Miracle-dz/UniFAR-FTUs huggingface dataset:
Multistage preictal seizure analysis using Hidden Markov Model

Epileptic seizures may be described as the population entrainment of low complexity activities. Multistage seizure detection implemented using hidden Markov model (HMM) was proposed for the analysis of intracranial EEG recordings of epilepsy patients. The number of hidden states and the number of Gaussian clusters of the HMM were obtained using an unsupervised method. Multiple dynamic stages had been found leading to ictal activity. High sensitivity and specificity can be achieved based on receiver operating characteristic curve analysis (with area > 0.85). Spatial generalisation features study suggested that HMM framework is independent of the subject and the testing recording location.
Convex Decomposition of Polyhedrons Using Occlusion Relations Among Edges/Facets

This paper presents a new method for convex decomposition of polyhedrons.In comparison with existing methods,the new method can improve the efficiency greatly with complexities reduced in many aspects of execution time,storage and added new vertices,and it is more advantageous in treating the polyhedrons with reflex edges in higher numbers.Its strategy is to gradually decompose a polyhedron in local operations by the occlusion relationships between facets and edges along certain orthogonal directions.In treating general polyhedrons in practice,the new method has its time and storage complexities both in O(n)approximately,and its produced new vertices are in a number not more than O(r+n~(0.5)),here n is the vertex number and r is the reflex edge number.By testing a large number of complex polyhedrons,it shows that,compared with the popularly used"cutting splitting"method,this new method can run 14～120 times faster,reduce the storage requirement to 1/2.3～1/7.4,and reduce the new points to at most 1/28,and even needs no new point in some cases.Because most convex polyhedrons decomposed by the method are tetrahedrons,the resulted convex polyhedrons by the new method are more than those by the"cutting splitting"method.However,if convex polyhedrons are required to be further decomposed to tetrahedrons,the new method can produce much fewer tetrahedrons,due to much fewer added vertices for decomposition by the new method.Besides,the new method can be conveniently used to treat the polyhedrons with holes,or even the non-manifold polyhedrons that contain isolated facets,edges or vertices.
Path Planning for Autonomous Robots Using Neural Networks

The realization that some forms of artificial potential fields (APF) for path planning can be developed by parallel distributed means has prompted research efforts in applying neural networks to the problem of generating these fields. This paper presents a neural network paradigm capable of exhibiting behavior that is useful for developing a variety of artificial potential fields for evaluating paths for autonomous robots. The neural network is called the Wave Expansion Neural Network or WENN. The WENN develops a topologically ordered neural map of the robot's environment, specifying the obstacles and the target configuration. The WENN neuron activity distribution is used as an APF to evaluate paths for the robot. The results are supported by computer simulations to illustrate the performance of the network.
Micro Expression Recognition Using the Eulerian Video Magnification Method

In this paper we propose a new approach for facial micro expressions recognition. For this purpose the Eulerian Video Magnification (EVM) method is used to retrieve the subtle motions of the face. The results of this method are obtained as in the magnified images sequence. In this study the numerical tests are performed on two databases: Spontaneous Micro expression (SMIC) and Category and Sourcing Managers Executive (CASME). We evaluate our proposed method in two phases using the eigenface method. In phase 1 we recognize the type of a micro expression, for example emotional versus unemotional in SMIC database. Phase 2 classifies the recognized micro expression as negative versus positive in SMIC database and happiness versus disgust in CASME database. The results show that the eigenface method by the EVM method for the retrieval of subtle motions of the face increases the performance of micro expression recognition. Moreover, the proposed approach is more accurate and promising than the previous works in micro expressions recognition.
Isotropic design optimization of robotic manipulators using a genetic algorithm method

In this paper, the kinematic isotropy has been considered as the performance evaluation criterion for optimal design of robotic manipulators. The global isotropy index (or GII) has been used as the measure of isotropy, which is based on the robot's behavior in its entire workspace. A "genetic algorithm" has been developed to solve the minimax optimization formulation of robot design in order to find the optimal design parameters such as link lengths of the best isotropic robot configurations at optimal working points of the end-effector. The algorithm has been implemented to optimize globally the throughout of the whole robot workspace. The method is demonstrated for 2-DOF robotic manipulators.
Simplification of a specific two-hidden-layer feedforward networks

