OpenRaiser/Intern-Atlas huggingface dataset:
The Neutrosophic Entropy and its Five Components
Venue: arXiv cs.AI 2015
Year: 2015

This paper presents two variants of penta-valued representation for neutrosophic entropy. The first is an extension of Kaufmann's formula and the second is an extension of Kosko's formula. Based on the primary three-valued information represented by the degree of truth, degree of falsity and degree of neutrality there are built some penta-valued representations that better highlights some specific features of neutrosophic entropy. Thus, we highlight five features of neutrosophic uncertainty such as ambiguity, ignorance, contradiction, neutrality and saturation. These five features are supplemented until a seven partition of unity by adding two features of neutrosophic certainty such as truth and falsity. The paper also presents the particular forms of neutrosophic entropy obtained in the case of bifuzzy representations, intuitionistic fuzzy representations, paraconsistent fuzzy representations and finally the case of fuzzy representations.
Exchange of Services in Networks: Competition, Cooperation, and Fairness
Venue: arXiv cs.GT 2015
Year: 2015

Exchange of services and resources in, or over, networks is attracting nowadays renewed interest. However, despite the broad applicability and the extensive study of such models, e.g., in the context of P2P networks, many fundamental questions regarding their properties and efficiency remain unanswered. We consider such a service exchange model and analyze the users' interactions under three different approaches. First, we study a centrally designed service allocation policy that yields the fair total service each user should receive based on the service it others to the others. Accordingly, we consider a competitive market where each user determines selfishly its allocation policy so as to maximize the service it receives in return, and a coalitional game model where users are allowed to coordinate their policies. We prove that there is a unique equilibrium exchange allocation for both game theoretic formulations, which also coincides with the central fair service allocation. Furthermore, we characterize its properties in terms of the coalitions that emerge and the equilibrium allocations, and analyze its dependency on the underlying network graph. That servicing policy is the natural reference point to the various mechanisms that are currently proposed to incentivize user participation and improve the efficiency of such networked service (or, resource) exchange markets.
Intractability of Optimal Multi-Robot Path Planning on Planar Graphs
Venue: arXiv cs.RO 2015
Year: 2015

We study the computational complexity of optimally solving multi-robot path planning problems on planar graphs. For four common time- and distance-based objectives, we show that the associated path optimization problems for multiple robots are all NP-complete, even when the underlying graph is planar. Establishing the computational intractability of optimal multi-robot path planning problems on planar graphs has important practical implications. In particular, our result suggests the preferred approach toward solving such problems, when the number of robots is large, is to augment the planar environment to reduce the sharing of paths among robots traveling in opposite directions on those paths. Indeed, such efficiency boosting structures, such as highways and elevated intersections, are ubiquitous in robotics and transportation applications.
A Review of Man-in-the-Middle Attacks
Venue: arXiv cs.CR 2015
Year: 2015

This paper presents a survey of man-in-the-middle (MIM) attacks in communication networks and methods of protection against them. In real time communication, the attack can in many situations be discovered by the use of timing information. The most common attacks occur due to Address Resolution Protocol (ARP) cache poisoning, DNS spoofing, session hijacking, and SSL hijacking.
Hand Posture's Effect on Touch Screen Text Input Behaviors: A Touch Area Based Study
Venue: arXiv cs.HC 2015
Year: 2015

Mobile devices with touch keyboards have become ubiquitous, but text entry on these devices remains slow and errorprone. Understanding touch patterns during text entry could be useful in designing robust error-correction algorithms for soft keyboards. In this paper, we present an analysis of text input behaviors on a soft QWERTY keyboard in three different text entry postures: index finger only, one thumb, and two thumb. Our work expands on the work of [1] by considering the entire surface area of digit contact with the smartphone keyboard, rather than interpreting each touch as a single point. To do this, we captured touch areas for every key in a lab study with 8 participants and calculated offsets, error rates, and size measurements. We then repeated the original experiment described in [1] and showed that significant differences exist when basing offset calculations on touch area compared to touch points for two postures.
Fackbook Implementation in Developing English Writing for Thai Students
Venue: arXiv cs.CY 2015
Year: 2015

The objectives of this research were to study FB implementation and attitudes in developing English writing skills of Thai students studying in the first year students program in EIC academic year 1/2014 at RMUTI, Surin Campus. The Purposive sampling was designed for data collecting. The instruments for this research were questionnaires and in-depth questions. The data analysis was analyzed by the Descriptive statistics to find out the value of the frequency and percentage.
On mining complex sequential data by means of FCA and pattern structures
Venue: arXiv cs.AI 2015
Year: 2015

