OpenRaiser/Intern-Atlas huggingface dataset:
Non-blind watermarking of network flows
Venue: arXiv cs.CR 2012
Year: 2012

Linking network flows is an important problem in intrusion detection as well as anonymity. Passive traffic analysis can link flows but requires long periods of observation to reduce errors. Active traffic analysis, also known as flow watermarking, allows for better precision and is more scalable. Previous flow watermarks introduce significant delays to the traffic flow as a side effect of using a blind detection scheme; this enables attacks that detect and remove the watermark, while at the same time slowing down legitimate traffic. We propose the first non-blind approach for flow watermarking, called RAINBOW, that improves watermark invisibility by inserting delays hundreds of times smaller than previous blind watermarks, hence reduces the watermark interference on network flows. We derive and analyze the optimum detectors for RAINBOW as well as the passive traffic analysis under different traffic models by using hypothesis testing. Comparing the detection performance of RAINBOW and the passive approach we observe that both RAINBOW and passive traffic analysis perform similarly good in the case of uncorrelated traffic, however, the RAINBOW detector drastically outperforms the optimum passive detector in the case of correlated network flows. This justifies the use of non-blind watermarks over passive traffic analysis even though both approaches have similar scalability constraints. We confirm our analysis by simulating the detectors and testing them against large traces of real network flows.
Diversity, Coding, and Multiplexing Trade-Off of Network-Coded Cooperative Wireless Networks
Venue: arXiv cs.IT 2012
Year: 2012

In this paper, we study the performance of network-coded cooperative diversity systems with practical communication constraints. More specifically, we investigate the interplay between diversity, coding, and multiplexing gain when the relay nodes do not act as dedicated repeaters, which only forward data packets transmitted by the sources, but they attempt to pursue their own interest by forwarding packets which contain a network-coded version of received and their own data. We provide a very accurate analysis of the Average Bit Error Probability (ABEP) for two network topologies with three and four nodes, when practical communication constraints, i.e., erroneous decoding at the relays and fading over all the wireless links, are taken into account. Furthermore, diversity and coding gain are studied, and advantages and disadvantages of cooperation and binary Network Coding (NC) are highlighted. Our results show that the throughput increase introduced by NC is offset by a loss of diversity and coding gain. It is shown that there is neither a coding nor a diversity gain for the source node when the relays forward a network-coded version of received and their own data. Compared to other results available in the literature, the conclusion is that binary NC seems to be more useful when the relay nodes act only on behalf of the source nodes, and do not mix their own packets to the received ones. Analytical derivation and findings are substantiated through extensive Monte Carlo simulations.
Vibrating Cantilever Transducer Incorporated in Dual Diaphragms Structure for Sensing Differential Pneumatic Pressure
Venue: arXiv cs.SY 2012
Year: 2012

Pneumatic pressure cells with thin metallic spherical diaphragm of shallow spherical shell configuration linked with vibrating wire pickup or vibrating cantilever pickup were reported in the past. In order to enhance the sensitivity of the pressure cell this work considers dual diaphragm structure fitted with cantilever pickup. The design and development of the pressure cell with this dual diaphragm structure having cantilever pickup is presented here. The geometrical design is optimally made as to sense either mono pressure or differential pressure resources. The cantilevers of the two diaphragms are excited to produce vibrations and the frequencies of vibrations are determined by picking up signals from orthogonally arranged opto-coupler links. With the computed frequency a lookup table is referred to obtain the pressure acting on the concerned diaphragm. In the external circuits, the average pressure and the differential pressure acting on two diaphragms are computed. Furthermore transmitting circuits taking the average pressure and differential pressure in digital form and analogue form to remote area are presented. Performance analysis of the proposed mechatronic pressure cell is made and its improved performance over other pressure cells is presented.
Knowledge-based antenna pattern extrapolation
Venue: arXiv cs.CE 2012
Year: 2012

