OpenRaiser/Intern-Atlas huggingface dataset:
The Impact of Operators' Performance in the Reliability of Cyber-Physical Power Distribution Systems
Venue: arXiv cs.SY 2016
Year: 2016

Cyber-Physical Systems are the result of integrating information and communication technologies into physical systems. One particular case are Cyber-Physical Power Systems (CPPS), which use communication technologies to perform real-time monitoring and operation. These kinds of systems have become more complex, impacting on the systems' characteristics, such as their reliability. In addition, it is already known that in terms of the reliability of Cyber-Physical Power Distribution Systems (CPPDS), the failures of the communication network are just as relevant as the electrical network failures. However, some of the operators' performances, such as response time and decision quality, during CPPDS contingencies have not been investigated yet. In this paper, we introduce a model to the operator response time, present a Sequential Monte Carlo Simulation methodology that incorporates the response time in CPPDS reliability indices estimation, and evaluate the impact of the operator response time in reliability indices. Our method is tested on a CPPDS using different values for the average response time of operators. The results show that the response time of the operators affects the reliability indices that are related to the durations of the failure, indicating that a fast decision directly contributes to the system performance. We conclude that the improvement of CPPDS reliability is not only dependent on the electric and communication components, but also dependent on operators' performance.
Simple Mechanisms For Agents With Complements
Venue: arXiv cs.GT 2016
Year: 2016

We study the efficiency of simple auctions in the presence of complements. [DMSW15] introduced the single-bid auction, and showed that it has a price of anarchy (PoA) of $O(\log m)$ for complement-free (i.e., subadditive) valuations. Prior to our work, no non-trivial upper bound on the PoA of single bid auctions was known for valuations exhibiting complements. We introduce a hierarchy over valuations, where levels of the hierarchy correspond to the degree of complementarity, and the PoA of the single bid auction degrades gracefully with the level of the hierarchy. This hierarchy is a refinement of the Maximum over Positive Hypergraphs (MPH) hierarchy [FFIILS15], where the degree of complementarity $d$ is captured by the maximum number of neighbors of a node in the positive hypergraph representation. We show that the price of anarchy of the single bid auction for valuations of level $d$ of the hierarchy is $O(d^2 \log(m/d))$, where $m$ is the number of items. We also establish an improved upper bound of $O(d \log m)$ for a subclass where every hyperedge in the positive hypergraph representation is of size at most 2 (but the degree is still $d$). Finally, we show that randomizing between the single bid auction and the grand bundle auction has a price of anarchy of at most $O(\sqrt{m})$ for general valuations. All of our results are derived via the smoothness framework, thus extend to coarse-correlated equilibria and to Bayes Nash equilibria.
Characterizing Interest Aggregation in Content-Centric Networks
Venue: arXiv cs.NI 2016
Year: 2016

The Named Data Networking (NDN) and Content-Centric Networking (CCN) architectures advocate Interest aggregation as a means to reduce end-to-end latency and bandwidth consumption. To enable these benefits, Interest aggregation must be realized through Pending Interest Tables (PIT) that grow in size at the rate of incoming Interests to an extent that may eventually defeat their original purpose. A thorough analysis is provided of the Interest aggregation mechanism using mathematical arguments backed by extensive discrete-event simulation results. We present a simple yet accurate analytical framework for characterizing Interest aggregation in an LRU cache, and use our model to develop an iterative algorithm to analyze the benefits of Interest aggregation in a network of interconnected caches. Our findings reveal that, under realistic assumptions, an insignificant fraction of Interests in the system benefit from aggregation, compromising the effectiveness of using PITs as an integral component of Content-Centric Networks.
An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype, and Experiments
Venue: arXiv cs.NI 2016
Year: 2016

Smart energy in buildings is an important research area of Internet of Things (IoT). Buildings as important parts of the smart grids, their energy efficiency is vital for the environment and global sustainability. Using a LEED-gold-certificated green office building, we built a unique IoT experimental testbed for our energy efficiency and building intelligence research. We first monitor and collect one-year-long building energy usage data and then systematically evaluate and analyze them. The results show that due to the centralized and static building controls, the actual running of green buildings may not be energy efficient even though they may be "green" by design. Inspired by "energy proportional computing" in modern computers, we propose a IoT framework with smart location-based automated and networked energy control, which uses smartphone platform and cloud computing technologies to enable multi-scale energy proportionality including building-, user-, and organizational-level energy proportionality. We further build a proof-of-concept IoT network and control system prototype and carried out real-world experiments which demonstrate the effectiveness of the proposed solution. We envision that the broad application of the proposed solution has not only led to significant economic benefits in term of energy saving, improving home/office network intelligence, but also bought in a huge social implication in terms of global sustainability.
Sparsity and Parallel Acquisition: Optimal Uniform and Nonuniform Recovery Guarantees
Venue: arXiv cs.IT 2016
Year: 2016

