Miracle-dz/UniFAR-FTUs huggingface dataset:
A framework for feature selection in high-dimensional domains

The introduction of DNA microarray technology has lead to enormous impact in cancer research, allowing researchers to analyze expression of thousands of genes in concert and relate gene expression patterns to clinical phenotypes. At the same time, machine learning methods have become one of the dominant approaches in an effort to identify cancer gene signatures, which could increase the accuracy of cancer diagnosis and prognosis. The central challenges is to identify the group of features (i.e. the biomarker) which take part in the same biological process or are regulated by the same mechanism, while minimizing the biomarker size, as it is known that few gene expression signatures are most accurate for phenotype discrimination. To account for these competing concerns, previous studies have proposed different methods for selecting a single subset of features that can be used as an accurate biomarker, capable of differentiating cancer from normal tissues, predicting outcome, detecting recurrence, and monitoring response to cancer treatment. The aim of this thesis is to propose a novel approach that pursues the concept of finding many potential predictive biomarkers. It is motivated from the biological assumption that, given the large numbers of different relationships which are possible between genes, it is highly possible to combine genes in many ways to produce signatures with similar predictive power. An intriguing advantage of our approach is that it increases the statistical power to capture more reliable and consistent biomarkers while a single predictor may not necessarily provide important clues as to biological differences of interest. Specifically, this thesis presents a framework for feature selection that is based upon a genetic algorithm, a well known approach recently proposed for feature selection. To mitigate the high computationally cost usually required by this algorithm, the framework structures the feature selection process into a multi-step approach which combines different categories of data mining methods. Starting from a ranking process performed at the first step, the following steps detail a wrapper approach where a genetic algorithm is coupled with a classifier to explore different feature subspaces looking for optimal biomarkers. The thesis presents in detail the framework and its validation on popular datasets which are usually considered as benchmark by the research community. The competitive classification power of the framework has been carefully evaluated and empirically confirms the benefits of its adoption. As well, experimental results obtained by the proposed framework are comparable to those obtained by analogous literature proposals. Finally, the thesis contributes with additional experiments which confirm the framework applicability to the categorization of the subject matter of documents.
PtiClic: A Game for Vocabulary Assessment combining JeuxDeMots and LSA

Abstract. One interesting path for designing software for vocabulary acquisition and assessment could be games. In this article we present PtiClic, a lexical game based on the principles behind JeuxDeMots and combined with LSA. Such a game can foster the interest of young people in developing lexical skills in either a tutored or an open environment . Keywords: Lexical acquisition, lexical network, serious games, LSA, JeuxDeMots 1 Introduction Developing software for vocabulary acquisition and/or assessment in general, and for young people in particular, is a risky business. There are several difficulties. First, we have to be able to design an activity that can foster an interest in learning which is not easy in the case of children. Then, the underlying dictionaries or lexical databases can prove tremendously hard to develop, especially if we want to go beyond just a list of words with parts-of-speech and venture into the realm of lexical functions and the relations intertwining terms. Automated acquisition of lexical or functional relations between terms is necessary in a large number of tasks in Natural Language Processing (NLP) outscoping largely Technology-Enhanced Learning of language. These relations that we find generally in thesauruses or ontologies can of course be revealed in a manual way; for example, one of the oldest thesauruses is Roget’s, its current version being (Kipfer, 2001), or the most famous lexical network is Wordnet (Miller, 1990). Such relations can be also determined computationally from corpora of texts, for example (Robertson and Spark Jones, 1976), (Lapata and Keller, 2005), or (Landauer et al 1998) in which statistical studies on the distributions of words are made. Moreover, many applications of NLP require information of various natures, like synonymy or antonymy, but also relations of hyperonymy / hyponymy, holonymy / meronymy etc. The building of such relations, when done manually by experts, requires resources
Gravitational wave background from neutrino-driven gamma-ray bursts

We discuss the gravitational wave background (GWB) from a cosmological population of gamma-ray bursts (GRBs). Among the various emission mechanisms for the gravitational waves (GWs), we pay particular attention to the vast anisotropic neutrino emissions from the accretion discs around black holes formed after so-called failed supernova explosions. The GWs produced by such mechanisms are known as 'burst with memory', and could dominate in the low-frequency regime below ∼10 Hz. To estimate their amplitudes, we derive general analytic formulae for gravitational waveforms from the axisymmetric jets. Based on the formulae, we first quantify the spectrum of GWs from a single GRB. Then, summing the cosmological population, we find that the resultant value of the density parameter becomes roughly Ω GW 10 -20 over the wide-band of the low-frequency region, f ∼ 10 -4 - 10 1 Hz. The amplitude of the GWB is significantly smaller than that of the primordial GWB originating from the inflationary epoch, and is far below the detection limit.
Research on sales forecasting based on ARIMA and BP neural network combined model

