Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Complex Event Processing in Sensor Networks

Mobile systems which include sensor networks, generate a continuous unmatched volume of primitive event streams with various properties where the interior semantic information of these events is generally very limited. Moreover, forwarding all these events to a central processing entity is not favored for limited-resource systems, it would deplete nodes' resources e.g., the energy. The goal of this work is to address these issues by extracting useful information locally, transmitting only this information and avoiding transmission of unnecessary low-level data between nodes. Therefore, an In-Network Distributed Complex Event Processing (INDCEP) based solution is proposed. INDCEP technology is used to perform the processing within the network by pushing complex event processing into network nodes. This research aims to develop a robust and high performance Distributed Complex Event Processing Engine for Mobile Systems (CEPEMS) based on aforementioned technology. It focuses on distributed event detection via disseminating distributed plans into a network of sensor nodes to perform complex event tasks. Furthermore, we present an optimization technique for the distributed queries and cost model to decide which query portions can be executed whether inside sensor nodes or at local CEP engine instance in a centralized point.
Emprego de métodos multicritério em decisões gerenciais: uma análise bibliométrica da produção científica brasileira

The decision-making process has an important role in an organization. The Operations Research, as a branch of science devoted to the decision, offers a variety of methods for managers to evaluate their alternatives. Among the possibilities, the MultiCriteria methods have been widely used in decision-making contexts involving the accounting, administrative and operations organizations. This research describes the analysis of Brazilian scientific literature on the use of MultiCriteria methods applied in the context of management decisions. In order to meet the proposed objective a bibliometric analysis of journals and annals of events in the areas of accounting, administration and production engineering was developed, taking into account the adequacy of thematic and editorial line. The scope of the research period has elapsed from the year 2004 until the year 2016. From an initial sample of 1,087 articles, we obtained a bibliographic portfolio of 623 articles. We identified the MACBETH and AHP methods as more employees, and the existence of networks of researchers in MultiCriteria area.
WEIGHTED SEMANTIC LOAD

In this paper, we propose a new model to calculate the similarity of two sentences. The proposed scheme is based on the amount of semantic load which is shared between two sentences. Since verb is the essential part of a sentence, the main focus of the proposed model is on the verbs of two sentences. We supposed the verb as the anchor of the sentence which carries the most semantic of the sentence. The proposed model depends on part of speech (POS), the partial order of words in the sentence and the words’ senses. The results by Precision and Recall are promising and benchmarks show that the proposed method improves the quality of the retrieved matched sentences.
A hybrid intrusion detection system based on different machine learning algorithms

Recently, Networks have developed quickly during the last many years, and attacks on network infrastructure presently are main threats against network and information security.With quickly growing unauthorized activities in network Intrusion Detection as a part of defense is extremely necessary because traditional firewall techniques cannot provide complete protection against intrusion. There are numerous study in intrusion detection system (IDS) especially with Genetic algorithms (GA) and Support Vector Machine (SVM) but most of them did not get the potential of hybrid SVM using GA. Hence this study aims to hybrid GA and forbids with high accuracy.The paper illustrates the benefit of hybrid SVM via GA also the paper has proven that by enhancing SVM with GA can reduce false alarms and mean square error (MSE) in detecting intrusion.
Computational Assessment of Nested Benders and Augmented Lagrangian Decomposition for Mean-Variance Multistage Stochastic Problems

We consider decomposition approaches for the solution of multistage stochastic programs that appear in financial applications. In particular, we discuss the performance of two algorithms that we test on the mean-variance portfolio optimization problem. The first algorithm is based on a regularized version of Benders decomposition, and we discuss its extension to the quadratic case. The second algorithm is an augmented lagrangian method. Our results indicate that the algorithm based on regularized Benders decomposition is more efficient, which is in line with similar studies performed in the linear setting.
Single-Carrier Frequency-Domain Equalization for Distributed Space-Time Block-Codes with Amplify-and-Forward Relaying

In this paper, we extended the conventional space-time block-codes single-carrier frequency-domain equalization (STBC SC-FDE) to distributed STBC SC-FDE for practical implementation of cooperative networks. We proposed a SC-FDE transmission scheme over frequency-selective fading channels for distributed STBC cooperative network with amplify-and-forward relaying. We showed that the order-two spatial diversity gain achieved by this distributed STBC SC-FDE scheme. Linear receiver processing collects full spatial diversity, while the overall receiver complexity remains comparable to that of conventional without-relay-assisted transmissions over frequency-selective channels. Simulation results were also provided to validate the distributed STBC SC-FDE scheme.
ITERATED EXTENDED KALMAN FILTER WITH ADAPTIVE STATE NOISE ESTIMATION FOR ELECTRICAL IMPEDANCE TOMOGRAPHY