We describe a theoretically-motivated algorithm for extrapolation of antenna radiation patterns from a small number of measurements. This algorithm exploits constraints on the antenna's underlying design to avoid ambiguities, but is sufficiently general to address many different antenna types. A theoretical basis for the robustness of this algorithm is developed, and its performance is verified in simulation using a number of popular antenna designs.
Degrees of Freedom of Time Correlated MISO Broadcast Channel with Delayed CSIT
Venue: arXiv cs.IT 2012
Year: 2012

We consider the time correlated multiple-input single-output (MISO) broadcast channel where the transmitter has imperfect knowledge on the current channel state, in addition to delayed channel state information. By representing the quality of the current channel state information as P^-α for the signal-to-noise ratio P and some constant α \geq 0, we characterize the optimal degree of freedom region for this more general two-user MISO broadcast correlated channel. The essential ingredients of the proposed scheme lie in the quantization and multicasting of the overheard interferences, while broadcasting new private messages. Our proposed scheme smoothly bridges between the scheme recently proposed by Maddah-Ali and Tse with no current state information and a simple zero-forcing beamforming with perfect current state information.
Average Consensus on General Strongly Connected Digraphs
Venue: arXiv cs.SY 2012
Year: 2012

We study the average consensus problem of multi-agent systems for general network topologies with unidirectional information flow. We propose two (linear) distributed algorithms, deterministic and gossip, respectively for the cases where the inter-agent communication is synchronous and asynchronous. Our contribution is that in both cases, the developed algorithms guarantee state averaging on arbitrary strongly connected digraphs; in particular, this graphical condition does not require that the network be balanced or symmetric, thereby extending many previous results in the literature. The key novelty of our approach is to augment an additional variable for each agent, called "surplus", whose function is to locally record individual state updates. For convergence analysis, we employ graph-theoretic and nonnegative matrix tools, with the eigenvalue perturbation theory playing a crucial role.
On using Multiple Quality Link Metrics with Destination Sequenced Distance Vector Protocol for Wireless Multi-Hop Networks
Venue: arXiv cs.NI 2012
Year: 2012

In this paper, we compare and analyze performance of five quality link metrics forWireless Multi-hop Networks (WMhNs). The metrics are based on loss probability measurements; ETX, ETT, InvETX, ML and MD, in a distance vector routing protocol; DSDV. Among these selected metrics, we have implemented ML, MD, InvETX and ETT in DSDV which are previously implemented with different protocols; ML, MD, InvETX are implemented with OLSR, while ETT is implemented in MR-LQSR. For our comparison, we have selected Throughput, Normalized Routing Load (NRL) and End-to-End Delay (E2ED) as performance parameters. Finally, we deduce that InvETX due to low computational burden and link asymmetry measurement outperforms among all metrics.
On Exact Algorithms for Permutation CSP
Venue: arXiv cs.CC 2012
Year: 2012

In the Permutation Constraint Satisfaction Problem (Permutation CSP) we are given a set of variables $V$ and a set of constraints C, in which constraints are tuples of elements of V. The goal is to find a total ordering of the variables, $π : V \rightarrow [1,...,|V|]$, which satisfies as many constraints as possible. A constraint $(v_1,v_2,...,v_k)$ is satisfied by an ordering $π$ when $π(v_1)<π(v_2)<...<π(v_k)$. An instance has arity $k$ if all the constraints involve at most $k$ elements. This problem expresses a variety of permutation problems including {\sc Feedback Arc Set} and {\sc Betweenness} problems. A naive algorithm, listing all the $n!$ permutations, requires $2^{O(n\log{n})}$ time. Interestingly, {\sc Permutation CSP} for arity 2 or 3 can be solved by Held-Karp type algorithms in time $O^*(2^n)$, but no algorithm is known for arity at least 4 with running time significantly better than $2^{O(n\log{n})}$. In this paper we resolve the gap by showing that {\sc Arity 4 Permutation CSP} cannot be solved in time $2^{o(n\log{n})}$ unless ETH fails.
Automated Synthesis of a Finite Complexity Ordering for Saturation
Venue: arXiv cs.LO 2012
Year: 2012

