Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic time warping and machine learning for signal quality assessment of pulsatile signals

In this work, we describe a beat-by-beat method for assessing the clinical utility of pulsatile waveforms, primarily recorded from cardiovascular blood volume or pressure changes, concentrating on the photoplethysmogram (PPG). Physiological blood flow is nonstationary, with pulses changing in height, width and morphology due to changes in heart rate, cardiac output, sensor type and hardware or software pre-processing requirements. Moreover, considerable inter-individual and sensor-location variability exists. Simple template matching methods are therefore inappropriate, and a patient-specific adaptive initialization is therefore required. We introduce dynamic time warping to stretch each beat to match a running template and combine it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped. The features were then presented to a multi-layer perceptron neural network to learn the relationships between the parameters in the presence of good- and bad-quality pulses. An expert-labeled database of 1055 segments of PPG, each 6 s long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test our algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was found. The algorithm could be deployed as a stand-alone signal quality assessment algorithm for vetting the clinical utility of PPG traces or any similar quasi-periodic signal.
SOA Grey Box Testing -- A Constraint-Based Approach

Service-Oriented Architectures (SOAs) offer attractive advantages in respect of reusability, interoperability and dynamics, and are nowadays widely accepted in industry. Achieving established software quality levels also with SOAs, while mandatory, is challenging, as, for instance, a SOA's dynamics and heterogeneity exacerbate verification issues like observability, controllability, and distribution. Regarding verification, we thus have to evolve available technology in order to enable the assessment of essential functional and non-functional system properties, including correctness, performance, stability, robustness and scalability. Adopting a model-based grey box testing approach that can exploit mixed description levels for individual (possibly 3rd party) services promises the required flexibility for successful development workflows. In this paper, we propose such a testing approach that, considering a SOA model, defines constraint satisfaction problems for the test case generation step. First empirical results for our approach are promising.
Patterns of Genomic Integration of Nuclear Chloroplast DNA Fragments in Plant Species

The transfer of organelle DNA fragments to the nuclear genome is frequently observed in eukaryotes. These transfers are thought to play an important role in gene and genome evolution of eukaryotes. In plants, such transfers occur from plastid to nuclear [nuclear plastid DNAs (NUPTs)] and mitochondrial to nuclear (nuclear mitochondrial DNAs) genomes. The amount and genomic organization of organelle DNA fragments have been studied in model plant species, such as Arabidopsis thaliana and rice. At present, publicly available genomic data can be used to conduct such studies in non-model plants. In this study, we analysed the amount and genomic organization of NUPTs in 17 plant species for which genome sequences are available. The amount and distribution of NUPTs varied among the species. We also estimated the distribution of NUPTs according to the time of integration (relative age) by conducting sequence similarity analysis between NUPTs and the plastid genome. The age distributions suggested that the present genomic constitutions of NUPTs could be explained by the combination of the rapidly eliminated deleterious parts and few but constantly existing less deleterious parts.
Efficient data selection for machine translation

Performance of statistical machine translation (SMT) systems relies on the availability of a large parallel corpus which is used to estimate translation probabilities. However, the generation of such corpus is a long and expensive process. In this paper, we introduce two methods for efficient selection of training data to be translated by humans. Our methods are motivated by active learning and aim to choose new data that adds maximal information to the currently available data pool. The first method uses a measure of disagreement between multiple SMT systems, whereas the second uses a perplexity criterion. We performed experiments on Chinese-English data in multiple domains and test sets. Our results show that we can select only one-fifth of the additional training data and achieve similar or better translation performance, compared to that of using all available data.
Statistical grid-based clustering over data streams

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Due to this reason, most algorithms for data streams sacrifice the correctness of their results for fast processing time. The processing time is greatly influenced by the amount of information that should be maintained. This paper proposes a statistical grid-based approach to clustering data elements of a data stream. Initially, the multidimensional data space of a data stream is partitioned into a set of mutually exclusive equal-size initial cells. When the support of a cell becomes high enough, the cell is dynamically divided into two mutually exclusive intermediate cells based on its distribution statistics. Three different ways of partitioning a dense cell are introduced. Eventually, a dense region of each initial cell is recursively partitioned until it becomes the smallest cell called a unit cell. A cluster of a data stream is a group of adjacent dense unit cells. In order to minimize the number of cells, a sparse intermediate or unit cell is pruned if its support becomes much less than a minimum support. Furthermore, in order to confine the usage of memory space, the size of a unit cell is dynamically minimized such that the result of clustering becomes as accurate as possible. The proposed algorithm is analyzed by a series of experiments to identify its various characteristics.
Microwave Photonic Noise Source From Microwave to Sub-Terahertz Wave Bands and Its Applications to Noise Characterization

