Miracle-dz/UniFAR-FTUs huggingface dataset:
A morphological model for large vocabulary speech recognition

A morphological model, applicable to inflected languages, which combines the robustness of the tripos model with the prediction power of the lemma is proposed. A semantic component acts at the lemma level, without taking into account the different inflections of a lemma, thus making its trainable even for 200000 words. The training corpus for the lemma model (consisting of 38 million words) is labeled in terms of lemma and part of speech, using a semiautomatic process. The results obtained with this new model are reported. The model shows another way to put knowledge in the pure probabilistic framework of hidden Markov models. <<ETX>>
Consumers Caring Health with End Products (Ayurvedic Medicines) Containing Neopicrorhiza scrophulariiflora in Nepal

Neopicrorhiza scrophulariiflora (Pennell) D. Y. Hong is an endangered herbaceous medicinal plant found in subalpine and alpine zone of eastern Himalayas comprising Sikkim, Nepal, Bhutan and China. Rhizomes of Neopicrorhiza scrophulariiflora (hereinafter referred to as Neopicrorhiza) have medicinal properties and are highly traded in Nepal, India and China. Information on driving factors for demand of Neopicrorhiza from consumer perspective is unknown, knowledge of which is important because it can allow insights into future demand and sustainability of future trade. In this context, a survey was conducted among purchasers and consumers (patients) of end products (Ayurvedic medicines) containing Neopicrorhiza (n=513) in five different cities of Nepal from March to July 2017 using structured questionnaire to explore characteristics of consumers and consumption of end products of Neopicrorhiza. The utilization of Ayurvedic medicines containing Neopicrorhiza for treatment of health disorders of human is prevalent in Nepal. Men and women patients of diverse ages and with different socio-economic profiles were found to consume Ayurvedic medicines containing Neopicrorhiza to treat health disorders associated with different organ systems of body. The patients receiving Ayurvedic medicines containing Neopicrorhiza were children, adults and elders ranging from 1 to 98 years old (av. 37.6 years). Employed and unemployed, educated and illiterate consumers were found to consume Ayurvedic medicines containing Neopicrorhiza to treat their health disorders in Nepal. Patients receiving those medicines were from households having diverse annual cash income levels. The purchase and consumption of Ayurvedic medicine containing Neopicrorhiza was most often guided by a doctor’s prescription. Most of the respondents believed that the prices of those Ayurvedic medicines were fairer than other types of medicines. Similar studies are recommended in other countries like India and China where the Neopicrorhiza is exported from Nepal.
Interacción remota con robots móviles basadas en internet

El objetivo de esta tesis es el desarrollo de una arquitectura software para un laboratorio remoto en el campo de robotica movil para facilitar la interaccion remota con robots moviles utilizando varios dispositivos de interaccion. Se ha realizado esta tesis en el marco del proyecto IECAT, que pretende desarrollar una red de laboratorios remotos entre seis universidades en Europa y EE.UU. en el campo de los sistemas autonomos y teleoperados. En la tesis se estudian las ventajas y los inconvenientes de utilizar la red Internet como medio de comunicacion en los sistemas de interaccion remota con robots moviles. Se ha utilizado el paradigma del control supervisado en el desarrollo del sistema propuesto para disminuir el ancho de banda necesario y la sensibilidad del sistema al retraso temporal. En la tesis se propone la utilizacion de los patrones de software para desarrollar interfaces hombre-robot flexibles y reutilizables. Se estudia tambien el uso de los dispositivos moviles tales como las PDAs y los telefonos moviles como elementos de interaccion con el robot. Finalmente, se propone una metodologia para construir entornos educativos innovadores para la robotica movil.
Effectiveness of Collision Avoidance Assistance System with Consideration of Driver's Dependence on the System