We present in this paper a new procedure to saturate a set of clauses with respect to a well-founded ordering on ground atoms such that A < B implies Var(A) {\subseteq} Var(B) for every atoms A and B. This condition is satisfied by any atom ordering compatible with a lexicographic, recursive, or multiset path ordering on terms. Our saturation procedure is based on a priori ordered resolution and its main novelty is the on-the-fly construction of a finite complexity atom ordering. In contrast with the usual redundancy, we give a new redundancy notion and we prove that during the saturation a non-redundant inference by a priori ordered resolution is also an inference by a posteriori ordered resolution. We also prove that if a set S of clauses is saturated with respect to an atom ordering as described above then the problem of whether a clause C is entailed from S is decidable.
Identifying Tipping Points in a Decision-Theoretic Model of Network Security
Venue: arXiv cs.CR 2012
Year: 2012

Although system administrators are frequently urged to protect the machines in their network, the fact remains that the decision to protect is far from universal. To better understand this decision, we formulate a decision-theoretic model of a system administrator responsible for a network of size n against an attacker attempting to penetrate the network and infect the machines with a virus or similar exploit. By analyzing the model we are able to demonstrate the cost sensitivity of smaller networks as well as identify tipping points that can lead the administrator to switch away from the decision to protect.
Square-Cut: A Segmentation Algorithm on the Basis of a Rectangle Shape
Venue: arXiv cs.CV 2012
Year: 2012

We present a rectangle-based segmentation algorithm that sets up a graph and performs a graph cut to separate an object from the background. However, graph-based algorithms distribute the graph's nodes uniformly and equidistantly on the image. Then, a smoothness term is added to force the cut to prefer a particular shape. This strategy does not allow the cut to prefer a certain structure, especially when areas of the object are indistinguishable from the background. We solve this problem by referring to a rectangle shape of the object when sampling the graph nodes, i.e., the nodes are distributed nonuniformly and non-equidistantly on the image. This strategy can be useful, when areas of the object are indistinguishable from the background. For evaluation, we focus on vertebrae images from Magnetic Resonance Imaging (MRI) datasets to support the time consuming manual slice-by-slice segmentation performed by physicians. The ground truth of the vertebrae boundaries were manually extracted by two clinical experts (neurological surgeons) with several years of experience in spine surgery and afterwards compared with the automatic segmentation results of the proposed scheme yielding an average Dice Similarity Coefficient (DSC) of 90.97\pm62.2%.
Streaming Transmitter over Block-Fading Channels with Delay Constraint
Venue: arXiv cs.IT 2012
Year: 2012

Data streaming transmission over a block fading channel is studied. It is assumed that the transmitter receives a new message at each channel block at a constant rate, which is fixed by an underlying application, and tries to deliver the arriving messages by a common deadline. Various transmission schemes are proposed and compared with an informed transmitter upper bound in terms of the average decoded rate. It is shown that in the single receiver case the adaptive joint encoding (aJE) scheme is asymptotically optimal, in that it achieves the ergodic capacity as the transmission deadline goes to infinity; and it closely follows the performance of the informed transmitter upper bound in the case of finite transmission deadline. On the other hand, in the presence of multiple receivers with different signal-to-noise ratios (SNR), memoryless transmission (MT), time sharing (TS) and superposition transmission (ST) schemes are shown to be more robust than the joint encoding (JE) scheme as they have gradual performance loss with decreasing SNR.
Statistical Characterization and Mitigation of NLOS Bias in UWB Localization Systems
Venue: arXiv cs.IT 2012
Year: 2012