The problem of multiple sensors simultaneously acquiring measurements of a single object can be found in many applications. In this paper, we present the optimal recovery guarantees for the recovery of compressible signals from multi-sensor measurements using compressed sensing. In the first half of the paper, we present both uniform and nonuniform recovery guarantees for the conventional sparse signal model in a so-called distinct sensing scenario. In the second half, using the so-called sparse and distributed signal model, we present nonuniform recovery guarantees which effectively broaden the class of sensing scenarios for which optimal recovery is possible, including to the so-called identical sampling scenario. To verify our recovery guarantees we provide several numerical results including phase transition curves and numerically-computed bounds.
Do You See What I Mean? Visual Resolution of Linguistic Ambiguities
Venue: arXiv cs.CV 2016
Year: 2016

Understanding language goes hand in hand with the ability to integrate complex contextual information obtained via perception. In this work, we present a novel task for grounded language understanding: disambiguating a sentence given a visual scene which depicts one of the possible interpretations of that sentence. To this end, we introduce a new multimodal corpus containing ambiguous sentences, representing a wide range of syntactic, semantic and discourse ambiguities, coupled with videos that visualize the different interpretations for each sentence. We address this task by extending a vision model which determines if a sentence is depicted by a video. We demonstrate how such a model can be adjusted to recognize different interpretations of the same underlying sentence, allowing to disambiguate sentences in a unified fashion across the different ambiguity types.
Online shopping behavior study based on multi-granularity opinion mining: China vs. America
Venue: arXiv cs.CY 2016
Year: 2016

With the development of e-commerce, many products are now being sold worldwide, and manufacturers are eager to obtain a better understanding of customer behavior in various regions. To achieve this goal, most previous efforts have focused mainly on questionnaires, which are time-consuming and costly. The tremendous volume of product reviews on e-commerce websites has seen a new trend emerge, whereby manufacturers attempt to understand user preferences by analyzing online reviews. Following this trend, this paper addresses the problem of studying customer behavior by exploiting recently developed opinion mining techniques. This work is novel for three reasons. First, questionnaire-based investigation is automatically enabled by employing algorithms for template-based question generation and opinion mining-based answer extraction. Using this system, manufacturers are able to obtain reports of customer behavior featuring a much larger sample size, more direct information, a higher degree of automation, and a lower cost. Second, international customer behavior study is made easier by integrating tools for multilingual opinion mining. Third, this is the first time an automatic questionnaire investigation has been conducted to compare customer behavior in China and America, where product reviews are written and read in Chinese and English, respectively. Our study on digital cameras, smartphones, and tablet computers yields three findings. First, Chinese customers follow the Doctrine of the Mean, and often use euphemistic expressions, while American customers express their opinions more directly. Second, Chinese customers care more about general feelings, while American customers pay more attention to product details. Third, Chinese customers focus on external features, while American customers care more about the internal features of products.
Energy-saving Pushing Based on Personal Interest and Context Information
Venue: arXiv cs.IT 2016
Year: 2016

Pushing files to users based on predicting the personal interest of each user may provide higher throughput gain than broadcasting popular files to users based on their common interests. However, the energy consumed at base station for pushing files individually to each user is also higher than broadcast. In this paper, we propose an energy-saving transmission strategy for pre-downloading the files to each user by exploiting the excess resources in the network during off-peak time. Specifically, a power allocation and scheduling algorithm is designed aimed to minimize the extra energy consumed for pushing, where network and user level context information are exploited. Simulation results show that when the energy of both content placement and content delivery is taken into account, the proposed unicast strategy consumes less energy and achieves higher throughput than broadcasting when the files popularity is not uniform and the personal interest prediction is with less uncertainty.
Bandwidth-aware Service Placement in Community Network Clouds
Venue: arXiv cs.NI 2016
Year: 2016

Seamless computing and service sharing in community networks have been gaining momentum due to the emerging technology of community network micro-clouds (CNMCs). However, running services in CNMCs can face enormous challenges such as the dynamic nature of micro-clouds, limited capacity of nodes and links, asymmetric quality of wireless links for services, deployment mod- els based on geographic singularities rather than network QoS, and etc. CNMCs have been increasingly used by network-intensive services that exchange significant amounts of data between the nodes on which they run, therefore the performance heavily relies on the available bandwidth resource in a network. This paper proposes a novel bandwidth-aware service placement algorithm which out- performs the current random placement adopted by Guifi.net. Our preliminary results show that the proposed algorithm consistently outperforms the current random placement adopted in Guifi.net by 35% regarding its bandwidth gain. More importantly, as the number of services increases, the gain tends to increase accordingly.
Channel Hopping Sequences for Maximizing Rendezvous Diversity in Cognitive Radio Networks
Venue: arXiv cs.IT 2016
Year: 2016

