OpenRaiser/Intern-Atlas huggingface dataset:
3D Channel Model in 3GPP
Venue: arXiv cs.IT 2015
Year: 2015

Multi-antenna techniques capable of exploiting the elevation dimension are anticipated to be an important air-interface enhancement targeted to handle the expected growth in mobile traffic. In order to enable the development and evaluation of such multi-antenna techniques, the 3rd generation partnership project (3GPP) has recently developed a 3-dimensional (3D) channel model. The existing 2-dimensional (2D) channel models do not capture the elevation channel characteristics lending them insufficient for such studies. This article describes the main components of the newly developed 3D channel model and the motivations behind introducing them. One key aspect is the ability to model channels for users located on different floors of a building (at different heights). This is achieved by capturing a user height dependency in modelling some channel characteristics including pathloss, line-of-sight (LOS) probability, etc. In general this 3D channel model follows the framework of WINNERII/WINNER+ while also extending the applicability and the accuracy of the model by introducing some height and distance dependent elevation related parameters.
A Self-Compiling Android Data Obfuscation Tool
Venue: arXiv cs.CR 2015
Year: 2015

Smartphones are becoming more significant in storing and transferring data. However, techniques ensuring this data is not compromised after a confiscation of the device are not readily available. DroidStealth is an open source Android application which combines data encryption and application obfuscation techniques to provide users with a way to securely hide content on their smartphones. This includes hiding the application's default launch methods and providing methods such as dial-to-launch or invisible launch buttons. A novel technique provided by DroidStealth is the ability to transform its appearance to be able to hide in plain sight on devices. To achieve this, it uses self-compilation, without requiring any special permissions. This Two-Layer protection aims to protect the user and its data from casual search in various situations.
Bitcoin Transaction Graph Analysis
Venue: arXiv cs.CR 2015
Year: 2015

Bitcoins have recently become an increasingly popular cryptocurrency through which users trade electronically and more anonymously than via traditional electronic transfers. Bitcoin's design keeps all transactions in a public ledger. The sender and receiver for each transaction are identified only by cryptographic public-key ids. This leads to a common misconception that it inherently provides anonymous use. While Bitcoin's presumed anonymity offers new avenues for commerce, several recent studies raise user-privacy concerns. We explore the level of anonymity in the Bitcoin system. Our approach is two-fold: (i) We annotate the public transaction graph by linking bitcoin public keys to "real" people - either definitively or statistically. (ii) We run the annotated graph through our graph-analysis framework to find and summarize activity of both known and unknown users.
Aggregation and Trunking of M2M Traffic via D2D Connections
Venue: arXiv cs.NI 2015
Year: 2015

Machine-to-Machine (M2M) communications is one of the key enablers of the Internet of Things (IoT). Billions of devices are expected to be deployed in the next future for novel M2M applications demanding ubiquitous access and global connectivity. In order to cope with the massive number of machines, there is a need for new techniques to coordinate the access and allocate the resources. Although the majority of the proposed solutions are focused on the adaptation of the traditional cellular networks to the M2M traffic patterns, novel approaches based on the direct communication among nearby devices may represent an effective way to avoid access congestion and cell overload. In this paper, we propose a new strategy inspired by the classical Trunked Radio Systems (TRS), exploiting the Device-to-Device (D2D) connectivity between cellular users and Machine-Type Devices (MTDs). The aggregation of the locally generated packets is performed by a user device, which aggregates the machine-type data, supplements it with its own data and transmits all of them to the Base Station. We observe a fundamental trade-off between latency and the transmit power needed to deliver the aggregate traffic, in a sense that lower latency requires increase in the transmit power.
Arrhythmia Detection using Mutual Information-Based Integration Method
Venue: arXiv cs.CE 2015
Year: 2015

The aim of this paper is to propose an application of mutual information-based ensemble methods to the analysis and classification of heart beats associated with different types of Arrhythmia. Models of multilayer perceptrons, support vector machines, and radial basis function neural networks were trained and tested using the MIT-BIH arrhythmia database. This research brings a focus to an ensemble method that, to our knowledge, is a novel application in the area of ECG Arrhythmia detection. The proposed classifier ensemble method showed improved performance, relative to either majority voting classifier integration or to individual classifier performance. The overall ensemble accuracy was 98.25%.
Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients
Venue: arXiv cs.CV 2015
Year: 2015

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.
Bipartite Synthesis Method applied to the Subset Sum Problem demonstrates capability as decision and optimization tool
Venue: arXiv cs.DS 2015
Year: 2015

