OpenRaiser/Intern-Atlas huggingface dataset:
A Low Cost Eeg Based Bci Prosthetic Using Motor Imagery
Venue: arXiv cs.HC 2016
Year: 2016

Brain Computer Interfaces (BCI) provide the opportunity to control external devices using the brain ElectroEncephaloGram (EEG) signals. In this paper we propose two software framework in order to control a 5 degree of freedom robotic and prosthetic hand. Results are presented where an Emotiv Cognitive Suite (i.e. the 1st framework) combined with an embedded software system (i.e. an open source Arduino board) is able to control the hand through character input associated with the taught actions of the suite. This system provides evidence of the feasibility of brain signals being a viable approach to controlling the chosen prosthetic. Results are then presented in the second framework. This latter one allowed for the training and classification of EEG signals for motor imagery tasks. When analysing the system, clear visual representations of the performance and accuracy are presented in the results using a confusion matrix, accuracy measurement and a feedback bar signifying signal strength. Experiments with various acquisition datasets were carried out and with a critical evaluation of the results given. Finally depending on the classification of the brain signal a Python script outputs the driving command to the Arduino to control the prosthetic. The proposed architecture performs overall good results for the design and implementation of economically convenient BCI and prosthesis.
Performance Analysis of Multi-Hop Underwater Wireless Optical Communication Systems (Extended Version)
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we evaluate the end-to-end bit error rate (BER) of point-to-point underwater wireless optical communication (UWOC) systems with multi-hop transmission. To do so, we analytically derive the BER expression of a single-hop UWOC link as the building block for end-to-end BER evaluation. We also apply photon-counting method to evaluate the system BER in the presence of shot noise. Moreover, we use Gauss-Hermite quadrature formula to obtain the closed-form solutions for the system BER in the case of log-normal underwater fading channels. Our analytical treatment involves all the impairing effects of the underwater optical channel, namely absorption, scattering and fading. Numerical results demonstrate that multi-hop transmission by alleviating the aforementioned impairing effects of the channel, can significantly improve the system performance and extend the viable end-to-end communication distance. For example, dual-hop transmission in $22.5$ m and $45$ m coastal water links can provide $17.5$ dB and $39$ dB performance enhancement at the BER of $10^{-6}$, respectively.
Weight Computation of Regular Tree Languages
Venue: arXiv cs.FL 2016
Year: 2016

We present a general framework to define an application-dependent weight measure on terms that subsumes e.g. total simplification orderings, and an O(n log n) algorithm for the simultaneous computation of the minimal weight of a term in the language of each nonterminal of a regular tree grammar, based on Barzdins' liquid-flow technique.
Discrete Preference Games in Heterogeneous Social Networks: Subverted Majorities and the Swing Player
Venue: arXiv cs.GT 2016
Year: 2016

We study discrete preference games in heterogeneous social networks. These games model the interplay between a player's private belief and his/her publicly stated opinion (which could be different from the player's belief) as a strategic game in which the players' strategies are the opinions and the cost of an opinion in a state is a convex combination through a parameter $α\in[0,1]$ of two factors: the disagreement between the player's opinion and his/her internal belief and the number of neighbors whose opinions differ from the one of the player. The parameter $α$ models how stubborn a player is: players with large $α$ change their opinion only if many neighbors disagree with his/her belief. We consider social networks that are heterogeneous in the sense that the parameter $α$ can vary from player to player. We ask if it is possible that the belief shared by the majority of the players does not coincide with the opinion that is publicly announced by the majority of the players in an equilibrium state. Our main result is a characterization of the social networks that admit an initial belief assignment for which there exists a sequence of best response moves that reach an equilibrium in which the initial majority is subverted. Our characterization is effective in the sense that can be tested efficiently and the initial belief assignment that can be subverted can be computed in time polynomial in the number of players. Our result is actually stronger as we show that in each initial belief assignment that can be subverted, subversion is actually obtained in a very strong way: it only takes one move of a single player, the swing player, to lead the social network to a point of no return in which any rational move from any player leads to a subverted majority.
Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code
Venue: arXiv cs.PL 2016
Year: 2016

The current trend in next-generation exascale systems goes towards integrating a wide range of specialized (co-)processors into traditional supercomputers. However, the integration of different specialized devices increases the degree of heterogeneity and the complexity in programming such type of systems. Due to the efficiency of heterogeneous systems in terms of Watt and FLOPS per surface unit, opening the access of heterogeneous platforms to a wider range of users is an important problem to be tackled. In order to bridge the gap between heterogeneous systems and programmers, in this paper we propose a machine learning-based approach to learn heuristics for defining transformation strategies of a program transformation system. Our approach proposes a novel combination of reinforcement learning and classification methods to efficiently tackle the problems inherent to this type of systems. Preliminary results demonstrate the suitability of the approach for easing the programmability of heterogeneous systems.
Deciphering the 2016 U.S. Presidential Campaign in the Twitter Sphere: A Comparison of the Trumpists and Clintonists
Venue: arXiv cs.SI 2016
Year: 2016

