Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid approach framework for sentiment classification on microblogging

Microblogging is used widely to express opinions toward an entity, knowing sentiment polarity can get benefit for decision making, planning, and visualization and so on. Outdated training data along with the nature of Microblogging which is short and noisy cause low accuracy. Existing approach requires human effort to manually label huge training data. To tackle these problems, we proposed a framework that used a hybrid approach between lexicon-based approach and machine learning approach. SentiWordnet has been used to automatically label training data and then using Support Vector Machine for sentiment classification. We study two scoring mechanisms for labeling training data, Word Sense Disambiguation and Non Word Sense Disambiguation. The framework also used MapReduce for computing large dataset. The result shows that Non Word Sense Disambiguation is optimal for this framework. The framework is functional, more automatically and less human efforts.
The Perfect Time: An Examination of Time- Synchronization Techniques

A combination of circumstances has made this the perfect time to look at time-synchronization techniques for power system monitoring devices: • Time synchronization of power system monitoring devices is now center stage because of the problems experienced with post-disturbance analysis following the August 14, 2003 blackout in the northeastern United States and southeastern Canada. NERC recommendations, based on the lessons learned from this postdisturbance analysis, place a high priority on implementing time-synchronization techniques that will eliminate or reduce the effort involved with comparing event information from distributed intelligent disturbance recording devices. • Maturing synchrophasor measurement technology is also creating a demand for very accurate time synchronization of devices in widely separated locations. • Global Positioning System (GPS) high-accuracy synchronized time code signals are now accessible through economical clock receivers that are designed for simple and reliable installation in the harsh environment of electric power facilities. • Time synchronization can be accomplished through communications network technologies that are becoming more prevalent in utility and industrial power facilities. This paper discusses available techniques to acquire synchronized time signals, various standard time signal formats, techniques for distributing these time signals to monitoring and recording devices, and the relative accuracy required by various power system timing and recording applications. Emphasis is given to the accuracy requirements of monitoring and recording devices, the relative accuracy of time signal sources, the effect that time signal distribution has on the accuracy of the signal, and the effect that device sampling and processing have on the accuracy of time tags applied to sampled data. Time-synchronization signal sources include earth-based radio transmission (WWV, WWVH, WWVB, and LORAN-C), satellite-based signal transmission (GOES, GPS, and GLONASS), and time-setting messages via communications networks and dial-up modems. All of these timesynchronization signal and time-setting sources can usually be traced back to common or coordinated precision time references operated and provided by government standards organizations. Coordinated Universal Time (UTC) has become the defacto standard time reference for many applications, including most electric power industry applications. This time is now the accepted standard because of its worldwide availability and relatively economical access through GPS satellite clock receivers. This paper focuses on the use of GPS satellites as a synchronized time source and compares and discusses various clocks and clock signal distribution methods to meet electric power industry time-synchronization applications. The other time-synchronization sources mentioned above are discussed in the appendix of this paper for reference purposes.
A new Java Thread model for concurrent programming of real-time systems

The Java ™ Virtual Machine (JVM) provides a high degree of platform independence, but being an interpreter, Java has a poor system performance. New compiler techniques and Java processors will gradually improve the performance of Java, but despite these developments, Java is still far from real-time. We propose the Communicating Java Threads (CJT) model, which eliminates several shortcomings, such as Java's non-deterministic behavior, Java's monitor weakness, and lack of reactiveness for real-time and embedded systems. CJT is based on CSP providing channels, composition constructs, and scheduling of processes. The CJT Java class library, which provides all necessary classes on top of Java, is readily available to interested users. The main power of the method is that it integrates these features in a natural way and in no way conflicts with other paradigms, such as PersonalJava ™, EmbeddedJava ™, JavaBeans ™, CORBA, and PVM. All activities, reported in this paper, are carried out as part of the JavaPP project.
Evaluation of the first phase of a specialist weight management programme in the UK National Health Service: prospective cohort study