This paper introduces a deterministic algorithm for solving an instance of the Subset Sum Problem based on a new method entitled the Bipartite Synthesis Method. The algorithm is described and shown to have worst-case limiting performance over similar to the best deterministic algorithms achieving run time complexity on the order of O(2^0.5n). This algorithm is representative of a more expansive capability that might convey significant advantages over existing deterministic or probabilistic methods, and it is amenable to blending with existing methods. The method introduced can be applied to a variety of decision and optimization problems.
On Wavelet Decomposition over Finite Fields
Venue: arXiv cs.IT 2015
Year: 2015

This paper introduces some foundations of wavelets over Galois fields. Standard orthogonal finite-field wavelets (FF-Wavelets) including FF-Haar and FF-Daubechies are derived. Non-orthogonal FF-wavelets such as B-spline over GF(p) are also considered. A few examples of multiresolution analysis over Finite fields are presented showing how to perform Laplacian pyramid filtering of finite block lengths sequences. An application of FF-wavelets to design spread-spectrum sequences is presented.
A framework for trustworthiness assessment based on fidelity in cyber and physical domains
Venue: arXiv cs.CR 2015
Year: 2015

We introduce a method for the assessment of trust for n-open systems based on a measurement of fidelity and present a prototypic implementation of a complaint architecture. We construct a MAPE loop which monitors the compliance between corresponding figures of interest in cyber- and physical domains; derive measures of the system's trustworthiness; and use them to plan and execute actions aiming at guaranteeing system safety and resilience. We conclude with a view on our future work.
Old Wine in New Skins? Revisiting the Software Architecture for IP Network Stacks on Constrained IoT Devices
Venue: arXiv cs.NI 2015
Year: 2015

In this paper, we argue that existing concepts for the design and implementation of network stacks for constrained devices do not comply with the requirements of current and upcoming Internet of Things (IoT) use cases. The IoT requires not only a lightweight but also a modular network stack, based on standards. We discuss functional and non-functional requirements for the software architecture of the network stack on constrained IoT devices. Then, revisiting concepts from the early Internet as well as current implementations, we propose a future-proof alternative to existing IoT network stack architectures, and provide an initial evaluation of this proposal based on its implementation running on top of state-of-the-art IoT operating system and hardware.
A Multistage Stochastic Programming Approach to the Dynamic and Stochastic VRPTW - Extended version
Venue: arXiv cs.AI 2015
Year: 2015

We consider a dynamic vehicle routing problem with time windows and stochastic customers (DS-VRPTW), such that customers may request for services as vehicles have already started their tours. To solve this problem, the goal is to provide a decision rule for choosing, at each time step, the next action to perform in light of known requests and probabilistic knowledge on requests likelihood. We introduce a new decision rule, called Global Stochastic Assessment (GSA) rule for the DS-VRPTW, and we compare it with existing decision rules, such as MSA. In particular, we show that GSA fully integrates nonanticipativity constraints so that it leads to better decisions in our stochastic context. We describe a new heuristic approach for efficiently approximating our GSA rule. We introduce a new waiting strategy. Experiments on dynamic and stochastic benchmarks, which include instances of different degrees of dynamism, show that not only our approach is competitive with state-of-the-art methods, but also enables to compute meaningful offline solutions to fully dynamic problems where absolutely no a priori customer request is provided.
Low Power Analog-to-Digital Conversion in Millimeter Wave Systems: Impact of Resolution and Bandwidth on Performance
Venue: arXiv cs.IT 2015
Year: 2015

The wide bandwidth and large number of antennas used in millimeter wave systems put a heavy burden on the power consumption at the receiver. In this paper, using an additive quantization noise model, the effect of analog-digital conversion (ADC) resolution and bandwidth on the achievable rate is investigated for a multi-antenna system under a receiver power constraint. Two receiver architectures, analog and digital combining, are compared in terms of performance. Results demonstrate that: (i) For both analog and digital combining, there is a maximum bandwidth beyond which the achievable rate decreases; (ii) Depending on the operating regime of the system, analog combiner may have higher rate but digital combining uses less bandwidth when only ADC power consumption is considered, (iii) digital combining may have higher rate when power consumption of all the components in the receiver front-end are taken into account.
MALL proof equivalence is Logspace-complete, via binary decision diagrams
Venue: arXiv cs.LO 2015
Year: 2015

Proof equivalence in a logic is the problem of deciding whether two proofs are equivalent modulo a set of permutation of rules that reflects the commutative conversions of its cut-elimination procedure. As such, it is related to the question of proofnets: finding canonical representatives of equivalence classes of proofs that have good computational properties. It can also be seen as the word problem for the notion of free category corresponding to the logic. It has been recently shown that proof equivalence in MLL (the multiplicative with units fragment of linear logic) is PSPACE-complete, which rules out any low-complexity notion of proofnet for this particular logic. Since it is another fragment of linear logic for which attempts to define a fully satisfactory low-complexity notion of proofnet have not been successful so far, we study proof equivalence in MALL- (multiplicative-additive without units fragment of linear logic) and discover a situation that is totally different from the MLL case. Indeed, we show that proof equivalence in MALL- corresponds (under AC0 reductions) to equivalence of binary decision diagrams, a data structure widely used to represent and analyze Boolean functions efficiently. We show these two equivalent problems to be LOGSPACE-complete. If this technically leaves open the possibility for a complete solution to the question of proofnets for MALL-, the established relation with binary decision diagrams actually suggests a negative solution to this problem.
Approximating ATSP by Relaxing Connectivity
Venue: arXiv cs.DS 2015
Year: 2015

