Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Piezoceramic Sensor for NDT Applications

The paper presents theoretical and experimental research concerning the manufacture of an intelligent piezoceramic sensor for non-destructive tests (NDT) applications in non-metallic materials. The non-destructive impact-echo method is a suitable technique for the flaw detection in the material structure, based on stress wave propagation. A transient stress pulse is introduced into the sample by an impact shock wave hammer placed on the element surface. This method could be applied for non-metallic materials with rugged and non-homogeneous structures, such as the concrete. The active element of the intelligent piezoceramic sensor for NDT applications is the piezoceramic disc which converts the vibrations into electrical signal, than this is amplified by an amplifier electronic circuit. The radial resonant frequency of the piezoceramic disc is 40.5 kHz, optimum for the ultrasound and low frequency sound vibration propagation into non-metallic materials. In conclusion, an improved device was manufactured, that is an intelligent sensor with piezoceramic disc and amplifier electronic circuit, put together into an aluminium case, in order to decrease the signal losses and obtain a good signal to noise ratio, suitable for applications in non-metallic materials.
Playful tagging: folksonomy generation using online games

Collaborative Tagging is a powerful method to create folksonomies that can be used to grasp/filter user preferences or enhance web search. Recent research has shown that depending on the number of users and the quality of user-provided tags powerful community-driven semantics or "ontologies" can emerge - as it was evident analyzing user data from social web applications such as del.icio. us or Flickr. Unfortunately, most web pages do not contain tags and, thus, no vocabulary that describes the information provided. A common problem in web page annotation is to motivate users for constant participation, i.e. tagging. In this paper we describe our approach of a binary verification game that embeds collaborative tagging into on-line games in order to produce domain specific folksonomies.
Catching target mobile node using time-of-arrival measurement in Wireless Sensor Networks

Target localization and mobile node tracking problems in WSNs have been receiving considerable concentration recently, because of the requirements to achieve high localization accuracy, with the output of minimum cost. The drawback involved is the crisis of target tracking in Wireless Sensor Networks. Localization primarily refers to revealing of the spatial coordinates of a node. Target tracking deals with spatial coordinates discovery and movement tracking of a moving object. The mobile node location algorithms like weighted tracking methods and Arrival Time (AT) are insufficient for finding the location of the mobile device. The location estimation and weighted tracking method are mainly based on mobile target signal's Arrival Time (AT) measurement model. In this paper work, location tracking process can be achieved into two ways. At first part we find the location of the mobile node along with the extended weighted tracking method. This can be used to track the mobile device and to estimate the weight for the particular mobile node. Secondly, the concentration is towards the AT measurement model for measuring the signal arrival time. Finally, the proposed algorithm finds the location of the mobile device and improves the performance of the AT measurement model in different places.
The Technology Behind Personal Digital Assistants: An overview of the system architecture and key components

We have long envisioned that one day computers will understand natural language and anticipate what we need, when and where we need it, and proactively complete tasks on our behalf. As computers get smaller and more pervasive, how humans interact with them is becoming a crucial issue. Despite numerous attempts over the past 30 years to make language understanding (LU) an effective and robust natural user interface for computer interaction, success has been limited and scoped to applications that were not particularly central to everyday use. However, speech recognition and machine learning have continued to be refined, and structured data served by applications and content providers has emerged. These advances, along with increased computational power, have broadened the application of natural LU to a wide spectrum of everyday tasks that are central to a user's productivity. We believe that as computers become smaller and more ubiquitous [e.g., wearables and Internet of Things (IoT)], and the number of applications increases, both system-initiated and user-initiated task completion across various applications and web services will become indispensable for personal life management and work productivity. In this article, we give an overview of personal digital assistants (PDAs); describe the system architecture, key components, and technology behind them; and discuss their future potential to fully redefine human? computer interaction.
Robust Parameter Learning in Bayesian Networks with Missing Data

Bayesian Belief Networks (BBNs) are a powerful formalism for knowledge representa tion and reasoning under uncertainty. During the past few years, Artificial Intelligence met· Statistics in the quest to develop effective methods to learn BBNs directly from databases. Unfortunately, real-world databases include missing and/or unreported data whose presence challenges traditional learning techniques, from both the theoretical and computational point of view. This paper introduces a new method to learn the probabilities defining a BBNs from databases with missing data. The intuition behind this method is close to the robust sensitivity analysis interpretation of probability: the method computes the extreme points of the set of possible distributions consistent with the available information and proceeds by refini·ng this ~et as more information be comes available. This paper outlines the description of this method and presents some experimental results comparing this approach to the Gibbs Samplings.
The evolution of parental care and egg size: a comparative analysis in frogs