This work proposes and evaluates the feasibility of the iter ated extended Kalman filter, incorporating an adap- tive noise technique, to solve the inverse problem of Electr ical Impedance Tomography. The study aims at the improve- ment of the method, in order to provide faster and more accura te estimates of the dielectric characteristics of a medium, mandatory requisites in medical applications. A cylindric al phantom filled with saline solution, in which a glass-obje ct is immersed, provides experimental data used to validate th e model. The immersion of the glass-object simulates a step- function perturbation on the medium. Current patterns are i njected and potentials are measured through brass electrod es placed around the border of the phantom. A two-phase identifi cation schema is employed. The approach is shown to be effective in determining the position of the glass-objec t and the contact impedances of the electrodes. Convergence criteria are used to assure correctness of the obtained esti mates.
Analysis of the mutual influence of supply mains and electrical consumer in tasks of induction motors diagnostics

This paper presents an approach to singling out the influence of supply mains low-quality, namely, non-sinusoidality and phase asymmetry, on the consumer of electric energy, particularly, induction motor. The main contribution of the paper is developed technique for analysis the composition of constant component of instantaneous power signal which is formed by instantaneous current and voltage harmonics of same frequencies. This analysis allows one to detect source of particular power harmonic: nonlinear consumer or non-sinusoidal and/or unsymmetrical supply mains. This information may be used in tasks of induction motor diagnostics basing on electrical signals spectra analysis to detect harmonics caused by supply mains low-quality and harmonics, caused by faults presence, which help improve reliability of diagnostics result.
Quantum chaotic communication

The attack induced by the eavesdropper will lower the bit rate or even lead to the failure of quantum communication. To solve this problem, we introduce a so-called quantum chaotic communication method to mask and thus actively protect the quantum information by chaotic signal from being attacked. The encoding and decoding processes are realized by transmitting the communicated quantum signals through the auxiliary chaotic devices at both sides of the sender and receiver. The method is applied to two examples, in which continuous-variable coherent states and qubit states are successfully transmitted without being influenced by eavesdropping.
Vital effects in foraminifera do not compromise the use of δ11B as a paleo‐pH indicator: Evidence from modeling

The stable boron isotope composition (δ11B) of fossil foraminiferal shells is used as a paleo-pH recorder and is therefore one of the most promising paleocarbonate chemistry proxies (“paleoacidimetry”). One crucial question regarding this proxy is whether foraminifera record the pH of the bulk seawater or the pH of the microenvironment (diffusive boundary layer, ∼500 μm), which is strongly influenced by life processes. Here we present a novel theoretical approach to address this question by using a diffusion-reaction model. Model results indicate that the δ11B in planktonic foraminifera is primarily controlled by the pH of the microenvironment. We therefore predict that the δ11B of different species (e.g., symbiont-bearing versus symbiont-barren) or of foraminifera grown in the dark and in the light should be offset from the δ11B of inorganic calcite. This theoretical prediction was experimentally confirmed while this paper was written [Honisch et al., 2003]. Most importantly, the model predicts that this offset is constant over a wide pH range. Thus the use of δ11B as a paleo-pH indicator is not compromised through vital effects as modeled here.
Algebraic specification of network security risk management

Existing risk analysis techniques are often hard to handle in real world contexts without the use of appropriate software because of their computational complexity. This makes managers and security analysts use simplified methods to evaluate security investments. However, these methods have been shown to be inefficient in most cases. Therefore, an automated tool for risk management would be of great interest, provided that it allows reasoning on attacks and helps building security decisions. This paper provides an algebraic specification of network security risk management activities. It constitutes a helpful mean to reason about automating the risk assessment process without taking into consideration implementations issues.
Linear Speed-Up for a Parallel Non-Approximate Recasting of Center-Based Clustering Algorithms, including K-Means, K-Harmonic Means, and EM 1

Data clustering is one of the fundamental techniques in scientific data analysis and data mining. It partitions a data set into groups of similar items, as measured by some distance metric. Over the years, data set sizes have grown rapidly with the exponential growth of computer storage and increasingly automated business and manufacturing processes. To cluster such large data sets, efficient parallel algorithms are called for, both to red uce the computation time, and to bring the resources of multiple machines to bear on a given large problem in order to scale up the largest problem size one can handle. We describe a technique for parallelizing center-based data clustering algorithms. The central idea is to communicate only sufficient statistics, yielding linear speed-up with excellent efficiency. The technique does not involve approximation and may be used orthogonally in conjunction with sampling or aggregation-based methods, such as BIRCH, to lessen the quality degradation of their approximation or to handle larger data sets. We have demonstrated elsewhere the decomposition and theoretical speed-up behaviors for three clustering algorithms: K-Means, K-Harmonic Means and Expectation Maximization (EM) [Zhang, Hsu, Forman ’00]. Here we present experimental measurements of their parallel behaviors, e.g., 93% speed-up efficiency for EM on a million data points spread over 128 computers connected via 100baseT Ethernet.
The Health and Well-being of Adopted Children