A single ARIMA model cannot meet higher standards of prediction accuracy. Moreover, it can only deal with small prediction periods in the forecasting work. For the sake of prediction accuracy, we combined an ARIMA model with BP neural network. Firstly, an ARIMA forecasting model is established. Secondly the BP neural network is used to improve the single ARIMA model. The residual of ARIMA model is trained and fitted by BP neural network. Finally, more accurate results are given through combination with the forecast results of ARIMA model. The practice turns out that, compared with single ARIMA model, the prediction accuracy of new ARIMA model improved by BP neural networks is obviously enhanced, with an average error of forecast decreasing 10.4% by a large margin. Thus, the combined model proposed by this paper can be used in future prediction researches and industrial data analysis.
Rationally connected varieties

This is an expository paper on rationally connected varieties. The aim is to provide an introduction to the subject, as well as to discuss a recent result by T. Graber, J. Harris and J. Starr. The paper is based on the talk I gave at the 2004 Summer Research Conference on Algebraic Geometry at Snowbird.
Formulating the influence of peer-to-peer systems scale up on access transparency

Distributed systems concepts have been presented since 1960 and there are many more attempts to develop these concepts to reach a real distributed system. But none of them able to implement all distributed systems goals and have some limitation. The major part of these limitations is related to no clear quantity relation are defined for distributed systems concepts. These limitations cause the developments were very special purpose like P2P system. P2P systems were developed on internet that is a dynamically scalable and heterogeneous system. Therefore scalability is one of the important challenges in P2P systems. On the other hand P2P systems have tried to provide transparent sharing in the Internet, especially transparent access to resources. Access transparency is more important to P2P systems because it affects more the system configuration, dynamicity and performance therefore the other important challenge in P2P systems is access transparency. However in distributed systems there isn't any clear mathematic relation between these two concepts, scalability and access transparency. Although we know that any increase in the scalability of a P2P systems cause complexity in access transparency establishing because the frequency of data exchanges as well as the system's process communication time increases exponentially. It is thus important for a P2P system to decide dynamically how far to allow the scale up, given the additional overheads it has to suffer from establishing data access transparency. This tradeoff decision in the statically or dynamically created P2P system can be made either quite centrally by a single manager, completely distributed by independent machines, or in a hybrid way. In this paper we first define scalability concept base on set theories and then adapted concept of access transparency to P2P systems and define some metrics for it. Finally formulates the relation between scalability and access transparency in P2P systems. The relation helps to calculate the overhead of establishing data access transparency when a P2P system is scaled up.
Automated Estimation of Fetal Cardiac Timing Events From Doppler Ultrasound Signal Using Hybrid Models

In this paper, a new noninvasive method is proposed for automated estimation of fetal cardiac intervals from Doppler Ultrasound (DUS) signal. This method is based on a novel combination of empirical mode decomposition (EMD) and hybrid support vector machines-hidden Markov models (SVM/HMM). EMD was used for feature extraction by decomposing the DUS signal into different components (IMFs), one of which is linked to the cardiac valve motions, i.e. opening (o) and closing (c) of the Aortic (A) and Mitral (M) valves. The noninvasive fetal electrocardiogram (fECG) was used as a reference for the segmentation of the IMF into cardiac cycles. The hybrid SVM/HMM was then applied to identify the cardiac events, based on the amplitude and timing of the IMF peaks as well as the sequence of the events. The estimated timings were verified using pulsed doppler images. Results show that this automated method can continuously evaluate beat-to-beat valve motion timings and identify more than 91% of total events which is higher than previous methods. Moreover, the changes of the cardiac intervals were analyzed for three fetal age groups: 16-29, 30-35, and 36-41 weeks. The time intervals from Q-wave of fECG to Ac (Systolic Time Interval, STI), Ac to Mo (Isovolumic Relaxation Time, IRT), Q-wave to Ao (Preejection Period, PEP) and Ao to Ac (Ventricular Ejection Time, VET) were found to change significantly (p <; 0.05) across these age groups. In particular, STI, IRT, and PEP of the fetuses with 36-41 week were significantly (p <; 0.05) different from other age groups. These findings can be used as sensitive markers for evaluating the fetal cardiac performance.
Lossless Image Compression with BCTW