A specific two-hidden-layer feedforward networks (TLFNs) proposed by G.B. Huang (2003) is presented in this paper. A method is introduced to simplify the structure of the TLFNs by introducing a new type of quantizers that unite two previous neurons A/sup (p)/ and B/sup (p)/ into a single neuron. Those new quantizers choose a special type of function as the neural network's activation function, which leads to the new TLFNs with 2/spl radic/((m+1)N) hidden neurons can learn N distinct samples (x/sub i/, t/sub i/) with negligibly small error, where m is the number of output neurons, and unlike Huang's TLFNs require 2/spl radic/((m+2)N) hidden neurons. Moreover, it is not necessary to estimate the quantizer value U defined in Huang's TLFNs, which is fixed in our new model of TLFNs. It can reduce significantly and markedly the complexity and computation of neural networks.
A Design Approach Dedicated to Pattern-Based and Conflict-Free Parallel Memory System

Hardware design of parallel interleaver architectures for TurboCodes (TB) and Low Density Parity Check (LDPC) is one of the hottest topics in channel coding domain. The throughput constraints that have to be supported by these architectures require avoiding memory collision (i.e. simultaneous read/write accesses should not target the same memory bank). Parallel TB or LDPC decoder typically includes several processing elements (PE) and memory banks (MB), an interconnection network allowing PEs to concurrently access MBs and a controller. In this paper we propose a methodology that finds collision-free mapping in the parallel memory banks and also respects user defined interconnection network. This allows to generate regular architectures where the steering logic and the controller complexities have been optimized.
Method for estimating and modeling age and gender using facial image processing

A preliminary questionnaire (enquete) examination of how effectively facial images could be used for gender and age estimations was executed by using 300 different faces and 21 examinees. Wrinkles appearing in the face and the shape and size of the facial parts are selected to model the age and gender estimation in this research based on this enquete. An image processing algorithm for wrinkle modeling is proposed. In addition, a method for making relationships between faces and their keywords is proposed by using latent semantic indexing. An efficient interface for displaying the relationships among keywords and facial images is introduced with 2D/3D-feature space defined by the indexing.
An introduction to a taxonomy of information privacy in collaborative environments

Information Privacy is becoming an increasingly important field of research with many new definitions and terminologies. Along similar rates of increase are the use, uptake and expansion of Collaborative Environments. There is a need for a better understanding and classification of information privacy concepts and terms. This is especially true in relation to their affect on the operation, creation and ongoing administration of Collaborative Environments. The knowledge provided from a information privacy taxonomy can be used to formulate better information privacy policies, practices, and privacy enhancing technologies (PET's). This paper provides a Information Privacy taxonomy for Collaborative Environments.
An Incrementally Deployable Data Centric Architechture Using Particle Swarm Optimization Algorithm

Today’s Internet users are majorly interested in accessing data only from server i.e. interested in data networks instead of host networks. But the current Internet architectures are host centric network and it is not practically possible to deploy a pure data-centric architecture. Therefore this paper describes such architecture which is future proof DCNA for Internet which is incrementally deployable that supports both data centric and host centric services having features like multi-homing and mobility. Keywords:-Ad-hoc network, incrementally deployable, future proof. Keywords: Ad-hoc network, Dyanamic route guidance, future proof, incrementally deployable.
Intelligent control of humanoid manipulation with uncertain weight objects

This article addresses a new intelligent full-body balancing control framework on a full-sized humanoid robot for stably tracking a given reference while grasping onto objects with unknown weight information. Dealing with external uncertainty from the inherent stability of humanoid is an interesting challenge for developing stable manipulation performance during their operation. To this end, intelligent fuzzy-proportional–integral control scheme where parameters are adaptively adjusted to respond to those uncertainties is proposed in this study. Application of momentum information at the torso as additional information is to make the improvement of the manipulation capability on a humanoid over an unknown weight object. Results from both experiment and simulation over a range of unknown weight objects on humanoid hand verified that the proposed scheme is more representative of the intelligent control on humanoid operation with external uncertainties.
A unified architecture to establish knowledge network

A unified architecture is proposed for the new emerging concept of knowledge networks. This architecture is based on a multi-aspect view to the problem of establishing such a network in organizations. By integrating infrastructure, knowledge and business layers mounted on two aspects of human-based and organizational supportive conditions in a unified framework, we proposed a novel architecture that contains intra-layer built-in assessment mechanisms as well. These assessment mechanisms ensure the gradual refinement of the network as the structure of each layer becomes fixed. In this paper, it has been discussed that how a multi-layer architecture with minimum amount of dependency among layers (that has the ability of interaction in business and knowledge layers) may facilitate knowledge management processes. It can also give some guidelines on how to establish such networks in inter-organization level. In order to demonstrate the applicability of this architecture, in the last section of the paper, we have shown that how the development guidelines of the common KM sub-systems -here a Document Management System- based on one of the most creditable standards of this domain -here ISO 15489- can be extracted in a straightforward manner through employing the proposed architecture of the paper with specific emphasis on the independencies among layers and the proposed self-assessment mechanisms.
Performance of a direct conversion receiver with pi /4-DQPSK modulated signal