Propagation in non-line-of-sight (NLOS) conditions is one of the major impairments in ultrawideband (UWB) wireless localization systems based on time-of-arrival (TOA) measurements. In this paper the problem of the joint statistical characterization of the NLOS bias and of the most representative features of LOS/NLOS UWB waveforms is investigated. In addition, the performance of various maximum-likelihood (ML) estimators for joint localization and NLOS bias mitigation is assessed. Our numerical results evidence that the accuracy of all the considered estimators is appreciably influenced by the LOS/NLOS conditions of the propagation environment and that a statistical knowledge of multiple signal features can be exploited to mitigate the NLOS bias, reducing the overall localization error.
Toward an example-based machine translation from written text to ASL using virtual agent animation
Venue: arXiv cs.CL 2012
Year: 2012

Modern computational linguistic software cannot produce important aspects of sign language translation. Using some researches we deduce that the majority of automatic sign language translation systems ignore many aspects when they generate animation; therefore the interpretation lost the truth information meaning. Our goals are: to translate written text from any language to ASL animation; to model maximum raw information using machine learning and computational techniques; and to produce a more adapted and expressive form to natural looking and understandable ASL animations. Our methods include linguistic annotation of initial text and semantic orientation to generate the facial expression. We use the genetic algorithms coupled to learning/recognized systems to produce the most natural form. To detect emotion we are based on fuzzy logic to produce the degree of interpolation between facial expressions. Roughly, we present a new expressive language Text Adapted Sign Modeling Language TASML that describes all maximum aspects related to a natural sign language interpretation. This paper is organized as follow: the next section is devoted to present the comprehension effect of using Space/Time/SVO form in ASL animation based on experimentation. In section 3, we describe our technical considerations. We present the general approach we adopted to develop our tool in section 4. Finally, we give some perspectives and future works.
A Comparative Study of Adaptive Crossover Operators for Genetic Algorithms to Resolve the Traveling Salesman Problem
Venue: arXiv cs.NE 2012
Year: 2012

Genetic algorithm includes some parameters that should be adjusting so that the algorithm can provide positive results. Crossover operators play very important role by constructing competitive Genetic Algorithms (GAs). In this paper, the basic conceptual features and specific characteristics of various crossover operators in the context of the Traveling Salesman Problem (TSP) are discussed. The results of experimental comparison of more than six different crossover operators for the TSP are presented. The experiment results show that OX operator enables to achieve a better solutions than other operators tested.
Integrated three-dimensional reconstruction using reflectance fields
Venue: arXiv cs.CV 2012
Year: 2012

A method to obtain three-dimensional data of real-world objects by integrating their material properties is presented. The material properties are defined by capturing the Reflectance Fields of the real-world objects. It is shown, unlike conventional reconstruction methods, the method is able to use the reflectance information to recover surface depth for objects having a non-Lambertian surface reflectance. It is, for recovering 3D data of objects exhibiting an anisotropic BRDF with an error less than 0.3%.
A Receding Horizon Strategy for Systems with Interval-Wise Energy Constraints
Venue: arXiv cs.SY 2012
Year: 2012

We propose a receding horizon control strategy that readily handles systems that exhibit interval-wise total energy constraints on the input control sequence. The approach is based on a variable optimization horizon length and contractive final state constraint sets. The optimization horizon, which recedes by N steps every N steps, is the key to accommodate the interval-wise total energy constraints. The varying optimization horizon along with the contractive constraints are used to achieve analytic asymptotic stability of the system under the proposed scheme. The strategy is demonstrated by simulation examples.
A note on Shannon entropy
Venue: arXiv cs.IT 2012
Year: 2012

We present a somewhat different way of looking on Shannon entropy. This leads to an axiomatisation of Shannon entropy that is essentially equivalent to that of Fadeev. In particular we give a new proof of Fadeev theorem.
Learning, Social Intelligence and the Turing Test - why an "out-of-the-box" Turing Machine will not pass the Turing Test
Venue: arXiv cs.AI 2012
Year: 2012