Nowadays data sets are available in very complex and heterogeneous ways. Mining of such data collections is essential to support many real-world applications ranging from healthcare to marketing. In this work, we focus on the analysis of "complex" sequential data by means of interesting sequential patterns. We approach the problem using the elegant mathematical framework of Formal Concept Analysis (FCA) and its extension based on "pattern structures". Pattern structures are used for mining complex data (such as sequences or graphs) and are based on a subsumption operation, which in our case is defined with respect to the partial order on sequences. We show how pattern structures along with projections (i.e., a data reduction of sequential structures), are able to enumerate more meaningful patterns and increase the computing efficiency of the approach. Finally, we show the applicability of the presented method for discovering and analyzing interesting patient patterns from a French healthcare data set on cancer. The quantitative and qualitative results (with annotations and analysis from a physician) are reported in this use case which is the main motivation for this work. Keywords: data mining; formal concept analysis; pattern structures; projections; sequences; sequential data.
Open Access Policy: Numbers, Analysis, Effectiveness
Venue: arXiv cs.DL 2015
Year: 2015

The PASTEUR4OA project analyses what makes an Open Access (OA) policy effective. The total number of institutional or funder OA policies worldwide is now 663 (March 2015), over half of them mandatory. ROARMAP, the policy registry, has been rebuilt to record more policy detail and provide more extensive search functionality. Deposit rates were measured for articles in institutions' repositories and compared to the total number of WoS-indexed articles published from those institutions. Average deposit rate was over four times as high for institutions with a mandatory policy. Six positive correlations were found between deposit rates and (1) Must-Deposit; (2) Cannot-Waive-Deposit; (3) Deposit-Linked-to-Research-Evaluation; (4) Cannot-Waive-Rights-Retention; (5) Must-Make-Deposit-OA (after allowable embargo) and (6) Can-Waive-OA. For deposit latency, there is a positive correlation between earlier deposit and (7) Must-Deposit-Immediately as well as with (4) Cannot-Waive-Rights-Retention and with mandate age. There are not yet enough OA policies to test whether still further policy conditions would contribute to mandate effectiveness but the present findings already suggest that it would be useful for current and future OA policies to adopt the seven positive conditions so as to accelerate and maximise the growth of OA.
The Early Bird Catches The Term: Combining Twitter and News Data For Event Detection and Situational Awareness
Venue: arXiv cs.SI 2015
Year: 2015

Twitter updates now represent an enormous stream of information originating from a wide variety of formal and informal sources, much of which is relevant to real-world events. In this paper we adapt existing bio-surveillance algorithms to detect localised spikes in Twitter activity corresponding to real events with a high level of confidence. We then develop a methodology to automatically summarise these events, both by providing the tweets which fully describe the event and by linking to highly relevant news articles. We apply our methods to outbreaks of illness and events strongly affecting sentiment. In both case studies we are able to detect events verifiable by third party sources and produce high quality summaries.
Point Decomposition Problem in Binary Elliptic Curves
Venue: arXiv cs.CR 2015
Year: 2015

We analyze the point decomposition problem (PDP) in binary elliptic curves. It is known that PDP in an elliptic curve group can be reduced to solving a particular system of multivariate non-linear system of equations derived from the so called Semaev summation polynomials. We modify the underlying system of equations by introducing some auxiliary variables. We argue that the trade-off between lowering the degree of Semaev polynomials and increasing the number of variables is worth.
On the covering dimension of a linear code
Venue: arXiv cs.IT 2015
Year: 2015

The critical exponent of a matroid is one of the important parameters in matroid theory and is related to the Rota and Crapo's Critical Problem. This paper introduces the covering dimension of a linear code over a finite field, which is analogous to the critical exponent of a representable matroid. An upper bound on the covering dimension is conjectured and nearly proven, improving a classical bound for the critical exponent. Finally, a construction is given of linear codes that attain equality in the covering dimension bound.
Survey of Operating Systems for the IoT Environment
Venue: arXiv cs.OS 2015
Year: 2015

This paper is a comprehensive survey of the various operating systems available for the Internet of Things environment. At first the paper introduces the various aspects of the operating systems designed for the IoT environment where resource constraint poses a huge problem for the operation of the general OS designed for the various computing devices. The latter part of the paper describes the various OS available for the resource constraint IoT environment along with the various platforms each OS supports, the software development kits available for the development of applications in the respective OS along with the various protocols implemented in these OS for the purpose of communication and networking.
Learning Arbitrary Statistical Mixtures of Discrete Distributions
Venue: arXiv cs.LG 2015
Year: 2015

