OpenRaiser/Intern-Atlas huggingface dataset:
Detection of Critical Number of People in Interlocked Doors for Security Access Control by Exploiting a Microwave Transceiver-Array
Venue: arXiv cs.CV 2015
Year: 2015

Counting the number of people is something many security application focus on, when dealing with controlling accesses in restricted areas, as it occurs with banks, airports, railway stations and governmental offices. This paper presents an automated solution for detecting the presence of more than one person into interlocked doors adopted in many accesses. In most cases, interlocked doors are small areas where other pieces of information and sensors are placed in order to detect the presence of guns, explosive, etc. The general goals and the required environmental condition, allowed us to implement a detection system at lower costs and complexity, with respect to other existing techniques. The system consists of a fixed array of microwave transceiver modules, whose received signals are processed to collect information related to a sort of volume occupied in the interlocked door cabin. The proposed solution has been statistically validated by using statistical analysis. The whole solution has been also implemented to be used in a real time environment and thus validated against real experimental measures.
Ontology Bulding vs Data Harvesting and Cleaning for Smart-city Services
Venue: arXiv cs.DB 2015
Year: 2015

Presently, a very large number of public and private data sets are available around the local governments. In most cases, they are not semantically interoperable and a huge human effort is needed to create integrated ontologies and knowledge base for smart city. Smart City ontology is not yet standardized, and a lot of research work is needed to identify models that can easily support the data reconciliation, the management of the complexity and reasoning. In this paper, a system for data ingestion and reconciliation smart cities related aspects as road graph, services available on the roads, traffic sensors etc., is proposed. The system allows managing a big volume of data coming from a variety of sources considering both static and dynamic data. These data are mapped to smart-city ontology and stored into an RDF-Store where they are available for applications via SPARQL queries to provide new services to the users. The paper presents the process adopted to produce the ontology and the knowledge base and the mechanisms adopted for the verification, reconciliation and validation. Some examples about the possible usage of the coherent knowledge base produced are also offered and are accessible from the RDF-Store.
Moving Towards a Socially-Driven Internet Architectural Design
Venue: arXiv cs.CY 2015
Year: 2015

This paper provides an interdisciplinary perspective concerning the role of prosumers on future Internet design based on the current trend of Internet user empowerment. The paper debates the prosumer role, and addresses models to develop a symmetric Internet architecture and supply-chain based on the integration of social capital aspects. It has as goal to ignite the discussion concerning a socially-driven Internet architectural design.
Evaluating color texture descriptors under large variations of controlled lighting conditions
Venue: arXiv cs.CV 2015
Year: 2015

The recognition of color texture under varying lighting conditions is still an open issue. Several features have been proposed for this purpose, ranging from traditional statistical descriptors to features extracted with neural networks. Still, it is not completely clear under what circumstances a feature performs better than the others. In this paper we report an extensive comparison of old and new texture features, with and without a color normalization step, with a particular focus on how they are affected by small and large variation in the lighting conditions. The evaluation is performed on a new texture database including 68 samples of raw food acquired under 46 conditions that present single and combined variations of light color, direction and intensity. The database allows to systematically investigate the robustness of texture descriptors across a large range of variations of imaging conditions.
Socially Constrained Structural Learning for Groups Detection in Crowd
Venue: arXiv cs.CV 2015
Year: 2015

Modern crowd theories agree that collective behavior is the result of the underlying interactions among small groups of individuals. In this work, we propose a novel algorithm for detecting social groups in crowds by means of a Correlation Clustering procedure on people trajectories. The affinity between crowd members is learned through an online formulation of the Structural SVM framework and a set of specifically designed features characterizing both their physical and social identity, inspired by Proxemic theory, Granger causality, DTW and Heat-maps. To adhere to sociological observations, we introduce a loss function (G-MITRE) able to deal with the complexity of evaluating group detection performances. We show our algorithm achieves state-of-the-art results when relying on both ground truth trajectories and tracklets previously extracted by available detector/tracker systems.
Interference Alignment Designs for Secure Multiuser MIMO Systems: Rank Constrained Rank Minimization Approach
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we formulate the interference alignment (IA) problem for a multiuser multiple-input multiple-output (MIMO) system in the presence of an eavesdropper as a rank constrained rank minimization (RCRM) problem. The aim of the proposed rank minimization IA schemes is to find the precoding and receiver subspace matrices to align interference and wiretapped signals into the lowest dimension subspaces while keeping the desired signal subspace spanning full available spatial dimensions. To deal with the nonconvexity of the rank function, we present two convex relaxations of the RCRM problem, namely nuclear norm (NN) and reweighted nuclear norm (RNN), and transform the rank constraints to equivalent and tractable ones. We then derive a coordinate decent approach to obtain the solutions for IA schemes. The simulation results show that our proposed IA designs outperform the conventional IA design in terms of average secrecy sum rate. On the other hand, our proposed designs perform the same or better than other secure IA schemes which account for low interference and wiretapped signal power rather than for low dimensions of interference and wiretapped signal matrices in the systems which achieve the perfect IA.
Secret key-based Identification and Authentication with a Privacy Constraint
Venue: arXiv cs.IT 2015
Year: 2015