In this paper, we present a microwave photonic technique to produce a noise signal from the microwave to sub-terahertz region with an optical amplified spontaneous emission noise, optical filter, and photodetector. With this scheme, it is easy to control the noise level by simply controlling the input optical noise power. In addition, the output noise spectral density with this scheme can be accurately estimated from the well-known spectral shape of an optical filter. For the demonstration of this scheme as a noise source, noise figure measurements for commercial devices such as amplifiers and a mixer, were performed in the frequency range from 0.1-20 to 293-357 GHz and the results were compared to those with conventional techniques.
Bee Venom Phospholipase A2, a Good “Chauffeur” for Delivering Tumor Antigen to the MHC I and MHC II Peptide-Loading Compartments of the Dendritic Cells: The Case of NY-ESO-1

Bee venom phospholipase A2 (bvPLA2) is a small, 15kDa enzyme which hydrolyses many phospholipids through interfacial binding. The mutated bvPLA2H34Q (bvPLA2m), in which histidine-34 is replaced by glutamine, is not catalytically active. This protein has been shown to be a suitable membrane anchor and has been suggested as a suitable tumor-antigen vector for the development of novel dendritic cell-based vaccines. To confirm this feature, in this study the fusion protein PNY, composed of NY-ESO-1(NY(s)) fused to the C-terminus of bvPLA2m, was engineered. bvPLA2m enhanced the binding of NY(s) to the membrane of human monocyte-derived dendritic cells (DCs) and, once taken up by the cells, the antigen fused to the vector was directed to both MHC I and MHC II peptide-loading compartments. bvPLA2m was shown to increase the cross-presentation of the NY(s)-derived, restricted HLA-A*02 peptide, NY-ESO-1157-165(NY157-165), at the T1 cell surface. DCs loaded with the fusion protein induced cross-priming of NY(s)-specific CD8 + T-cells with greater efficiency than DCs loaded with NY(s). Sixty-five percent of these NY(s)-specific CD8+ T-cell lines could also be activated with the DCs pulsed with the peptide, NY157-165. Of these CD8+ T-cell lines, two were able to recognize the human melanoma cell line, SK-MEL-37, in a context of HLA-A*02. Only a small number of bvPLA2m CD8+ T-cell lines were induced, indicating the low immunogenicity of the protein. It was concluded that bvPLA2m can be used as a membrane-binding vector to promote MHC class II peptide presentation and MHC class I peptide cross-presentation. Such a system can, therefore, be tested for the preparation of cell-based vaccines.
A framework for selection of SAAS by evaluating the quality of Freemium model

The cloud computing has emerged as highly constructive and challenging approach in the field of computing as it provides benefits of high performance, availability, scalability e.t. c. at much reduced cost than in house development. Cloud computing claims to condense IT obstacles hence it helps people to strictly focus on business requirements. However, selecting a service that actually meets the requirements is still a hot potato. In this paper we have proposed a framework to evaluate the quality of SAAS Freemium Model for selection of high quality service. For the resolution we have extracted different quality factors from software quality models. On the basis of Quality factors a framework is proposed to compare and evaluate the available SAAS and selecting one that is satisfying most with respect to requirements. Multi criteria decision making approach ANP is used for the purpose of evaluation of SAAS. Experience and domain knowledge of the stake holders will accompany this approach for selection of SAAS according to requirements.
A Compare between Shor's quantum factoring algorithm and General Number Field Sieve

Factoring large integers has been one of the most difficult problems in the history of mathematics and computer science. There was no efficient solution of this problem until Shor's algorithm emerged. Shor's algorithm is a polynomial time factoring algorithm which works on a quantum computer. Quantum computing is a new paradigm of computing that uses quantum mechanical phenomena in solving problems. The computers we are using right now are called classical computer. The most efficient classical factoring algorithm is General Number Field Sieve (GNFS). GNFS also cannot factor integers in polynomial time. In this paper, we compared these two algorithms in factoring integer in a standalone system.
Socially-Optimal ISP-Aware P2P Content Distribution via a Primal-Dual Approach