We study the problem of learning from unlabeled samples very general statistical mixture models on large finite sets. Specifically, the model to be learned, $\vartheta$, is a probability distribution over probability distributions $p$, where each such $p$ is a probability distribution over $[n] = \{1,2,\dots,n\}$. When we sample from $\vartheta$, we do not observe $p$ directly, but only indirectly and in very noisy fashion, by sampling from $[n]$ repeatedly, independently $K$ times from the distribution $p$. The problem is to infer $\vartheta$ to high accuracy in transportation (earthmover) distance. We give the first efficient algorithms for learning this mixture model without making any restricting assumptions on the structure of the distribution $\vartheta$. We bound the quality of the solution as a function of the size of the samples $K$ and the number of samples used. Our model and results have applications to a variety of unsupervised learning scenarios, including learning topic models and collaborative filtering.
Panther: Fast Top-k Similarity Search in Large Networks
Venue: arXiv cs.SI 2015
Year: 2015

Estimating similarity between vertices is a fundamental issue in network analysis across various domains, such as social networks and biological networks. Methods based on common neighbors and structural contexts have received much attention. However, both categories of methods are difficult to scale up to handle large networks (with billions of nodes). In this paper, we propose a sampling method that provably and accurately estimates the similarity between vertices. The algorithm is based on a novel idea of random path, and an extended method is also presented, to enhance the structural similarity when two vertices are completely disconnected. We provide theoretical proofs for the error-bound and confidence of the proposed algorithm. We perform extensive empirical study and show that our algorithm can obtain top-k similar vertices for any vertex in a network approximately 300x faster than state-of-the-art methods. We also use identity resolution and structural hole spanner finding, two important applications in social networks, to evaluate the accuracy of the estimated similarities. Our experimental results demonstrate that the proposed algorithm achieves clearly better performance than several alternative methods.
Blade: A Data Center Garbage Collector
Venue: arXiv cs.DC 2015
Year: 2015

An increasing number of high-performance distributed systems are written in garbage collected languages. This removes a large class of harmful bugs from these systems. However, it also introduces high tail-latency do to garbage collection pause times. We address this problem through a new technique of garbage collection avoidance which we call Blade. Blade is an API between the collector and application developer that allows developers to leverage existing failure recovery mechanisms in distributed systems to coordinate collection and bound the latency impact. We describe Blade and implement it for the Go programming language. We also investigate two different systems that utilize Blade, a HTTP load-balancer and the Raft consensus algorithm. For the load-balancer, we eliminate any latency introduced by the garbage collector, for Raft, we bound the latency impact to a single network round-trip, (48 μs in our setup). In both cases, latency at the tail using Blade is up to three orders of magnitude better.
A Simple Parallel Implementation of Interaction Nets in Haskell
Venue: arXiv cs.PL 2015
Year: 2015

Due to their "inherent parallelism", interaction nets have since their introduction been considered as an attractive implementation mechanism for functional programming. We show that a simple highly-concurrent implementation in Haskell can achieve promising speed-ups on multiple cores.
Towards Practical Graph-Based Verification for an Object-Oriented Concurrency Model
Venue: arXiv cs.SE 2015
Year: 2015

To harness the power of multi-core and distributed platforms, and to make the development of concurrent software more accessible to software engineers, different object-oriented concurrency models such as SCOOP have been proposed. Despite the practical importance of analysing SCOOP programs, there are currently no general verification approaches that operate directly on program code without additional annotations. One reason for this is the multitude of partially conflicting semantic formalisations for SCOOP (either in theory or by-implementation). Here, we propose a simple graph transformation system (GTS) based run-time semantics for SCOOP that grasps the most common features of all known semantics of the language. This run-time model is implemented in the state-of-the-art GTS tool GROOVE, which allows us to simulate, analyse, and verify a subset of SCOOP programs with respect to deadlocks and other behavioural properties. Besides proposing the first approach to verify SCOOP programs by automatic translation to GTS, we also highlight our experiences of applying GTS (and especially GROOVE) for specifying semantics in the form of a run-time model, which should be transferable to GTS models for other concurrent languages and libraries.
Gradient of Probability Density Functions based Contrasts for Blind Source Separation (BSS)
Venue: arXiv cs.LG 2015
Year: 2015

The article derives some novel independence measures and contrast functions for Blind Source Separation (BSS) application. For the $k^{th}$ order differentiable multivariate functions with equal hyper-volumes (region bounded by hyper-surfaces) and with a constraint of bounded support for $k>1$, it proves that equality of any $k^{th}$ order derivatives implies equality of the functions. The difference between product of marginal Probability Density Functions (PDFs) and joint PDF of a random vector is defined as Function Difference (FD) of a random vector. Assuming the PDFs are $k^{th}$ order differentiable, the results on generalized functions are applied to the independence condition. This brings new sets of independence measures and BSS contrasts based on the $L^p$-Norm, $ p \geq 1$ of - FD, gradient of FD (GFD) and Hessian of FD (HFD). Instead of a conventional two stage indirect estimation method for joint PDF based BSS contrast estimation, a single stage direct estimation of the contrasts is desired. The article targets both the efficient estimation of the proposed contrasts and extension of the potential theory for an information field. The potential theory has a concept of reference potential and it is used to derive closed form expression for the relative analysis of potential field. Analogous to it, there are introduced concepts of Reference Information Potential (RIP) and Cross Reference Information Potential (CRIP) based on the potential due to kernel functions placed at selected sample points as basis in kernel methods. The quantities are used to derive closed form expressions for information field analysis using least squares. The expressions are used to estimate $L^2$-Norm of FD and $L^2$-Norm of GFD based contrasts.
Discrete All-Pay Bidding Games
Venue: arXiv cs.GT 2015
Year: 2015