The evolution of parental care and egg size has attracted considerable attention and theoretical debate. Several different hypotheses have been proposed concerning the trajectories of parental care and egg size evolution and the order of specific evolutionary transitions. Few comparative studies have investigated the predictions of these hypotheses. Here, we investigate the evolutionary association between parental care and egg size in frogs in a phylogenetic context. Data on egg size and presence or absence of parental care in various species of frogs was gathered from the scientific literature. As a basis for our comparative analyses, we developed a phylogenetic supertree, by combining the results of multiple phylogenetic analyses in the literature using matrix representation parsimony. Using phylogenetic pairwise comparisons we demonstrated a significant association between the evolution of parental care and large egg size. We then used recently developed maximum likelihood methods to infer the evolutionary order of specific transitions. This analysis revealed that the evolution of large egg size typically precedes the evolution of parental care, rather than the reverse. We discuss the relevance of our results to previous hypotheses concerning the evolution of parental care and egg size.
On-site Likelihood Identification of Tweets for Tourism Information Analysis

Tourism is one of the most important key industries. The Web contains much information for the tourism, such as impressions and sentiments about sightseeing areas. Analyzing the information is a significant task for tourism informatics. One approach to extract tourism information is to extract sentences with keywords related to target facilities and events. However, all sentences with keywords might be not tourism information. In this paper, we propose a method for measuring tourism information likelihood. The target resource for the analysis is information on Twitter. The task is to identify whether each tweet has high on-site likelihood. We introduce a filtering process and a machine learning technique for the task. Our method obtained 80.5% on the precision rate.
Robust Carotid Artery Recognition in Longitudinal B-Mode Ultrasound Images

Automatic segmentation of the arterial lumen from ultrasound images is an important task in clinical diagnosis. Carotid artery recognition, the first task in lumen segmentation, should be performed in a fully automated, fast, and reliable way to further facilitate the low-level task of arterial delineation. In this paper, a user-independent, real-time algorithm is introduced for carotid artery localization in longitudinal B-mode ultrasound images. The proposed technique acts directly on the raw image, and exploits basic statistics along with anatomical knowledge. The method's evaluation and parameter value optimization were performed on a threefold cross validation basis. In addition, the introduced algorithm was systematically compared with another algorithm for common carotid artery recognition in B-mode scans, separately for multi-frame and single-frame data. The data sets used included 2,149 images from 100 subjects taken from three different institutions and covering a wide range of possible lumen and surrounding tissue representations. Using the optimized values, the carotid artery was recognized in all the processed images in both multi-frame and single-frame data. Thus, the introduced technique will further reinforce automatic segmentation in longitudinal B-mode ultrasound images.
X-ray spectral evolution of SAX J1747.0-2853 during outburst activity and confirmation of its transient nature

SAX J1747.0-2853 is an X-ray transient which exhibited X-ray outbursts yearly between 1998 and 2001, and most probably also in 1976. The outburst of 2000 was the longest and brightest. We have analyzed X-ray data sets that focus on the 2000 outburst and were obtained with BeppoSAX, XMM-Newton and RXTE. The data cover unabsorbed 2–10 keV fluxes between 0.1 and $5.3\times10^{-9}$ erg s -1 cm -2 . The equivalent luminosity range is $6\times10^{35}$ to $2\times10^{37}$ erg s -1 . The 0.3–10 keV spectrum is well described by a combination of a multi-temperature disk blackbody, a hot Comptonization component and a narrow Fe–K emission line at 6.5 to 6.8 keV with an equivalent width of up to 285 eV. The hydrogen column density in the line of sight is $(8.8\pm0.5)\times10^{22}$ cm -2 . The most conspicuous spectral changes in this model are represented by variations of the temperature and radius of the inner edge of the accretion disk, and a jump of the equivalent width of the Fe–K line in one observation. Furthermore, 45 type-I X-ray bursts were unambiguously detected between 1998 and 2001 which all occurred during or close to outbursts. We derive a distance of $7.5\pm1.3$ kpc which is consistent with previous determinations. Our failure to detect bursts for prolonged periods outside outbursts provides indirect evidence that the source returns to quiescence between outbursts and is a true transient.
Survey Data on Perceptions of Contraceptive Methods as Compositional Tables

