Miracle-dz/UniFAR-FTUs huggingface dataset:
Neuro-Symbolic Integration with Association Rule Mining

Most of the knowledge-based systems require the extraction of meaningful patterns from large amount of text data to handle the indeterminacy present in the system. In this paper, we propose one such method which identifies the relationships between the data with in a neuro-symbolic integration framework to extract meaning ful patterns. The method provides an integration of neuro computing with the symbolic structures and with the association rule based methods. The integration is in such a way that to extract the knowledge from data in the form of symbolic rules and then to derive infereces from that with the help of association rules for making proper decisions. We describe our method with a case study on a medical diagnosis problem by identifying the relationship between diseases and the associated symptoms for decision making tasks. We used an association rule mining algorithm to find the relationships between the diseases and symptoms. With the implementation of association rule mining in neurules, the accuracy of the inference mechanism and the generalization performance is improved.
Determination Of Near-surface Wind Speeds From Sunglint Data Ill Band 2 Of The Averr And Comparison With Meterorological Data For The Western Mediterranean

We have used the well-known theory of Cox and Munk to determine near-surface windspeeds from AVHRR visible/near-infrared band data for 23 May, 21 June, 28 June 'nd 5 July 1982 for areas of sunglint in the western Mediterranean. The results have been compared with meteorological data obtained from the UK Meteorological Office for these dates in this area. The discrepancies are generally not unreasonably large, moreover it is by no means clear whether the discrepancies arise from errors in the meteorological data or from errors in the retrieval from the satellite-data. · The relevance of this work to the validation of wind-speeds that are expected to be derived from ERS-1 microwave data is considered briefly. An example of the temperature rise associated with the skin effect in an area of calm water in one scene has also been calculated.
Der orale Biofilm – neue Perspektiven zu einem alten Thema ? The oral biofilm – new views on an old topic ?

Einleitung: Der kariespathogene Biofilm ist Gegenstand umfassender Studien mit modernen biowissenschaftlichen Techniken. Material und Methode: Dazu zählen molekularbiologische Verfahren zur Identifizierung des Mikrobioms ebenso wie Untersuchungen zum Metabolom und zur Funktion, Komposition und Ultrastruktur der extrazellulären Matrix. Prinzipiell können mit den teilweise sehr aufwendigen Methoden umfassende Informationen und Daten generiert werden. Ergebnisse: Für einen tatsächlichen Erkenntnisgewinn zu den Pathomechanismen von Kariesinitiation und Kariesprogression ist es jedoch erforderlich, die Daten adäquat statistisch aufzubereiten und die Ergebnisse der verschiedenen Methoden vernetzt zu interpretieren. Auf diese Weise können neue Strategien zur Früherkennung kariesgefährdeter Individuen aber auch neue Ansätze für die Kariesprävention und für das Biofilmmanagement entwickelt werden. Schlussfolgerung: Die aktuellen Entwicklungen belegen die Relevanz von biowissenschaftlicher Grundlagenforschung in der Zahnmedizin zu den Hintergründen der häufigsten Infektionskrankheit – der Karies. Der vorliegende Übersichtsartikel gibt einen Überblick über aktuelle Studien und methodische Ansätze. (Dtsch Zahnärztl Z 2014; 69: 658–673)
Numerical investigations of the wall shear stress behaviour in curved coronary arteries

This paper presents numerical investigations on wall shear stress (WSS) profiles in three-dimensional pulsatile flow through curved stenotic coronary arteries. Clinical evidence has shown that plaque tends to form in the low and oscillatory WSS regions. Direct numerical simulations are carried out in threedimensional models of the curved arteries with three different degrees of stenosis (0%, 50% and 90%). Low and oscillatory WSS is found near the inner wall of curved artery distal to the stenosis. It is only in the severe stenosis case that a chaotic WSS region is observed downstream of stenosis.
A High-Performance Low Cost SAD Architecture for Video Coding

This paper presents a high-performance low cost sum of absolute difference (SAD) architecture for motion estimation, which consumes a lot of computation and resource in video coding. Unlike many hardware implementations based on complement adders, this paper features the comparison of unsigned numbers to generate partial results of SAD and determine minimum SAD. Furthermore, the compression array unit is implemented by 4-2 compressor. This general-purpose architecture is implemented with a 2-stage pipeline and it is suitable for block-based video compression standards, such as MPEG-4, H.263 and H.264/AVC. Performance analysis shows that compared with other SAD architectures, the proposed architecture reduces 15.3%-22.9% area at almost no cost of latency.
Case-Based Software Quality Prediction

