OpenRaiser/Intern-Atlas huggingface dataset:
Hierarchical Cooperation for Operator-Controlled Device-to-Device Communications: A Layered Coalitional Game Approach
Venue: arXiv cs.NI 2015
Year: 2015

Device-to-Device (D2D) communications, which allow direct communication among mobile devices, have been proposed as an enabler of local services in 3GPP LTE-Advanced (LTE-A) cellular networks. This work investigates a hierarchical LTE-A network framework consisting of multiple D2D operators at the upper layer and a group of devices at the lower layer. We propose a cooperative model that allows the operators to improve their utility in terms of revenue by sharing their devices, and the devices to improve their payoff in terms of end-to-end throughput by collaboratively performing multi-path routing. To help understanding the interaction among operators and devices, we present a game-theoretic framework to model the cooperation behavior, and further, we propose a layered coalitional game (LCG) to address the decision making problems among them. Specifically, the cooperation of operators is modeled as an overlapping coalition formation game (CFG) in a partition form, in which operators should form a stable coalitional structure. Moreover, the cooperation of devices is modeled as a coalitional graphical game (CGG), in which devices establish links among each other to form a stable network structure for multi-path routing.We adopt the extended recursive core, and Nash network, as the stability concept for the proposed CFG and CGG, respectively. Numerical results demonstrate that the proposed LCG yields notable gains compared to both the non-cooperative case and a LCG variant and achieves good convergence speed.
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Venue: arXiv cs.NI 2015
Year: 2015

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs.
On Enhancing The Performance Of Nearest Neighbour Classifiers Using Hassanat Distance Metric
Venue: arXiv cs.LG 2015
Year: 2015

We showed in this work how the Hassanat distance metric enhances the performance of the nearest neighbour classifiers. The results demonstrate the superiority of this distance metric over the traditional and most-used distances, such as Manhattan distance and Euclidian distance. Moreover, we proved that the Hassanat distance metric is invariant to data scale, noise and outliers. Throughout this work, it is clearly notable that both ENN and IINC performed very well with the distance investigated, as their accuracy increased significantly by 3.3% and 3.1% respectively, with no significant advantage of the ENN over the IINC in terms of accuracy. Correspondingly, it can be noted from our results that there is no optimal algorithm that can solve all real-life problems perfectly; this is supported by the no-free-lunch theorem
A new algorithm for multiplying two Dirac numbers
Venue: arXiv cs.DS 2015
Year: 2015

In this work a rationalized algorithm for Dirac numbers multiplication is presented. This algorithm has a low computational complexity feature and is well suited to FPGA implementation. The computation of two Dirac numbers product using the naïve method takes 256 real multiplications and 240 real additions, while the proposed algorithm can compute the same result in only 88 real multiplications and 256 real additions. During synthesis of the discussed algorithm we use the fact that Dirac numbers product may be represented as vector-matrix product. The matrix participating in the product has unique structural properties that allow performing its advantageous decomposition. Namely this decomposition leads to significant reducing of the computational complexity.
Inverse Renormalization Group Transformation in Bayesian Image Segmentations
Venue: arXiv cs.CV 2015
Year: 2015

A new Bayesian image segmentation algorithm is proposed by combining a loopy belief propagation with an inverse real space renormalization group transformation to reduce the computational time. In results of our experiment, we observe that the proposed method can reduce the computational time to less than one-tenth of that taken by conventional Bayesian approaches.
Fast forward feature selection for the nonlinear classification of hyperspectral images
Venue: arXiv cs.CV 2015
Year: 2015

A fast forward feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM) classifier. GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectral features that maximizes an estimation of the classification rate. The estimation is done using the k-fold cross validation. In order to perform fast in terms of computing time, an efficient implementation is proposed. First, the GMM can be updated when the estimation of the classification rate is computed, rather than re-estimate the full model. Secondly, using marginalization of the GMM, sub models can be directly obtained from the full model learned with all the spectral features. Experimental results for two real hyperspectral data sets show that the method performs very well in terms of classification accuracy and processing time. Furthermore, the extracted model contains very few spectral channels.
Learning to Recognize Pedestrian Attribute
Venue: arXiv cs.CV 2015
Year: 2015

Learning to recognize pedestrian attributes at far distance is a challenging problem in visual surveillance since face and body close-shots are hardly available; instead, only far-view image frames of pedestrian are given. In this study, we present an alternative approach that exploits the context of neighboring pedestrian images for improved attribute inference compared to the conventional SVM-based method. In addition, we conduct extensive experiments to evaluate the informativeness of background and foreground features for attribute recognition. Experiments are based on our newly released pedestrian attribute dataset, which is by far the largest and most diverse of its kind.
Sidecoin: a snapshot mechanism for bootstrapping a blockchain
Venue: arXiv cs.CR 2015
Year: 2015