Peer-to-peer (P2P) technology is popularly exploited to enable large-scale content distribution (e.g., live and on-demand video streaming) with low server costs. Most P2P protocols in place are network agnostic, where peers download content chunks from each other regardless of their ISP belonging, leading to significant amounts of inter-ISP traffic. It has been a daunting challenge how to design protocols that optimize the P2P content distribution topology, such that inter-ISP traffic is minimized while the dissemination performance is maximized, not to mention one that motivates peer's voluntary ISP-aware peer selection. In this paper, we formulate a social welfare maximization framework for dynamical construction of the P2P content distribution topology % (namely deciding who is downloading which chunk from whom), taking into consideration both peers' gain due to chunk downloading and inter-ISP traffic that is incurred. Given its nature of an assignment problem, we resort to a primal-dual framework to design the solution algorithm, which can be practically implemented as a set of distributed, interleaving auctions, where peers bid for bandwidth to download chunks at other peers considering the potential inter-ISP traffic as a cost factor. Such an auction-based mechanism encourages peers to download from neighbors with low network costs in between, in order to succeed in bandwidth acquisition for chunk downloading. We analyze and prove the social optimality achieved by the distributed auctions and verify the performance of our proposal using realistic emulation experiments with real P2P traffic.
On Site Investigation and Health Monitoring of a Historic Tower in Mantua, Italy

The paper describes the strategy adopted to assess the structural condition of the tallest historic tower in Mantua (Italy) after the Italian seismic sequence of May–June 2012 and exemplifies the application of health monitoring using (automated) operational modal analysis. The post-earthquake survey (including extensive visual inspection, historic and documentary research, non-destructive (ND) material testing, and ambient vibration tests) highlighted the poor state of preservation of the upper part of the tower; subsequently, a dynamic monitoring system (consisting of a few accelerometers and one temperature sensor) was installed in the building to address the preservation of the historic structure, and automated modal identification was continuously performed. Despite the low levels of vibration that existed in operational conditions, the analysis of data collected over a period of about 15 months allowed to assess and model the effects of changing temperature on modal frequencies and to detect the occurrence of abnormal behavior and damage under the changing environment. The monitoring results demonstrate the potential key role of vibration-based structural health monitoring, implemented through low-cost hardware solutions and appropriate software tools, in the preventive conservation and the condition-based maintenance of historic towers.
Egorov theorems and equidistribution of eigenfunctions for the quantized sawtooth and Baker maps

Nous demontrons des estimations d'Egorov semi-classiques pour les applications dents de scie quantifiees ainsi que pour l'application du boulanger quantifiee. Ces applications du tore preservent les aires et elles sont uniformement hyperboliques, mais discontinues. Par consequent, le Theoreme d'Egorov usuel n'est plus valable. Les estimations que nous obtenons permettent encore de montrer l'existence d'une suite de fonctions propres des applications quantifiees, de densite egale a un, et qui equidistribue dans la limite classique.
SECURITY MECHANISM IN BODY AREA NETWORK-A SURVEY

Wireless Body Area Network is an Emerging technology in Wireless Sensor Network for various applications in healthcare, entertainment, defense etc., In Body Area Network, sensors are used to monitor the human's activities and their actions like health parameters so it is necessary to secure the privacy of the user and the necessary information are collected by the sensors from the body of the user. In this paper we discussed introduction, architecture, issues, challenges, and security approaches of Body Area Network. Different types of security protocols are discussed for Body Area Network and their comparison is also made.
The Web Library of Babel: evaluating genre collections

We present experiments in automatic genre classification on web corpora, comparing a wide variety of features on several different genreannotated datasets (HGC, I-EN, KI-04, KRYS-I, MGC and SANTINIS). We investigate the performance of several types of features (POS n-grams, character n-grams and word n-grams) and show that simple character n-grams perform best on current collections because of their ability to generalise both lexical and syntactic phenomena related to genres. However, we also show that these impressive results might not be transferrable to the wider web due to the lack of comparability between different annotation labels (many webpages cannot be described in terms of the genre labels in individual collections), lack of representativeness of existing collections (many genres are represented by webpages coming from a small number of sources) as well as problems in the reliability of genre annotation (many pages from the web are difficult to interpret in terms of the labels available). This suggests that more research is needed to understand genres on the Web.
Increased Birth Weight Associated with Regular Pre-Pregnancy Deworming and Weekly Iron-Folic Acid Supplementation for Vietnamese Women

