptible to inversion attacks. We analyze this phenomenon formally.
Serifos: Workload Consolidation and Load Balancing for SSD Based Cloud Storage Systems
Venue: arXiv cs.DC 2015
Year: 2015

Achieving high performance in virtualized data centers requires both deploying high throughput storage clusters, i.e. based on Solid State Disks (SSDs), as well as optimally consolidating the workloads across storage nodes. Nowadays, the only practical solution for cloud storage providers to offer guaranteed performance is to grossly over-provision the storage nodes. The current workload scheduling mechanisms used in production do not have the intelligence to optimally allocate block storage volumes based on the performance of SSDs. In this paper, we introduce Serifos, an autonomous performance modeling and load balancing system designed for SSD-based cloud storage. Serifos takes into account the characteristics of the SSD storage units and constructs hardware dependent workload consolidation models. Thus Serifos is able to predict the latency caused by workload interference and the average latency of concurrent workloads. Furthermore, Serifos leverages an I/O load balancing algorithm to dynamically balance the volumes across the cluster. Experimental results indicate that Serifos consolidation model is able to maintain the mean prediction error of around 10% for heterogeneous hardware. As a result of Serifos load balancing, we found that the variance and the maximum average latency are reduced by 82% and 52%, respectively. The supported Service Level Objectives (SLOs) on the testbed improve 43% on average latency, 32% on the maximum read and 63% on the maximum write latency.
From Discrepancy to Majority
Venue: arXiv cs.DS 2015
Year: 2015

We show how to select an item with the majority color from $n$ two-colored items, given access to the items only through an oracle that returns the discrepancy of subsets of $k$ items. We use $n/\lfloor\tfrac{k}{2}\rfloor+O(k)$ queries, improving a previous method by De Marco and Kranakis that used $n-k+k^2/2$ queries. We also prove a lower bound of $n/(k-1)-O(n^{1/3})$ on the number of queries needed, improving a lower bound of $\lfloor n/k\rfloor$ by De Marco and Kranakis.
Remote Health Coaching System and Human Motion Data Analysis for Physical Therapy with Microsoft Kinect
Venue: arXiv cs.CV 2015
Year: 2015

This paper summarizes the recent progress we have made for the computer vision technologies in physical therapy with the accessible and affordable devices. We first introduce the remote health coaching system we build with Microsoft Kinect. Since the motion data captured by Kinect is noisy, we investigate the data accuracy of Kinect with respect to the high accuracy motion capture system. We also propose an outlier data removal algorithm based on the data distribution. In order to generate the kinematic parameter from the noisy data captured by Kinect, we propose a kinematic filtering algorithm based on Unscented Kalman Filter and the kinematic model of human skeleton. The proposed algorithm can obtain smooth kinematic parameter with reduced noise compared to the kinematic parameter generated from the raw motion data from Kinect.
Secrecy Wireless Information and Power Transfer: Challenges and Opportunities
Venue: arXiv cs.IT 2015
Year: 2015

Wireless information and power transfer (WIPT) enables more sustainable and resilient communications owing to the fact that it avoids frequent battery charging and replacement. However, it also suffers from possible information interception due to the open nature of wireless channels. Compared to traditional secure communications, secrecy wireless information and power transfer (SWIPT) carries several distinct characteristics. On one hand, wireless power transfer may increase the vulnerability of eavesdropping, since a power receiver, as a potential eavesdropper, usually has a shorter access distance than an information receiver. On the other hand, wireless power transfer can be exploited to enhance wireless security. This article reviews the security issues in various SWIPT scenarios, with an emphasis on revealing the corresponding challenges and opportunities for implementing SWIPT. Furthermore, we provide a survey on a variety of physical layer security techniques to improve secrecy performance. In particular, we propose to use massive multiple-input multiple-output (MIMO) techniques to enhance power transfer efficiency and secure information transmission simultaneously. Finally, we discuss several potential research directions to further enhance the security in SWIPT systems.
Flexible Attribute-Based Encryption Applicable to Secure E-Healthcare Records
Venue: arXiv cs.CR 2015
Year: 2015