The standard LP relaxation of the asymmetric traveling salesman problem has been conjectured to have a constant integrality gap in the metric case. We prove this conjecture when restricted to shortest path metrics of node-weighted digraphs. Our arguments are constructive and give a constant factor approximation algorithm for these metrics. We remark that the considered case is more general than the directed analog of the special case of the symmetric traveling salesman problem for which there were recent improvements on Christofides' algorithm. The main idea of our approach is to first consider an easier problem obtained by significantly relaxing the general connectivity requirements into local connectivity conditions. For this relaxed problem, it is quite easy to give an algorithm with a guarantee of 3 on node-weighted shortest path metrics. More surprisingly, we then show that any algorithm (irrespective of the metric) for the relaxed problem can be turned into an algorithm for the asymmetric traveling salesman problem by only losing a small constant factor in the performance guarantee. This leaves open the intriguing task of designing a "good" algorithm for the relaxed problem on general metrics.
Can Other People Make You Less Creative?
Venue: arXiv cs.MA 2015
Year: 2015

This paper explains in layperson's terms how an agent-based model was used to investigate the hypothesis that culture evolves more effectively when novelty-generating creative processes are tempered by imitation processes that preserve proven successful ideas. Using EVOC, an agent-based model of cultural evolution we found that (1) the optimal ratio of inventing to imitating ranged from 1:1 to 2:1 depending on the fitness function, (2) there was a trade-off between the proportion of creators to conformers and how creative the creators were, and (3) when agents in increased or decreased their creativity depending on the success of their latest creative efforts, they segregated into creators and conformers, and the mean fitness of ideas across the society was higher. It is tentatively suggested that through the unconscious use of social cues, members of a society self-organizes to achieve a balanced mix of creators and conformers.
Contextual Online Learning for Multimedia Content Aggregation
Venue: arXiv cs.MM 2015
Year: 2015

The last decade has witnessed a tremendous growth in the volume as well as the diversity of multimedia content generated by a multitude of sources (news agencies, social media, etc.). Faced with a variety of content choices, consumers are exhibiting diverse preferences for content; their preferences often depend on the context in which they consume content as well as various exogenous events. To satisfy the consumers' demand for such diverse content, multimedia content aggregators (CAs) have emerged which gather content from numerous multimedia sources. A key challenge for such systems is to accurately predict what type of content each of its consumers prefers in a certain context, and adapt these predictions to the evolving consumers' preferences, contexts and content characteristics. We propose a novel, distributed, online multimedia content aggregation framework, which gathers content generated by multiple heterogeneous producers to fulfill its consumers' demand for content. Since both the multimedia content characteristics and the consumers' preferences and contexts are unknown, the optimal content aggregation strategy is unknown a priori. Our proposed content aggregation algorithm is able to learn online what content to gather and how to match content and users by exploiting similarities between consumer types. We prove bounds for our proposed learning algorithms that guarantee both the accuracy of the predictions as well as the learning speed. Importantly, our algorithms operate efficiently even when feedback from consumers is missing or content and preferences evolve over time. Illustrative results highlight the merits of the proposed content aggregation system in a variety of settings.
Optimal Multiuser Scheduling Schemes for Simultaneous Wireless Information and Power Transfer
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we study the downlink multiuser scheduling problem for systems with simultaneous wireless information and power transfer (SWIPT). We design optimal scheduling algorithms that maximize the long-term average system throughput under different fairness requirements, such as proportional fairness and equal throughput fairness. In particular, the algorithm designs are formulated as non-convex optimization problems which take into account the minimum required average sum harvested energy in the system. The problems are solved by using convex optimization techniques and the proposed optimization framework reveals the tradeoff between the long-term average system throughput and the sum harvested energy in multiuser systems with fairness constraints. Simulation results demonstrate that substantial performance gains can be achieved by the proposed optimization framework compared to existing suboptimal scheduling algorithms from the literature.
Dispersing Instant Social Video Service Across Multiple Clouds
Venue: arXiv cs.NI 2015
Year: 2015