OBJECTIVE. We compared the health and well-being of adopted and biological children and examined whether observed differences may be a result of differences between these 2 groups in demographic characteristics and special health care needs. METHODS. The 2003 National Survey of Children's Health was funded by the Maternal and Child Health Bureau, Health Resources and Services Administration, and was conducted as a module of the State and Local Area Integrated Telephone Survey by the National Center for Health Statistics, Centers for Disease Control and Prevention. The nationally representative sample consisted of 102 353 children, including 2903 adopted children. We compared estimates for 31 indicators of health and well-being for adopted and biological children and present adjusted estimates that control for differences in demographic characteristics and special health care needs prevalence. RESULTS. Adopted children are more likely than biological children to have special health care needs, current moderate or severe health problems, learning disability, developmental delay or physical impairment, and other mental health difficulties. However, adopted children are more likely than biological children to have had a preventive medical visit or a combination of preventive medical and dental visits during the previous year, to receive needed mental health care, and to receive care in a medical home; they are more likely to have consistent health insurance coverage, to be read to daily, or to live in neighborhoods that are supportive, and they are less likely to live in households in which someone smokes. These differences between adopted and biological children remain statistically significant even after adjustments for differences in demographic characteristics and the prevalence of special health care needs. CONCLUSION. The results suggest that, although adopted children may have poorer health than biological children, their parents may be doing more to ensure that they have needed health care and supportive environments.
Transgenic Expression of Human Connexin32 in Myelinating Schwann Cells Prevents Demyelination in Connexin32-Null Mice

Mutations in Gap Junction β1 (GJB1), the gene encoding the gap junction protein connexin32 (Cx32), cause the X-linked form of Charcot-Marie-Tooth disease (CMT1X), an inherited demyelinating neuropathy. We investigated the possibility that the expression of mutant Cx32 in other cells besides myelinating Schwann cells contributes to the development of demyelination. Human Cx32 was expressed in transgenic mice using a rat myelin protein zero (Mpz) promoter, which is exclusively expressed by myelinating Schwann cells. Male mice expressing the human transgene were crossed with female Gjb1/cx32-null mice; the resulting male offspring were all cx32-null (on the X chromosome), and one-half were transgene positive. In these transgenic mice, all of the Cx32 was derived from the expression of the transgene and was found in the sciatic nerve but not in the spinal cord or the liver. Furthermore, the Cx32 protein was properly localized (within incisures and paranodes) in myelinating Schwann cells. Finally, the expression of human Cx32 protein “rescued” the phenotype of cx32-null mice, because the transgenic mice have significantly fewer demyelinated or remyelinated axons than their nontransgenic littermates. These results indicate that the loss of Schwann-cell-autonomous expression of Cx32 is sufficient to account for demyelination in CMT1X.
Social cognition in adults with autism spectrum disorders: Validation of the Edinburgh Social Cognition Test (ESCoT)

Abstract Objective Many existing tests of social cognition are not appropriate for clinical use, due to their length, complexity or uncertainty in what they are assessing. The Edinburgh Social Cognition Test (ESCoT) is a new test of social cognition that assesses affective and cognitive Theory of Mind as well as inter- and intrapersonal understanding of social norms using animated interactions. Method To support the development of the ESCoT as a clinical tool, we derived cut-off scores from a neurotypical population (n = 236) and sought to validate the ESCoT in a sample of Autism Spectrum Disorder (ASD; n = 19) adults and neurotypical controls (NC; n = 38) matched on age and education. The ESCoT was administered alongside established tests and questionnaire measures of ASD, empathy, systemizing traits and intelligence. Results Performance on the subtests of the ESCoT and ESCoT total scores correlated with performance on traditional tests, demonstrating convergent validity. ASD adults performed poorer on all measures of social cognition. Unlike the ESCoT, performance on the established tests was predicted by verbal comprehension abilities. Using a ROC curve analysis, we showed that the ESCoT was more effective than existing tests at differentiating ASD adults from NC. Furthermore, a total of 42.11% of ASD adults were impaired on the ESCoT compared to 0% of NC adults. Conclusions Overall these results demonstrate that the ESCoT is a useful test for clinical assessment and can aid in the detection of potential difficulties in ToM and social norm understanding.
On residual prediction in voice conversion task

