Miracle-dz/UniFAR-FTUs huggingface dataset:
A wireless charging system

A wireless charging system (10) comprises a wireless charger (12) comprising an energy transfer element (12a) and a control / regulating means (12b), wherein the energy transfer element (12a) is configured to transfer energy to a portable terminal (11), which has an energy receiving part (11a) to be transmitted in a contactless manner, wherein the control / regulating means (12b) is configured to control / adjust one of the power transmitting portion (12a) performed power transmission, and a stop by the energy transmission to control / regulate; and a wireless vehicle communication device (14) having an antenna (14a), which is configured to perform a wireless communication with an electronic key (13) from an occupant of the vehicle (1), wherein the wireless charger (12) with a predetermined equipment which is provided on the vehicle (1) communicably connected, and when a wireless communication between the wireless vehicle communication device (14) and the electronic key (13) is carried out, the control / regulating means (12b) stops power transmission, which of the energy transfer element (12a) is carried out, is received a predetermined signal, which is transmitted from the predetermined equipment in a case in which, before the antenna (14a) is operated.
Evaluation of anakinra for the treatment of systemic juvenile idiopathic arthritis

Introduction: Recombinant interleukin-1 receptor antagonist (rIL-1RA or anakinra) is a recombinant IL-1RA protein capable of binding with high affinity to the IL-1 receptor type I, thus competitively blocking IL-1 signaling in IL-1-receptor-expressing cells. It is FDA-approved for a periodic fever syndrome called cryopyrin-associated periodic syndrome, one of the monogenetic auto-inflammatory disorders. It was initially registered for (adult) rheumatoid arthritis, but is rarely used for this indication nowadays. Here, we discuss its use as an orphan drug in an acquired childhood auto-inflammatory disorder: systemic juvenile idiopathic arthritis (sJIA). Areas covered: Rationale for IL-1 blockade in sJIA, clinical efficacy, timing of rIL-1RA in the treatment of sJIA, safety issues and side effects. Expert opinion: Clinical efficacy of anakinra in sJIA was shown in one placebo-controlled trial and several cohort studies. Anakinra seems to yield impressive response rates, especially in the early phase of sJIA. Recent data suggest that, when started early in the disease course, there is a window of opportunity for IL-1 blockade to change the biology of the disease in sJIA patients. This argues both for the early and short-term (< 1 year) use of anakinra in sJIA. Moreover, anakinra can be considered in the treatment of sJIA-associated macrophage activation syndrome (MAS), especially in steroid-resistant MAS.
Long-term adherence to evidence-based secondary prevention therapies in coronary artery disease

Background -Studies have examined the use of evidence-based therapies for coronary artery disease(CAD) in the short term and at hospital discharge, but few have evaluated long-term use. Methods and Results -Using the Duke Databank for Cardiovascular Disease for the years 1995 to 2002, we determined the annual prevalence and consistency of serf-reported use of aspirin, β-blockers, lipid-lowering agents, and their combinations in all CAD patients and of angiotensin-converting enzyme inhibitors(ACEIs) in those with and without heart failure. Logistic-regression models identified characteristics associated with consistent use(reported on ≥2 consecutive follow-up surveys and then through death,withdrawal, or study end), and Cox proportional-hazards models explored the association of consistent use with mortality. Use of all agents and combinations thereof increased yearly. In 2002, 83%reported aspirin use; 61%, β-blocker use; 63%, lipid-lowering therapy use; 54%, aspirin and β-blocker use; and 39%, use of all 3. Consistent use was as follows: For aspirin, 71%; β-blockers, 46%; lipid-lowering therapy, 44%; aspirin and β-blockers, 36%; and all 3, 21%. Among patients without heart failure, 39%reported ACEI use in 2002; consistent use was 20%. Among heart failure patients, ACEI use was 51%in 2002 and consistent use, 39%. Except for ACEIs among patients without heart failure, consistent use was associated with lower adjusted mortality: Aspirin hazard ratio(HR), 0.58 and 95%confidence interval(CI), 0.54 to 0.62; β-blockers, HR, 0.63 and 95%CI, 0.59 to 0.67; lipid-lowering therapy, HR, 0.52 and 95%CI, 0.42 to 0.65; all 3, HR, 0.67 and 95%CI, 0.59 to 0.77; aspirin and β-blockers, HR, 0.61 and 95%CI, 0.57 to 0.65; and ACEIs among heart failure patients, HR, 0.75 and 95%CI, 0.67 to 0.84. Conclusions -Use of evidence-based therapies for CAD has improved but remains suboptimal. Although improved discharge prescription of these agents is needed, considerable attention must also be focused on understanding and improving long-term adherence.
Bi-directional NDVI and atmosphere-coupled BRDF inversion