In this paper, we study follower demographics of Donald Trump and Hillary Clinton, the two leading candidates in the 2016 U.S. presidential race. We build a unique dataset US2016, which includes the number of followers for each candidate from September 17, 2015 to December 22, 2015. US2016 also includes the geographical location of these followers, the number of their own followers and, very importantly, the profile image of each follower. We use individuals' number of followers and profile images to analyze four dimensions of follower demographics: social status, gender, race and age. Our study shows that in terms of social influence, the Trumpists are more polarized than the Clintonists: they tend to have either a lot of influence or little influence. We also find that compared with the Clintonists, the Trumpists are more likely to be either very young or very old. Our study finds no gender affinity effect for Clinton in the Twitter sphere, but we do find that the Clintonists are more racially diverse.
Scenario Submodular Cover
Venue: arXiv cs.DS 2016
Year: 2016

Many problems in Machine Learning can be modeled as submodular optimization problems. Recent work has focused on stochastic or adaptive versions of these problems. We consider the Scenario Submodular Cover problem, which is a counterpart to the Stochastic Submodular Cover problem studied by Golovin and Krause. In Scenario Submodular Cover, the goal is to produce a cover with minimum expected cost, where the expectation is with respect to an empirical joint distribution, given as input by a weighted sample of realizations. In contrast, in Stochastic Submodular Cover, the variables of the input distribution are assumed to be independent, and the distribution of each variable is given as input. Building on algorithms developed by Cicalese et al. and Golovin and Krause for related problems, we give two approximation algorithms for Scenario Submodular Cover over discrete distributions. The first achieves an approximation factor of O(log Qm), where m is the size of the sample and Q is the goal utility. The second, simpler algorithm achieves an approximation bound of O(log QW), where Q is the goal utility and W is the sum of the integer weights. (Both bounds assume an integer-valued utility function.) Our results yield approximation bounds for other problems involving non-independent distributions that are explicitly specified by their support.
Most Compact Parsimonious Trees
Venue: arXiv cs.DS 2016
Year: 2016

Construction of phylogenetic trees has traditionally focused on binary trees where all species appear on leaves, a problem for which numerous efficient solutions have been developed. Certain application domains though, such as viral evolution and transmission, paleontology, linguistics, and phylogenetic stemmatics, often require phylogeny inference that involves placing input species on ancestral tree nodes (live phylogeny), and polytomies. These requirements, despite their prevalence, lead to computationally harder algorithmic solutions and have been sparsely examined in the literature to date. In this article we prove some unique properties of most parsimonious live phylogenetic trees with polytomies, and describe novel algorithms to find the such trees without resorting to exhaustive enumeration of all possible tree topologies.
A Set Theoretic Approach for Knowledge Representation: the Representation Part
Venue: arXiv cs.AI 2016
Year: 2016

In this paper, we propose a set theoretic approach for knowledge representation. While the syntax of an application domain is captured by set theoretic constructs including individuals, concepts and operators, knowledge is formalized by equality assertions. We first present a primitive form that uses minimal assumed knowledge and constructs. Then, assuming naive set theory, we extend it by definitions, which are special kinds of knowledge. Interestingly, we show that the primitive form is expressive enough to define logic operators, not only propositional connectives but also quantifiers.
Near-Optimal Active Learning of Halfspaces via Query Synthesis in the Noisy Setting
Venue: arXiv cs.AI 2016
Year: 2016

In this paper, we consider the problem of actively learning a linear classifier through query synthesis where the learner can construct artificial queries in order to estimate the true decision boundaries. This problem has recently gained a lot of interest in automated science and adversarial reverse engineering for which only heuristic algorithms are known. In such applications, queries can be constructed de novo to elicit information (e.g., automated science) or to evade detection with minimal cost (e.g., adversarial reverse engineering). We develop a general framework, called dimension coupling (DC), that 1) reduces a d-dimensional learning problem to d-1 low dimensional sub-problems, 2) solves each sub-problem efficiently, 3) appropriately aggregates the results and outputs a linear classifier, and 4) provides a theoretical guarantee for all possible schemes of aggregation. The proposed method is proved resilient to noise. We show that the DC framework avoids the curse of dimensionality: its computational complexity scales linearly with the dimension. Moreover, we show that the query complexity of DC is near optimal (within a constant factor of the optimum algorithm). To further support our theoretical analysis, we compare the performance of DC with the existing work. We observe that DC consistently outperforms the prior arts in terms of query complexity while often running orders of magnitude faster.
Cycle and flow trusses in directed networks
Venue: arXiv cs.SI 2016
Year: 2016

