Miracle-dz/UniFAR-FTUs huggingface dataset:
CoNFV: An Endhost-Cloud Collaborated Network Function Virtualization Framework

T381 popularity of Network Function Virtualization (NFV) is evidenced by its proliferation. There are two existing ways to deploy NFV: in the cloud and at the endhost. However, neither the cloud nor the endhost alone can achieve low latency, low cost and high flexibility simultaneously. In this paper, we propose CoNFV, a framework that combines the cloud and endhosts together, and can achieve all the three goals above. We present a novel NF abstraction that divides NF processing into the cloud-side part and the endhost-side part. CoNFV then incorporates the carefully-designed communication mechanism and offloads the compute-intensive rule generation task to the cloud, which largely reduces the dependency on network bandwidth and also eliminates network latency. Our evaluation result shows that CoNFV is able to reduce latency by 41.8% and reduce the bandwidth usage by 90.6%. To our best knowledge, CoNFV is the first NFV framework that enables endhost-cloud collaboration.
Adapted GooLeNet for Answer Selection

S377 matching is of central significance to the answer selection task which aims to select correct answers for a given question from a candidate answer pool. A useful method is to employ neural networks to model sentence by generating sentences representations and then measuring their distance. In this work, we introduce an effective architecture ‐‐Adapted GooLeNet (AG)‐‐ into the answer selection task for sentence modeling. This architecture can capture more levels of language granularities in parallel, because of the various sizes of filters comparing with single-layer CNN and multi-layer CNN. The empirical study on three various benchmark tasks of answer selection demonstrates that capturing sentence features on different levels of granularities benefit sentence modeling by utilizing AG, comparing with single-layer CNNs, multi-layer CNNs and biLSTM.
High-dose omega-3 fatty acids have no effect on platelet aggregation or coagulation measured with static and flow-based aggregation instruments and Sonoclot; an observational study in healthy volunteers

Abstract The effect of omega-3 fatty acids on platelet aggregation and coagulation is highly unclear. Studies both support and refute the impacts of omega-3 fatty acids on prolonged bleeding time and platelet inhibition as well as its purported positive effects on cardiovascular disease. In a previous pilot study we suggested an inhibition of platelet aggregation measured with multiple electrode aggregometry. Following on that, the aim of the present study was to investigate the effects of supplementary high doses of omega-3 fatty acids on platelet aggregation and coagulation in a sample-size calculated number of healthy volunteers using Sonoclot, multiple electrode aggregometry, and flow-based Cellix instruments after 10 days of omega-3 fatty acid intake. Twelve healthy human volunteers ingested 2520 mg of supplementary omega-3 fatty acids per day for 10 days. Venous blood was sampled and platelet aggregation and coagulation were measured before and after the treatment period. The viscoelastic test instrument Sonoclot, multiple electrode aggregometry, and flow-based Cellix instruments with collagen-coated channels were used to evaluate platelet aggregation and coagulation. There were no differences in any of the measured variables after the treatment period as compared to before. In this well-powered study on healthy volunteers, no effects of high doses of omega-3 fatty acids after 10 days of intake could be demonstrated, either on coagulation or platelet function. Further studies are needed to clarify whether omega-3 fatty acids have a role in the regulation of the putative complex processes in vivo.
An Automated Approach for Epilepsy Detection Based on Tunable Q-Wavelet and Firefly Feature Selection Algorithm