espanolResumen Este trabajo estudia la percepcion relativa de los jovenes de los posibles beneficios asociados a tres metodos para evitar infecciones de transmision sexual/SIDA y/o embarazos no deseados (condones, pildoras anticonceptivas, pildoras poscoitales o del dia despues). Al efecto de evaluar estos tres metodos se paso una encuesta a 145 estudiantes de pregrado (76% mujeres, 24% hombres) con respecto a diez items diferentes. Los resultados muestran que, en comparacion con las pildoras, los condones se valoran positivamente como proteccion contra infecciones de transmision sexual/SIDA, las parejas estan interesadas en utilizarlas para proteger su salud, y son apreciados por su falta de efectos secundarios. Las dos pildoras estan mejor valoradas que los condones con respecto a los sentimientos de placer para ambos sexos y no tan valoradas en la prevencion de infecciones de transmision sexual. Las evaluaciones de las mujeres son menos extremas que las de los hombres en cinco de los seis items, pero son mas pronunciadas con respecto a la suposicion de que el uso de condones muestra un interes por la pareja cuidando su salud sexual. El estudio muestra una preferencia general hacia los condones en lugar de las pildoras anticonceptivas y las pildoras poscoitales, pero es mucho mas probable que los hombres elijan esta opcion. Las conclusiones se basan en un analisis composicional de tablas de doble entrada. EnglishAbstract This paper studies the relative evaluation of young people and the possible benefits associated with three methods of avoiding sexually transmitted infections/AIDS and/or unwanted pregnancies (condoms, contraceptive pills, morning-after pills). A survey evaluating these three methods, with respect to ten different items, was given to 145 undergraduate students (76% females, 24% males). Results show that, compared to pills, condoms are positively valued as protection against sexually transmitted infections/AIDS, couples are interested in using them to protect their health, and they are appreciated for their lack of side effects. Pills are better valued than condoms with respect to feelings of pleasure for both sexes and not as valued in preventing sexually transmitted infections. Females' assessments are less extreme than male's for five of the six items, but are more pronounced with respect to the assumption that using condoms shows an interest in the couple taking care of their sexual health. The study shows a general preference towards condoms rather than contraceptive pills and postcoital pills, but men are far more likely to choose this option. The conclusions are based on a compositional analysis of two way tables.
Multi-level sequence alignment: a trade-off between speed and accuracy in similar text searching

A fingerprinting algorithm and sequence alignment are used widely to calculate the similarity of documents. The fingerprinting method is simple and fast but it cannot find specific similar regions. A string alignment method is used to identify similar regions by arranging sequences of strings. This has the advantage that it can find specific similar regions, but it also has the disadvantage that it requires more computational time. Multi-level alignment (MLA) is a new method, which was designed to exploit the advantages of both methods. MLA divides input documents into uniform length blocks, before extracting the fingerprint from each block and calculating the similarity of block pairs by comparing fingerprints. A similarity table is created during this process. Finally, sequence alignment is used to identify the longest similar regions in the similarity table. MLA allows users to change the block's size to control the relative proportion of the fingerprint algorithm and sequence alignment. A document is divided into several block, so similar regions are also fragmented into two or more blocks. To address this fragmentation problem, we propose a united block method. The united block method integrates adjacent fragmented similar regions to increase the similarity value. Our experiments demonstrated that computing a document's similarity using the united block method was more accurate than the original MLA method, with minor reductions in time.
Detection of Anatoxin-a and Three Analogs in Anabaena spp. Cultures: New Fluorescence Polarization Assay and Toxin Profile by LC-MS/MS

Anatoxin-a (ATX) is a potent neurotoxin produced by several species of Anabaena spp. Cyanobacteria blooms around the world have been increasing in recent years; therefore, it is urgent to develop sensitive techniques that unequivocally confirm the presence of these toxins in fresh water and cyanobacterial samples. In addition, the identification of different ATX analogues is essential to later determine its toxicity. In this paper we designed a fluorescent polarization (FP) method to detect ATXs in water samples. A nicotinic acetylcholine receptor (nAChR) labeled with a fluorescein derivative was used to develop this assay. Data showed a direct relationship between the amount of toxin in a sample and the changes in the polarization degree of the emitted light by the labeled nAChR, indicating an interaction between the two molecules. This method was used to measure the amount of ATX in three Anabaena spp. cultures. Results indicate that it is a good method to show ATXs presence in algal samples. In order to check the toxin profile of Anabaena cultures a LC-MS/MS method was also developed. Within this new method, ATX-a, retention time (RT) 5 min, and three other molecules with a mass m/z 180.1 eluting at 4.14 min, 5.90 min and 7.14 min with MS/MS spectra characteristic of ATX toxin group not previously identified were detected in the Anabaena spp. cultures. These ATX analogues may have an important role in the toxicity of the sample.
Nanomolar Oxytocin Synergizes with Weak Electrical Afferent Stimulation to Activate the Locomotor CPG of the Rat Spinal Cord In Vitro