The normalized difference vegetation index (NDVI) has been widely applied in optical remote sensing. It has been demonstrated that NDVI is still partially affected by atmospheric path scattering and bi-directional (illumination and viewing geometry) effects. We present a feature of using bi-directional NDVI. Based on the assumption that a clear day has a larger NDVI value than a dusty day (smaller atmospheric path scattering in near infrared band and larger atmospheric path scattering in red band), we used the square ratio of observed NDVI values and expected NDVI values as weights for the observations. The initial weights for each observation are calculated by the ratio of the observed NDVI value and mean NDVI value of all bi-directional observations. The inversion process will loop until all weights converge, while the expected NDVI values are calculated from the previous loop's model prediction. Our preliminary research on the early Terra/MODIS data using the semi-empirical kernel-driven bi-directional reflectance distribution function (BRDF) model (RossThick-LiTransit) shows that this new method can improve the inversion results when some of the cloudy pixels are not filtered out. As sub-pixel cloudiness is always a problem, this technique should still be very useful even as cloud detection and atmospheric correction get better.
Data Mining Techniques for Communities’ Detection in Dynamic Social Networks

Social network analysis studies relationships between individuals and aims at identifying interesting substructures such as communities. This type of network structure is intuitively defined as a subset of nodes more densely linked, when compared with the rest of the network. Such dense subgraphs gather individuals sharing similar property depending on the type of relation encoded in the graph. In this chapter we tackle the problem of identifying communities in dynamic networks where relationships among entities evolve over time. Meaningful patterns in such structured data must capture the strong interactions between individuals but also their temporal relationships. We propose a pattern discovery method to identify evolving patterns defined by constraints. In this paradigm, constraints are parameter- ized by the user to drive the discovery process towards potentially interesting patterns, with the positive side effect of achieving a more efficient computation. In the proposed approach, dense and isolated subgraphs, defined by two user-parameterized constraints, are first computed in the dynamic network restricted at a given time stamp. Second, the temporal evolution of such patterns is captured by associ- ating a temporal event types to each subgraph. We consider five basic temporal events: the formation, dissolution, growth, diminution and stability of subgraphs from one time stamp to the next one. We propose an algorithm that finds such subgraphs in a time series of graphs processed incrementally. The extraction is feasible thanks to efficient pruning patterns strategies. Experimental results on real-world data confirm the practical feasibility of our approach. We evaluate the added-value of the method, both in terms of the relevancy of the extracted evolving patterns and in terms of scalability, on two dynamic sensor networks and on a dynamic mobility network.
Android Based Rush And Drunk Driver Alerting System

The Rush driving and driving after consumption of alcohol is a major cause of road accident worldwide. In this paper we proposed an idea to alert rush and drunk driver at early stage before actual accident take place. Complete system works on a cell phone having software installed in it with some sensor. The program calculates the sensor reading and matches with the pattern already present in system, if match found then an alert will give to driver and also a text message will send to the relative asking for help with location details. This system will design on an android based cell phone. Our proposed system will prevent many accidents to occur and save lives.
A Comparative Study of Digital Filtering Techniques in Ultrasound and Carotid Bifurcation Atherosclerosis