We consider the problem of identification and authentication based on secret key generation from some user-generated source data (e.g., a biometric source). The goal is to reliably identify users pre-enrolled in a database as well as authenticate them based on the estimated secret key while preserving the privacy of the enrolled data and of the generated keys. We characterize the optimal tradeoff between the identification rate, the compression rate of the users' source data, information leakage rate, and secret key rate. In particular, we provide a coding strategy based on layered random binning which is shown to be optimal. In addition, we study a related secure identification/authentication problem where an adversary tries to deceive the system using its own data. Here the optimal tradeoff between the identification rate, compression rate, leakage rate, and exponent of the maximum false acceptance probability is provided. The results reveal a close connection between the optimal secret key rate and the false acceptance exponent of the identification/authentication system.
Knowledge, Level of Symmetry, and Time of Leader Election
Venue: arXiv cs.DC 2015
Year: 2015

We study the time needed for deterministic leader election in the ${\cal LOCAL}$ model, where in every round a node can exchange any messages with its neighbors and perform any local computations. The topology of the network is unknown and nodes are unlabeled, but ports at each node have arbitrary fixed labelings which, together with the topology of the network, can create asymmetries to be exploited in leader election. We consider two versions of the leader election problem: strong LE in which exactly one leader has to be elected, if this is possible, while all nodes must terminate declaring that leader election is impossible otherwise, and weak LE, which differs from strong LE in that no requirement on the behavior of nodes is imposed, if leader election is impossible. We show that the time of leader election depends on three parameters of the network: its diameter $D$, its size $n$, and its level of symmetry $λ$, which, when leader election is feasible, is the smallest depth at which some node has a unique view of the network. It also depends on the knowledge by the nodes, or lack of it, of parameters $D$ and $n$.
Replication and Generalization of PRECISE
Venue: arXiv cs.CL 2015
Year: 2015

This report describes an initial replication study of the PRECISE system and develops a clearer, more formal description of the approach. Based on our evaluation, we conclude that the PRECISE results do not fully replicate. However the formalization developed here suggests a road map to further enhance and extend the approach pioneered by PRECISE. After a long, productive discussion with Ana-Maria Popescu (one of the authors of PRECISE) we got more clarity on the PRECISE approach and how the lexicon was authored for the GEO evaluation. Based on this we built a more direct implementation over a repaired formalism. Although our new evaluation is not yet complete, it is clear that the system is performing much better now. We will continue developing our ideas and implementation and generate a future report/publication that more accurately evaluates PRECISE like approaches.
Collaborative Total Variation: A General Framework for Vectorial TV Models
Venue: arXiv cs.CV 2015
Year: 2015

Even after over two decades, the total variation (TV) remains one of the most popular regularizations for image processing problems and has sparked a tremendous amount of research, particularly to move from scalar to vector-valued functions. In this paper, we consider the gradient of a color image as a three dimensional matrix or tensor with dimensions corresponding to the spatial extend, the differences to other pixels, and the spectral channels. The smoothness of this tensor is then measured by taking different norms along the different dimensions. Depending on the type of these norms one obtains very different properties of the regularization, leading to novel models for color images. We call this class of regularizations collaborative total variation (CTV). On the theoretical side, we characterize the dual norm, the subdifferential and the proximal mapping of the proposed regularizers. We further prove, with the help of the generalized concept of singular vectors, that an $\ell^{\infty}$ channel coupling makes the most prior assumptions and has the greatest potential to reduce color artifacts. Our practical contributions consist of an extensive experimental section where we compare the performance of a large number of collaborative TV methods for inverse problems like denoising, deblurring and inpainting.
Fuzzy Logic Based Direct Torque Control Of Induction Motor With Space Vector Modulation
Venue: arXiv cs.SY 2015
Year: 2015