Sidecoin is a mechanism that allows a snapshot to be taken of Bitcoin's blockchain. We compile a list of Bitcoin's unspent transaction outputs, then use these outputs and their corresponding balances to bootstrap a new blockchain. This allows the preservation of Bitcoin's economic state in the context of a new blockchain, which may provide new features and technical innovations.
Impact of the Temperature and Humidity Variations on Link Quality of xm1000 Mote Sensors
Venue: arXiv cs.NI 2015
Year: 2015

The core motivations of deploying a sensor network for a specific application come from the autonomy of sensors, their reduced size, and their capabilities for computing and communicating in a short range. However, many challenges for sensor networks still exist: minimizing energy consumption, and ensuring the performance of communication that may be affected by many parameters. The work described in this paper covers mainly the analysis of the impact of the temperature and humidity variations on link quality of XM1000 operating under TinyOS. Two-way ANOVA test has been applied and the obtained results show that both the temperature and humidity variations impact RSSI.
A Novel Technique for Grading of Dates using Shape and Texture Features
Venue: arXiv cs.CV 2015
Year: 2015

This paper presents a novel method to grade the date fruits based on the combination of shape and texture features. The method begins with reducing the specular reflection and small noise using a bilateral filter. Threshold based segmentation is performed for background removal and fruit part selection from the given image. Shape features is extracted using the contour of the date fruit and texture features are extracted using Curvelet transform and Local Binary Pattern (LBP) from the selected date fruit region. Finally, combinations of shape and texture features are fused to grade the dates into six grades. k-Nearest Neighbour(k-NN) classifier yields the best grading rate compared to other two classifiers such as Support Vector Machine (SVM) and Linear Discriminant(LDA) classifiers. The experiment result shows that our technique achieves highest accuracy.
Experimental Research Data Quality In Materials Science
Venue: arXiv cs.DB 2015
Year: 2015

In materials sciences, a large amount of research data is generated through a broad spectrum of different experiments. As of today, experimental research data including meta-data in materials science is often stored decentralized by the researcher(s) conducting the experiments without generally accepted standards on what and how to store data. The conducted research and experiments often involve a considerable investment from public funding agencies that desire the results to be made available in order to increase their impact. In order to achieve the goal of citable and (openly) accessible materials science experimental research data in the future, not only an adequate infrastructure needs to be established but the question of how to measure the quality of the experimental research data also to be addressed. In this publication, the authors identify requirements and challenges towards a systematic methodology to measure experimental research data quality prior to publication and derive different approaches on that basis. These methods are critically discussed and assessed by their contribution and limitations towards the set goals. Concluding, a combination of selected methods is presented as a systematic, functional and practical quality measurement and assurance approach for experimental research data in materials science with the goal of supporting the accessibility and dissemination of existing data sets.
Interference Spins: Scheduling of Multiple Interfering Two-Way Wireless Links
Venue: arXiv cs.IT 2015
Year: 2015

Two-way is a dominant mode of communication in wireless systems. Departing from the tradition to optimize each transmission direction separately, recent work has demonstrated that, for time-division duplex (TDD) systems, optimizing the schedule of the two transmission directions depending on traffic load and interference condition leads to performance gains. In this letter, a general network of multiple interfering two-way links is studied under the assumption of a balanced load in the two directions for each link. Using the notion of interference spin, we introduce an algebraic framework for the optimization of two-way scheduling, along with an efficient optimization algorithm that is based on the pruning of a properly defined topology graph and dynamic programming. Numerical results demonstrate multi-fold rate gains with respect to baseline solutions, especially for worst-case (5%-ile) rates.
Application and network layers design for wireless sensor network to supervise chemical active product warehouse
Venue: arXiv cs.NI 2015
Year: 2015

Wireless sensor networks have profound effects on many application fields like security management which need an immediate and fast system reaction. Indeed, the monitoring of a dangerous product warehouse is a major issue in chemical industry field. This paper describes the design of chemical warehouse security system using the concept of active products and wireless sensor networks. A security application layer is developed to supervise and exchange messages between nodes and the control center to prevent industrial accident. Different security rules are proposed on this layer to monitor the internal state and incompatible products distance. If a critical event is detected, the application generates alert message which need a short end to end delay and low packet loss rate constraints by network layer. Thus, a QoS routing protocol is also developed in the network layer. The proposed solution is implemented in Castalia/OMNeT++ simulator. Simulation results show that the system reacts perfectly for critical event and can meet the QoS constraints of alert message.
Requirement analysis, Architectural design and Formal verification of a multi-agent based University Information Management System
Venue: arXiv cs.SE 2015
Year: 2015

