OpenRaiser/Intern-Atlas huggingface dataset:
Semantic-based Detection of Segment Outliers and Unusual Events for Wireless Sensor Networks
Venue: arXiv cs.OH 2014
Year: 2014

Environmental scientists have increasingly been deploying wireless sensor networks to capture valuable data that measures and records precise information about our environment. One of the major challenges associated with wireless sensor networks is the quality of the data and more specifically the detection of segment outliers and unusual events. Most previous research has focused on detecting outliers that are errors that are caused by unreliable sensors and sensor nodes. However, there is an urgent need for the development of new tools capable of identifying, tagging and visualizing erroneous segment outliers and unusual events from sensor data streams. In this paper, we present a SOUE Detector (Segment Outlier and Unusual Event-Detector) system for wireless sensor networks that combines statistical analyses using Dynamic Time Warping (DTW) with domain expert knowledge (captured via an ontology and semantic inferencing rules). The resulting Web portal enables scientist to efficiently search across a collection of wireless sensor data streams and identify, retrieve and display segment outliers (both erroneous and genuine) within the data streams. In this paper, we firstly describe the detection algorithms, the implementation details and the functionality of the SOUE Detector system. Secondly we evaluate our approach using data that comprises sensor observations collected from a sensor network deployed in the Springbrook National Park in Queensland, Australia. The experimental results show that the SOUE-Detector can efficiently detect segment outliers and unusual events with high levels of precision and recall.
GeoTravel: Harvesting Ambient Geographic Footprints from GPS Trajectories
Venue: arXiv cs.CY 2014
Year: 2014

This study is about harvesting point of interest from GPS trajectories. Trajectories are the paths that moving objects move by follow through space in a function of time while GPS trajectories generally are point-sequences with geographic coordinates, time stamp, speed and heading. User can get information from GPS enable device. For example, user can acquire present location, search the information around them and design driving routes to a destination and thus design travel itineraries. By sharing GPS logs among each other, people are able to find some places that attract them from other people's travel route. Analysis on the GPS logs can get the point of interest that is popular. By present the point of interest, user can choose travel place easily and the travel itineraries is plan based on the user preferences.
Core-Periphery in Networks: An Axiomatic Approach
Venue: arXiv cs.SI 2014
Year: 2014

Recent evidence shows that in many societies worldwide the relative sizes of the economic and social elites are continuously shrinking. Is this a natural social phenomenon? What are the forces that shape this process? We try to address these questions by studying a Core-Periphery social structure composed of a social elite, namely, a relatively small but well-connected and highly influential group of powerful individuals, and the rest of society, the periphery. Herein, we present a novel axiom-based model for the forces governing the mutual influences between the elite and the periphery. Assuming a simple set of axioms, capturing the elite's dominance, robustness, compactness and density, we are able to draw strong conclusions about the elite-periphery structure. In particular, we show that a balance of powers between elite and periphery and an elite size that is sub-linear in the network size are universal properties of elites in social networks that satisfy our axioms. We note that the latter is in controversy to the common belief that the elite size converges to a linear fraction of society (most recently claimed to be 1%). We accompany these findings with a large scale empirical study on about 100 real-world networks, which supports our results.
On Finding Minimal Infrequent Elements in Multi-dimensional Data Defined over Partially Ordered Sets
Venue: arXiv cs.DB 2014
Year: 2014

We consider databases in which each attribute takes values from a partially ordered set (poset). This allows one to model a number of interesting scenarios arising in different applications, including quantitative databases, taxonomies, and databases in which each attribute is an interval representing the duration of a certain event occurring over time. A natural problem that arises in such circumstances is the following: given a database $\mathcal{D}$ and a threshold value $t$, find all collections of "generalizations" of attributes which are "supported" by less than $t$ transactions from $\mathcal{D}$. We call such collections infrequent elements. Due to monotonicity, we can reduce the output size by considering only \emph{minimal} infrequent elements. We study the complexity of finding all minimal infrequent elements for some interesting classes of posets. We show how this problem can be applied to mining association rules in different types of databases, and to finding "sparse regions" or "holes" in quantitative data or in databases recording the time intervals during which a re-occurring event appears over time. Our main focus will be on these applications rather than on the correctness or analysis of the given algorithms.
Trade-Offs in Exploiting Body Morphology for Control: from Simple Bodies and Model-Based Control to Complex Bodies with Model-Free Distributed Control Schemes
Venue: arXiv cs.RO 2014
Year: 2014