Digital Object Identifier 10.32754/JMT.2018. 1.01 5 Abstract—The current paper presents a method for speckle cancellation in digital images obtained in ultrasound and carotid bifurcation atherosclerosis. Filtering techniques are usually used for noise reduction and contrast enhancement in digital images. Due to interferences and other phenomena in the acquisition chain, ultrasound images usually have perturbations and artifacts. Speckle is often considered a powerful source of noise in the ultrasound imagery and it must be filtered without affecting the rest of the most important features in the image. Linear, nonlinear, diffusion and wavelet filters are applied on various ultrasound images of carotid bifurcation. In order to evaluate the quality difference between original and anti-speckle filtered images, a comparative study of results is presented at the end.
Employing Evidence in Evaluating Complementary Therapies: Findings from an Ethnography of Integrative Pain Management at a Large Urban Pediatric Hospital.

BACKGROUND Complementary and Integrative Medicine (CIM) shows positive clinical benefit with minimal side effects, yet, challenges to effective integration of CIM providers in biomedical health care settings remain. This study aimed to better understand the role evidence played in the process of integration of complementary therapies into a large urban pediatric hospital from the perspective of patients, caregivers, providers, and administrators through applied medical ethnography. METHODS An ethnography was conducted over the course of 6 months in a large urban pediatric hospital in the Southern United States. At the time, the hospital was piloting an integrative medicine (IM) pain consult service. Purposive sampling was used to select providers, patients, administrators, and caregivers to follow as they engaged with both the preexisting pain service and pilot IM pain services. Field observation and interviews were conducted with 34 participants. Thematic content analysis was used to analyze field notes, interview transcripts, and documents collected. FINDINGS Analysis of the data revealed five themes regarding the role of evidence in the process of integration: Anecdotal Evidence and Personal Experience Effecting CIM Use, Open to Trying Whatever Works, Resistance to the Unfamiliar, Patients and Parents Trusting Doctors as Experts, and Importance of Scientific Evidence to Biomedicine. All themes address the role of evidence in relationship to acceptance of complementary therapies and correspond with three thought processes: critical thinking, relying on others for evidence, and reactionary thinking. CONCLUSION Both scientific and anecdotal evidence informed CIM use in this context. Biomedical environments seeking to integrate CIM into pain treatment regimens will need to engage all relevant stakeholders in building education, clinical training, and research programs that are critically informed about the context and evidence for CIM.
Viewing the Dictionary as a Classification System

Information retrieval is one of the earliest applications of computers. Starting with the speculative wode of Vannevar Bush on Memex [Bush 45], to the development of Key Word in Context (KWIC) indexing by H.P. Luhn [Luhn 60] and Boolean retrieval by John Horty [Horty 62], to the statistical techniques for automatic indexing and document retrieval done in the 1960's and continuing to the present [Salton and McGill 83], Information Retrieval has continued to develop and progress. However, there is a growing consensus that current generation statistical techniques have gone about as far as they can go, and that further improvement requires the use of natural language processing and knowledge representation. We believe that the best place to start is by focusing on the lexicon, and to index documents not by words, but by word senses. Why use word senses? Conventional approaches advocate either indexing by the words themselves, or by manual indexing using a controlled vocabulary. Manual indexing offers some of the advantage of word senses, in that the terms are not ambiguous, but it suffers from problems of consistency. In addition, as text data bases continue to grow, it will only be possible to index a fraction of them by hand. In advocating word senses as indices we are not suggesting that they are the ultimate answer. There is much more to the meaning of a document then the senses of the words it contains; we are just saying that senses are a good start. Any approach to providing a semantic analysis must deal with the problem of word meaning. Existing retrieval systems try to go beyond single words by using a thesaurus,l but this has the problem that words are not synonymous in all contexts. The word 'term' may be synonymous with 'word' (as in a vocabulary term), 'sentence' (as in a prison term), or 'condition' (as in 'terms of agreement'). If we expand the query with words from a thesaurus, we must be careful to use the right senses of those words. We not only have to know the sense of the word in the query (in this example, the sense of the word 'term '), but the sense of the word that is being used to augment it (e.g., the appropriate sense of the word 'sentence'). The thesaurus we use should be one in which the senses of words are explicitly indicated [Chodorow et al. 88]. We contend that the best place to obtain word senses is a machine-readable dictionary. Although it is possible that another list of senses might be manually constructed, this strategy might cause some senses to be overlooked, and the task will entail a great degree of effort.
Text Mining Process, Techniques and Tools : an Overview

