Miracle-dz/UniFAR-FTUs huggingface dataset:
Food proteins as a source of bioactive peptides with diverse functions

In addition to supplying essential nutrients, some food proteins can confer additional health benefits beyond nutrition. The presence of bioactive proteins and peptides in different foods is a factor not currently taken into consideration when assessing the dietary quality of food proteins. The range of described physiological benefits attributed to bioactive proteins and peptides is diverse. Multiple factors can potentially impact on the ability of a bioactive peptide or protein to elicit an effect. Although some food proteins act directly in their intact form to elicit their effects, generally it is peptides derived from digestion, hydrolysis or fermentation that are of most interest. The levels of bioactive peptides generated must be sufficient to elicit a response, but should not be so high as to be unsafe, thus causing negative effects. In addition, some peptides cause systemic effects and therefore must be absorbed, again in sufficient amounts to elicit their action. Many studies to date have been carried out in vitro; therefore it is important that further trials are conducted in vivo to assess efficacy, dose response and safety of the peptides, particularly if health related claims are to be made. Therefore, methods must be developed and standardised that enable the measurement of health benefits and also the level of bioactive peptides which are absorbed into the bloodstream. Once standardised, such methods may provide a new perspective and an additional mechanism for analysing protein quality which is currently not encompassed by the use of the protein digestibility-corrected amino acid score (PDCAAS).
Preference-based Search and Machine Learning for Collaborative Filtering: the "Film-Conseil" Movie Recommender System

This paper introduces a new approach for decision support on the internet. It is characterized by a preference-based filtering relying on the integration of content-based and collaborative filtering principles. We present algorithms that support a key aspect of recommender systems that was absent in early systems: the ability to explain and justify recommendations. A tight integration of preference modeling and machine learning is proposed to support both collaborative decision making and active rating. This integration addresses the problem of both efficiency and storage to scale up with large community of users. For the sake of illustration, we present the main features of the “film-conseil” system which implements the proposed approachfor movie recommendation tasks on the internet.
General Quantum Modeling of Combining Concepts: A Quantum Field Model in Fock Space

We extend a quantum model in Hilbert space developed in Aerts (2007a) into a quantum eld theoric model in Fock space for the modeling of the combination of concepts. Items and concepts are represented by vectors in Fock space and membership weights of items are modeled by quantum probabilities. We apply this theory to model the disjunction of concepts and show that the predictions of our theory for the membership weights of items regarding the disjunction of concepts match with great accuracy the complete set of results of an experiment conducted by Hampton (1988b). It are the quantum eects of interference and superposition of that are at the origin of the eects of overextension and underextension observed by Hampton as deviations from a classical use of the disjunction. It is essential for the perfect matches we obtain between the predictions of the quantum eld model and Hampton’s experimental data that items can be in superpositions of ‘dierent numbers states’ which proves that the genuine structure of quantum eld theory is
Tracking interacting targets with laser scanner via on-line supervised learning

Successful multi-target tracking requires locating the targets and labeling their identities. For the laser based tracking system, the latter becomes significantly more challenging when the targets frequently interact with each other. This paper presents a novel on-line supervised learning based method for tracking interacting targets with laser scanner. When the targets do not interact with each other, we collect samples and train a classifier for each target. When the targets are in close proximity, we use these classifiers to assist in tracking. Different evaluations demonstrate that this method has a better tracking performance than previous methods when interactions occur, and can maintain correct tracking under various complex tracking situations.
Renal transplantation in systemic vasculitis: when is it safe?

BACKGROUND There are no clear guidelines on renal transplantation in patients with antineutrophil cytoplasmic antibody (ANCA)-positive vasculitis. METHODS We undertook a survey of transplant centres across Europe to assess whether there was consensus about how to manage transplantation in patients with vasculitis. We then identified 107 renal allograft recipients whose primary disease was systemic vasculitis and assessed their outcome post-transplant. RESULTS All questionnaire respondents felt that vasculitis should be in remission at transplantation, 16% believed that ANCA should be negative pre-transplant and 40% felt that one should wait >12 months after remission before transplanting. Remission was defined by all as an absence of clinical symptoms of vasculitis, but three respondents (13%) also required a negative ANCA test. Overall graft survival was 70% after 10 years (95% C.I. 58-82). A total of 30 (41% of those with known ANCA status) were ANCA-positive peri-transplantation, while 15 (14%) were transplanted <1 year post-remission. Severe vasculopathy occurred more frequently in ANCA-positive recipients (odds ratio 4.4, 95% C.I. 1.1-16.8, P < 0.05), although causation cannot be determined from this study. Vasculopathy significantly reduced 10-year graft survival to 47% (P < 0.05). However, ANCA status per se was not significantly associated with graft failure. The strongest predictor of death was transplantation <1 year post-vasculitis remission on both univariate and multivariate analysis (hazard ratio 2.3, P < 0.05). CONCLUSIONS In conclusion, circulating ANCA at transplant was associated with the development of vascular lesions in the graft but was not significantly correlated with graft survival. Most grafts were lost due to patient death, which was more likely if transplantation occurred <12 months following induction of remission of ANCA-positive vasculitis.
Two Phase Integrated Rule based Model (TPC-IRBM) for Clustering of Gene Expression Data of CA1 Region of Rat Hippocampus