When we represent real-world systems as networks, the directions of links often convey valuable information. Finding module structures that respect link directions is one of the most important tasks for analyzing directed networks. Although many notions of a directed module have been proposed, no consensus has been reached. This lack of consensus results partly because there might exist distinct types of modules in a single directed network, whereas most previous studies focused on an independent criterion for modules. To address this issue, we propose a generic notion of the so-called truss structures in directed networks. Our definition of truss is able to extract two distinct types of trusses, named the cycle truss and the flow truss, from a unified framework. By applying the method for finding trusses to empirical networks obtained from a wide range of research fields, we find that most real networks contain both cycle and flow trusses. In addition, the abundance of (and the overlap between) the two types of trusses may be useful to characterize module structures in a wide variety of empirical networks. Our findings shed light on the importance of simultaneously considering different types of modules in directed networks.
DSCMC: Distributed Stateless Code Model Checker
Venue: arXiv cs.PL 2016
Year: 2016

Stateless code model checking is an effective verification technique, which is more applicable than stateful model checking to the software world. Existing stateless model checkers support the verification of neither LTL formulae nor the information flow security properties. This paper proposes a distributed stateless code model checker (DSCMC) designed based on the Actor model, and has the capability of verifying code written in different programming languages. This tool is implemented using Erlang, which is an actor-based programming language. DSCMC is able to detect deadlocks, livelocks, and data races automatically. In addition, the tool can verify information flow security and the properties specified in LTL. Thanks to its actor-based architecture, DSCMC provides a wide range of capabilities. The parallel architecture of the tool exploiting the rich concurrency model of Erlang is suited to the time-intensive process of stateless code model checking.
Matrix factoring by fraction-free reduction
Venue: arXiv cs.SC 2016
Year: 2016

We consider exact matrix decomposition by Gauss-Bareiss reduction. We investigate two aspects of the process: common row and column factors and the influence of pivoting strategies. We identify two types of common factors: systematic and statistical. Systematic factors depend on the process, while statistical factors depend on the specific data. We show that existing fraction-free QR (Gram-Schmidt) algorithms create a common factor in the last column of Q. We relate the existence of row factors in LU decomposition to factors appearing in the Smith normal form of the matrix. For statistical factors, we identify mechanisms and give estimates of the frequency. Our conclusions are tested by experimental data. For pivoting strategies, we compare the sizes of output factors obtained by different strategies. We also comment on timing differences.
Smart Context-aware Rejuvenation of Engagement on Urban Ambient Augmented Things
Venue: arXiv cs.CY 2016
Year: 2016

The concern over global urbanization trend imposes smart-city as enabling information and communication technology (ICT) to improve urban governance. However, the light trance on better living space is stimulated by socioeconomic impact of escalated senior generation. Hence, ambient assisted living (AAL) emerges for the autonomous provisioning of pervasive things or objects from relevant perturbation for advanced scientific instrumentation. Meanwhile, citizens are observed in being transfixed by lively stimuli of monotonous urban events with the advent of virtual reality or augmented things. Thus, due to the involvement of situation-awareness or contextualization, engagement/participation information as a utility promises to improve urban experience. However, it is complex to grapple meaningful concepts due to personalization obstacles, such as citizen psychology, information gap, service-visualization. Moreover, recommended practices deficit adaptation to monochromatic choice, disparate impairments, mobility and annotation-richness in urban space. Hence, rejuvenation of engagement relates to monitoring and quantification of 'service consumption and graceful degradation' of experience. However, paramount challenges are imposed on this stipulation, such as, unobservability, independence and composite relationship of contexts. Therefore, a parametric Bayesian based model is envisioned to address observability and scalability of contexts and its conjugal relationship with engagement. Last but not the least, systematic framework is demonstrated, which pinpoints key goals of context-aware engagement from participants' opinions, usages and feed-backs.
Region Graph Based Method for Multi-Object Detection and Tracking using Depth Cameras
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we propose a multi-object detection and tracking method using depth cameras. Depth maps are very noisy and obscure in object detection. We first propose a region-based method to suppress high magnitude noise which cannot be filtered using spatial filters. Second, the proposed method detect Region of Interests by temporal learning which are then tracked using weighted graph-based approach. We demonstrate the performance of the proposed method on standard depth camera datasets with and without object occlusions. Experimental results show that the proposed method is able to suppress high magnitude noise in depth maps and detect/track the objects (with and without occlusion).
Degree Four Plane Spanners: Simpler and Better
Venue: arXiv cs.CG 2016
Year: 2016