Instant social video sharing which combines the online social network and user-generated short video streaming services, has become popular in today's Internet. Cloud-based hosting of such instant social video contents has become a norm to serve the increasing users with user-generated contents. A fundamental problem of cloud-based social video sharing service is that users are located globally, who cannot be served with good service quality with a single cloud provider. In this paper, we investigate the feasibility of dispersing instant social video contents to multiple cloud providers. The challenge is that inter-cloud social \emph{propagation} is indispensable with such multi-cloud social video hosting, yet such inter-cloud traffic incurs substantial operational cost. We analyze and formulate the multi-cloud hosting of an instant social video system as an optimization problem. We conduct large-scale measurement studies to show the characteristics of instant social video deployment, and demonstrate the trade-off between satisfying users with their ideal cloud providers, and reducing the inter-cloud data propagation. Our measurement insights of the social propagation allow us to propose a heuristic algorithm with acceptable complexity to solve the optimization problem, by partitioning a propagation-weighted social graph in two phases: a preference-aware initial cloud provider selection and a propagation-aware re-hosting. Our simulation experiments driven by real-world social network traces show the superiority of our design.
Restricted Isometry Property of Subspace Projection Matrix Under Random Compression
Venue: arXiv cs.IT 2015
Year: 2015

Structures play a significant role in the field of signal processing. As a representative of structural data, low rank matrix along with its restricted isometry property (RIP) has been an important research topic in compressive signal processing. Subspace projection matrix is a kind of low rank matrix with additional structure, which allows for further reduction of its intrinsic dimension. This leaves room for improving its own RIP, which could work as the foundation of compressed subspace projection matrix recovery. In this work, we study the RIP of subspace projection matrix under random orthonormal compression. Considering the fact that subspace projection matrices of $s$ dimensional subspaces in $\mathbb{R}^N$ form an $s(N-s)$ dimensional submanifold in $\mathbb{R}^{N\times N}$, our main concern is transformed to the stable embedding of such submanifold into $\mathbb{R}^{N\times N}$. The result is that by $O(s(N-s)\log N)$ number of random measurements the RIP of subspace projection matrix is guaranteed.
How Hard is Computing Parity with Noisy Communications?
Venue: arXiv cs.DC 2015
Year: 2015

We show a tight lower bound of $Ω(N \log\log N)$ on the number of transmissions required to compute the parity of $N$ input bits with constant error in a noisy communication network of $N$ randomly placed sensors, each having one input bit and communicating with others using local transmissions with power near the connectivity threshold. This result settles the lower bound question left open by Ying, Srikant and Dullerud (WiOpt 06), who showed how the sum of all the $N$ bits can be computed using $O(N \log\log N)$ transmissions. The same lower bound has been shown to hold for a host of other functions including majority by Dutta and Radhakrishnan (FOCS 2008). Most works on lower bounds for communication networks considered mostly the full broadcast model without using the fact that the communication in real networks is local, determined by the power of the transmitters. In fact, in full broadcast networks computing parity needs $θ(N)$ transmissions. To obtain our lower bound we employ techniques developed by Goyal, Kindler and Saks (FOCS 05), who showed lower bounds in the full broadcast model by reducing the problem to a model of noisy decision trees. However, in order to capture the limited range of transmissions in real sensor networks, we adapt their definition of noisy decision trees and allow each node of the tree access to only a limited part of the input. Our lower bound is obtained by exploiting special properties of parity computations in such noisy decision trees.
Using Heavy Clique Base Coarsening to Enhance Virtual Network Embedding
Venue: arXiv cs.DC 2015
Year: 2015

Network virtualization allows cloud infrastructure providers to accommodate multiple virtual networks on a single physical network. However, mapping multiple virtual network resources to physical network components, called virtual network embedding (VNE), is known to be non-deterministic polynomial-time hard (NP-hard). Effective virtual network embedding increases the revenue by increasing the number of accepted virtual networks. In this paper, we propose virtual network embedding algorithm, which improves virtual network embedding by coarsening virtual networks. Heavy Clique matching technique is used to coarsen virtual networks. Then, the coarsened virtual networks are enhanced by using a refined Kernighan-Lin algorithm. The performance of the proposed algorithm is evaluated and compared with existing algorithms using extensive simulations, which show that the proposed algorithm improves virtual network embedding by increasing the acceptance ratio and the revenue.
Generalized switching signals for input-to-state stability of switched systems
Venue: arXiv cs.SY 2015
Year: 2015

This article deals with input-to-state stability (ISS) of continuous-time switched nonlinear systems. Given a family of systems with exogenous inputs such that not all systems in the family are ISS, we characterize a new and general class of switching signals under which the resulting switched system is ISS. Our stabilizing switching signals allow the number of switches to grow faster than an affine function of the length of a time interval, unlike in the case of average dwell time switching. We also recast a subclass of average dwell time switching signals in our setting and establish analogs of two representative prior results.