Text Mining has become an important research area, which refers to the application of machine learning (or data mining) techniques in the study of Information Retrieval and Natural Language Processing. In sense, it is defined as the way of discovering knowledge from ubiquitous text data which are easily accessible over the Internet or the Intranet. The survey of Text Mining techniques, Text Mining Applications, literature survey of various applications and tools has been presented. Text Mining techniques like Document clustering and Document Classification have been presented. Text mining based framework for applications like Summarization, Topic Discovery, Information Extraction, Information Retrieval terms and techniques in each method has been discussed. Various text mining and data visualization tools for application to patent information like their working mode, capabilities, data sources and result output have been presented. In depth analysis of algorithm related to classification techniques its advantages and disadvantages and the working mode has been presented.
Sistema de Previsions Horàries d'Energia Eòlica basat en Màquines de Vectors de Suport

En aquest treball es descriu el sentit estrategic de realitzar previsions de la produccio d’energia eolica i es proposa un sistema de prediccio a curt termini de la potencia electrica horaria generada per un parc eolic basat en els models no parametrics desenvolupats originalment per Vladimir Vapknik: les maquines de vectors de suport. Concretament es desenvolupen estrategies basades en l’aplicacio d’aquests algorismes per a regressio. D’aquesta manera, s’avalua la precisio dels models utilitzant unicament variables predictores numeriques de les produccions del parc eolic, sense tenir en compte les condicions meteorologiques o altres factors fisics de l’entorn.
Pulse-density modulation technique in VLSI implementations of neural network algorithms

New implementations of fully connected neural network architecture are explored, and some efficient implementations based on the pulse-density modulation technique are presented. These VLSI circuits are fully programmable, thereby usable in many applications. The architecture is implemented by using two different approaches: analog implementation with switched-capacitor structures and fully digital implementation. The approaches are also compared from the VLSI point of view. The advantage of the switched-capacitor implementation is the small area of a synapse, thus relatively large networks can be implemented. The architecture of the network is also regular, modular, and easy to expand. For the same complexity of network architecture, the digital implementation requires 30% more silicon area, which can be considered quite insignificant. The advantage of the fully digital implementation is good expandability to larger networks. In addition, single circuits can be joined together to form very large networks. >
Modèles Probabilistes pour l'Interaction entre agents

Dans un contexte d'interaction Humain-Machine, notre objectif est l'elaboration d'un modele probabiliste d'interaction generique et vraisemblable capable de commander a la fois un Agent Conversationnel Anime (ACA) dans un cadre d'interaction et un Agent Musical Creatif (AMC) dans un contexte d'improvisation musicale.
ABSTRACT OF PhD THESIS Automatic Semantic Role Labeling using Selectional Preferences with Very Large Corpora Determinación Automática de Roles Semánticos usando Preferencias de Selección sobre Corpus muy Grandes