Tailoring the design of robot bodies for control purposes is implicitly performed by engineers, however, a methodology or set of tools is largely absent and optimization of morphology (shape, material properties of robot bodies, etc.) is lagging behind the development of controllers. This has become even more prominent with the advent of compliant, deformable or "soft" bodies. These carry substantial potential regarding their exploitation for control---sometimes referred to as "morphological computation" in the sense of offloading computation needed for control to the body. Here, we will argue in favor of a dynamical systems rather than computational perspective on the problem. Then, we will look at the pros and cons of simple vs. complex bodies, critically reviewing the attractive notion of "soft" bodies automatically taking over control tasks. We will address another key dimension of the design space---whether model-based control should be used and to what extent it is feasible to develop faithful models for different morphologies.
Model-Parallel Inference for Big Topic Models
Venue: arXiv cs.DC 2014
Year: 2014

In real world industrial applications of topic modeling, the ability to capture gigantic conceptual space by learning an ultra-high dimensional topical representation, i.e., the so-called "big model", is becoming the next desideratum after enthusiasms on "big data", especially for fine-grained downstream tasks such as online advertising, where good performances are usually achieved by regression-based predictors built on millions if not billions of input features. The conventional data-parallel approach for training gigantic topic models turns out to be rather inefficient in utilizing the power of parallelism, due to the heavy dependency on a centralized image of "model". Big model size also poses another challenge on the storage, where available model size is bounded by the smallest RAM of nodes. To address these issues, we explore another type of parallelism, namely model-parallelism, which enables training of disjoint blocks of a big topic model in parallel. By integrating data-parallelism with model-parallelism, we show that dependencies between distributed elements can be handled seamlessly, achieving not only faster convergence but also an ability to tackle significantly bigger model size. We describe an architecture for model-parallel inference of LDA, and present a variant of collapsed Gibbs sampling algorithm tailored for it. Experimental results demonstrate the ability of this system to handle topic modeling with unprecedented amount of 200 billion model variables only on a low-end cluster with very limited computational resources and bandwidth.
Modeling Word Relatedness in Latent Dirichlet Allocation
Venue: arXiv cs.CL 2014
Year: 2014

Standard LDA model suffers the problem that the topic assignment of each word is independent and word correlation hence is neglected. To address this problem, in this paper, we propose a model called Word Related Latent Dirichlet Allocation (WR-LDA) by incorporating word correlation into LDA topic models. This leads to new capabilities that standard LDA model does not have such as estimating infrequently occurring words or multi-language topic modeling. Experimental results demonstrate the effectiveness of our model compared with standard LDA.
A Statistical Analysis of Multipath Interference for Impulse Radio UWB Systems
Venue: arXiv cs.NI 2014
Year: 2014

In this paper, we develop a statistical characterization of the multipath interference in an Impulse Radio (IR)-UWB system, considering the standardized IEEE 802.15.4a channel model. In such systems, the chip length has to be carefully tuned as all the propagation paths located beyond this limit can cause interframe/intersymbol interferences (IFI/ISI). Our approach aims at computing the probability density function (PDF) of the power of all multipath components with delays larger than the chip time, so as to prevent such interferences. Exact analytical expressions are derived first for the probability that the chip length falls into a particular cluster of the multipath propagation model and for the statistics of the number of paths spread over several contiguous clusters. A power delay profile (PDP) approximation is then used to evaluate the total interference power as the problem appears to be mathematically intractable. Using the proposed closed-form expressions, and assuming minimal prior information on the channel state, a rapid update of the chip time value is enabled so as to control the signal to interference plus noise ratio.
The Selfish Algorithm
Venue: arXiv cs.CC 2014
Year: 2014

The principle of Generalized Natural Selection (GNS) states that in nature, computational processes of high computational sophistication are more likely to maintain/abide than processes of lower computational sophistication provided that sufficiently many resources are around to sustain the processes. In this paper we give a concrete set-up how to test GNS in a weak sense. In particular, we work in the setting of Cellular Automata and see how GNS can manifest itself in this setting.
The Johnson-Lindenstrauss lemma is optimal for linear dimensionality reduction
Venue: arXiv cs.IT 2014
Year: 2014

For any $n>1$ and $0<\varepsilon<1/2$, we show the existence of an $n^{O(1)}$-point subset $X$ of $\mathbb{R}^n$ such that any linear map from $(X,\ell_2)$ to $\ell_2^m$ with distortion at most $1+\varepsilon$ must have $m = Ω(\min\{n, \varepsilon^{-2}\log n\})$. Our lower bound matches the upper bounds provided by the identity matrix and the Johnson-Lindenstrauss lemma, improving the previous lower bound of Alon by a $\log(1/\varepsilon)$ factor.
On Timing Synchronization for Quantity-based Modulation in Additive Inverse Gaussian Channel with Drift
Venue: arXiv cs.ET 2014
Year: 2014