In an all-pay auction, only one bidder wins but all bidders must pay the auctioneer. All-pay bidding games arise from attaching a similar bidding structure to traditional combinatorial games to determine which player moves next. In contrast to the established theory of single-pay bidding games, optimal play involves choosing bids from some probability distribution that will guarantee a minimum probability of winning. In this manner, all-pay bidding games wed the underlying concepts of economic and combinatorial games. We present several results on the structures of optimal strategies in these games. We then give a fast algorithm for computing such strategies for a large class of all-pay bidding games. The methods presented provide a framework for further development of the theory of all-pay bidding games.
A Deep Embedding Model for Co-occurrence Learning
Venue: arXiv cs.LG 2015
Year: 2015

Co-occurrence Data is a common and important information source in many areas, such as the word co-occurrence in the sentences, friends co-occurrence in social networks and products co-occurrence in commercial transaction data, etc, which contains rich correlation and clustering information about the items. In this paper, we study co-occurrence data using a general energy-based probabilistic model, and we analyze three different categories of energy-based model, namely, the $L_1$, $L_2$ and $L_k$ models, which are able to capture different levels of dependency in the co-occurrence data. We also discuss how several typical existing models are related to these three types of energy models, including the Fully Visible Boltzmann Machine (FVBM) ($L_2$), Matrix Factorization ($L_2$), Log-BiLinear (LBL) models ($L_2$), and the Restricted Boltzmann Machine (RBM) model ($L_k$). Then, we propose a Deep Embedding Model (DEM) (an $L_k$ model) from the energy model in a \emph{principled} manner. Furthermore, motivated by the observation that the partition function in the energy model is intractable and the fact that the major objective of modeling the co-occurrence data is to predict using the conditional probability, we apply the \emph{maximum pseudo-likelihood} method to learn DEM. In consequence, the developed model and its learning method naturally avoid the above difficulties and can be easily used to compute the conditional probability in prediction. Interestingly, our method is equivalent to learning a special structured deep neural network using back-propagation and a special sampling strategy, which makes it scalable on large-scale datasets. Finally, in the experiments, we show that the DEM can achieve comparable or better results than state-of-the-art methods on datasets across several application domains.
Data Science and Ebola
Venue: arXiv cs.AI 2015
Year: 2015

Data Science---Today, everybody and everything produces data. People produce large amounts of data in social networks and in commercial transactions. Medical, corporate, and government databases continue to grow. Sensors continue to get cheaper and are increasingly connected, creating an Internet of Things, and generating even more data. In every discipline, large, diverse, and rich data sets are emerging, from astrophysics, to the life sciences, to the behavioral sciences, to finance and commerce, to the humanities and to the arts. In every discipline people want to organize, analyze, optimize and understand their data to answer questions and to deepen insights. The science that is transforming this ocean of data into a sea of knowledge is called data science. This lecture will discuss how data science has changed the way in which one of the most visible challenges to public health is handled, the 2014 Ebola outbreak in West Africa.
Quantitative Analysis of Whether Machine Intelligence Can Surpass Human Intelligence
Venue: arXiv cs.AI 2015
Year: 2015

Whether the machine intelligence can surpass the human intelligence is a controversial issue. On the basis of traditional IQ, this article presents the Universal IQ test method suitable for both the machine intelligence and the human intelligence. With the method, machine and human intelligences were divided into 4 major categories and 15 subcategories. A total of 50 search engines across the world and 150 persons at different ages were subject to the relevant test. And then, the Universal IQ ranking list of 2014 for the test objects was obtained.
Antenna Array Signal Processing for Quaternion-Valued Wireless Communication Systems
Venue: arXiv cs.SY 2015
Year: 2015

Quaternion-valued wireless communication systems have been studied in the past. Although progress has been made in this promising area, a crucial missing link is lack of effective and efficient quaternion-valued signal processing algorithms for channel equalisation and beamforming. With most recent developments in quaternion-valued signal processing, in this work, we fill the gap to solve the problem and further derive the quaternion-valued Wiener solution for block-based calculation.