In e-healthcare record systems (EHRS), attribute-based encryption (ABE) appears as a natural way to achieve fine-grained access control on health records. Some proposals exploit key-policy ABE (KP-ABE) to protect privacy in such a way that all users are associated with specific access policies and only the ciphertexts matching the users' access policies can be decrypted. An issue with KP-ABE is that it requires an a priori formulation of access policies during key generation, which is not always practicable in EHRS because the policies to access health records are sometimes determined after key generation. In this paper, we revisit KPABE and propose a dynamic ABE paradigm, referred to as access policy redefinable ABE (APR-ABE). To address the above issue, APR-ABE allows users to redefine their access policies and delegate keys for the redefined ones; hence a priori precise policies are no longer mandatory. We construct an APR-ABE scheme with short ciphertexts and prove its full security in the standard model under several static assumptions.
Fingerprinting Software-defined Networks
Venue: arXiv cs.CR 2015
Year: 2015

Software-defined networking (SDN) eases network management by centralizing the control plane and separating it from the data plane. The separation of planes in SDN, however, introduces new vulnerabilities in SDN networks since the difference in processing packets at each plane allows an adversary to fingerprint the network's packet-forwarding logic. In this paper, we study the feasibility of fingerprinting the controller-switch interactions by a remote adversary, whose aim is to acquire knowledge about specific flow rules that are installed at the switches. This knowledge empowers the adversary with a better understanding of the network's packet-forwarding logic and exposes the network to a number of threats. In our study, we collect measurements from hosts located across the globe using a realistic SDN network comprising of OpenFlow hardware and software switches. We show that, by leveraging information from the RTT and packet-pair dispersion of the exchanged packets, fingerprinting attacks on SDN networks succeed with overwhelming probability. We also show that these attacks are not restricted to active adversaries, but can be equally mounted by passive adversaries that only monitor traffic exchanged with the SDN network. Finally, we discuss the implications of these attacks on the security of SDN networks, and we present and evaluate an efficient countermeasure to strengthen SDN networks against fingerprinting. Our results demonstrate the effectiveness of our countermeasure in deterring fingerprinting attacks on SDN networks.
Beyond Classification: Latent User Interests Profiling from Visual Contents Analysis
Venue: arXiv cs.IR 2015
Year: 2015

User preference profiling is an important task in modern online social networks (OSN). With the proliferation of image-centric social platforms, such as Pinterest, visual contents have become one of the most informative data streams for understanding user preferences. Traditional approaches usually treat visual content analysis as a general classification problem where one or more labels are assigned to each image. Although such an approach simplifies the process of image analysis, it misses the rich context and visual cues that play an important role in people's perception of images. In this paper, we explore the possibilities of learning a user's latent visual preferences directly from image contents. We propose a distance metric learning method based on Deep Convolutional Neural Networks (CNN) to directly extract similarity information from visual contents and use the derived distance metric to mine individual users' fine-grained visual preferences. Through our preliminary experiments using data from 5,790 Pinterest users, we show that even for the images within the same category, each user possesses distinct and individually-identifiable visual preferences that are consistent over their lifetime. Our results underscore the untapped potential of finer-grained visual preference profiling in understanding users' preferences.
An Evaluation of Yelp Dataset
Venue: arXiv cs.SI 2015
Year: 2015

Yelp is one of the largest online searching and reviewing systems for kinds of businesses, including restaurants, shopping, home services et al. Analyzing the real world data from Yelp is valuable in acquiring the interests of users, which helps to improve the design of the next generation system. This paper targets the evaluation of Yelp dataset, which is provided in the Yelp data challenge. A bunch of interesting results are found. For instance, to reach any one in the Yelp social network, one only needs 4.5 hops on average, which verifies the classical six degree separation theory; Elite user mechanism is especially effective in maintaining the healthy of the whole network; Users who write less than 100 business reviews dominate. Those insights are expected to be considered by Yelp to make intelligent business decisions in the future.
Proving Continuity of Coinductive Global Bisimulation Distances: A Never Ending Story
Venue: arXiv cs.LO 2015
Year: 2015