This paper presents an approach based on the analysis, design, and formal verification of a multi-agent based university Information Management System (IMS). University IMS accesses information, creates reports and facilitates teachers as well as students. An orchestrator agent manages the coordination between all agents. It also manages the database connectivity for the whole system. The proposed IMS is based on BDI agent architecture, which models the system based on belief, desire, and intentions. The correctness properties of safety and liveness are specified by First-order predicate logic.
Arabic Text Categorization Algorithm using Vector Evaluation Method
Venue: arXiv cs.IR 2015
Year: 2015

Text categorization is the process of grouping documents into categories based on their contents. This process is important to make information retrieval easier, and it became more important due to the huge textual information available online. The main problem in text categorization is how to improve the classification accuracy. Although Arabic text categorization is a new promising field, there are a few researches in this field. This paper proposes a new method for Arabic text categorization using vector evaluation. The proposed method uses a categorized Arabic documents corpus, and then the weights of the tested document's words are calculated to determine the document keywords which will be compared with the keywords of the corpus categorizes to determine the tested document's best category.
Roman Urdu Opinion Mining System (RUOMiS)
Venue: arXiv cs.CL 2015
Year: 2015

Convincing a customer is always considered as a challenging task in every business. But when it comes to online business, this task becomes even more difficult. Online retailers try everything possible to gain the trust of the customer. One of the solutions is to provide an area for existing users to leave their comments. This service can effectively develop the trust of the customer however normally the customer comments about the product in their native language using Roman script. If there are hundreds of comments this makes difficulty even for the native customers to make a buying decision. This research proposes a system which extracts the comments posted in Roman Urdu, translate them, find their polarity and then gives us the rating of the product. This rating will help the native and non-native customers to make buying decision efficiently from the comments posted in Roman Urdu.
Online Computation of Abelian Runs
Venue: arXiv cs.FL 2015
Year: 2015

Given a word $w$ and a Parikh vector $\mathcal{P}$, an abelian run of period $\mathcal{P}$ in $w$ is a maximal occurrence of a substring of $w$ having abelian period $\mathcal{P}$. We give an algorithm that finds all the abelian runs of period $\mathcal{P}$ in a word of length $n$ in time $O(n\times |\mathcal{P}|)$ and space $O(σ+|\mathcal{P}|)$.
Median evidential c-means algorithm and its application to community detection
Venue: arXiv cs.AI 2015
Year: 2015

Median clustering is of great value for partitioning relational data. In this paper, a new prototype-based clustering method, called Median Evidential C-Means (MECM), which is an extension of median c-means and median fuzzy c-means on the theoretical framework of belief functions is proposed. The median variant relaxes the restriction of a metric space embedding for the objects but constrains the prototypes to be in the original data set. Due to these properties, MECM could be applied to graph clustering problems. A community detection scheme for social networks based on MECM is investigated and the obtained credal partitions of graphs, which are more refined than crisp and fuzzy ones, enable us to have a better understanding of the graph structures. An initial prototype-selection scheme based on evidential semi-centrality is presented to avoid local premature convergence and an evidential modularity function is defined to choose the optimal number of communities. Finally, experiments in synthetic and real data sets illustrate the performance of MECM and show its difference to other methods.
Implementation of a Stream Cipher Based on Bernoulli's Map
Venue: arXiv cs.CR 2015
Year: 2015

A stream cipher was implemented on a FPGA. The keystream, for some authors the most important element, was developed using an algorithm based on Bernoullis chaotic map. When dynamic systems are digitally implemented, a normal degradation appears and disturbs their behavior; for such reason, a mechanism was needed. The proposed mechanism gives a solution for degradation issue and its implementation is not complicated. Finally, the implemented cipher includes 8 stages and 2 pseudo-random number generators (PRNG), such cipher was tested using NIST testes. Once its designing stage, it was implemented using a developing FPGA board.
Implementation of Auto Monitoring and Short-Message-Service System via GSM Modem
Venue: arXiv cs.CV 2015
Year: 2015

Auto-Monitoring and Short-Messaging-Service System is a real-time monitoring system for any critical operational environments. It detects an undesired event occurring in the environment, generates an alert with detailed message and sends it to the user to prevent hazards. This system employs a Friendly ARM as main controller while, sensors and terminals to interact with the real world. A GSM network is utilized to bridge the communication between monitoring system and user. This paper presents details of prototyping the system.
GammaCHI: a package for the inversion and computation of the gamma and chi-square cumulative distribution functions (central and noncentral)
Venue: arXiv cs.MS 2015
Year: 2015