Highly reliable software is becoming an essential ingredient in many systems. However, assuring reliability often entails time-consuming costly development processes. One cost-effective strategy is to target reliability-enhancement activities to those modules that are likely to have the most problems. Software quality prediction models can predict the number of faults expected in each module early enough for reliability enhancement to be effective. This paper introduces a case-based reasoning technique for the prediction of software quality factors. Case-based reasoning is a technique that seeks to answer new problems by identifying similar "cases" from the past. A case-based reasoning system can function as a software quality prediction model. To our knowledge, this study is the first to use case-based reasoning systems for predicting quantitative measures of software quality. A case study applied case-based reasoning to software quality modeling of a family of full-scale industrial software systems. The case-based reasoning system's accuracy was much better than a corresponding multiple linear regression model in predicting the number of design faults. When predicting faults in code, its accuracy was significantly better than a corresponding multiple linear regression model for two of three test data sets and statistically equivalent for the third.
Intelligent visual data analysis service in the Internet

Descartes is a knowledge-based system supporting exploratory analysis of spatially referenced data with the use of maps. It is capable of automated generation of maps and other graphical displays that represent data selected for analysis. Besides, the system selects and combines appropriate analytical instruments (graphical displays, data transformations, interactive operations etc.) depending on the analysis task the user needs to perform and instructs the user how to employ these instruments for this task. The intelligent behaviour of the system is based on three kinds of knowledge: the rules of data representation depending on the characteristics of the data and relationships between data components, the possible analysis tasks and correspondence between the tasks and the available instruments, and domain-specific knowledge allowing the system to "understand" the content of data. The services of the system are available in the WWW.
Extended Bootstrap Bias Correction with Application to Multilevel Modelling of Survey Data under Informative Sampling

Data available to analysts are often obtained from complex sample surveys in which population units are selected by stratified multi-s tage designs with unequal selection probabilities. Unweighted estimators of the model p arameters may be severely biased in such cases if the selection probabilities are relat ed to the outcome values even after conditioning on the model covariates (informative s ampling). Probability weighting reduces the bias very significantly but does not el iminate it, unless the sample sizes at the various levels of the model hierarchy are very larg e. In this paper we propose a general approach for bias correction based on resampling pr ocedure. We assess the performance of our proposed approach by an extensive simulation study using probability weighted estimators of two-level model parameters when fitti ng complex survey data under informative sampling designs. The proposed method showed to be effective in bias reduction in all the cases considered.
Explicit algebraic Reynolds stress models for boundary layer flows 1

1. Explicit algebraic models Two explicit algebraic models are here compared in order to assess their predictive capabilities in the simulation of boundary layer flow cases. The studied models are both coupled with a twoequations closure following the so-called effective viscosity concept. The turbulent velocityand length-scale are determined by solving conservation equations for the turbulent kinetic energy (k) and the commonly used scalar turbulent dissipation rate (ε). In particular, the transport equation for the dissipation rate (ε) is implemented on the basis of the model proposed by Jones and Launder (1972). The carried out numerical campaigns are limited to high-Reynolds number (HRN) flow regimes, thus the anisotropic turbulence closures under investigation are applied to the classical synthetic wall treatment. A detailed description of the adopted set of wall boundary conditions is given in section 4. Plane channel flow numerical model. Both the algebraic models are evaluated against the performance characteristics of the standard k-ε model proposed by Jones and Launder (1972), labeled as JL72.
Numerical Simulations on Drag Reduction Mechanism by Microbubbles

Kazuyasu, SUGIYAMA*, Takafumi KAWAMURA**, Shu TAKAGI*** and Yoichiro MATSUMOTO*** *Ship Performance Division, National Maritime Research Institute 6-38-1, Shinkawa, Mitaka-shi, Tokyo, 181-0004, JAPAN **Department of Environmental and Ocean Engineering, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, JAPAN ***Department of Mechanical Engineering, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, JAPAN
Inhabitants Tracking System in a Cluttered Home Environment Via Floor Load Sensors