Detection of epileptic seizures using an electroencephalogram (EEG) signals is a challenging task that requires a high level of skilled neurophysiologists. Therefore, computer-aided detection provides an asset to the neurophysiologist in interpreting the EEG. This paper introduces a novel approach to recognize and classify the epileptic seizure and seizure-free EEG signals automatically by an intelligent computer-aided method. Moreover, the prediction of the preictal phase of the epilepsy is proposed to assist the neurophysiologist in the clinic. The proposed method presents two perspectives for the EEG signal processing to detect and classify the seizures and seizure-free signals. The first perspectives consider the EEG signal as a nonlinear time series. A tunable Q-wavelet is applied to decompose the signal into smaller segments called subbands. Then a chaotic, statistical, and power spectrum features sets are extracted from each subband. The second perspectives process the EEG signal as an image; hence the gray-level co-occurrence matrix is determined from the image to obtain the textures of contrast, correlation, energy, and homogeneity. Due to a large number of features obtained, a feature selection algorithm based on firefly optimization was applied. The firefly optimization reduces the original set of features and generates a reduced compact set. A random forest classifier is trained for the classification and prediction of the seizures and seizure-free signals. Afterward, a dataset from the University of Bonn, Germany, is used for benchmarking and evaluation. The proposed approach provided a significant result compared with other recent work regarding accuracy, recall, specificity, F-measure, and Matthew's correlation coefficient.
Input Dominance and Development of Home Language in Russian-German Bilinguals

Bilingual children experience a rapid shift in language preference and input dominance from L1 to L2 upon entering kindergarten when regular contact with L2 starts. Though this change in dominance affects further L1 development, little is known about how various factors shape this. The present study examines the combined influence of different background factors including not only chronological age, age of onset of L2 (L2 AoO), and gender, but also various L1 input measures on L1 receptive and expressive lexical and morphological (case and verb inflections) development in Russian-German bilingual children. For lexical skills, we found a general strong impact of chronological age, gender, and input factors but a differential impact of L2 AoO. Only expressive lexical skills were influenced by language dominance). Morphological development was influenced in the following way: chronological age and gender were most relevant for the acquisition of verb inflection, whereas age, L1 use in the nuclear family and L2 AoO affected the acquisition of case on nouns. This pattern explains the findings of the second series of analyses of longitudinal data, which showed that case is more vulnerable than verb inflection to language attrition – or, taking another perspective – to heritage Russian grammar restructuring.
Efficient Power Extraction from SPV system in Partially Shaded Condition: A Comparative Study between Classical and Fuzzy Logic Control

Curbing global warming and simultaneously meeting enhanced electrical energy demand have become a challenge to developing country, like India. Uncontrolled use of fossil fuels causes emission of greenhouse gases and serious environmental degradation. Use of non-conventional energy resources, like photovoltaic (pv) solar energy is a necessity to maintain sustainable development in energy sector. A common goal is to extract maximum available power from the pv source with the use of different maximum power point tracking (MPPT) algorithms. However, the capability of tracking MPP under rapidly changing solar insolation level by different MPPT algorithm is not satisfactory. In present work, a comparison has been made among three different MPPT algorithm namely, perturb and observe (P&O), incremental conductance method and fuzzy logic based algorithm in tracking maximum power under partial shading conditions of two series connected 2.1 kWp pv arrays interfaced with a DC-DC boost converter.
Web Page Classification Algorithm Based on Semi-Supervised Support Vector Machine

Most web page classification algorithms are learning algorithms under the single-instance single-label framework. Multi-Instance Multi-Label learning is a new machine learning framework. MIMLSVM+ algorithm, using degenerate strategy to transform multiple instances into single instances, and then decomposing multiple labels into a series of two types of classification problems, that is, building an SVM for each label. Because the MIMLSVM+ algorithm degrades the multi-label problem into a series of two types of classification problems, the processing of each label will lose the contact information between the labels independently. Therefore, multi-tasking technology is introduced to make use of the multi-tasking learning framework based on nuclear to validate the MIMLSVM+ expand, get E-MIMLSVM+ algorithm. The paper improves E-MIMLSVM+ algorithm by using the semi-supervised learning method. Experimental results show that the proposed method can achieve higher classification accuracy.
A Statistical Approach to Generation Shift Keys