Let ${\cal P}$ be a set of $n$ points embedded in the plane, and let ${\cal C}$ be the complete Euclidean graph whose point-set is ${\cal P}$. Each edge in ${\cal C}$ between two points $p, q$ is realized as the line segment $[pq]$, and is assigned a weight equal to the Euclidean distance $|pq|$. In this paper, we show how to construct in $O(n\lg{n})$ time a plane spanner of ${\cal C}$ of maximum degree at most 4 and stretch factor at most 20. This improves a long sequence of results on the construction of plane spanners of ${\cal C}$. Our result matches the smallest known upper bound of 4 by Bonichon et al. on the maximum degree of plane spanners of ${\cal C}$, while significantly improving their stretch factor upper bound from 156.82 to 20. The construction of our spanner is based on Delaunay triangulations defined with respect to the equilateral-triangle distance, and uses a different approach than that used by Bonichon et al. Our approach leads to a simple and intuitive construction of a well-structured spanner, and reveals useful structural properties of the Delaunay triangulations defined with respect to the equilateral-triangle distance. The structure of the constructed spanner implies that when ${\cal P}$ is in convex position, the maximum degree of this spanner is at most 3. Combining the above degree upper bound with the fact that 3 is a lower bound on the maximum degree of any plane spanner of ${\cal C}$ when the point-set ${\cal P}$ is in convex position, the results in this paper give a tight bound of 3 on the maximum degree of plane spanners of ${\cal C}$ for point-sets in convex position.
Comparative Advantage Driven Resource Allocation for Virtual Network Functions
Venue: arXiv cs.NI 2016
Year: 2016

As Communication Service Providers (CSPs) adopt the Network Function Virtualization (NFV) paradigm, they need to transition their network function capacity to a virtualized infrastructure with different Network Functions running on a set of heterogeneous servers. This abstract describes a novel technique for allocating server resources (compute, storage and network) for a given set of Virtual Network Function (VNF) requirements. Our approach helps the telco providers decide the most effective way to run several VNFs on servers with different performance characteristics. Our analysis of prior VNF performance characterization on heterogeneous/different server resource allocations shows that the ability to arbitrarily create many VNFs among different servers' resource allocations leads to a comparative advantage among servers. We propose a VNF resource allocation method called COMPARE that maximizes the total throughput of the system by formulating this resource allocation problem as a comparative advantage problem among heterogeneous servers. There several applications for using the VNF resource allocation from COMPARE including transitioning current Telco deployments to NFV based solutions and providing initial VNF placement for Service Function Chain (SFC) provisioning.
Intrusion Detection A Text Mining Based Approach
Venue: arXiv cs.CR 2016
Year: 2016

Intrusion Detection is one of major threats for organization. The approach of intrusion detection using text processing has been one of research interests which is gaining significant importance from researchers. In text mining based approach for intrusion detection, system calls serve as source for mining and predicting possibility of intrusion or attack. When an application runs, there might be several system calls which are initiated in the background. These system calls form the strong basis and the deciding factor for intrusion detection. In this paper, we mainly discuss the approach for intrusion detection by designing a distance measure which is designed by taking into consideration the conventional Gaussian function and modified to suit the need for similarity function. A Framework for intrusion detection is also discussed as part of this research.
Neural Discourse Relation Recognition with Semantic Memory
Venue: arXiv cs.CL 2016
Year: 2016

Humans comprehend the meanings and relations of discourses heavily relying on their semantic memory that encodes general knowledge about concepts and facts. Inspired by this, we propose a neural recognizer for implicit discourse relation analysis, which builds upon a semantic memory that stores knowledge in a distributed fashion. We refer to this recognizer as SeMDER. Starting from word embeddings of discourse arguments, SeMDER employs a shallow encoder to generate a distributed surface representation for a discourse. A semantic encoder with attention to the semantic memory matrix is further established over surface representations. It is able to retrieve a deep semantic meaning representation for the discourse from the memory. Using the surface and semantic representations as input, SeMDER finally predicts implicit discourse relations via a neural recognizer. Experiments on the benchmark data set show that SeMDER benefits from the semantic memory and achieves substantial improvements of 2.56\% on average over current state-of-the-art baselines in terms of F1-score.
The coherent matching distance in 2D persistent homology
Venue: arXiv cs.CG 2016
Year: 2016

Comparison between multidimensional persistent Betti numbers is often based on the multidimensional matching distance. While this metric is rather simple to define and compute by considering a suitable family of filtering functions associated with lines having a positive slope, it has two main drawbacks. First, it forgets the natural link between the homological properties of filtrations associated with lines that are close to each other. As a consequence, part of the interesting homological information is lost. Second, its intrinsically discontinuous definition makes it difficult to study its properties. In this paper we introduce a new matching distance for 2D persistent Betti numbers, called coherent matching distance and based on matchings that change coherently with the filtrations we take into account. Its definition is not trivial, as it must face the presence of monodromy in multidimensional persistence, i.e. the fact that different paths in the space parameterizing the above filtrations can induce different matchings between the associated persistent diagrams. In our paper we prove that the coherent 2D matching distance is well-defined and stable.