Synergizing the effect of afferent fibre stimulation with pharmacological interventions is a desirable goal to trigger spinal locomotor activity, especially after injury. Thus, to better understand the mechanisms to optimize this process, we studied the role of the neuropeptide oxytocin (previously shown to stimulate locomotor networks) on network and motoneuron properties using the isolated neonatal rat spinal cord. On motoneurons oxytocin (1 nM–1 μM) generated sporadic bursts with superimposed firing and dose-dependent depolarization. No desensitization was observed despite repeated applications. Tetrodotoxin completely blocked the effects of oxytocin, demonstrating the network origin of the responses. Recording motoneuron pool activity from lumbar ventral roots showed oxytocin mediated depolarization with synchronous bursts, and depression of reflex responses in a stimulus and peptide-concentration dependent fashion. Disinhibited bursting caused by strychnine and bicuculline was accelerated by oxytocin whose action was blocked by the oxytocin antagonist atosiban. Fictive locomotion appeared when subthreshold concentrations of NMDA plus 5HT were coapplied with oxytocin, an effect prevented after 24 h incubation with the inhibitor of 5HT synthesis, PCPA. When fictive locomotion was fully manifested, oxytocin did not change periodicity, although cycle amplitude became smaller. A novel protocol of electrical stimulation based on noisy waveforms and applied to one dorsal root evoked stereotypic fictive locomotion. Whenever the stimulus intensity was subthreshold, low doses of oxytocin triggered fictive locomotion although oxytocin per se did not affect primary afferent depolarization evoked by dorsal root pulses. Among the several functional targets for the action of oxytocin at lumbar spinal cord level, the present results highlight how small concentrations of this peptide could bring spinal networks to threshold for fictive locomotion in combination with other protocols, and delineate the use of oxytocin to strengthen the efficiency of electrical stimulation to activate locomotor circuits.
Mining Using HMM and PPM

Text mining involves the use of statistical and machine lear ning techniques to learn structural elements of text in order to search for useful inf ormation in previously unseen text. The need for these techniques have emerged out o f the rapidly growing information era. Token identification is an important compo nent of any text mining tool. The accomplishment of this task enhances the function of diverse applications involving searching for patterns in textual data. Several different identification methods have been reporte d in the literature. HMMs and PPM models have been successfully used in language proce ssing tasks. They have also been applied separately to learning-based token i dentification. Most of the existing systems are domainand language-dependent. In this thesis, we implement a system that bridges the two wel l known methods through words new to the identification model. The system is f ully domainand language-independent. No changes of code are necessary whe n applying to other domains or languages. The only thing required is an annotate d corpus. The system has been tested on two corpora and achieved an over all F-measure of 76:59% for TCC, and69:02% for BIB. This is not as good as would be expected from a system which includes language-dependent component s. However, our system is more generalized. The identification of date has the be st result,73% and92% of correct tokens are identified respectively. The system al so performs reasonably well on people’s name with correct tokens of 68% for TCC, and76% for BIB.
Privacy by design in big data: An overview of privacy enhancing technologies in the era of big data analytics

The extensive collection and processing of personal information in big data analytics has given rise to serious privacy concerns, related to wide scale electronic surveillance, profiling, and disclosure of private data. To reap the benefits of analytics without invading the individuals' private sphere, it is essential to draw the limits of big data processing and integrate data protection safeguards in the analytics value chain. ENISA, with the current report, supports this approach and the position that the challenges of technology (for big data) should be addressed by the opportunities of technology (for privacy). We first explain the need to shift from "big data versus privacy" to "big data with privacy". In this respect, the concept of privacy by design is key to identify the privacy requirements early in the big data analytics value chain and in subsequently implementing the necessary technical and organizational measures. After an analysis of the proposed privacy by design strategies in the different phases of the big data value chain, we review privacy enhancing technologies of special interest for the current and future big data landscape. In particular, we discuss anonymization, the "traditional" analytics technique, the emerging area of encrypted search and privacy preserving computations, granular access control mechanisms, policy enforcement and accountability, as well as data provenance issues. Moreover, new transparency and access tools in big data are explored, together with techniques for user empowerment and control. Achieving "big data with privacy" is no easy task and a lot of research and implementation is still needed. Yet, it remains a possible task, as long as all the involved stakeholders take the necessary steps to integrate privacy and data protection safeguards in the heart of big data, by design and by default.