Abstract Objective To evaluate the first phase of a specialist weight management programme provided entirely within the UK National Health Service. Design Prospective cohort study using multiple logistic regression analysis to report odds of ≥5 kg weight loss in all referrals and completers, and odds of completion, with 95 % confidence intervals. Anxiety and depression ‘caseness’ were measured by the Hospital Anxiety and Depression Scale. Setting Glasgow and Clyde Weight Management Service (GCWMS) is a specialist multidisciplinary service, with clinical psychology support, for patients with BMI ≥35 kg/m2 or BMI ≥30 kg/m2 with co-morbidities. Subjects All patients referred to GCWMS between 2004 and 2006. Results Of 2976 patients referred to GCWMS, 2156 (72·4 %) opted into the service and 809 completed phase 1. Among 809 completers, 35·5 % (n 287) lost ≥5 kg. Age ≥40 years, male sex (OR = 1·39, 95 % CI 1·05, 1·82), BMI ≥ 50 kg/m2 (OR = 1·70, 95 % CI 1·14, 2·54) and depression (OR = 1·81, 95 % CI 1·35, 2·44) increased the likelihood of losing ≥5 kg. Diabetes mellitus (OR = 0·55, 95 % CI 0·38, 0·81) and socio-economic deprivation were associated with poorer outcomes. Success in patients aged ≥40 years and with BMI ≥50 kg/m2 was associated with higher completion rates of the programme. Patients from the most deprived areas were less likely to lose ≥5 kg because of non-completion of the programme. Conclusions Further improvements in overall effectiveness might be achieved through targeting improvements in appropriateness of referrals, retention and effective interventions at specific populations of patients.
Evolutionary inevitability of sexual antagonism

Sexual antagonism, whereby mutations are favourable in one sex and disfavourable in the other, is common in natural populations, yet the root causes of sexual antagonism are rarely considered in evolutionary theories of adaptation. Here, we explore the evolutionary consequences of sex-differential selection and genotype-by-sex interactions for adaptation in species with separate sexes. We show that sexual antagonism emerges naturally from sex differences in the direction of selection on phenotypes expressed by both sexes or from sex-by-genotype interactions affecting the expression of such phenotypes. Moreover, modest sex differences in selection or genotype-by-sex effects profoundly influence the long-term evolutionary trajectories of populations with separate sexes, as these conditions trigger the evolution of strong sexual antagonism as a by-product of adaptively driven evolutionary change. The theory demonstrates that sexual antagonism is an inescapable by-product of adaptation in species with separate sexes, whether or not selection favours evolutionary divergence between males and females.
The 2-Aminoethylphosphonate-Specific Transaminase of the 2-Aminoethylphosphonate Degradation Pathway

ABSTRACT The 2-aminoethylphosphonate transaminase (AEPT; the phnW gene product) of the Salmonella enterica serovar Typhimurium 2-aminoethylphosphonate (AEP) degradation pathway catalyzes the reversible reaction of AEP and pyruvate to form phosphonoacetaldehyde (P-Ald) and l-alanine (l-Ala). Here, we describe the purification and characterization of recombinant AEPT. pH rate profiles (log Vm and log Vm/Km versus pH) revealed a pH optimum of 8.5. At pH 8.5, Keq is equal to 0.5 and the kcat values of the forward and reverse reactions are 7 and 9 s−1, respectively. The Km for AEP is 1.11 ± 0.03 mM; for pyruvate it is 0.15 ± 0.02 mM, for P-Ald it is 0.09 ± 0.01 mM, and for l-Ala it is 1.4 ± 0.03 mM. Substrate specificity tests revealed a high degree of discrimination, indicating a singular physiological role for the transaminase in AEP degradation. The 40-kDa subunit of the homodimeric enzyme is homologous to other members of the pyridoxalphosphate-dependent amino acid transaminase superfamily. Catalytic residues conserved within well-characterized members are also conserved within the seven known AEPT sequences. Site-directed mutagenesis demonstrated the importance of three selected residues (Asp168, Lys194, and Arg340) in AEPT catalysis.
Planar Hidden Markov Modeling: From Speech to Optical Character Recognition