This paper propose a semi supervised clustering model TPCIRBM(Two phase clustering-Integrated rule based model) for clustering large data set such as gene expression data. TPCIRBM works in two phases to cluster the gene expression data set. The proposed model is based on rule based models CRT,C5,CHAID and QUEST. In the first phase of the model 30 % data(which may vary) is extracted to prepare training, testing and validation data (TTV data)using suitable heuristic or neural network based clustering techniques. The output of first phase is used as build the models and generate the rule base fitting to TTV data using aforesaid models. The proposed model is then constructed by selecting and integrating the quality rules of various models using qualifying criteria corresponding to every cluster.The number of quality rules in proposed model is much more compared to that of CRT,C5,CHAID and QUEST.The performance in terms of accuracy is better compared to the models. Although in some cases Neural Network based models performance is slightly better but a very high cost of complexity for very large data set.
An analog dual delay locked loop using coarse and fine programmable delay elements

In the paper we consider architecture of an analog dual delay locked loop (DLL). It uses an simply-implementable coarse delay line to push the loop close to lock state, and a fine programmable delay line to make small adjustments to the output phase. Both coarse- as well as fine-delay line are implemented as a cascades of variable-delay elements based on single-ended Schmitt triggers. For the correction of a duty cycle of the delayed output signal, particularly in the coarse delay segment, a very effective duty cycle correction (DCC) has been applied. The complete architecture of the DLL utilizing aforementioned building blocks, has also been simulated using Spectre and BSIM3V3 device models for 1.8V 180nm CMOS technology from UMC. Most important simulation results are presented.
Multilingual Acquisition of Large Scale Knowledge Resources

Ph. D. thesis in Computer Science written by Montse Cuadros under the supervision of Dr. Lluis Padro and Dr. German Rigau. The thesis defense was done on 22th November 2011 at the Computer Science Faculty of the Universitat Politecnica de Catalunya. The Doctoral Examination Committee was composed by Dr. Horacio Rodriguez (Universitat Politecnica de Catalunya), Prof. Dr. Piek Vossen (Vrije Universiteit Amsterdam), Dra. Arantza Diaz de Ilarraza (Universidad del Pais Vasco), Dra. Irene Castellon (Universitat de Barcelona) and Dr. Roberto Navigli (Sapienza University of Rome). The thesis was graded Cum Laude.
Impovement of a Label Extraction Method for a Risk Search System

This paper proposes an improvement method of clas- sification efficiency in a classification model. The model is used in a risk search system and extracts specific labels from articles posted at bulletin board sites. The system can analyze the important discussions composed of the articles. The improvement method introduces ensemble learning methods that use multiple classification models. Also, it introduces expressions related to the specific labels into generation of word vectors. The paper applies the improvement method to articles collected from three bulletin board sites selected by users and verifies the effectiveness of the improvement method. Keywords—Text mining, Risk search system, Corporate reputa- tion, Bulletin board site, Ensemble learning I. INTRODUCTION Owing to the progress of internet environments and com- puter environments, many texts are uploaded to the Web. There is a need to analyze the texts, because new knowledge can be buried in the texts. Hu and Liu (6) proposed a method that analyzes customer reviews on the Web. The method mines product features, identifies opinion sentences, and summarizes the results. Morinaga et al. (9) proposed a method that analyzes product reputations. The method extracts opinions by using syntactic and linguistic rules. The method attaches the positive/negative determination, the product name, and the degree of the confidence to the opinions. Also, Kobayashi et al. (7) proposed a method that extracts attribute-value pairs. The method judges whether the pairs express an opinion of the author. In addition, Esuli and Sebastiani (2) proposed SENTIWORDNET for opinion mining. It is a lexical resource in which WordNet synset (8) is associated with three numerical scores. The scores show how objective, positive, and negative the terms contained in the synset are. On the other hand, we have been studying a method of analyzing corporate reputations on bulletin board sites. Sakurai and Orihara (10) proposed a method that extracts labels from articles posted at bulletin board sites, decides important threads based on extracted labels, and extracts characteristic expres- sions of the threads. Also, Sakurai and Orihara (11) proposed a method that revises both the decision as to which threads are important and the extraction of characteristic expressions. Sakurai and Orihara (10) (11) and Sakurai (12) verified its effectiveness. This paper proposes the method, aiming at the improvement of label extraction in order to decrease the workload for checking threads, and verifies the effectiveness of the method through numerical experiments.
A Saliency Detection Model Based on Multi-feature Fusion