We present a new lossless image compression algorithm called BCTW, for bitplane context tree weighting, and a corresponding study into lossless image compression using several number representations and various algorithms. BCTW processes the image bitplane by bitplane and uses context tree weighting (CTW) to estimate the probability of each pixel bit being a 1 or 0. The bit is then compressed with an entropy coder, most likely an arithmetic coder. BCTW is a very good lossless image compressor. In the study, we also look at several number representations (twos-complement, Gray coding, and sign-magnitude) and compare BCTW to MedBzip2 and JPEG-LS. On the Waterloo Bragzone GreySet2 dataset, BCTW outperforms JPEG-LS by 13% and MedBzip2 by 5%. We find that representing the pixels in a Gray code does not help. Sign-magnitude helps sometimes, but not in BCTW. The MED transform helps bzip2, but not BCTW.
Signal Framing Methods of Automotive Embedded Networked Control System for Bandwidth Consumption Optimization

Parameter (or variable) of automotive embedded networked control system is named as signal. In order to improve system performance and provide more remaining bandwidth for system extension, it is necessary to minimize the bandwidth consumption of frames transmitting the datum of signals. In this paper, the signal framing problem and methods of automotive embedded networked control system for bandwidth consumption optimization are studied. Besides OSpF (One Signal per Frame), other different signal framing methods, BFD-FNO (Best Fit Decreasing for Frame Number Optimization), BFBCD-BCO (Best Fit with Bandwidth Consumption Decreasing for Bandwidth Consumption Optimization) and BFPI-BCO (Best Fit with Period Increasing for Bandwidth Consumption Optimization), are presented based on bin-packing heuristic algorithm. Applying NETCARBENCH and algorithm implementation tool, the signal framing heuristic algorithm performance of automotive embedded networked control system for bandwidth consumption optimization is analyzed and evaluated. By comparing the performances of different heuristic algorithms, it is concluded that no matter the ECU number and signal load, BFD-FNO heuristic algorithm retains the signal framing efficiency for bandwidth consumption optimization.
Out of the Box: Television, Documentary Fiction, and the Art of Healing

Abstract:Reflecting on her own healing from the trauma of losing her husband during the April 16, 2007, Virginia Tech massacre, the author explores how viewers can use television as an individualized tool for emotional strengthening. Television can give the freedom to explore post-traumatic feelings at one's own pace, assisting the ability to achieve control of emotions. In the essay, the author explores her repulsion toward fiction, caused by the need for moral witnessing, and how the HBO documentary fiction, Treme, challenged that response to trauma. The show entailed many features of moral witnessing, and its fictional components allowed viewers to access the inaccessible—the trauma caused by Hurricane Katrina—without risking exploitation of personal memories and intrusion of privacy. Ultimately, Treme paved the way for the author's return to fiction as a source of pleasure.
Recognition and understanding of meetings

This paper is about interpreting human communication in meetings using audio, video and other signals. Automatic meeting recognition and understanding is extremely challenging, since communication in a meeting is spontaneous and conversational, and involves multiple speakers and multiple modalities. This leads to a number of significant research problems in signal processing, in speech recognition, and in discourse interpretation, taking account of both individual and group behaviours. Addressing these problems requires an interdisciplinary effort. In this paper, I discuss the capture and annotation of multi-modal meeting recordings---resulting in the AMI meeting corpus---and how we have built on this to develop techniques and applications for the recognition and interpretation of meetings.
SATIN 2 : How to support self-efficacy and diversity in end-user development

We live in an era where information technology services and devices are more spread than ever, forming a basis for new innovations, even among ordinary people. And yet, producers of these services and devices are mostly men with programming skills. This means that women's participation as producers of computer-based products is far from as influential as that of men. An approach to this situation is to provide end-user development environments where ordinary people with no programming experiences have the opportunity to develop their own smartphone applications. SATIN 2, a collaboration project between two universities, and IT-companies, funded by EU Structural Funds, has taken such an approach, with a focus on supporting particularly female end-users. Qualitative observation studies of 11 subjects have focused on how to support computer selfefficacy, and end-user programming strategies that women prefer to a higher degree than men. Observations indicate that the women where as positive to making use of the system as the men. The test subjects also showed signs of motivation as well as creativity while exploring the system. To support women's own perceptions of self-efficacy related to developing computer-based systems is challenging, still indications of acceptance and enthusiasm for the system were observed.
Production of polyclonal antisera to manganese superoxide dismutase expressed in downy mildew resistant pearl millet and its application for immunodiagnosis