Home automation systems should evolve to the next phase in which they are aware of contexts, because providing services based on contexts will upgrade the service quality, thus making people more comfortable and home living more convenient. In particular, location is a piece of important and useful context information for seeking appropriate services, as well as providing them to people living in a home. So far, there have been several studies focused on tracking inhabitants in smart home environments. However, these approaches are often intrusive or require inhabitants to wear some sort of devices, which may make the inhabitants uncomfortable or even inconvenient. This problem could devastate the ultimate goal, which is to provide convenient services, and hence cause such approaches somewhat controversial. In this study, we utilize a number of load sensors together to construct a sensory floor on which exerted pressure can be detected and cover its surface with wooden flooring as in a normal home environment. Although the wooden flooring provides a flat surface for inhabitants to walk on, it also causes clutter in the sensor readings, which lead to difficulty in clearly identifying the location(s) of pressure source(s). Thus, we apply probabilistic data association technique and LeZi-Update approach to analyze the cluttered pressure readings collected by the load sensors so as to determine the positions of the inhabitants, as well as to track their movements. With our nonintrusive approach, there is no need for inhabitants to wear any devices, and this also solves the cross-walking problem in the home environment. Note to Practitioners-This system aims for detecting the location of multiple inhabitants in the home environment. We adopt the sensory floor as our tracking sensor, which consists of many blocks, each with a load cell to collect the human body weight. These blocks are covered with conventional wooden flooring to provide a flat surface on which inhabitants can walk freely. After collecting data, we apply mathematical techniques to analyze them, thus determining inhabitants' locations and tracking their movements. The limitation is that the system cannot differentiate different persons if they have close weight readings, which is the natural limitation of the load cell.
BIOMASS ENERGY PRODUCTION BY CULTIVATING FIBER SORGHUM IN GREECE UNDER INTELLIGENT IRRIGATION METHODS

Sorghum (Sorghum bicolor L. Moench) has been recognized as an alternative energy crop cultivated for biomass which used as a raw material in production of syrup or dry matter. In Central Greece (Velestino, Volos) its main growth period coincides with the dry season and irrigation is needed for reasonable biomass production. The objective of this work is to research the biomass production under intelligent irrigation methods using surface and subsurface drip irrigation systems. Experiments were carried out during 2007 cultivating period. Three irrigation treatments: I50, I70, I100 corresponding to the water supply, surface (DI) and subsurface (SDI), of 50% of daily evapotranspiration (ETd), 70% ETd, 100% ETd, respectively, were experimented with each other and with a fourth complete automated one (AI100). The daily water needs were determined by an A class evaporation pan. The AI100 treatment included soil moisture sensor which measured the fluctuation of moisture content. The amount of water was equal to 100% ETd and irrigation time lasted until soil moisture content rose from 70% to 100% of field capacity. Maximum above ground dry biomass was 42.88Mg/ha in subsurface treatment SDI100. The total dry biomass production and the water use efficiency were affected.
2 Related Work 2 . 1 Argument Structure Analysis

Argumentative relation classification is the task of determining the type of relation (e.g., support or attack) that holds between two argument units. Current state-of-the-art models primarily exploit surface-linguistic features including discourse markers, modals or adverbials to classify argumentative relations. However, a system that performs argument analysis using mainly rhetorical features can be easily fooled by the stylistic presentation of the argument as opposed to its content, in cases where a weak argument is concealed by strong rhetorical means. This paper explores the difficulties and the potential effectiveness of knowledge-enhanced argument analysis, with the aim of advancing the state-of-the-art in argument analysis towards a deeper, knowledge-based understanding and representation of arguments. We propose an argumentative relation classification system that employs linguistic as well as knowledge-based features, and investigate the effects of injecting background knowledge into a neural baseline model for argumentative relation classification. Starting from a Siamese neural network that classifies pairs of argument units into support vs. attack relations, we extend this system with a set of features that encode a variety of features extracted from two complementary background knowledge resources: ConceptNet and DBpedia. We evaluate our systems on three different datasets and show that the inclusion of background knowledge can improve the classification performance by considerable margins. Thus, our work offers a first step towards effective, knowledge-rich argument analysis. 2012 ACM Subject Classification Computing methodologies → Neural networks; Information systems → Graph-based database models; Information systems → Clustering and classification
The Digital Emily project: photoreal facial modeling and animation

This course describes how high-resolution face scanning, advanced character rigging, and performance-driven facial animation were combined to create Digital Emily, a believably photorealistic digital actor. Actress Emily O'Brien was scanned in the USC ICT light stage in 35 different facial poses using a new high-resolution face-scanning process capable of capturing geometry and textures down to the level of skin pores and fine wrinkles. These scans were assembled into a rigged digital character, which could then be driven by Image Metrics video-based facial animation technology. The real Emily was captured speaking on a small set, and her movements were used to drive a complete digital face replacement of her character, including its diffuse, specular, and animated displacement maps. HDRI lighting reconstruction techniques were used to reproduce the lighting on her original performance. The most recent results show new real-time animation and rendering research for the Digital Emily character.
Halftone image resampling by interpolation and error-diffusion

Halftoning schemes are developed for reserving the quality after transforming continuous tone images to binary images under human vision. It is wildly used in printing and outputting. So far most of halftoning schemes focus on the algorithms for halftoning transformation and inverse halftoning transformation. However, the resampling algorithm for halftone images is still insufficient, only a few research focusing on shirking halftone images. In this paper, we propose a new halftone image resampling scheme based on interpolation and error-diffusion. The proposed scheme can directly shrink and magnify halftone images to any desired scale. It does not need to transform back and forth form continuous-tone images anymore. In the experimental results, the quality of halftone image is superior to previous works.
Creating an online tutorial to support information literacy and academic skills development