We investigated the effectiveness of collision avoidance assistance system in a driving simulator. In this study, we analyzed the combined error frequency of a man-machine system, with respect to driver performance and system reliability. In the analysis of driver performance, braking timing to avoid the collision both in normal operation and malfunction of collision avoidance assistance system were analyzed. In short, not only the positive effects of the driving assistance system but also the negative effects results from driver's excessive dependence on the systems were considered to analyze the combined error frequency. It was revealed that the combined error frequency when the system was used is lower than that when they were not used, even though the drivers' dependence on the warning system (i.e. delay of braking timing to avoid the collision) was observed.
How well can machine learning predict demographics of social media users

The wide use of social media sites and other digital technologies have resulted in an unprecedented availability of digital data that are being used to study human behavior across research domains. Although unsolicited opinions and sentiments are available on these platforms, demographic details are usually missing. Demographic information is pertinent in fields such as demography and public health, where significant differences can exist across sex, racial and socioeconomic groups. In an attempt to address this shortcoming, a number of academic studies have proposed methods for inferring the demographics of social media users using details such as names, usernames, and network characteristics. Gender is the easiest trait to accurately infer, with measures of accuracy higher than 90 percent in some studies. Race, ethnicity and age tend to be more challenging to predict for a variety of reasons including the novelty of social media to certain age groups and a lack of significant deviations in user details across racial and ethnic groups. Although the endeavor to predict user demographics is plagued with ethical questions regarding privacy and data ownership, knowing the demographics in a data sample can aid in addressing issues of bias and population representation, so that existing societal inequalities are not exacerbated.
Learning Priors for Adversarial Autoencoders

Most deep latent factor models choose simple priors for simplicity, tractability or not knowing what prior to use. Recent studies show that the choice of the prior may have a profound effect on the expressiveness of the model, especially when its generative network has limited capacity. In this paper, we propose to learn a proper prior from data for adversarial autoencoders (AAEs). We introduce the notion of code generators to transform manually selected simple priors into ones that can better characterize the data distribution. Experimental results show that the proposed model can generate better image quality and learn better disentangled representations than AAEs in both supervised and unsupervised settings. Lastly, we present its ability to do cross-domain translation in a text-to-image synthesis task.
Multi-document statistical fact extraction and fusion

This dissertation presents original techniques for statistical fact extraction and fusion from multiple documents. Fact extraction, or relationship extraction, is a process where natural language text is scanned to find instances of a predetermined class of facts (e.g. birthday(x,y)). A framework for training statistical fact extractors from example is used wherein a set of examples and a target model are used to annotate an automatically collected corpus. This annotation is then used to provide training data for classifiers (Phrase Conditional Likelihood and Native Bayes) or sequence models (Conditional Random Fields). Fact extractors are used in two information retrieval tasks. In question answering the set of candidate answers is narrowed using fine-grained proper noun ontological facts (is-a(X, Y)) extracted from a corpus by rote classifiers leading to higher performance. Extracted facts are also used for name-referent disambiguation, or cross-document coreference, where one personal name may refer to multiple potential people in the world. The distinguishing biographic facts for each person, such as birthday(x,y) and occupation (x,y), are automatically extracted from plain text and these biographic facts are used along with other statistical methods to distinguish between mentions of each of the referents. This dissertation presents novel techniques for fusion which integrate facts extracted from multiple sources. For the task of biographic fact extraction, fusion of factual information extracted from multiple documents improves the precision of the resulting information. Further improvements result from cascaded fact extraction, where certain facts are extracted and fused and then these facts are used to extract additional information. The technique of cascaded fact extraction and fusion is also applied to time-bounded facts, where a cascade of fact extractors produce a timeline of corporate management succession. Collectively, this research demonstrates the utility of multi-document fact extraction and fusion. It shows that facts can serve as a building-block for deeper text processing such as finding coreferent names in a series of documents, finding the answers to questions, and constructing a timeline for time-variable facts. The key aspects to the process are training with minimal supervision, high-performance statistical fact extraction, fusion across multiple sources of information, and cascaded extraction.
Multi-keywords Query Expansion with OLCA Based Concept Tree Pruning