Level of superoxide dismutase (SOD) in the seedlings of downy mildew resistant pearl millet genotype (IP 18293) increased 3-fold upon inoculation with Sclerospora graminicola. Mn-SOD was purified from pearl millet by ion-exchange and gel filtration chromatography. Polyclonal antibody was raised in rabbit against Mn-SOD of pearl millet. Anti-Mn SOD had an antibody titer of 1:20,000. ELISA results revealed a 1.5-fold increase in pure Mn-SOD preparation compared to the crude preparation. The antibody reactivity as detected by ELISA revealed that Mn-SOD is higher at 4 hrs post-inoculation in resistant pearl millet seedlings. The reactivity also showed the Mn-SOD is more in roots of inoculated resistant seedlings. SOD reactivity was also determined by DIBA (Dot-immunobinding assay). The results suggested that the Mn-SOD antibody reacted more strongly with crude SOD of the inoculated resistant seedlings than inoculated resistant seedlings. Western blot analysis revealed the induction of 35 kDa SOD protein in the resistant genotype. Its appearance on western blot coincided with high SOD activity. Immunolocalization experiments showed that SOD protein was abundant in the vascular bundles. The antibody produced to Mn-SOD was specific as judged by ELISA, DIBA, western blot and immunofluorescence assays. The work described here investigates the possibility of using the serological techniques to assess the reactivity of antibody with the SOD antigen.
The Fluorescence Detector of the Pierre Auger Observatory — a Calorimeter for UHECR

The Pierre Auger Observatory is a hybrid detector for ultrahigh energy cosmic rays (UHECR) with energies above 1018.5 eV. Currently the first part of the Observatory nears completion in the southern hemisphere in Argentina. One detection technique uses over 1600 water Cherenkov tanks at ground where samples of secondary particles of extensive air showers (EAS) are detected. The second technique is a calorimetric measurement of the energy deposited by EAS in the atmosphere. Charged secondary particles of EAS lose part of their energy in the atmosphere via ionization. The deposited energy is converted into excitation of molecules of the air and afterwards partly emitted as fluorescence light mainly from nitrogen in the wavelength region between 300 and 400 nm. This light is observed with 24 fluorescence telescopes in 4 stations placed at the boundary of the surface array. This setup provides a combined measurement of the longitudinal shower development and the lateral particle distribution at ground of the .
Bridging the gap: from multi document Template Detection to single document Content Extraction

Template Detection algorithms use collections of web documents to determine the structure of a common underlying template. Content Extraction algorithms instead operate on a single document and use heuristics to determine the main content. In this paper we propose a way to combine the reliability and theoretic underpinning of the first world with the single document based approach of the latter. Starting from a single initial document we use the set of hyperlinked web pages to build the required training set for Template Detection automatically. By clustering the documents in this set according to their underlying templates we clean the training set from documents based on different templates. We confirm the applicability of the approach by using an entropy based Template Detection algorithm to build a Content Extractor.
An improved collaborative filtering recommendation algorithm based on case-based reasoning

Collaborative filtering recommendation is a popular recommendation algorithm in electronic commerce, but the disadvantage of cold start still exists in the reason of new coming users and items. CBR (case-based reasoning) is used to get source case in history according to the notation of target case, and the source case plays a guiding role in solving the problem of the target case. It is a useful algorithm to evaluate the solution of target case, and explain the abnormal phenomenon of target case. In this paper we applied case-based reason with forgetting mechanism to solve the cold start problem in collaborative filtering, to deduce the score of items which is not scored by user, and then to recommend items with TOP-N collaborative filtering. Experimental results show that the proposed collaborative filtering combining case-based reasoning could significantly ease cold start problem.
DISTRIBUTED DATA MANAGEMENT: UNSOLVED PROBLEMS AND NEW ISSUES *

Distributed database technology is expected to have a significant impact on data processing in the upcoming years. With the introduction of commercial products, expectations are that distributed database management systems will by and large replace centralized ones within the next decade. In this paper, we reflect on the promises of distributed database technology, take stock of where we are, and discuss the issues that remain to be solved. We also present new research issues, such as distributed object-oriented systems, distributed knowledge bases, multiprocessor data servers, and distributed multidatabase systems, that arise with the introduction of new technology and the subsequent relaxation of some of the assumptions underlying current systems.
Experiences from building a EUD business portal

End user development (EUD) is the idea of providing end users, professionals outside computer science community, means to develop their own software. This idea has received special attention by domain experts who would like to easily modify software applications to their needs, without intervention of technical professionals and without learning conventional programming languages. The end user development is in fact a system property, closely related to system adaptability that should be built into a system to enable number of modifications at different system levels securing powerful expressions that would satisfy the end user needs. This is a challenging requirement for every software provider. There are a number of identified system perspectives, critical to EUD, and general guiding principles that should be addressed. However, in reality, building these properties into the concrete system, a number of complex and interrelated issues have to be solved. In this paper we report challenges that we addressed while implementing end user development capability into a software system within business domain.