Analog implementation of direct conversion receivers suffers from a variety of detriments such as DC offset, which can be caused by the self-mixing of on and off channel signals, carrier feedthrough from high local oscillator signals, and phase and gain imbalance in the in-phase and quadrature-phase branches. The performance of a direct conversion receiver is investigated. The performance degradation due to various impairments is discussed, and simulated and measured results are compared. Imperfections in the receiver front end as well as degradations due to spurious signals are studied. Signal-to-noise penalty curves for four specific imperfections are presented and possible solutions are recommended to minimize their effects. The results presented are based on a raised cosine Nyquist channel with a roll-off of 0.35. <<ETX>>
Modelo de un algoritmo eficiente para el alineamiento de secuencias biomoleculares basado en programación paralela

At present there has been a considerable effort to develop algorithms that compare the sequences of biological macromolecules (proteins, DNA and RNA), which aims to detect evolutionary relationships both structural and functional. This is the main problem of computational biology. These tasks are currently performed bioinformatics tools that have been developed with sequential algorithms. Dynamic programming, both local alignment algorithms (Smith-Waterman) and global alignment (Needleman-Wunsch) determining optimal alignment of two sequences. Currently the computers that have more one core are available for the common user, and to use multiple computer processors need to know parallel programming paradigms. This paper presents a new algorithmic proposed for global alignment using parallel programming, this requires a new reformulation of the algorithm of Needleman Wunsh. The implementation of parallel algorithm has required to make a matrix filled with scores for their antidiagonales with all available processors. The software used for this was the C # with the library TPL (Task Parallel Library). The application compares Needleman-Wunsch algorithm with this new algorithm, checking the response time. The results show that the proposed parallel algorithm reduces the response time in comparison with the global alignment algorithm of Needleman-Wunsch.
Character Alignment for High-Capacity PCM Systems Using MS43 Line Code

Alphabetic pseudoternary codes, which are being used largely for high-capacity pulse-code modulation (PCM) transmission, require character alignment of the received ternary sequence for correct conversion from pseudoternary to straight binary. In this paper a method and a strategy of character alignment for MS43 line code are proposed. The method is based on the recognition of the constraints and correlations involved by the conversion rules of MS43; the strategy, which is similar to that already used for frame alignment in PCM time-division-multiplex (PCM-TDM) systems, allows limiting of the effects of simulations and line errors on the processes of loss and recovery of character alignment. Statistical properties of these processes are analyzed both theoretically and by computer simulation. The results are used to design the character alignment strategy for a 34.368 Mbit/s PCM system using MS43 as line code.
A Many-objective Ant Colony Optimization applied to the Traveling Salesman Problem

Evolutionary algorithms present performance drawbacks when applied to Many-objective Optimization Problems (MaOPs). In this work, a novel approach based on Ant Colony Optimization theory (ACO), denominated ACO λ base-p algorithm, is proposed in order to handle Manyobjective instances of the well-known Traveling Salesman Problem (TSP). The proposed algorithm was applied to several Many-objective TSP instances, verifying the quality of the experimental results using the Hypervolume metric. A comparison with other state-of-the-art Multi Objective ACO algorithms as MAS, M3AS and MOACS as well as NSGA2 evolutionary algorithm was made, verifying that the best experimental results were obtained when the proposed algorithm was used, proving a good applicability to MaOPs
Multi-Layer Perceptron Having Neuro-Glia Network

In this paper, we propose a Multi-Layer Perceptron (MLP) having neuro-glia network. Neuro-glia network is a network of model which glial cells interrelate neurons in the brain. Glial cells aﬀect to neurons’ thresholds and neurons aﬀect to glial cells’ output. By computer simulations for solving Two Spiral Problem (TSP), we conﬁrm that the proposed MLP having neuro-glia network gains better performance than the conventional MLP.
Making sense of learner and learning Big Data: reviewing five years of Data Wrangling at the Open University UK

ABSTRACT Most distance learning institutions collect vast amounts of learning data. Making sense of this ‘Big Data’ can be a challenge, in particular when data are stored at different data warehouses and require advanced statistical skills to interpret complex patterns of data. As a leading institute on learning analytics, the Open University UK instigated in 2012 a Data Wrangling initiative. This provided every Faculty with a dedicated academic with expertise data analysis and whose task is to provide strategic, pedagogical and sense-making advice to staff and senior management. Given substantial changes within the OU (e.g. new Faculty structure, real-time dashboards, two large-scale adoptions of predictive analytics approaches, increased reliance on analytics), this embedded case study provides an in-depth review of lessons learned of five years of data wrangling. We will elaborate on the design of the new structure, its strengths and potential weaknesses, and affordances to be adopted by other institutions.
Resolución automática de la anáfora indirecta en el español