We have developed a notion of global bisimulation distance between processes which goes somehow beyond the notions of bisimulation distance already existing in the literature, mainly based on bisimulation games. Our proposal is based on the cost of transformations: how much we need to modify one of the compared processes to obtain the other. Our original definition only covered finite processes, but a coinductive approach allows us to extend it to cover infinite but finitary trees. After having shown many interesting properties of our distance, it was our intention to prove continuity with respect to projections, but unfortunately the issue remains open. Nonetheless, we have obtained several partial results that are presented in this paper.
HDR - A Hysteresis-Driven Routing Algorithm for Energy Harvesting Tag Networks
Venue: arXiv cs.NI 2015
Year: 2015

The work contains a first attempt to treat the problem of routing in networks with energy harvesting units. We propose HDR - a Hysteresis Based Routing Algorithm and analyse it in a simple diamond network. We also consider a network with three forwarding nodes. The results are used to give insight into its application in general topology networks and to general harvesting patterns.
Evaluation of Time-Critical Communications for IEC 61850-Substation Network Architecture
Venue: arXiv cs.NI 2015
Year: 2015

Present-day developments, in electrical power transmission and distribution, require considerations of the status quo. In other meaning, international regulations enforce increasing of reliability and reducing of environment impact, correspondingly they motivate developing of dependable systems. Power grids especially intelligent (smart grids) ones become industrial solutions that follow standardized development. The International standardization, in the field of power transmission and distribution, improve technology influences. The rise of dedicated standards for SAS (Substation Automation Systems) communications, such as the leading International Electro-technical Commission standard IEC 61850, enforces modern technological trends in this field. Within this standard, a constraint of low ETE (End-to-End) latency should be respected, and time-critical status transmission must be achieved. This experimental study emphasis on IEC 61850 SAS communication standard, e.g. IEC 61850 GOOSE (Generic Object Oriented Substation Events), to implement an investigational method to determine the protection communication delay. This method observes GOOSE behaviour by adopting monitoring and analysis capabilities. It is observed by using network test equipment, i.e. SPAN (Switch Port Analyser) and TAP (Test Access Point) devices, with on-the-shelf available hardware and software solutions.
Implementation of deep learning algorithm for automatic detection of brain tumors using intraoperative IR-thermal mapping data
Venue: arXiv cs.CV 2015
Year: 2015

The efficiency of deep machine learning for automatic delineation of tumor areas has been demonstrated for intraoperative neuronavigation using active IR-mapping with the use of the cold test. The proposed approach employs a matrix IR-imager to remotely register the space-time distribution of surface temperature pattern, which is determined by the dynamics of local cerebral blood flow. The advantages of this technique are non-invasiveness, zero risks for the health of patients and medical staff, low implementation and operational costs, ease and speed of use. Traditional IR-diagnostic technique has a crucial limitation - it involves a diagnostician who determines the boundaries of tumor areas, which gives rise to considerable uncertainty, which can lead to diagnosis errors that are difficult to control. The current study demonstrates that implementing deep learning algorithms allows to eliminate the explained drawback.
News Across Languages - Cross-Lingual Document Similarity and Event Tracking
Venue: arXiv cs.IR 2015
Year: 2015

In today's world, we follow news which is distributed globally. Significant events are reported by different sources and in different languages. In this work, we address the problem of tracking of events in a large multilingual stream. Within a recently developed system Event Registry we examine two aspects of this problem: how to compare articles in different languages and how to link collections of articles in different languages which refer to the same event. Taking a multilingual stream and clusters of articles from each language, we compare different cross-lingual document similarity measures based on Wikipedia. This allows us to compute the similarity of any two articles regardless of language. Building on previous work, we show there are methods which scale well and can compute a meaningful similarity between articles from languages with little or no direct overlap in the training data. Using this capability, we then propose an approach to link clusters of articles across languages which represent the same event. We provide an extensive evaluation of the system as a whole, as well as an evaluation of the quality and robustness of the similarity measure and the linking algorithm.