The Turing Test (TT) checks for human intelligence, rather than any putative general intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a macro-level post-hoc test in contrast to the definition of a Turing Machine (TM), which is a prior micro-level definition. This raises the question of whether learning is just another computational process, i.e. can be implemented as a TM. Here we argue that learning or adaption is fundamentally different from computation, though it does involve processes that can be seen as computations. To illustrate this difference we compare (a) designing a TM and (b) learning a TM, defining them for the purpose of the argument. We show that there is a well-defined sequence of problems which are not effectively designable but are learnable, in the form of the bounded halting problem. Some characteristics of human intelligence are reviewed including it's: interactive nature, learning abilities, imitative tendencies, linguistic ability and context-dependency. A story that explains some of these is the Social Intelligence Hypothesis. If this is broadly correct, this points to the necessity of a considerable period of acculturation (social learning in context) if an artificial intelligence is to pass the TT. Whilst it is always possible to 'compile' the results of learning into a TM, this would not be a designed TM and would not be able to continually adapt (pass future TTs). We conclude three things, namely that: a purely "designed" TM will never pass the TT; that there is no such thing as a general intelligence since it necessary involves learning; and that learning/adaption and computation should be clearly distinguished.
Coded Cooperative Data Exchange in Multihop Networks
Venue: arXiv cs.IT 2012
Year: 2012

Consider a connected network of n nodes that all wish to recover k desired packets. Each node begins with a subset of the desired packets and exchanges coded packets with its neighbors. This paper provides necessary and sufficient conditions which characterize the set of all transmission schemes that permit every node to ultimately learn (recover) all k packets. When the network satisfies certain regularity conditions and packets are randomly distributed, this paper provides tight concentration results on the number of transmissions required to achieve universal recovery. For the case of a fully connected network, a polynomial-time algorithm for computing an optimal transmission scheme is derived. An application to secrecy generation is discussed.
Compiling Possibilistic Networks: Alternative Approaches to Possibilistic Inference
Venue: arXiv cs.AI 2012
Year: 2012

Qualitative possibilistic networks, also known as min-based possibilistic networks, are important tools for handling uncertain information in the possibility theory frame- work. Despite their importance, only the junction tree adaptation has been proposed for exact reasoning with such networks. This paper explores alternative algorithms using compilation techniques. We first propose possibilistic adaptations of standard compilation-based probabilistic methods. Then, we develop a new, purely possibilistic, method based on the transformation of the initial network into a possibilistic base. A comparative study shows that this latter performs better than the possibilistic adap- tations of probabilistic methods. This result is also confirmed by experimental results.
Probabilistic Similarity Logic
Venue: arXiv cs.AI 2012
Year: 2012

Many machine learning applications require the ability to learn from and reason about noisy multi-relational data. To address this, several effective representations have been developed that provide both a language for expressing the structural regularities of a domain, and principled support for probabilistic inference. In addition to these two aspects, however, many applications also involve a third aspect-the need to reason about similarities-which has not been directly supported in existing frameworks. This paper introduces probabilistic similarity logic (PSL), a general-purpose framework for joint reasoning about similarity in relational domains that incorporates probabilistic reasoning about similarities and relational structure in a principled way. PSL can integrate any existing domain-specific similarity measures and also supports reasoning about similarities between sets of entities. We provide efficient inference and learning techniques for PSL and demonstrate its effectiveness both in common relational tasks and in settings that require reasoning about similarity.
Inferring deterministic causal relations
Venue: arXiv cs.LG 2012
Year: 2012

We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints to determine which of the two variables is the cause, we presently show that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference. Our method is based on the idea that if the function and the probability density of the cause are chosen independently, then the distribution of the effect will, in a certain sense, depend on the function. We provide a theoretical analysis of this method, showing that it also works in the low noise regime, and link it to information geometry. We report strong empirical results on various real-world data sets from different domains.