Al iniciar la investigación se estableció la necesidad de descubrir los marcadores que identificaban la presencia de la anáfora indirecta en los textos escritos; conforme avanzaba el análisis fue necesario profundizar en la relación existente entre la sintaxis, la semántica y la pragmática cuando participan activamente en el discurso textual; con el fin de integrar el contexto lingüístico como parte del discurso comunicativo se requirió la identificación del rol de las expresiones referenciales, marcadas por los determinantes en las frases nominales; finalmente el descubrimiento de la fuerte interrelación entre la referencia, correferencia y la anáfora permitió desarrollar un modelo de escenario que identifica más claramente la presencia de la anáfora indirecta.
Scalable discovery of networked data : Algorithms, Infrastructure, Applications

The OpenKnowledge project aims at knowledge sharing through open and flexible peer interactions. Within this project, we are developing a system that supports searching, developing and sharing of interactions/workflows consisting of roles implemented by software that can be shared and executed by peers. Part of this system is a discovery service, which will be the focus of this chapter. This service aspires to fulfill the above requirements featuring a Peer-to-Peer architecture and Distributed Hash Tables (DHTs) to achieve robustness through redundancy and scalability through decentralization. Resources are discovered using a set of attributevalue pairs. A straightforward DHT-based approach that creates a distributed inverted index suffers from a linear increase of messages and replicas with the number of attributes. We try to reduce this number by proposing an efficient multi-attribute routing algorithm. We emulate and test our implementation on the DAS-2 distributed supercomputer.
Intelligent speculation for pipelined multithreading

In recent years, microprocessor manufacturers have shifted their focus from single-core to multi-core processors. Since many of today’s applications are single-threaded and since it is likely that many of tomorrow’s applications will have far fewer threads than there will be processor cores, automatic thread extraction is an essential tool for effectively leveraging today’s multi-core and tomorrow’s many-core processors. A recently proposed technique, Decoupled Software Pipelining (DSWP), has demonstrated promise by partitioning loops into long-running threads organized into a pipeline. Using a pipeline organization and execution decoupled by inter-core communication queues, DSWP offers increased execution efficiency that is largely independent of inter-core communication latency and variability in intra-thread performance. This dissertation extends the pipelined parallelism paradigm with speculation. Using speculation, dependences that manifest infrequently or are easily predictable can be safely ignored by the compiler allowing it to carve more, and better balanced, thread-based pipeline stages from a single thread of execution. Prior speculative threading proposals were obligated to speculate most, if not all, loop-carried dependences to squeeze the code segment under consideration into the mold required by the parallelization paradigm. Unlike those techniques, this dissertation demonstrates that speculation need only break the longest few dependence cycles to enhance the applicability and scalability of the pipelined multi-threading paradigm. By speculatively breaking these cycles, instructions that were formerly restricted to a single thread to ensure decoupling are now free to span multiple threads. To demonstrate the effectiveness of speculative pipelined multi-threading, this dissertation presents the design and experimental evaluation of our fully automatic compiler transformation, Speculative Decoupled Software Pipelining, a speculative extension to DSWP. This dissertation additionally introduces multi-threaded transactional memories to support speculative pipelined multi-threading. Similar to past speculative parallelization approaches, speculative pipelined multi-threading relies on runtime-system support to buffer speculative modifications to memory. However, this dissertation demonstrates that existing proposals to buffer speculative memory state, transactional memories, are insufficient for speculative pipelined multi-threading because the speculative buffers are restricted to a single thread. Further, this dissertation demonstrates that this limitation leads to modularity and composability problems even for transactional programming, thus limiting the potential of that approach also. To surmount these limitations, this thesis introduces multi-threaded transactional memories and presents an initial hardware implementation.
Predictive Control for Linear and Hybrid Systems

Model Predictive Control (MPC), the dominant advanced control approach in industry over the past twenty-five years, is presented comprehensively in this unique book. With a simple, unified approach, and with attention to real-time implementation, it covers predictive control theory including the stability, feasibility, and robustness of MPC controllers. The theory of explicit MPC, where the nonlinear optimal feedback controller can be calculated efficiently, is presented in the context of linear systems with linear constraints, switched linear systems, and, more generally, linear hybrid systems. Drawing upon years of practical experience and using numerous examples and illustrative applications, the authors discuss the techniques required to design predictive control laws, including algorithms for polyhedral manipulations, mathematical and multiparametric programming and how to validate the theoretical properties and to implement predictive control policies. The most important algorithms feature in an accompanying free online MATLAB toolbox, which allows easy access to sample solutions. Predictive Control for Linear and Hybrid Systems is an ideal reference for graduate, postgraduate and advanced control practitioners interested in theory and/or implementation aspects of predictive control.