In semantic-based query expansion,computing expansion words and its weight is a key step to describe the needed query.We proposed a method called OLCA(keyword to concept method),the idea comes from LCA(Local context analysis). We made some i mprovement and applied it to multi-keywords query with different weight according to their attribute to the query.Combined with concept tree based on WordNet,we made the query expansion performed fromboth semantic and the real query documents aspects,and calculated the weight of expansion term based on this technique.Compared with the one based on semantic or the traditional expansion which merelyis based on statistic,the experi ments reveal that this method can achieve a better query quality.
On startups and teaching computer architecture

The ACM 2001 curriculum [1] states that "[s]tudents need to understand computer architecture in order to structure a program so that it runs more efficiently on a real machine. In selecting a system to use, they should be able to understand the tradeoff among various components, such as CPU clock speed vs. memory size. " Generally, the computer architecture (CA) class in computer science departments is taught in two flavors: one where the lectures are centered on textbooks such as Staellings[2] or Hennessy and Patterson[3], the other one---usually taught by faculty with more engineering backgrounds---where a digital electronics perspective is presented in lectures and laboratories, and in which case textbooks such as Mano[4] are used. In recent years, we have seen new textbooks appear offering Hardware Description Language (VHSL) as a conduit for understanding and then programming logic design and microprocessor electronics [5]. In this approach a language, typically Verilog, is used as a support for understanding and designing simple logic circuits that are easily encapsulated in modules, and in turn integrated into a microprocessor design. Rigo et al. [6] describe their experience teaching architecture using a similar approach based on the Architecture Description Language (ADL). Similarly, Gray [7] proposes a whole computer architecture course based on FPGAs.
Influence of cryotherapy on balance and joint position sense in healthy subjects: randomized clinical trial.

Background: Cryotherapy has been widely used in clinical practice, particularly for the treatment of acute injuries to soft tissues and various joints. However, since decrease in temperature can results in reduced nerve conduction velocity and proprioception, it can therefore be assumed that cryotherapy, applied before exercise, can result in a greater predisposition of the joints to lesion. Thus, the objective of this study was to evaluate the influence of cryotherapy on balance and knee joint position sense (JPS). Methods: We conducted a randomized single-blinded clinical trial, with thirty volunteers of both genders (age: 23.3 ± 2.5 years; BMI: 22.2 ± 2.1 Kg/m 2 ), who were randomly distributed into two groups with 15 subjects in each: control group - 20 minutes at rest; and experimental group - application of cryotherapy on the knee of the dominant lower limb, for 20 minutes. All subjects were submitted to the assessment of the balance, by means of computerized baropodometry and the JPS of the knee of the dominant lower limb (DLL) using an electrogoniometer, both open kinetic chain (OKC) and closed kinetic chain (CKC), before and after the interventions. Statistical analysis was performed using SPSS 20.0 software. Kolmogorov-Smirnov test was used to check the data normality and two-way ANOVA to verify intra and inter-group differences. The study was approved by the local Research and Ethics Committee (Approval no. 099/10) Results: There was a difference on the JPS assessed in OKC in the experimental group (p= 0.03). There was no alteration in the balance and the JPS with CKC, in none of the groups tested ( p > 0.05). Conclusion: Cryotherapy when applied on knee did not affect balance or the JPS with CKC of the knee, although alteration was observed in OKC. We therefore conclude that application of cryotherapy before exercises do not posses increased risk of myoarticular injuries, since the most of these activities is performed in CKC.
Image Retrieval System Using Improved Local Binary Patterns and GLCM Matrices

Medical images produced by different hospitals and health care centers are increasing day by day. Medical images play a vital role in identification of various diseases, clinical diagnosis and prognosis, medical training and surgical planning. Management, indexing and retrieval of such a huge collection of images tend to be very expensive and time consuming when manual methods are employed. A lot of work has been done on generic image retrieval systems using content based information. Many feature descriptors have been proposed for image retrieval in the past few years. Most of them stick to a particular type of images. In this work content based image retrieval system has been proposed which uses both spatial as well as frequency domain texture features of input images and classify them into relevant categories. System has been tested by considering mix databases in which medical datasets of different types has been included along with face image or other object images. Center symmetric Local binary patterns and local phase quantization features has been used as spatial and frequency domain features respectively along with GLCM texture features of the images to extract the features from variety of datasets. Four different types of distance measure metrics i.e. Chi-square, Manhattan, Canbarra and Euclidian has been used for getting the similarity between query and dataset images. Experimental results shows high precision ratio when canbarra distance metrics has been considered.
FAQs about Info-Gap Decision Theory