This paper develops a novel two-step method to establish Generation Shift Keys (GSKs) on any desired aggregation level. Estimates are derived from econometric models, utilizing regression coefficients to measure how net position deviations affect power outputs, isolated from the influence of renewable energy generation and load. This enables GSK estimations based on actual common dispatch decisions, decoupled from fixed generating unit parameters and merit-order assumptions. The method's first step, the Generation Share Key (GShK), is applied to the four German TSOs. The obtained results are robust, suggesting the implementation for other countries with sufficiently stable markets. The second step is conducted for the generating units of 50Hertz's control region. For most analyzed units, size and variability of the estimated GSK values are appropriate regarding capacity and primary energy source. Also, the analysis discusses possibilities to account for the susceptibility of GShKs and GSKs to prevailing market situations.
Functional Characterization of Arabidopsis PHL4 in Plant Response to Phosphate Starvation

Plants have evolved an array of adaptive responses to cope with phosphate (Pi) starvation. These responses are mainly controlled at the transcriptional level. In Arabidopsis, PHR1, a member of the MYB-CC transcription factor family, is a key component of the central regulatory system controlling plant transcriptional responses to Pi starvation. Its homologs in the MYB-CC family, PHL1 (PHR1-LIKE 1), PHL2, and perhaps also PHL3, act redundantly with PHR1 to regulate plant Pi starvation responses. The functions of PHR1’s closest homolog in this family, PHL4, however, have not been characterized due to the lack of its null mutant. In this work, we generated two phl4 null mutants using the CRISPR/Cas9 technique and investigated the functions of PHL4 in plant responses to Pi starvation. The results indicated that the major developmental, physiological, and molecular responses of the phl4 mutants to Pi starvation did not significantly differ from those of the wild type. By comparing the phenotypes of the phr1 single mutant and phr1phl1 and phr1phl4 double mutants, we found that PHL4 also acts redundantly with PHR1 to regulate plant Pi responses, but that its effects are weaker than those of PHL1. We also found that the overexpression of PHL4 suppresses plant development under both Pi-sufficient and -deficient conditions. Taken together, the results indicate that PHL4 has only a minor role in the regulation of plant responses to Pi starvation and is a negative regulator of plant development.
Unusual dicistronic expression from closely spaced initiation codons in an umbravirus subgenomic RNA

Abstract Translation commencing at closely spaced initiation codons is common in RNA viruses with limited genome space. In the subgenomic RNA (sgRNA) of Pea enation mosaic virus 2, two closely spaced, out-of-frame start codons direct synthesis of movement/stability proteins p26 and p27. Efficient translation from AUG26/AUG27 is dependent on three 3′-proximal cap-independent translation enhancers (3′CITEs), whereas translation of the genomic (gRNA) requires only two. Contrary to strictly scanning-dependent initiation at the gRNA, sequence context of AUG26/AUG27 does not conform with Kozak requirements and insertion of efficient upstream AUGs had pronounced effects for AUG26 but only moderate effects for AUG27. Insertion of a hairpin within an extended 5′ UTR did not significantly impact translation from AUG26/AUG27. Furthermore, AUG27 repressed translation from upstream AUG26 and this effect was mitigated when inter-codon spacing was reduced. Addition of a stable hairpin to the very 5′ end of the sgRNA severely restricted translation, testifying that this 3′CITE-driven initiation is 5′ end-dependent. Similar to gRNA, sgRNA reporter transcripts were nearly exclusively associated with light polysomes and 3′CITE-promoted long-distance interaction connecting the sgRNA ends affected the number of templates translated and not the initiation rate. We propose a non-canonical, 3′CITE-driven mechanism for efficient dicistronic expression from umbravirus sgRNAs.
Study of Degradation Phenomena of Solid Insulation used in Extra High Voltage Cable under Alternating Voltage Stress