A Fortran 90 module (GammaCHI) for computing and inverting the gamma and chi-square cumulative distribution functions (central and noncentral) is presented. The main novelty of this package are the reliable and accurate inversion routines for the noncentral cumulative distribution functions. Additionally, the package also provides routines for computing the gamma function, the error function and other functions related to the gamma function. The module includes the routines cdfgamC, invcdfgamC, cdfgamNC, invcdfgamNC, errorfunction, inverfc, gamma, loggam, gamstar and quotgamm for the computation of the central gamma distribution function (and its complementary function), the inversion of the central gamma distribution function, the computation of the noncentral gamma distribution function (and its complementary function), the inversion of the noncentral gamma distribution function, the computation of the error function and its complementary function, the inversion of the complementary error function, the computation of: the gamma function, the logarithm of the gamma function, the regulated gamma function and the ratio of two gamma functions, respectively.
A DNF Blocking Scheme Learner for Heterogeneous Datasets
Venue: arXiv cs.DB 2015
Year: 2015

Entity Resolution concerns identifying co-referent entity pairs across datasets. A typical workflow comprises two steps. In the first step, a blocking method uses a one-many function called a blocking scheme to map entities to blocks. In the second step, entities sharing a block are paired and compared. Current DNF blocking scheme learners (DNF-BSLs) apply only to structurally homogeneous tables. We present an unsupervised algorithmic pipeline for learning DNF blocking schemes on RDF graph datasets, as well as structurally heterogeneous tables. Previous DNF-BSLs are admitted as special cases. We evaluate the pipeline on six real-world dataset pairs. Unsupervised results are shown to be competitive with supervised and semi-supervised baselines. To the best of our knowledge, this is the first unsupervised DNF-BSL that admits RDF graphs and structurally heterogeneous tables as inputs.
An Effective Image Feature Classiffication using an improved SOM
Venue: arXiv cs.CV 2015
Year: 2015

Image feature classification is a challenging problem in many computer vision applications, specifically, in the fields of remote sensing, image analysis and pattern recognition. In this paper, a novel Self Organizing Map, termed improved SOM (iSOM), is proposed with the aim of effectively classifying Mammographic images based on their texture feature representation. The main contribution of the iSOM is to introduce a new node structure for the map representation and adopting a learning technique based on Kohonen SOM accordingly. The main idea is to control, in an unsupervised fashion, the weight updating procedure depending on the class reliability of the node, during the weight update time. Experiments held on a real Mammographic images. Results showed high accuracy compared to classical SOM and other state-of-art classifiers.
Load-Modulated Single-RF MIMO Transmission for Spatially Multiplexed QAM Signals
Venue: arXiv cs.IT 2015
Year: 2015

Today, MIMO has become an indispensable scheme for providing significant spectral efficiency in wireless communication and for future wireless system, recently, it goes to two extremes: massive MIMO and single-RF MIMO. This paper, which is put in the latter, utilizes load-modulated arrays with only reactance loads for single-RF transmission of spatially multiplexed QAM signals. To alleviate the need for iterative processes while considering mutual coupling in the compact antenna, we present a novel design methodology for the loading network, which enables the exact computation of the three reactance loads per antenna element and also the perfect matching to the source with the opportunity to select appropriate analog tunable loads. We verify the design methodology by comparing the calculated values for some key parameters with the values from the circuit simulation. In addition, as an evaluation of the proposed architecture, we perform the bit error rate (BER) comparison which shows that our scheme with ideal loading is comparable to the conventional MIMO.
Time Reversal-based Transmissions with Distributed Power Allocation for Two-Tier Networks
Venue: arXiv cs.IT 2015
Year: 2015

Radio pollution and power consumption problems lead to innovative development of green heterogeneous networks (HetNet). Time reversal (TR) technique which has been validated from wide- to narrow-band transmissions is evaluated as one of most prominent linear precoders with superior capability of harvesting signal energy. In this paper, we consider a new HetNet model, in which TR-employed femtocell is proposed to attain saving power benefits whereas macrocell utilizes the beam-forming algorithm based on zero-forcing principle, over frequency selective channels. In the considered HetNet, the practical case of limited signaling information exchanged via backhaul connections is also taken under advisement. We hence organize a distributed power loading strategy, in which macrocell users are treated with a superior priority compared to femtocell users. By Monte-Carlo simulation, the obtained results show that TR is preferred to zero-forcing in the perspective of beamforming technique for femtocell environments due to very high achievable gain in saving energy, and the validity of power loading strategy is verified over multipath channels.