In this paper, we develop a saliency detection model which combines low and high level features. This model can be useful to the problems of content missing in image with large scale foreground and false detection in image with complex background when detecting salient regions with the existing models. To seek a solution to avoid content missing, our approach firstly adopted the blur region inhibition to reduce false detection to some extent, and then merged the saliency information both in space and in frequency domain. Experimental results show that the proposed model can solve the problem of content missing and false detection. Comparing to the other five typical models, our method can increase the detection accuracy by at least 16% while can outperform other systems in ROC analysis.
Network Coding-Based Mutual Anonymity Communication Protocol for Mobile P2P Networks

To protect user privacy in mobile peer to peer (MP2P) networks, a network coding-based mutual anonymity communication protocol (NMA) is proposed. Our contributions are described as below. We first design a network coding scheme which can defend against various omniscient adversary attacks. Then a novel anonymous communication protocol is presented to meet the anonymity requirement for MP2P applications. The novel anonymous communication protocol is comprised of three steps: query issuance, reply-confirm and file delivery. They all employ the network coding scheme to split and encrypt the signaling and data information. The splitted fragments are flooded at a certain number of hops until some intermediate peers called agents, can collect enough fragments to recover the original information. Next, the agents forward the messages to their neighboring peers. For the query issuance, the neighboring peers forward the query message to the responders by random walk mechanism. For the rest steps, the data information is delivered along the reversed paths discovered by the way of onion routing plus buffer information in routing table. In the entire process, the identities and sensitive information about the initiator and responder are completely hidden. The advantages of the scheme lie in the fact that the network coding and mutli-agent can improve the load balance, the successful rate of information transmission and anonymity degree. The experimental results demonstrate that when the percentage of malicious peers is lower than 50%, the various performances of the NMA, including the response time and the success rate, outperform other mutual anonymity schemes.
Use of an artificial neural network to capture the domain knowledge of a conventional hydraulic simulation model

As part of the POWADIMA research project, this paper describes the technique used to predict the consequences of different control settings on the performance of the water-distribution network, in the context of real-time, near-optimal control. Since the use of a complex hydraulic simulation model is somewhat impractical for real-time operations as a result of the computational burden it imposes, the approach adopted has been to capture its domain knowledge in a far more efficient form by means of an artificial neural network (ANN). The way this is achieved is to run the hydraulic simulation model off-line, with a large number of different combinations of initial tank-storage levels, demands, pump and valve settings, to predict future tank-storage water levels, hydrostatic pressures and flow rates at critical points throughout the network. These input/output data sets are used to train an ANN, which is then verified using testing sets. Thereafter, the ANN is employed in preference to the hydraulic simulation model within the optimization process. For experimental purposes, this technique was initially applied to a small, hypothetical water-distribution network, using EPANET as the hydraulic simulation package. The application to two real networks is described in subsequent papers of this series.
Apoyo de las relaciones públicas a los programas de responsabilidad social en las empresas del sector eléctrico colombiano

Las relaciones publicas y la responsabilidad social empresarial comparten perspectivas para gestionar las interacciones entre la organizacion y sus grupos de interes. Sin embargo, desde algunas visiones teoricas y aplicadas, las relaciones publicas han tenido una connotacion negativa, de apariencia y ocultamiento de informacion. En este articulo se presentan los resultados de una investigacion de tipo exploratorio, orientada a determinar cual es el rol que cumplen las relaciones publicas en la gestion de los programas de responsabilidad social de un grupo de empresas del sector electrico colombiano. Los resultados indican que entre los consultados no se maneja un concepto unificado sobre las relaciones publicas y se presentan casos en los que la vision que se tiene sobre ellas es negativa
4 Polygonal Representation of Digital Curves