In Diffusion-based Molecular Communications, the channel between Transmitter Nano-machine (TN) and Receiver Nano-machine (RN) can be modeled by Additive Inverse Gaussian Channel, that is the first hitting time of messenger molecule released from TN and captured by RN follows Inverse Gaussian distribution. In this channel, a quantity-based modulation embedding message on the different quantity levels of messenger molecules relies on a time-slotted system between TN and RN. Accordingly, their clocks need to synchronize with each other. In this paper, we discuss the approaches to make RN estimate its timing offset between TN efficiently by the arrival times of molecules. We propose many methods such as Maximum Likelihood Estimation (MLE), Unbiased Linear Estimation (ULE), Iterative ULE, and Decision Feedback (DF). The numerical results shows the comparison of them. We evaluate these methods by not only the Mean Square Error, but also the computational complexity.
Enabling Data Exchange in Interactive State Estimation under Privacy Constraints
Venue: arXiv cs.IT 2014
Year: 2014

Data collecting agents in large networks, such as the electric power system, need to share information (measurements) for estimating the system state in a distributed manner. However, privacy concerns may limit or prevent this exchange leading to a tradeoff between state estimation fidelity and privacy (referred to as competitive privacy). This paper builds upon a recent information-theoretic result (using mutual information to measure privacy and mean-squared error to measure fidelity) that quantifies the region of achievable distortion-leakage tuples in a two-agent network. The objective of this paper is to study centralized and decentralized mechanisms that can enable and sustain non-trivial data exchanges among the agents. A centralized mechanism determines the data sharing policies that optimize a network-wide objective function combining the fidelities and leakages at both agents. Using common-goal games and best-response analysis, the optimal policies allow for distributed implementation. In contrast, in the decentralized setting, repeated discounted games are shown to naturally enable data exchange without any central control nor economic incentives. The effect of repetition is modeled by a time-averaged payoff function at each agent which combines its fidelity and leakage at each interaction stage. For both approaches, it is shown that non-trivial data exchange can be sustained for specific fidelity ranges even when privacy is a limiting factor.
Generating S-Boxes from Semi-fields Pseudo-extensions
Venue: arXiv cs.CR 2014
Year: 2014

Specific vectorial boolean functions, such as S-Boxes or APN functions have many applications, for instance in symmetric ciphers. In cryptography they must satisfy some criteria (balancedness, high nonlinearity, high algebraic degree, avalanche, or transparency) to provide best possible resistance against attacks. Functions satisfying most criteria are however difficult to find. Indeed, random generation does not work and the S-Boxes used in the AES or Camellia ciphers are actually variations around a single function, the inverse function in F_2^n. Would the latter function have an unforeseen weakness (for instance if more practical algebraic attacks are developped), it would be desirable to have some replacement candidates. For that matter, we propose to weaken a little bit the algebraic part of the design of S-Boxes and use finite semifields instead of finite fields to build such S-Boxes. Since it is not even known how many semifields there are of order 256, we propose to build S-Boxes and APN functions via semifields pseudo-extensions of the form S_{2^4}^2, where S_{2^4} is any semifield of order 16 . Then, we mimic in this structure the use of functions applied on a finite fields, such as the inverse or the cube. We report here the construction of 12781 non equivalent S-Boxes with with maximal nonlinearity, differential invariants, degrees and bit interdependency, and 2684 APN functions.
Constructions of Optimal and Near-Optimal Multiply Constant-Weight Codes
Venue: arXiv cs.IT 2014
Year: 2014

Multiply constant-weight codes (MCWCs) have been recently studied to improve the reliability of certain physically unclonable function response. In this paper, we give combinatorial constructions for MCWCs which yield several new infinite families of optimal MCWCs. Furthermore, we demonstrate that the Johnson type upper bounds of MCWCs are asymptotically tight for fixed weights and distances. Finally, we provide bounds and constructions of two dimensional MCWCs.
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
Venue: arXiv cs.LG 2014
Year: 2014

Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space. Our pipeline effectively unifies joint image-text embedding models with multimodal neural language models. We introduce the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder. The encoder allows one to rank images and sentences while the decoder can generate novel descriptions from scratch. Using LSTM to encode sentences, we match the state-of-the-art performance on Flickr8K and Flickr30K without using object detections. We also set new best results when using the 19-layer Oxford convolutional network. Furthermore we show that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic e.g. *image of a blue car* - "blue" + "red" is near images of red cars. Sample captions generated for 800 images are made available for comparison.
Applications of sampling Kantorovich operators to thermographic images for seismic engineering
Venue: arXiv cs.CV 2014
Year: 2014

In this paper, we present some applications of the multivariate sampling Kantorovich operators $S_w$ to seismic engineering. The mathematical theory of these operators, both in the space of continuous functions and in Orlicz spaces, show how it is possible to approximate/reconstruct multivariate signals, such as images. In particular, to obtain applications for thermographic images a mathematical algorithm is developed using MATLAB and matrix calculus. The setting of Orlicz spaces is important since allow us to reconstruct not necessarily continuous signals by means of $S_w$. The reconstruction of thermographic images of buildings by our sampling Kantorovich algorithm allow us to obtain models for the simulation of the behavior of structures under seismic action. We analyze a real world case study in term of structural analysis and we compare the behavior of the building under seismic action using various models.
A chain rule for the expected suprema of Gaussian processes
Venue: arXiv cs.LG 2014
Year: 2014