The induction motors have wide range of applications for due to its well-known advantages like brushless structures, low costs and robust performances. Over the past years, many kind of control methods are proposed for the induction motors and direct torque control has gained huge importance inside of them due to fast dynamic torque responses and simple control structures. However, the direct torque control method has still some handicaps against the other control methods and most of the important of these handicaps is high torque ripple. This paper suggests a new approach, Fuzzy logic based space vector modulation, on the direct torque controlled induction motors and aim of the approach is to overcome high torque ripple disadvantages of conventional direct torque control. In order to test and compare the proposed direct torque control method with conventional direct torque control method simulations, in Matlab/Simulink,have been carried out in different working conditions. The simulation results showed that a significant improvement in the dynamic torque and speed responses when compared to the conventional direct torque control method.
Arabic Text Watermarking: A Review
Venue: arXiv cs.MM 2015
Year: 2015

The using of the internet with its technologies and applications have been increased rapidly. So, protecting the text from illegal use is too needed . Text watermarking is used for this purpose. Arabic text has many characteristics such existing of diacritics , kashida (extension character) and points above or under its letters .Each of Arabic letters can take different shapes with different Unicode. These characteristics are utilized in the watermarking process. In this paper, several methods are discussed in the area of Arabic text watermarking with its advantages and disadvantages .Comparison of these methods is done in term of capacity, robustness and Imperceptibility.
A Unified Approach to 3 Basic Challenges in Shared Autonomy
Venue: arXiv cs.RO 2015
Year: 2015

We discuss some of the challenges facing shared autonomy. In particular, we explore 1) shared autonomy over unreliable networks, 2) how we can model individual human operators (in contrast to the average of a human operator), and 3) how our approach naturally models and integrates sliding autonomy into the joint human-machine system. We include a Background Section for completeness.
Recoverable DTN Routing based on a Relay of Cyclic Message-Ferries on a MSQ Network
Venue: arXiv cs.DC 2015
Year: 2015

An interrelation between a topological design of network and efficient algorithm on it is important for its applications to communication or transportation systems. In this paper, we propose a design principle for a reliable routing in a store-carry-forward manner based on autonomously moving message-ferries on a special structure of fractal-like network, which consists of a self-similar tiling of equilateral triangles. As a collective adaptive mechanism, the routing is realized by a relay of cyclic message-ferries corresponded to a concatenation of the triangle cycles and using some good properties of the network structure. It is recoverable for local accidents in the hierarchical network structure. Moreover, the design principle is theoretically supported with a calculation method for the optimal service rates of message-ferries derived from a tandem queue model for stochastic processes on a chain of edges in the network. These results obtained from a combination of complex network science and computer science will be useful for developing a resilient network system.
Automata networks for memory loss effects in the formation of linguistic conventions
Venue: arXiv cs.CL 2015
Year: 2015

This work attempts to give new theoretical insights to the absence of intermediate stages in the evolution of language. In particular, it is developed an automata networks approach to a crucial question: how a population of language users can reach agreement on a linguistic convention? To describe the appearance of sharp transitions in the self-organization of language, it is adopted an extremely simple model of (working) memory. At each time step, language users simply loss part of their word-memories. Through computer simulations of low-dimensional lattices, it appear sharp transitions at critical values that depend on the size of the vicinities of the individuals.
Applying Deep Learning to Answer Selection: A Study and An Open Task
Venue: arXiv cs.CL 2015
Year: 2015

We apply a general deep learning framework to address the non-factoid question answering task. Our approach does not rely on any linguistic tools and can be applied to different languages or domains. Various architectures are presented and compared. We create and release a QA corpus and setup a new QA task in the insurance domain. Experimental results demonstrate superior performance compared to the baseline methods and various technologies give further improvements. For this highly challenging task, the top-1 accuracy can reach up to 65.3% on a test set, which indicates a great potential for practical use.
A Stronger Soft-Covering Lemma and Applications
Venue: arXiv cs.IT 2015
Year: 2015

Wyner's soft-covering lemma is a valuable tool for achievability proofs of information theoretic security, resolvability, channel synthesis, and source coding. The result herein sharpens the claim of soft-covering by moving away from an expected value analysis. Instead, a random codebook is shown to achieve the soft-covering phenomenon with high probability. The probability of failure is doubly-exponentially small in the block-length, enabling more powerful applications through the union bound.
Linear Programming Decoding of Binary Linear Codes for Symbol-Pair Read Channels
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we develop a new decoding algorithm of a binary linear codes for symbol-pair read channels. Symbol-pair read channel has recently been introduced by Cassuto and Blaum to model channels with high write resolution but low read resolution. The proposed decoding algorithm is based on a linear programming (LP). It is proved that the proposed LP decoder has the maximum-likelihood (ML) certificate property, i.e., the output of the decoder is guaranteed to be the ML codeword when it is integral. We also introduce the fractional pair distance $d_{fp}$ of a code which is a lower bound on the pair distance. It is proved that the proposed LP decoder will correct up to $\lceil d_{fp}/2\rceil-1$ pair errors.
Study of Phonemes Confusions in Hierarchical Automatic Phoneme Recognition System
Venue: arXiv cs.CL 2015
Year: 2015