Approximating digital curves using polygonal approximations is required in many image processing applications [Kolesnikov & Fränti, 2003, 2005; Lavallee & Szeliski, 1995; Leung, 1990; Mokhtarian & Mackworth, 1986; Prasad, et al., 2011; Prasad & Leung, 2010a, 2010b; Prasad & Leung, 2010; Prasad & Leung, 2012; Prasad, et al., 2011a]. Such representation is used for representing noisy digital curves in a more robust manner, reducing the computational resources required for processing and storing them, and for computing various geometrical properties of digital curves. Specifically, properties like curvature estimation, tangent estimation, detecting inflexion points, perimeter of the curves, etc., which are very sensitive to the digitization noise. Polygonal approximation is also useful in topological representation, segmentation and contour feature extraction in the applications of object detection, face detection, etc.
Prediction of human collision avoidance behavior by lifelong learning for socially compliant robot navigation

In order to act socially compliant with humans, mobile robots need to show several behaviors that require the prediction of people's motion. For example, when a robot avoids a person, it needs to respect the human's personal space [1] and the avoidance behavior needs to be smooth, so that it is understandable to the interaction partner. To achieve this, the robot needs to reason about future paths a person is likely to follow. Because humans adapt their avoidance behavior to the robot's motion, the proposed method performs lifelong learning of the people's behavior while it adapts its own behavior to their motion. The human avoidance behavior is modeled by a discrete, multi-modal, spatio-temporal distribution over the people's future occurrences. This prediction is based on the people's positions and their velocities relatively to the robot and the obstacle situation of the robot's environment. The proposed prediction method is significantly better than a simple linear prediction. Particularly, for tactical decisions, like whether to avoid a moving person on the left or on the right side, this approach is well suited. Furthermore, when the humans get used to a robot, also a long-term change of the human behavior towards the robot can be learned by our approach.
The language of equus : exploring equine-assisted psychotherapy (EAP) using the Equine Assisted Growth and Learning Association (EAGALA) model

This qualitative study set out to explore equine-assisted psychotherapy (EAP) under the Equine Assisted Growth and Learning Association (EAGALA) model. As EAP is still a fairly new modality of therapy, the intention of this study is to add to the growing EAP literature. Interviews were conducted with fifteen EAGALA certified mental health practitioners (MH) and equine specialists (ES) with the intent to explore their professional insights, personal experiences and perspectives regarding EAP. EAP facilitators discussed the structure, benefits and challenges of the EAGALA model. These EAGALA model EAP facilitators also discussed the therapeutic modality of EAP -revealing its own benefits and challenges. Study results supported existing literature in terms of the benefits that EAP can offer to diverse populations who are experiencing a range of psychological challenges. Insights revealed facilitators’ observations about the power and effectiveness behind EAP. The experiential nature of working with horses as a therapeutic tool offers hope to situations where there may have been none. Findings also indicated EAGALA’s credibility and valuable training and certification program for equine therapy. Implications of this investigation point to the need for further quantitative research, as equine therapy is still an emerging treatment modality offering considerable therapeutic benefits.
Models of pattern formation applied to plant development

Plants are the beautiful results of a chain of complex pattern forming events. Pattern formation—the generation of regular differences in space—occurs at several levels of organization. For example, a particular group of cells at the shoot apex may receive a signal to form a leaf. The leaf will get a polarity, developing an upper and a lower side. Some cells of the leaf may develop into stomata, while chains of other cells may form vascular strands. Individual cells may become polar. All these patterns must ultimately arise from similar mechanisms although they do not act at the same structural level. In this chapter we would like to discuss basic signalling systems among cells that allow to generate different cell types in a defined spatial arrangement and apply them to plant development. We will further show that a linkage of several such pattern forming systems allows the reproducible generation of complex patterns. Models for the generation of the primary shoot-root axis and for the generation of leaf primordia around the meristem will be elaborated. These models are certainly not complete. For instance, shape changes of a tissue (see Gierer, 1977a) or the tissue-specific control of cell proliferation will not be discussed. Comparisons with pattern formation in the freshwater polyp Hydra, with the formation of imaginal disks in insects or the patterning on the shells of some mollusks will reveal that in plants mechanisms are at work that are closely related to those in other systems that, at first sight, look very different from plants. In plants, as in most other higher organisms, the adult structure develops from a single cell. The final pattern cannot already be present in this cell in a hidden form. Regulatory phenomena observed after an experimental interference indicate that cells communicate with each other to achieve the translation from the genetic information into the three-dimensional structure. We assume that the formation of a given structure is initiated by a particular biochemical signal that may consist of a high concentration of one ore more substances. If mechanical forces are involved that bring a particular tissue into shape, it will be assumed that this is initiated by a preceding chemical signal that caused, for instance, a rearrangement of the cytoskeleton. These models are therefore different from models assuming pattern formation directly on a mechanical level (Green and Poething, 1982 ; Oster et al, 1983).