Miracle-dz/UniFAR-FTUs huggingface dataset:
Transitive probabilistic CLIR models

Transitive translation could be a useful technique to enlarge the number of supported language pairs for a cross-language information retrieval (CLIR) system in a cost-effective manner. The paper describes several setups for transitive translation based on probabilistic translation models. The transitive CLIR models were evaluated on the CLEF test collection and yielded a retrieval effectiveness up to 83% of monolingual performance, which is significantly better than a baseline using the synonym operator.
Adaptive unsupervised learning of human actions

Automatic detection of suspicious activities in CCTV camera feeds is crucial to the success of video surveillance systems. Such a capability can help transform the dumb CCTV cameras into smart surveillance tools for fighting crime and terror. Learning and classification of basic human actions is a precursor to detecting suspicious activities. Most of the current approaches rely on a non-realistic assumption that a complete dataset of normal human actions is available. This paper presents a different approach to deal with the problem of understanding human actions in video when no prior information is available. This is achieved by working with an incomplete dataset of basic actions which are continuously updated. Initially, all video segments are represented by Bags-Of-Words (BOW) method using only Term Frequency-Inverse Document Frequency (TF-IDF) features. Then, a data-stream clustering algorithm is applied for updating the system's knowledge from the incoming video feeds. Finally, all the actions are classified into different sets. Experiments and comparisons are conducted on the well known Weizmann and KTH datasets to show the efficacy of the proposed approach.
Breast Cancer Classification From Histological Images with Multiple Features and Random Subspace Classifier Ensemble

Histological image is important for diagnosis of breast cancer. In this paper, we present a novel automatic breaset cancer classification scheme based on histological images. The image features are extracted using the Curvelet Transform, statistics of Gray Level Co‐occurence Matrix (GLCM) and Completed Local Binary Patterns (CLBP), respectively. The three different features are combined together and used for classification. A classifier ensemble approach, called Random Subspace Ensemble (RSE), are used to select and aggregate a set of base neural network classifiers for classification. The proposed multiple features and random subspace ensemble offer the classification rate 95.22% on a publically available breast cancer image dataset, which compares favorably with the previously published result 93.4%.
Comparison of CELP speech coder with a wavelet method

OF THESIS Comparison of CELP speech coder with a wavelet method This thesis compares the speech quality of Code Excited Linear Predictor (CELP, Federal Standard 1016) speech coder with a new wavelet method to compress speech. The performances of both are compared by performing subjective listening tests. The test signals used are clean signals (i.e. with no background noise), speech signals with room noise and speech signals with artificial noise added. Results indicate that for clean signals and signals with predominantly voiced components the CELP standard performs better than the wavelet method but for signals with room noise the wavelet method performs much better than the CELP. For signals with artificial noise added, the results are mixed depending on the level of artificial noise added with CELP performing better for low level noise added signals and the wavelet method performing better for higher noise levels.
Game Mechanics: Advanced Game Design

This in-depth resource teaches you to craft mechanics that generate challenging, enjoyable, and well-balanced gameplay. Youll discover at what stages to prototype, test, and implement mechanics in games and learn how to visualize and simulate game mechanics in order to design better games. Along the way, youll practice what youve learned with hands-on lessons. A free downloadable simulation tool developed by Joris Dormans is also available in order to follow along with exercises in the book in an easy-to-use graphical environment. In Game Mechanics: Advanced Game Design, youll learn how to: * Design and balance game mechanics to create emergent gameplay before you write a single line of code. * Visualize the internal economy so that you can immediately see what goes on in a complex game. * Use novel prototyping techniques that let you simulate games and collect vast quantities of gameplay data on the first day of development. * Apply design patterns for game mechanicsfrom a library in this bookto improve your game designs. * Explore the delicate balance between game mechanics and level design to create compelling, long-lasting game experiences. * Replace fixed, scripted events in your game with dynamic progression systems to give your players a new experience every time they play. "I've been waiting for a book like this for ten years: packed with game design goodness that tackles the science without undermining the art." --Richard Bartle, University of Essex, co-author of the first MMORPG Game Mechanics: Advanced Game Design by Joris Dormans & Ernest Adams formalizes game grammar quite well. Not sure I need to write a next book now! -- Raph Koster, author of A Theory of Fun for Game Design.
Fault Tolerant CORBA Specication