This paper explores an approach to creating an online tutorial to support postgraduate distance learners in the School of Geography at the University of Leeds. The process used by the author, the faculty team librarian for geography, is described through the planning and design stages, to development and application, and finally evaluation and future uses of the tutorial. The aim of the tutorial was to provide a resource students could use to improve and develop their information literacy (IL) and academic skills, addressing the process of producing a written assignment, from planning, to selecting search tools and techniques, to reading and critical thinking, through to writing up and referencing. In planning and designing the tutorial, the content covered in the face-to-face workshop for postgraduate students on finding, managing and evaluating information was reviewed, and information on library support for distance learners added. To accommodate differing learning styles, and encourage learning by doing, activities such as drag-and-drop exercises, quizzes and videos were used. A learning technologist was consulted for advice on look and feel, navigation and techniques to improve interactivity. The tutorial was made available via the virtual learning environment (VLE) and students were encouraged by their tutor to look at the tutorial and fill in a short feedback survey. Obtaining qualitative feedback from students in an online environment proved challenging, and ultimately was a major limitation in providing a thorough evaluation of the tutorial. There were also difficulties in obtaining quantitative data on use of the tutorial. With little evidence of whether the tutorial had been helpful to students, or had enabled them to improve their IL skills, decisions as to its continuation had to be made based on feedback from academic staff and the perceived benefits, identified by library staff, of reusing the tutorial as a resource to support face-to-face workshops. While not every IL practitioner will have access to the software used (Articulate), or have the assistance of a learning technologist, the principles of developing online tutorial learning materials, the advice from the learning technologist, and the explanation of limitations and lessons learned will be of interest to any library professional or IL practitioner wishing to create online learning resources.
A retrospective glance at automatic detection of epileptic spike in electroencephalogram

Automatic detection of epileptic spikes is an important clinical application. It has been developed nearly 40 years. Yet the current automatic detection results are still not as reliable as experienced human interpreters, mainly due to the complex morphology of spikes and the similarity between paroxysmal events in brain activities. By reviewing the previous work, it is noticeable that the implementation of wavelet, ANN and spatiotemporal analysis show promising prospect. By reasonable combination of detection strategies, the detection rate can reach 90% with acceptable false detection rate. Current researches also reveal the reality of lacking of uniform dataset and rules for algorithm comparison. This review aims to compare the pros and cons of current researches and discuss the trend of development in the future.
Local distribution for IMTV

The local distribution system for an interactive multimedia television (IMTV) system builds on a fiber-to-the-curb (FTTC) architecture that delivers the IMTV signals to and from the home over telephone wiring and coaxial cable. The downstream IMTV channel, from the curb to the home, operates at a data rate of 51.84 Mbps, and the upstream channel, from the home to the curb, operates at a data rate of 1.62 Mbps.
Hepatopancreaticoduodenectomy for Biliary Cancer ality and Acceptable Long-term Outcome Strategies for Near-zero Operative

Objective: The purpose of this study was to evaluate the safety and efficacy of hepatopancreaticoduodenectomy (HPD) for patients with biliary cancer. Background: HPD is thought to be the only curative treatment for widespread bile duct cancer and for some advanced cases of gallbladder cancer; however, HPD has not yet been accepted as a standard operative procedure because of concerns over morbidity and mortality. Methods: Fifty-two patients undergoing HPD were retrospectively reviewed. The patient and tumor characteristics, preoperative treatments, operative results, and survival outcomes were investigated. Results: Preoperative biliary drainage and portal vein embolization were applied for all patients undergoing right-sided HPD or a left trisectionectomy. A major hepatectomy was performed in 42 patients, and a 2-stage pancreaticojejunostomy was selected in all the cases. The 90-day mortality was 0; however, 1 patient died because of a liver abscess 230 days after surgery. Postoperative significant complications (grade III or greater) and liver insufficiency were observed in 19 (37%) and 2 (3.8%) patients, respectively, and no abdominal bleeding events after the formation of a pancreatic fistula were encountered. The 5-year overall survival rate was 44.5%, and a significant difference was not observed between patients with bile duct cancer and those with gallbladder cancer. The operative procedure was switched to an HPD in 13 patients based on intraoperative findings, and the recurrence-free survival rate for these patients was poorer than that for patients who did not require a switch in operative procedure (P 1⁄4 0.004). Conclusions: HPD can be safely performed using the presently reported surgical strategies with acceptable short and long-term outcomes. A precise assessment of the extent of tumor spread might improve patient outcome.