Background Hookworm infections are significant public health issues in South-East Asia. In women of reproductive age, chronic hookworm infections cause iron deficiency anaemia, which, upon pregnancy, can lead to intrauterine growth restriction and low birth weight. Low birth weight is an important risk factor for neonatal and infant mortality and morbidity. Methodology We investigated the association between neonatal birth weight and a 4-monthly deworming and weekly iron-folic acid supplementation program given to women of reproductive age in north-west Vietnam. The program was made available to all women of reproductive age (estimated 51,623) in two districts in Yen Bai Province for 20 months prior to commencement of birth weight data collection. Data were obtained for births at the district hospitals of the two intervention districts as well as from two control districts where women did not have access to the intervention, but had similar maternal and child health indicators and socio-economic backgrounds. The primary outcome was low birth weight. Principal Findings The birth weights of 463 infants born in district hospitals in the intervention (168) and control districts (295) were recorded. Twenty-six months after the program was started, the prevalence of low birth weight was 3% in intervention districts compared to 7.4% in control districts (adjusted odds ratio 0.29, 95% confidence interval 0.10 to 0.81, p = 0.017). The mean birth weight was 124 g (CI 68 - 255 g, p<0.001) greater in the intervention districts compared to control districts. Conclusions/Significance The findings of this study suggest that providing women with regular deworming and weekly iron-folic acid supplements before pregnancy is associated with a reduced prevalence of low birth weight in rural Vietnam. The impact of this health system-integrated intervention on birth outcomes should be further evaluated through a more extensive randomised-controlled trial.
A DEA approach for Supplier Selection with AHP and risk consideration

Supplier Selection is a problem that supply chain managers have been facing for many years. Selecting the appropriate suppliers is no longer as simple as choosing only based on the price they offer. There are many criteria, which can be either quantitative or qualitative, to be considered. There is thus a need for an approach that can handle these criteria. Besides, as supply chains are becoming more complex these days, it is also important to incorporate supply risks in the evaluation of the suppliers. This paper offers an approach that mainly focuses on Data Envelopment Analysis (DEA) to analyze and compare the relative efficiencies of the suppliers. Since DEA can only handle quantitative attributes, Analytic Hierarchy Process (AHP) is being utilized to help to analyze qualitative analysis. Risks are also being considered in the evaluation of the suppliers. The proposed approach seeks to offer a holistic approach to tackle the Supplier Selection problem.
Children’s understanding of pupils’ responsibility. A self-responsibility model.

The aim of the study was to present the concept of pupils' esponsibility as understood by younger primary school children. The interviews and responsibility dilemma tests conducted during the experiment on a group of 100 younger primary school pupils provid­ed the basis for: (1) developing a model of self-responsibility, and (2) determining the factors that stimulate and promote responsibility. The self-responsibility model was verified empirically to consist of two dimensions: (1) subjective responsibility versus formal responsibility, and (2) responsibility to be borne (negative) versus responsibility to be taken (posiiive). The obtained results are presented in the paper. The three-year action research project enabled us also to determine the factors stimulating the development of responsibility.
Comparative analysis of middleware for multi-processor system-on-chip (MPSoC)

Efficient software is required in order to make optimum utilization of the full-scale features of the rapidly developing MPSoC hardware. Embedded MPSoCs are characterized by their heterogeneity and resource limitation. In addition, embedded MPSoCs systems would be required to deal with a complex set of tasks whose resource requirements cannot be easily determined statically at design time. Middleware is a particularly important software component of embedded MPSoC that enables the system to overcome heterogeneity and manage resources dynamically at run-time. An important part of the middleware is the run-time system manager responsible for the distributed resource manager of the MPSoC-based system. System management at run-time is important for many modern embedded systems because the tasks performed by the system vary over time. In this paper we explore design trade-offs for middleware systems targeting multi-core embedded systems. The experimental platform implemented for the project is presented and future work is highlighted.
Modeling clicks using document popularity

It is commonly believed, that implicit user feedback such as clicks from search logs, can be a strong indicator of user preferences during web search. Moreover, proper click log data analysis can lead to accurate document relevance estimation. As a result, an effective approach to properly interpret users' search behaviour was the development of user click models, designed to reduce position bias and produce more attractive users' search needs. Here, in this paper we present a novel click model based on a Bayesian network, for estimating document relevance from click-through data. In contrast with previous works, our model focuses only on the estimation of actual document relevance and not on perceived relevance, which we take to be inferred directly from document popularity. We design the model to be scalable and incremental according to modern computational needs and experiments show that our proposed model achieves better scores in log-likelihood and predicts relevance more accurately than current state-of-the-art.
Latent Semantic Tensor Indexing for Community-based Question Answering

Retrieving similar questions is very important in community-based question answering(CQA). In this paper, we propose a unified question retrieval model based on latent semantic indexing with tensor analysis, which can capture word associations among different parts of CQA triples simultaneously. Thus, our method can reduce lexical chasm of question retrieval with the help of the information of question content and answer parts. The experimental result shows that our method outperforms the traditional methods.