This article — more accurately working paper — is a LTEX version of the page info-gap.moshe-online.com/faqs.html on my website. I plan to use the material discussed in this paper in a book I am currently working on, whose envisaged title is: The Rise and Rise of Voodoo Decision-Making Please note that this is a “work-in-progress” project, so the article will be in a permanent state of “Draft”. My aim is to maintain the discussion in each FAQ as self-contained as possible, hence the overlap between the discussions in a number of FAQs and the repetition of a number of figures and equations. You are welcome to send me any FAQs that you may have about InfoGap Decision Theory.
Weighted Combination of Sample Based and Block Based Intra Prediction in Video Coding

The latest standard within video compression, HEVC/H.265, was released during 2013 and provides a significant improvement from its predecessor AVC/H.264. However, with a constantly increasing demand for high denition video and streaming of large video files, there are still improvements that can be done. Difficult content in video sequences, for example smoke, leaves and water that moves irregularly, is being hard to predict and can be troublesome at the prediction stage in the video compression. In this thesis, carried out at Ericsson in Stockholm, the combination of sample based intra prediction (SBIP) and block based intra prediction (BBIP) is tested to see if it could improve the prediction of video sequences containing difficult content, here focusing on water. The combined methods are compared to HEVC intra prediction. All implementations have been done in Matlab. The results show that a combination reduces the Mean Squared Error (MSE) as well as could improve the Visual Information Fidelity (VIF) and the mean Structural Similarity (MSSIM). Moreover the visual quality was improved by more details and less blocking artefacts.
Grouping Domain Names using DNS Query Graph

Many malwares have the network activities for command and control, update, propagation, and so on. Because of the use of domain names while the network activities, those malicious activities are observed and have been blocked on the DNS. However, to evade static blacklists, recent malwares are using numbers of newly generated domain names. In this paper, we introduce a domain name grouping using DNS query graph containing query strategy of DNS clients. By grouping the domain names which have the statistically and sequentially similar query strategy, we extract the malicious domain names groups of the multi-domain malwares from the numerous numbers of domain names. From the experiments with the DNS trace of an ISP network, we find tens of multi-domain malwares, and commonly observed unique DNS query strategies. As the contribution of method, the grouping result enhances the efficiency of blacklists by detecting newly appeared malicious domain names.
Penggunaan Aplikasi Duolingo Dalam Meningkatkan Kamampuan Kosakata Bahasa Inggris Pada Tenaga Pengajar Bimbingan Belajar Omega Sains Institut