In order to balance the power demand against the population growth the applications of power cables are getting important role in the field of extra high voltage power transmission. Most of the power cables are insulated with Cross Linked Polyethylene (XLPE) as it offers high dielectric strength against the high voltage stress. Such power cables are failed to carry the huge power due to internal discharge i.e., partial discharge occur inside the insulation. Partial discharge in polymeric insulation of high voltage power cable causes cumulative damage that progressively deteriorates the insulation, leading to eventual failure. In view of the above, the condition monitoring of such power cables is very much essential to get early indication of degradation processes inside the XLPE insulation which leads to catastrophic failure in power system network and moreover to cope up with the huge power demand. In this work, the formation of electrical tree inside the XLPE insulation consider as one of the vital cause of insulation failure due to partial discharge has been conducted in high voltage laboratory.
Inverse Transport Networks

We introduce inverse transport networks as a learning architecture for inverse rendering problems where, given input image measurements, we seek to infer physical scene parameters such as shape, material, and illumination. During training, these networks are evaluated not only in terms of how close they can predict groundtruth parameters, but also in terms of whether the parameters they produce can be used, together with physically-accurate graphics renderers, to reproduce the input image measurements. To en- able training of inverse transport networks using stochastic gradient descent, we additionally create a general-purpose, physically-accurate differentiable renderer, which can be used to estimate derivatives of images with respect to arbitrary physical scene parameters. Our experiments demonstrate that inverse transport networks can be trained efficiently using differentiable rendering, and that they generalize to scenes with completely unseen geometry and illumination better than networks trained without appearance- matching regularization.
Study and simulation of WBAN MAC protocols for emergency data traffic in healthcare

This paper presents issues, limitations and challenges of designing WBAN MAC protocols for handling emergency based medical data transmission in healthcare applications. Human health related data of WBAN are monitored in regular intervals in normal scenario. But in case of sudden illness, accident or any emergency, data rate may abruptly vary. Moreover, based on context, priority of data sending may need to be dynamically changed. Considering all these challenges, MAC protocol design for WBAN is still an emerging field of research. The work here compares different MAC protocols and focuses on research challenges and future directions of MAC protocol design in WBAN based on state-of-the-art with thrust on priority based medical data transmission in emergency healthcare scenario. Here, we have simulated IEEE 802.15. 4, IEEE 802.15. 6, SMAC and TMAC protocols using Castalia 3.3 and compare their results in term of latency and energy consumption.
Modified SERI technique in the treatment of hallux valgus combined with arthritis

Introduction: Few reports encompass the treatment of first metatarsophalangeal joint (MTPJ) arthritis complicating hallux valgus. Indeed, in the literature, there is a lack of guidance concerning the management of patients affected by mild to moderate first MTPJ arthritis complicating hallux valgus. The aim of this study is to present the mid-term results of an original joint-preserving surgical technique. Materials and Methods: A group of 50 patients (60 feet) affected by mild to moderate first MTPJ arthritis complicating hallux valgus who underwent modified Giannini’s SERI (simple, effective, rapid, inexpensive) surgical procedure were prospectively reviewed. They were clinically and radiographically evaluated preoperatively and after surgery and were also assessed using the American Orthopedic Foot and Ankle Society (AOFAS) score and Coughlin and Shurnas classification. The average follow-up was 38 months. A multivariate analysis was performed to evaluate the existing relationship between postoperative AOFAS results and radiological preoperative measured angles and sesamoid grade of dislocation. Results: The average AOFAS score significantly improved at last follow-up to an average of 90.5 ± 14.5, while preoperatively it was 45.6 ± 15.3 (p < 0.001). On multivariate analysis, a statistically significant correlation resulted among all the radiographic preoperative registered parameters and clinical postoperative results. However, it was shown that any radiological parameter alone could not predict a better clinical recovery. Conclusions: The surgical technique proposed here could extend the indications of the SERI procedure to those patients affected by mild to moderate hallux valgus combined with mild to moderate osteoarthritis.
Vandermonde Factorization of Hankel Matrix for Complex Exponential Signal Recovery—Application in Fast NMR Spectroscopy