Nowadays, voice conversion is a problem which is i ntensively analyzed by many researchers. A large group of existing voic e conversion systems is based on RELP re-synthesis. Within these systems, the spe ech signal is pitchsynchronously segmented and described with LSF para meters. A transformation function is acquired by employing pairs of equal ti me-aligned utterances from source and target speaker. The conversion function for LSFs is often derived from probabilistic description of LSF pairs. The residua l signal is also important for speech perception; it is transformed by so called r esidual prediction. Usually, a suitable residual signal is estimated from converte d LSFs. We proposed two alternative approaches. First, we tried to predict the target speaker’s residual signal directly from source speaker’s LSFs. Then we combined both aforementioned approaches and used both source and converted LSFs for residual signal estimation. Moreover within each of these me thods, the probabilistic and Euclidian-metrics-based descriptions of LSF paramet er space were employed. Various pairs of source and target speaker were tes t d. Objective evaluation of converted speech using performance metrics and orie ntat on listening tests was performed. The preliminary experiments revealed tha t no approach is generally better then others. However, in particular cases on method is usually preferable to others. Thus a universal voice conversion system sh ould automatically decide which technique of residual prediction will be util ized.
Using OWA fuzzy operator to merge retrieval system results

With rapid growth of information sources, it is essential to develop methods that retrieve most relevant information according to the user requirements. One way of improving the quality of retrieval is to use more than one retrieval engine and then merge the retrieved results and show a single ranked list to the user. There are studies that suggest combining the results of multiple search engines will improve ranking when these engine are treated as independent experts. In this study, we investigated performance of Persian retrieval by merging four different language modeling methods and two vector space models with Lnu.ltu and Lnc.btc weighting schemes. The experiments were conducted on a large Persian collection of news archives called Hamshari Collection. Different variations of the Ordered Weighted Average (OWA) fuzzy operators method, called a quantifier based OWA operator and a degree-ofimportance based OWA operator method have been tested for merging the results. Our experimental results show that the OWA operators produce better precision and ranking in comparison with weaker retrieval methods. But in comparison with stronger retrieval models they only produce minimal improvements. 1 This work was partially supported by Iranian Telecommunication Research Center (ITRC) contract No. 500/12204.
Human-Computer adaptation for EEG based communication

Brain-computer interface (BCI) systems allow the user to interact with a computer by merely thinking. Successful BCI operation depends on the continuous adaptation of the system to the user and on the user motivation. This paper presents a model of continuous adaptation using kernel based dynamic data characterization. Additionally, the adaptive capabilities of the brain are engaged by providing feedback to the user who can modulate her mental activity so as to make the BCI accomplish her intents.
Journal of Theoretical and Applied Information Technology

This paper concentrates on the difficulty of the value of the set of revealed association rules. This problem is important since real-life databases capitulate most of the time several thousands of rules with high confidence and propose new algorithms based on closed sets to reduce the mining to bases for exact and estimated rules. Once frequent closed itemsets which constitute a generating set for both frequent itemsets and association rules have been discovered. Proposed algorithms for efficiently generating bases for association rules. A basis is a set of non-redundant rules from which all association rules can be derived, thus it captures all useful information. Moreover, its size is significantly reduced compared with the set of all possible rules because redundant and thus useless rules are discarded. New approach has a twofold advantage on one hand, the user is provided with a smaller set of resulting rules, easier to handle, and information of improved quality. On the other hand, execution times are reduced compared with the discovering of all association rules. Chess dataset used for experiments.
Effect of Monotherapy with Darunavir/Ritonavir on Viral Load in Seminal Fluid, and Quality Parameters of Semen in HIV-1-Positive Patients

Patients with human immunodeficiency virus type 1 (HIV-1) who receive antiretroviral therapy (ART) often achieve increased survival and improved quality of life. In this respect, monotherapy with darunavir/ritonavir (mDRV/r) can be a useful treatment strategy. This prospective study analyses the effect of mDRV/r on sperm quality and viral load in a group of 28 patients who had previously been given conventional ART and who had recorded a viral load <20 copies/mL for at least six months. These patients were given mDRV/r at a dose of 800/100 mg for 48 weeks. At baseline (V0), CD4, CD8, FSH, LH and testosterone levels were measured, together with HIV-1 viral load in plasma and semen. In addition, seminal fluid quality was studied before mDRV/r treatment was prescribed. At week 48 (V1), HIV-1 viral load in plasma and semen and the quality of the seminal fluid were again measured. The results obtained indicate that at V0, 10% of the patients with ART had a positive viral load in seminal fluid (>20 copies/ml), and that at V1, after mDRV/r treatment, this figure had fallen to 3%. The quality of seminal fluid was close to normal in 57% of patients at V0 and in 62% at V1. We conclude that, similar to ART, mDRV/r maintains HIV-1 viral load in most patients, and that there is no worsening in seminal fluid quality.