The expected supremum of a Gaussian process indexed by the image of an index set under a function class is bounded in terms of separate properties of the index set and the function class. The bound is relevant to the estimation of nonlinear transformations or the analysis of learning algorithms whenever hypotheses are chosen from composite classes, as is the case for multi-layer models.
A novel approach for mobility management inf lte femtocells
Venue: arXiv cs.NI 2014
Year: 2014

LTE is an emerging wireless data communication technology to provide broadband ubiquitous Internet access. Femtocells are included in 3GPP since Release 8 to enhance the indoor network coverage and capacity. The main challenge of mobility management in hierarchical LTE structure is to guarantee efficient handover to or from/to/between Femtocells. This paper focuses, on different types of Handover and comparison performance between different decision algorithms. Furthermore, a speed based Handover algorithm for macro-femto scenario is proposed with simulation results
Vulnerabilities of Smart Grid State Estimation against False Data Injection Attack
Venue: arXiv cs.CR 2014
Year: 2014

In recent years, Information Security has become a notable issue in the energy sector. After the invention of The Stuxnet worm in 2010, data integrity, privacy and confidentiality has received significant importance in the real-time operation of the control centres. New methods and frameworks are being developed to protect the National Critical Infrastructures like energy sector. In the recent literatures, it has been shown that the key real-time operational tools (e.g., State Estimator) of any Energy Management System (EMS) are vulnerable to Cyber Attacks. In this chapter, one such cyber attack named False Data Injection Attack is discussed. A literature review with a case study is considered to explain the characteristics and significance of such data integrity attacks.
Distributed Storage Allocations for Neighborhood-based Data Access
Venue: arXiv cs.IT 2014
Year: 2014

We introduce a neighborhood-based data access model for distributed coded storage allocation. Storage nodes are connected in a generic network and data is accessed locally: a user accesses a randomly chosen storage node, which subsequently queries its neighborhood to recover the data object. We aim at finding an optimal allocation that minimizes the overall storage budget while ensuring recovery with probability one. We show that the problem reduces to finding the fractional dominating set of the underlying network. Furthermore, we develop a fully distributed algorithm where each storage node communicates only with its neighborhood in order to find its optimal storage allocation. The proposed algorithm is based upon the recently proposed proximal center method--an efficient dual decomposition based on accelerated dual gradient method. We show that our algorithm achieves a $(1+ε)$-approximation ratio in $O(d_{\mathrm{max}}^{3/2}/ε)$ iterations and per-node communications, where $d_{\mathrm{max}}$ is the maximal degree across nodes. Simulations demonstrate the effectiveness of the algorithm.
Logical Limitations to Machine Ethics with Consequences to Lethal Autonomous Weapons
Venue: arXiv cs.CY 2014
Year: 2014

Lethal Autonomous Weapons promise to revolutionize warfare -- and raise a multitude of ethical and legal questions. It has thus been suggested to program values and principles of conduct (such as the Geneva Conventions) into the machines' control, thereby rendering them both physically and morally superior to human combatants. We employ mathematical logic and theoretical computer science to explore fundamental limitations to the moral behaviour of intelligent machines in a series of "Gedankenexperiments": Refining and sharpening variants of the Trolley Problem leads us to construct an (admittedly artificial but) fully deterministic situation where a robot is presented with two choices: one morally clearly preferable over the other -- yet, based on the undecidability of the Halting problem, it provably cannot decide algorithmically which one. Our considerations have surprising implications to the question of responsibility and liability for an autonomous system's actions and lead to specific technical recommendations.
QoS in IEEE 802.11-based Wireless Networks: A Contemporary Survey
Venue: arXiv cs.NI 2014
Year: 2014

Apart from mobile cellular networks, IEEE 802.11-based wireless local area networks (WLANs) represent the most widely deployed wireless networking technology. With the migration of critical applications onto data networks, and the emergence of multimedia applications such as digital audio/video and multimedia games, the success of IEEE 802.11 depends critically on its ability to provide quality of service (QoS). A lot of research has focused on equipping IEEE 802.11 WLANs with features to support QoS. In this survey, we provide an overview of these techniques. We discuss the QoS features incorporated by the IEEE 802.11 standard at both physical (PHY) and media access control (MAC) layers, as well as other higher-layer proposals. We also focus on how the new architectural developments of software-defined networking (SDN) and cloud networking can be used to facilitate QoS provisioning in IEEE 802.11-based networks. We conclude this paper by identifying some open research issues for future consideration.