Many signals are modeled as a superposition of exponential functions in spectroscopy of chemistry, biology, and medical imaging. This paper studies the problem of recovering exponential signals from a random subset of samples. We exploit the Vandermonde structure of the Hankel matrix formed by the exponential signal and formulate signal recovery as Hankel matrix completion with Vandermonde factorization (HVaF). A numerical algorithm is developed to solve the proposed model and its sequence convergence is analyzed theoretically. Experiments on synthetic data demonstrate that HVaF succeeds over a wider regime than the state-of-the-art nuclear-norm-minimization-based Hankel matrix completion method, while it has a less restriction on frequency separation than the state-of-the-art atomic norm minimization and fast iterative hard thresholding methods. The effectiveness of HVaF is further validated on biological magnetic resonance spectroscopy data.
Understanding the influence of Individual's Self-efficacy for Information Systems Security Innovation Adoption: A Systematic Literature Review

Information Systems security cannot be fully apprehended if the user lacks the required knowledge and skills to effectively apply the safeguard measures. Knowledge and skills enhance one's self-efficacy. Individual self-efficacy is an important element in ensuring Information Systems safeguard effectiveness. In this research, we explore the role of individual's self-efficacy for Information Systems security adoption. The study uses the method of Systematic Literature Review using 42 extant studies to evaluate individual self- efficacy for Information Systems security innovation adoption. The systematic review findings reveal the appropriateness of the existing empirical investigations on the individual self-efficacy for Information Systems security adoption. Furthermore, the review results confirmed the significance of the relationship between individual self-efficacy and Information Systems security adoption. In addition, the study validates the past administration of the research on this subject in terms of sample size, sample subject and theoretical grounds.
Optimisation of an Energy System in Finland Using NSGA-II Evolutionary Algorithm

Energy system optimisation often includes multiple objectives and their relative trade-offs, e.g. system costs, CO2 emissions and generation adequacy. Without a conversion factor between the objectives, result of the optimisation is a set of solutions instead of a single optimal solution, commonly known as a Pareto-optimal set. Instead of working with a single solution at a time, evolutionary algorithms work with a population of solutions, and can hence be used to find multiple optimal solutions in a single optimisation run. This paper uses a well-known multi-objective evolutionary algorithm, NSGA-II (nondominated sorting genetic algorithm II), to optimise an imaginary energy system in Finland with conflicting objectives. Furthermore, we analyse two optimal points in the far ends of the resulting Pareto-optimal set in generation 10,000. Our results indicate that evolutionary algorithms are not always the most accurate optimisation method, but they have potential to be applied more widely to energy system optimisation.
Efficient GPU-based implementation of the median filter based on a multi-pixel-per-thread framework

Median filtering has become a ubiquitous smoothing tool for image denoising tasks, with its complexity generally determined by the median algorithm used (usually on the order of O(n log(n)) when computing the median of n elements). Most algorithms were formulated for scalar single processor computers, with few of them successfully adapted and implemented for computers with a parallel architecture. However, the redundancy for processing neighboring pixels has not yet been fully exploited for parallel implementations. Additionally, most of the implementations are only suitable for fixed point images, but not for floating point.In this paper we propose an efficient parallel implementation of the 2D median filter, based on a multiple pixel-per-thread framework, and test its implementation on a CUDA-capable GPU either for fixed point or floating point data. Our computational results show that our proposed methods outperforms state-of the art implementations, with the difference increasing significantly as the filter size grows.
Pool-Based Market with Bids from Electric Vehicles in the Presence of Uncertain Renewable Generation

A pool-based electricity model which incorporates the bidding from electric vehicles (EVs) and conventional generators and which accounts for the uncertainty of forecasted wind generation is presented in this paper. Electric vehicles are allowed to bid multi-part bids, through the aggregator to the market operator. The market model solves the stochastic optimization problem by maximizing the social welfare with two-stage constraints. We have shown that the participation of EVs into the auction allows the efficient utilization o f w ind energy,