We propose in this paper a statistical model (planar hidden Markov model - PHMM) describing statistical properties of images. The model generalizes the single-dimensional HMM, used for speech processing, to the planar case. For this model to be useful an efficient segmentation algorithm, similar to the Viterbi algorithm for HMM, must exist. We present conditions in terms of the PHMM parameters that are sufficient to guarantee that the planar segmentation problem can be solved in polynomial time, and describe an algorithm for that. This algorithm aligns optimally the image with the model, and therefore is insensitive to elastic distortions of images. Using this algorithm a joint optimal segmentation and recognition of the image can be performed, thus overcoming the weakness of traditional OCR systems where segmentation is performed independently before the recognition leading to unrecoverable recognition errors. The PHMM approach was evaluated using a set of isolated handwritten digits. An overall digit recognition accuracy of 95% was achieved. An analysis of the results showed that even in the simple case of recognition of isolated characters, the elimination of elastic distortions enhances the performance Significantly. We expect that the advantage of this approach will be even more significant for tasks such as connected writing recognition/spotting, for which there is no known high accuracy method of recognition.
Context-Aware Prediction of Pathogenicity of Missense Mutations Involved in Human Disease

Amino-acid substitutions are implicated in a wide range of human diseases, many of which are lethal. Distinguishing such mutations from polymorphisms without significant effect on human health is a necessary step in understanding the etiology of such diseases. Computational methods can be used to select interesting mutations within a larger set, to corroborate experimental findings and to elucidate the cause of the deleterious effect. In this work, we show that taking into account the sequence context in which the mutation appears allows to improve the predictive and explanatory power of such methods. We present an unsupervised approach based on the direct-coupling analysis of homologous proteins. We show its capability to quantify mutations where methods without context dependence fail. We highlight cases where the context dependence is interpretable as functional or structural constraints and show that our simple and unsupervised method has an accuracy similar to state-of-the-art methods, including supervised ones.
Two-Pole $X$ -Band-Tunable Ferroelectric Filters With Tunable Center Frequency, Fractional Bandwidth, and Return Loss

Two two-pole X-band-tunable ferroelectric filters with constant fractional bandwidths and constant return losses are presented in this paper. The center frequency, the fractional bandwidth, and the return loss of the filters are tuned simultaneously through high-Q barium-strontium-titanate capacitors with fast switching speed. The constant fractional bandwidth is achieved while tuning the center frequency and keeping the constant return loss. A center frequency of 10 GHz and a frequency tuning range of 740 MHz (7.4%) are achieved with a low dc bias voltage of 30 V. The first tunable filter with constant fractional bandwidth has an equal-ripple bandwidth of 8.45%. The second filter with a constant fractional bandwidth and a constant return loss exhibits an equal-ripple bandwidth of 8% and a return loss of 17 dB. The measured insertion loss is between 2.7-2.2 dB for both filters, with a dc bias range of 0-30 V.
Design of a single-chip pH sensor using a conventional 0.6-/spl mu/m CMOS process

A pH sensor fabricated on a single chip by an unmodified, commercial 0.6-/spl mu/m CMOS process is presented. The sensor comprises a circuit for making differential measurements between an ion-sensitive field-effect transistor (ISFET) and a reference FET (REFET). The ISFET has a floating-gate structure and uses the silicon nitride passivation layer as a pH-sensitive insulator. As fabricated, it has a large threshold voltage that is postulated to be caused by a trapped charge on the floating gate. Ultraviolet radiation and bulk-substrate biasing is used to permanently modify the threshold voltage so that the ISFET can be used in a battery-operated circuit. A novel post-processing method using a single layer of photoresist is used to define the sensing areas and to provide robust encapsulation for the chip. The complete circuit, operating from a single 3-V supply, provides an output voltage proportional to pH and can be powered down when not required.
Research of Anomaly Detection Based on Time Series