In cognitive radio networks (CRNs), establishing a communication link between a pair of secondary users (SUs) requires them to rendezvous on a common channel which is not occupied by primary users (PUs). Under time-varying PU traffic, asynchronous sequence-based channel hopping (CH) with the maximal rendezvous diversity is a representative technique to guarantee an upper bounded time-torendezvous (TTR) for delay-sensitive services in CRNs, without requiring global clock synchronization. Maximum TTR (MTTR) and maximum conditional TTR (MCTTR) are two commonly considered metrics for evaluating such CH sequences, and minimizing these two metrics is the primary goal in the sequence design of various paper reported in the literature. In this paper, to investigate the fundamental limits of these two metrics, we first derive lower bounds on the MCTTR and MTTR, and then propose an asymmetric design which has the minimum MCTTR and an improvement on MTTR than other previously known algorithms. Moreover, when the number of licensed channels is odd, our proposed design achieves the minimum MTTR. We also present the TTR performance of the proposed design via simulation.
Predictive Modeling of Opinion and Connectivity Dynamics in Social Networks
Venue: arXiv cs.SI 2016
Year: 2016

Recent years saw an increased interest in modeling and understanding the mechanisms of opinion and innovation spread through human networks. Using analysis of real-world social data, researchers are able to gain a better understanding of the dynamics of social networks and subsequently model the changes in such networks over time. We developed a social network model that both utilizes an agent-based approach with a dynamic update of opinions and connections between agents and reflects opinion propagation and structural changes over time as observed in real-world data. We validate the model using data from the Social Evolution dataset of the MIT Human Dynamics Lab describing changes in friendships and health self-perception in a targeted student population over a nine-month period. We demonstrate the effectiveness of the approach by predicting changes in both opinion spread and connectivity of the network. We also use the model to evaluate how the network parameters, such as the level of `openness' and willingness to incorporate opinions of neighboring agents, affect the outcome. The model not only provides insight into the dynamics of ever changing social networks, but also presents a tool with which one can investigate opinion propagation strategies for networks of various structures and opinion distributions.
Towards Machine Intelligence
Venue: arXiv cs.AI 2016
Year: 2016

There exists a theory of a single general-purpose learning algorithm which could explain the principles of its operation. This theory assumes that the brain has some initial rough architecture, a small library of simple innate circuits which are prewired at birth and proposes that all significant mental algorithms can be learned. Given current understanding and observations, this paper reviews and lists the ingredients of such an algorithm from both architectural and functional perspectives.
Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing
Venue: arXiv cs.NE 2016
Year: 2016

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that i) approach state-of-the-art classification accuracy across 8 standard datasets, encompassing vision and speech, ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1200 and 2600 frames per second and using between 25 and 275 mW (effectively > 6000 frames / sec / W) and iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. For the first time, the algorithmic power of deep learning can be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.
An Extended Stochastic Model for Quantitative Security Analysis of Networked Systems
Venue: arXiv cs.CR 2016
Year: 2016

Quantitative security analysis of networked computer systems is one of the decades-long open problems in computer security. Recently, a promising approach was proposed in \cite{XuTDSC11}, which however made some strong assumptions including the exponential distribution of, and the independence between, the relevant random variables. In this paper, we substantially weaken these assumptions while offering, in addition to the same types of analytical results as in \cite{XuTDSC11}, methods for obtaining the desired security quantities in practice. Moreover, we investigate the problem from a higher-level abstraction, which also leads to both analytical results and practical methods for obtaining the desired security quantities. These would represent a significant step toward ultimately solving the problem of quantitative security analysis of networked computer systems.
Cyber Epidemic Models with Dependences
Venue: arXiv cs.CR 2016
Year: 2016

Studying models of cyber epidemics over arbitrary complex networks can deepen our understanding of cyber security from a whole-system perspective. In this paper, we initiate the investigation of cyber epidemic models that accommodate the {\em dependences} between the cyber attack events. Due to the notorious difficulty in dealing with such dependences, essentially all existing cyber epidemic models have assumed them away. Specifically, we introduce the idea of Copulas into cyber epidemic models for accommodating the dependences between the cyber attack events. We investigate the epidemic equilibrium thresholds as well as the bounds for both equilibrium and non-equilibrium infection probabilities. We further characterize the side-effects of assuming away the due dependences between the cyber attack events, by showing that the results thereof are unnecessarily restrictive or even incorrect.
A Stochastic Model of Active Cyber Defense Dynamics
Venue: arXiv cs.CR 2016
Year: 2016