Pendidikan mempunyai peran penting dalam kehidupan, khususnya dalam mempelajari bahasa asing yang dianggap sering kali membosankan. Tidak dapat dipungkiri pendidikan terpengaruholehkemajuan Teknologi, banyak manfaat dari teknologi yang dapat digunakan.Sebagai contoh, dengan mudahnya akses internet guru ataupun tenaga pengajar melalui internet, materi tersebut dapat dengan mudah mencari materi pendukung pengajaran melalui internet, materi tersebut dapat berupa artikel online, dll.Penggunaan media pembelajaran sangat mempengaruhi hasil dari tujuan pembelajaran.Karena para guru tidak perlu merasa kesulitan memahami dan khususnya melatih kosakata (vocabulary), yang sering kali menjadikendalanya adalah para guru, kurang memiliki waktu khusus dalam melatih kemampuan kosakata mereka.Banyak media pembelajaran disekitar kita yang dapat dimanfaatkan untuk mendukung kegiatan pembelajaran, dengan kemajuan Informasi dan Teknologi sekarang ini, sebagai contoh dengan aplikasi smartphone Duolingo. Aplikasi ini sengaja mengusung konsep “bermain sambil belajar” agar terasa lebih menyenangkan, dan mudah digunakan oleh semua kalangan umur.Aplikasi ini dapat dibuat sendiri untuk mendukung kegiatan pembelajaran sesuai topik harian, dandapat mempermudah kita khususnya para praktisi pendidikan, untuk melatih kemampuan bahasa Ingris dalam hal tes kosakata ( vocabulary ). Tujuan diadakannya pelatihan pemanfaatan media storyboards (Duolingo) ini adalah untuk meningkatkan kompetensi para guru bahasa Inggris. Sehingga nantinya jika kompetensi mereka menigkat, otomatis kemampuan mengajar mereka dikelas pun meningkat. Pada akhirnya akan tercipta suasan kegiatan belajar mengajar yang lebih efektif khususnya bagi siswa dalam memahami bahasa Inggris. Kata Kunci: Aplikasi Duolingo ; Kosakata; Bahasa Inggris .
Global analysis of SNPs, proteins and protein-protein interactions: approaches for the prioritisation of candidate disease genes.

Understanding the etiology of complex disease remains a cha llenge in biology. In recent years there has been an explosion in biological data, th is study investigates machine learning and network analysis methods as tools to aid candid te isease gene prioritisation, specifically relating to hypertension and cardiovasc ular disease. This thesis comprises four sets of analyses: Firstly, non sy non mous single nucleotide polymorphisms (nsSNPs) were analysed in terms of sequence a nd structure based properties using a classifier to provide a model for predicting de leterious nsSNPs. The degree of sequence conservation at the nsSNP position was found to b e the single best attribute but other sequence and structural attributes in combinatio were also useful. Predictions for nsSNPs within Ensembl have been made publicly available . Secondly, predicting protein function for proteins with an bsence of experimental data or lack of clear similarity to a sequence of known functi on was addressed. Protein domain attributes based on physicochemical and predic ted structural characteristics of the sequence were used as input to classifiers for predicti ng membership of large and diverse protein superfamiles from the SCOP database. An enr ichment method was investigated that involved adding domains to the training dat aset that are currently absent from SCOP. This analysis resulted in improved classifier acc uracy, optimised classifiers achieved 66.3% for single domain proteins and 55.6% when inc luding domains from multi domain proteins. The domains from superfamilies with low sequence similarity, share global sequence properties enabling applications to be developed which compliment profile methods for detecting distant sequence relatio nships. Thirdly, a topological analysis of the human protein intera ctome was performed. The results were combined with functional annotation and seque nce based properties to build models for predicting hypertension associated proteins. T he study found that predicted hypertension related proteins are not generally associate d with network hubs and do not exhibit high clustering coefficients. Despite this, the y t nd to be closer and better connected to other hypertension proteins on the interactio n network than would be expected by chance. Classifiers that combined PPI network, ami no acid sequence and functional properties produced a range of precision and recall s ore according to the applied
Document and Schema XML Updates

XML (W3C, 1998) is nowadays everywhere employed for the representation and exchange of information on the Web. The document structure is often described through a DTD or an XML schema (W3C, 2004). Both XML native (e.g., Tamino, eXist, TIMBER) and enabled (e.g., Oracle, IBM DB2, SQL Server) DBMSs have been so far proposed (Bourret, 2007) for storing and querying XML documents. Some enabled DBMSs support XML schema for the specification of a mapping between the XML schema and internal relational or object-relational representation of XML documents (Florescu & ABSTRACT
Developing a Scientific Knowledge of Simulation/Gaming

This article first speculates on why such little progress has been made regarding the effective application of educational simulation/games. It suggests that the field's eclectic foundation has been a virtue for its development but a vice regarding its rigorous assessment. The article then outlines the antecedents for generating a practical and cumulative body of literature, concluding with a number of recommendations as to how the field might accelerate the rate at which its literature accumulates.