With the continuous deterioration of the network environment, a variety of viruses, Trojans continue to affect the security of the network. Through the network traffic anomaly detection and analysis can efficiently find problems existing in the network. This paper discusses the network traffic flow data predict and network anomaly detection, network traffic prediction using ARMA model, network anomaly detection using the exponential smoothing model. ARMA model supplies the expectation value to abnormal detection, at the same time exponential smoothing model can restoration historical flow data, making the following traffic forecast more accurate. A network traffic predict and network anomaly detection system has been developed, with which can find network anomaly and send alarms, thus improve network stability and robustness.
Endovascular Therapy of 623 Patients With Anterior Circulation Stroke

Background and Purpose— Endovascular therapy of acute ischemic stroke has been shown to be beneficial for selected patients. The purpose of this study is to determine predictors of outcome in a large cohort of patients treated with intra-arterial thrombolysis, mechanical revascularization techniques, or both. Methods— We prospectively acquired data for 623 patients with acute cerebral infarcts in the carotid artery territory who received endovascular treatment at a single center. Logistic regression analysis was performed to determine predictors of outcome. Results— Median National Institutes of Health Stroke Scale (NIHSS) at admission was 15. Partial or complete recanalization was achieved in 70.3% of patients; it was independently associated with hypercholesterolemia (P=0.02), absence of coronary artery disease (P=0.023), and more proximal occlusion site (P<0.0001). After 3 months, 80.5% of patients had survived, and 48.9% of patients reached favorable outcome (modified Rankin scale score 0–2). Good collaterals (P<0.0001), recanalization (P=0.023), hypercholesterolemia (P=0.03), lower NIHSS at admission (P=0.001), and younger age (P<0.0001) were independent predictors for survival. More peripheral occlusion site (P<0.0001), recanalization (P<0.0001), hypercholesterolemia (P=0.002), good collaterals (P=0.002), lower NIHSS (P<0.0001), younger age (P<0.0001), absence of diabetes (P=0.002), and no previous antithrombotic therapy (P=0.036) predicted favorable outcome. Time to treatment was only a predictor of outcome, when collaterals were excluded from the model. Symptomatic intracerebral hemorrhage occurred in 5.5% and was independently predicted by poor collaterals (P=0.004). Conclusions— Several independent predictors for outcome and complications were identified. Unlike in intravenous thrombolysis trials, time to treatment was a predictor of outcome only when collaterals were excluded from the model, indicating the important role of collaterals for the time window.
Fast summarization and anonymization of multivariate big time series

Sequential, predominantly temporal nature of the vast amounts of big data released every day from many different sources could potentially be linked, aligned along the time and deliver new evidence for the next generation predictive systems or knowledge discovery engines. However, big data owners are reluctant to share their data due to legally binding privacy and identity protection concerns, thereby posing a major hurdle preventing shared exploitation of big data on a massive scale. Data anonymization is expected to solve this problem, yet the current approaches are limited predominantly to univariate time series generalized by aggregation or clustering to eliminate identifiable uniqueness of individual data points or patterns. For multivariate time series, uniqueness among of the combination of values or patterns across multiple dimensions is much harder to eliminate due the to exponentially growing number of unique configurations of point values across multiple dimensions. Our method implements linearly scalable asynchronous summarization of multivariate time series independently at every dimension. As a result the series retain only a small subset of defining points at different times along multiple dimensions effectively breaking up the multivariate time series into a collection of summarized univariate time series that are perturbed from the original series in terms of actual points and pattern shapes. Current implementation of the anonymizing summarization involves shape preserving greedy elimination and aggregation that supports parallel cluster processing for big data implementation.
Joint Routing and Scheduling Optimization in Wireless Mesh Networks with Directional Antennas