We present a method for recognizing semantic roles for Spanish sentences. This method is based on dependency parsing using heuristic rules to infer dependency relationships between words, and word co-occurrence statistics (learnt in an unsupervised manner) to resolve ambiguities such as prepositional phrase attachment. If a complete parse cannot be produced, a partial structure is built with some (if not all) dependency relations identified. Evaluation shows that in spite of its simplicity, the parser’s accuracy is superior to the available existing parsers for Spanish. Though certain grammar rules, as well as the lexical resources used, are specific for Spanish, the suggested approach is language-independent. A particularly interesting ambiguity which we have decided to analyze deeper, is the Prepositional Phrase Attachment Disambiguation. The system uses an ordered set of simple heuristic rules for determining iteratively the relationships between words to which a governor has not been yet assigned. For resolving certain cases of ambiguity we use cooccurrence statistics of words collected previously in an unsupervised manner, whether it be from big corpora, or from the Web (through a search engine such as Google). Collecting these statistics is done by using Selectional Preferences. In order to evaluate our system, we developed a Method for Converting a Gold Standard from a constituent format to a dependency format. Additionally, each one of the modules of the system (Selectional Preferences Acquisition and Prepositional Phrase Attachment Disambiguation), is evaluated in a separate and independent way to verify that they work properly. Finally we present some Applications of our system: Word Sense Disambiguation and Linguistic Steganography.
Bilateral medial epicondyle fractures with elbow dislocations in an adolescent female athlete

To date, only two cases of bilateral elbow dislocations with medial epicondyle fractures have been reported. The current case is an 11-year-old girl who sustained bilateral elbow dislocations with medial epicondyle fractures by landing on extended elbows while performing a back handspring on a trampoline. Closed reduction and splinting were performed in the emergency room. Open reduction with screw fixation of the medial epicondyle fractures was performed in the operating room. Post-operatively, the patient was splinted for eight days followed by range of motion exercises. She returned to full sports participation at 15 weeks. On final follow-up 24 months later, the patient had full range of motion, normal strength, and normal neurovascular examination. Compared to the two previous case reports in the literature, this is the only case report demonstrating full range of motion, full strength, and full return to sports after bilateral elbow dislocation with medial epicondyle fractures. Keys to complete recovery included internal fixation with a screw and washer followed by early range of motion.
Characterisation and Applications of MANET Routing Algorithms in Wireless Sensor Networks

Wireless Sensor Networks (WSN) are networks of low-cost, low-power, multifunctional devices that can be used to monitor physical phenomena. Communication between nodes in a vast network requires routing mechanisms. A Specknet is a programmable computational network of large numbers of minute semiconductor components with integrated sensing, processing and wireless networking capabilities called Specks. This thesis sets to analyse the feasibility of usingMobile Ad-hoc Network (MANET) routing mechanisms in Wireless Sensor Networks, and to investigate application domains where MANET algorithms can be used. Therefore, the Destination Sequenced Distance Vector (DSDV) and Zone Routing Protocol (ZRP) algorithms, and a Visitor Tracking System (VTS) application using the DSDV algorithm were implemented on prototypes of Specks called the ProSpeckz. The suitability of the algorithms and the performance of the application are analysed using several experiments. The results of the experiments indicate that MANET algorithms such as the DSDV algorithm are candidates for use in certain WSN applications. It is also learnt that the DSDV algorithm, while more responsive when used in small networks, is less scalable than the ZRP algorithm. Future work includes improving the performance of the implementations analysed by performing optimisations to MAC algorithm characteristics and additions to network layer functionality, and extending the ZRP algorithm to new application domains.
Spelling with Brain-Computer Interfaces Current trends and prospects

Brain-Computer Interfaces (BCIs) have become a large research field that include challenges mainly in neuroscience, signal processing, machine learning and user interface. A BCI can allow the direct communication between humans and computers by analyzing brain activity. The main purpose for BCIs is to enable communication for people with severe disabilities. Indeed, spelling was one of the first BCI application. While spelling can be the most basic application, it remains a benchmark for communication application and one major challenge in the BCI community. This paper focuses on the current main strategies for spelling words. It includes recent BCIs based on P300, Steady-State Visual Evoked Potentials (SSVEP) and motor imagery. This work highlights some current challenges in BCI spellers and virtual keyboards.