The concept of active cyber defense has been proposed for years. However, there are no mathematical models for characterizing the effectiveness of active cyber defense. In this paper, we fill the void by proposing a novel Markov process model that is native to the interaction between cyber attack and active cyber defense. Unfortunately, the native Markov process model cannot be tackled by the techniques we are aware of. We therefore simplify, via mean-field approximation, the Markov process model as a Dynamic System model that is amenable to analysis. This allows us to derive a set of valuable analytical results that characterize the effectiveness of four types of active cyber defense dynamics. Simulations show that the analytical results are inherent to the native Markov process model, and therefore justify the validity of the Dynamic System model. We also discuss the side-effect of the mean-field approximation and its implications.
Optimizing Active Cyber Defense
Venue: arXiv cs.CR 2016
Year: 2016

Active cyber defense is one important defensive method for combating cyber attacks. Unlike traditional defensive methods such as firewall-based filtering and anti-malware tools, active cyber defense is based on spreading "white" or "benign" worms to combat against the attackers' malwares (i.e., malicious worms) that also spread over the network. In this paper, we initiate the study of {\em optimal} active cyber defense in the setting of strategic attackers and/or strategic defenders. Specifically, we investigate infinite-time horizon optimal control and fast optimal control for strategic defenders (who want to minimize their cost) against non-strategic attackers (who do not consider the issue of cost). We also investigate the Nash equilibria for strategic defenders and attackers. We discuss the cyber security meanings/implications of the theoretic results. Our study brings interesting open problems for future research.
Principled Random Finite Set Approximations of Labeled Multi-Object Densities
Venue: arXiv cs.SY 2016
Year: 2016

As a fundamental piece of multi-object Bayesian inference, multi-object density has the ability to describe the uncertainty of the number and values of objects, as well as the statistical correlation between objects, thus perfectly matches the behavior of multi-object system. However, it also makes the set integral suffer from the curse of dimensionality and the inherently combinatorial nature of the problem. In this paper, we study the approximations for the universal labeled multi-object (LMO) density and derive several principled approximations including labeled multi-Bernoulli, labeled Poisson and labeled independent identically clustering process based approximations. Also, a detailed analysis on the characteristics (e.g., approximation error and computational complexity) of the proposed approximations is provided. Then some practical suggestions are made for the applications of these approximations based on the preceding analysis and discussion. Finally, an numerical example is given to support our study.
$k$-shot Broadcasting in Ad Hoc Radio Networks
Venue: arXiv cs.DC 2016
Year: 2016

We study distributed broadcasting protocols with few transmissions (`shots') in radio networks where the topology is unknown. In particular, we examine the case in which a bound $k$ is given and a node may transmit at most $k$ times during the broadcasting protocol. Initially, we focus on oblivious algorithms for $k$-shot broadcasting, that is, algorithms where each node decides whether to transmit or not with no consideration of the transmission history. Our main contributions are (a) a lower bound of $Ω(n^2/k)$ on the broadcasting time of any oblivious $k$-shot broadcasting algorithm and (b) an oblivious broadcasting protocol that achieves a matching upper bound, namely $O(n^2/k)$, for every $k \le \sqrt{n}$ and an upper bound of $O(n^{3/2})$ for every $k > \sqrt{n}$. We also study the general case of adaptive broadcasting protocols where nodes decide whether to transmit based on all the available information, namely the transmission history known by each. We prove a lower bound of $Ω\left(n^{\frac{1+k}{k}}\right)$ on the broadcasting time of any protocol by introducing the \emph{transmission tree} construction which generalizes previous approaches.
Content-Centric Networking Using Anonymous Datagrams
Venue: arXiv cs.NI 2016
Year: 2016

Using Interests (requests that elicit content) and maintaining per-Interest forwarding state in Pending Interest Tables (PIT) are integral to the design of the Named Data Networking (NDN) and Content-Centric Networking (CCNx) architectures. However, using PITs makes the network vulnerable to Interest-flooding attacks, and PITs can become very large. It is shown that in-network caching eliminates the need for Interest aggregation and obviates the use of PITs. A new approach to content-centric networking (CCN-GRAM) is introduced that provides all the benefits of NDN and CCNx, eliminates the use of PITs by means of anonymous datagrams, and is immune to Interest-flooding attacks. Routers maintain routes to the anonymous origins of Interests using an on-demand routing approach in the data plane that can also be used to provide native support for multicasting in the dat a plane. Simulation experiments show that the number of forwarding entries required in CCN-GRAM is orders of magnitude smaller than the number of PIT entries.