Wireless Mesh Networks (WMNs) have recently emerged as a technology for next-generation wireless networking. WMNs partially replace wired backbone networks, and it is therefore reasonable to plan carefully radio resource assignment to provide quality guarantees to traffic flows. Directional transmissions allow to reduce radio interference, thus exploiting spatial reuse. Therefore, as a main contribution, in this paper we study the joint routing and scheduling optimization problem in Wireless Mesh Networks where nodes are equipped with directional antennas. To this aim, we assume a Spatial reuse Time Division Multiple Access (STDMA) scheme, a dynamic power control able to vary the emitted power slot-by-slot, and a rate adaptation mechanism that sets transmission rates according to the Signal- to-Interference-and-Noise Ratio (SINR). We provide column generation-based heuristic approaches for the proposed models in a set of realistic-size instances and discuss the impact of different parameters on the network performance. The results show that our schemes increase considerably the total traffic accepted by the network, providing bounds to the achievable performance.
An efficient cntfet-based 7-input minority gate

Complementary metal oxide semiconductor technology (CMOS) has been faced critical challenges in nano-scale regime. CNTFET (Carbon Nanotube Field effect transistor) technology is a promising alternative for CMOS technology. In this paper, we proposed a novel 7-input minority gate in CNTFET technology that has only 9 CNTFETs. Minority function is utilized in the voting systems for decision making and also it is used in data mining. This proposed 7-input minority gate is utilized less fewer transistors than the conventional CMOS method which utilizes many transistors for implementing sum of products. By means of this proposed 7-input minority gate, a 4-input NAND gate can be implemented, which gets better the conventional design in terms of delay and energy efficiency and has much more deriving power at its output.
Hybrid Instance-Based System for Predicting Ocean Temperatures

An instance-based problem solving model is presented in which the aim is to forecast, in real time, the physical parameter values of a complex and dynamic environment: the ocean. The situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. In such a situation it has been found that an instance-based reasoning system can provide a more effective means of performing such predictions than other connectionist or symbolic techniques. The instance-based reasoning system incorporates a radial basis function artificial neural network for the instance adaptation. The results obtained from experiments, in which the system operated in real time in the oceanographic environment, are presented.
Influence of Genotype on Warfarin Maintenance Dose Predictions Produced Using a Bayesian Dose Individualization Tool

Background: A previously established Bayesian dosing tool for warfarin was found to produce biased maintenance dose predictions. In this study, we aimed (1) to determine whether the biased warfarin dose predictions previously observed could be replicated in a new cohort of patients from 2 different clinical settings, (2) to explore the influence of CYP2C9 and VKORC1 genotype on predictive performance of the Bayesian dosing tool, and (3) to determine whether the previous population used to develop the kinetic–pharmacodynamic model underpinning the Bayesian dosing tool was sufficiently different from the test (posterior) population to account for the biased dose predictions. Methods: The warfarin maintenance doses for 140 patients were predicted using the dosing tool and compared with the observed maintenance dose. The impact of genotype was assessed by predicting maintenance doses with prior parameter values known to be altered by genetic variability (eg, EC50 for VKORC1 genotype). The prior population was evaluated by fitting the published kinetic–pharmacodynamic model, which underpins the Bayesian tool, to the observed data using NONMEM and comparing the model parameter estimates with published values. Results: The Bayesian tool produced positively biased dose predictions in the new cohort of patients (mean prediction error [95% confidence interval]; 0.32 mg/d [0.14–0.5]). The bias was only observed in patients requiring ≥7 mg/d. The direction and magnitude of the observed bias was not influenced by genotype. The prior model provided a good fit to our data, which suggests that the bias was not caused by different prior and posterior populations. Conclusions: Maintenance doses for patients requiring ≥7 mg/d were overpredicted. The bias was not due to the influence of genotype nor was it related to differences between the prior and posterior populations. There is a need for a more mechanistic model that captures warfarin dose–response relationship at higher warfarin doses.