In this paper, we have analyzed the impact of confusions on the robustness of phoneme recognitions system. The confusions are detected at the pronunciation and the confusions matrices of the phoneme recognizer. The confusions show that some similarities between phonemes at the pronunciation affect significantly the recognition rates. This paper proposes to understand those confusions in order to improve the performance of the phoneme recognition system by isolating the problematic phonemes. Confusion analysis leads to build a new hierarchical recognizer using new phoneme distribution and the information from the confusion matrices. This new hierarchical phoneme recognition system shows significant improvements of the recognition rates on TIMIT database.
Practical Algorithms for Finding Extremal Sets
Venue: arXiv cs.DS 2015
Year: 2015

The minimal sets within a collection of sets are defined as the ones which do not have a proper subset within the collection, and the maximal sets are the ones which do not have a proper superset within the collection. Identifying extremal sets is a fundamental problem with a wide-range of applications in SAT solvers, data-mining and social network analysis. In this paper, we present two novel improvements of the high-quality extremal set identification algorithm, \textit{AMS-Lex}, described by Bayardo and Panda. The first technique uses memoization to improve the execution time of the single-threaded variant of the AMS-Lex, whilst our second improvement uses parallel programming methods. In a subset of the presented experiments our memoized algorithm executes more than $400$ times faster than the highly efficient publicly available implementation of AMS-Lex. Moreover, we show that our modified algorithm's speedup is not bounded above by a constant and that it increases as the length of the common prefixes in successive input \textit{itemsets} increases. We provide experimental results using both real-world and synthetic data sets, and show our multi-threaded variant algorithm out-performing AMS-Lex by $3$ to $6$ times. We find that on synthetic input datasets when executed using $16$ CPU cores of a $32$-core machine, our multi-threaded program executes about as fast as the state of the art parallel GPU-based program using an NVIDIA GTX 580 graphics processing unit.
Reducible Cyclic Codes Constructed as the Direct Sum of Two Semiprimitive Cyclic Codes
Venue: arXiv cs.IT 2015
Year: 2015

We present a family of reducible cyclic codes constructed as the direct sum of two different semiprimitive two-weight irreducible cyclic codes. This family generalizes the class of reducible cyclic codes that was reported in the main result of B. Wang, {\em et al.} \cite{once}. Moreover, despite of what was stated therein, we show that, at least for the codes studied here, it is still possible to compute the frequencies of their weight distributions through the cyclotomic numbers in a very easy way.
Mimicry Is Presidential: Linguistic Style Matching in Presidential Debates and Improved Polling Numbers
Venue: arXiv cs.CL 2015
Year: 2015

The current research used the contexts of U.S. presidential debates and negotiations to examine whether matching the linguistic style of an opponent in a two-party exchange affects the reactions of third-party observers. Building off communication accommodation theory (CAT), interaction alignment theory (IAT), and processing fluency, we propose that language style matching (LSM) will improve subsequent third-party evaluations because matching an opponent's linguistic style reflects greater perspective taking and will make one's arguments easier to process. In contrast, research on status inferences predicts that LSM will negatively impact third-party evaluations because LSM implies followership. We conduct two studies to test these competing hypotheses. Study 1 analyzed transcripts of U.S. presidential debates between 1976 and 2012 and found that candidates who matched their opponent's linguistic style increased their standing in the polls. Study 2 demonstrated a causal relationship between LSM and third-party observer evaluations using negotiation transcripts.
Trend-driven information cascades on random networks
Venue: arXiv cs.SI 2015
Year: 2015

Threshold models of global cascades have been extensively used to model real-world collective behavior, such as the contagious spread of fads and the adoption of new technologies. A common property of those cascade models is that a vanishingly small seed fraction can spread to a finite fraction of an infinitely large network through local infections. In social and economic networks, however, individuals' behavior is often influenced not only by what their direct neighbors are doing, but also by what the majority of people are doing as a trend. A trend affects individuals' behavior while individuals' behavior creates a trend. To analyze such a complex interplay between local- and global-scale phenomena, I generalize the standard threshold model by introducing a new type of node, called \textit{global nodes} (or \textit{trend followers}), whose activation probability depends on a global-scale trend; specifically the percentage of activated nodes in the population. The model shows that global nodes play a role as accelerating cascades once a trend emerges while reducing the probability of a trend emerging. Global nodes thus either facilitate or inhibit cascades, suggesting that a moderate share of trend followers may maximize the average size of cascades.