You may view the pending issues for this specification from the OMG revision issues web page http://www.omg. org/issues/; however, at the time of this writing there were no pending issues. The FTF Recommendation and Report for this specification will be published on December 15, 2000. If you are reading this after that date, please download the available specification from the OMG formal specifications web page 27 Note – The OMG documents used to create this chapter were orbos/00-01-19 and ptc/2000-03-04.
A Hybrid High-Order Markov Chain Model for Computer Intrusion Detection

A hybrid model based mostly on a high-order Markov chain and occasionally on a statistical-independence model is proposed for profiling command sequences of a computer user in order to identify a “signature behavior” for that user. Based on the model, an estimation procedure for such a signature behavior driven by maximum likelihood (ML) considerations is devised. The formal ML estimates are numerically intractable, but the ML-optimization problem can be substituted by a linear inverse problem with positivity constraint (LININPOS), for which the EM algorithm can be used as an equation solver to produce an approximate ML-estimate. The intrusion detection system works by comparing a user's command sequence to the user's and others' estimated signature behaviors in real time through statistical hypothesis testing. A form of likelihood-ratio test is used to detect if a given sequence of commands is from the proclaimed user, with the alternative hypothesis being a masquerader user. Applying the model to real-life data collected from AT&T Labs–Research indicates that the new methodology holds some promise for intrusion detection.
Using supervised machine learning on large-scale online forums to classify course-related Facebook messages in predicting learning achievement within the personal learning environment

ABSTRACT This paper demonstrated the use of the supervised Machine Learning (ML) for text classification to predict students’ final course grades in a hybrid Advanced Statistics course and exhibited the potential of using ML classified messages to identify students at risk of course failure. We built three classification models with training data of 76,936 posts from two large online forums and applied the models to classify messages into statistics-related and non-statistics-related posts in a private Facebook group. Three ML algorithms were compared in terms of classification effectiveness and congruency with human coding. Students with more messages endorsed by two or more ML algorithms as statistics-related had higher final course grades. Students who failed the course also had significantly fewer messages endorsed by all three ML algorithms than those who passed. Results suggest that ML can be used for identifying students in need of support within the personal learning environment and for quality control of the large-scale educational data.
Richness of semantic encoding in a hypermedia-assisted instructional environment for ESP: effects on incidental vocabulary retention among learners with low ability in the target language

The objective of this study is to investigate how low-proficiency English for Specific Purposes (ESP) students benefit from a hypermedia-enhanced learning environment, specifically in terms of incidental acquisition of lexical items in the target language. Additionally, this study aims at finding out how frequently the comprehension tools available from within the application were used by the learners. The hypermedia-assisted learning environment used in this study provides a rich environment where learners gain exposure to foreign language texts by listening and reading in the target language. Learners are invited to explore Chemistry-related video segments through listening comprehension questions in combination with access to various comprehension tools, for example, L1 translations of questions and answers, L2 video manuscript, translation of manuscript sentences, specific video segments containing answers to questions and video control tools. A total of 40 subjects with a low-intermediate proficiency level were exposed to the treatment. An achievement test was administered before the treatment as a pre-test, and two weeks following the completion of the treatment as a post-test, to evaluate retention. The courseware was set up by the researcher to record every user action and compile these data into user logs of behaviour data which were subsequently submitted to analysis. The findings of this experimental study are conclusive in suggesting that hypermedia-based instruction can provide an effective learning environment to build vocabulary among ESP students. It can be claimed that the access to a variety of comprehension tools results in a deep processing of the lexical items and can therefore contribute to favour retention.
Comparing Yiq and Ycbcr Colour Image Transforms for Semi-Fragile Medical Image Steganography

Visible light photography diagnostic images are coloured ex vivo medical images popularly used in Dermatology and Endoscopy for diagnosis and monitoring. The need to protect the integrity of these images as well as associated patient data calls for techniques such as image steganography and watermarking. This research explores and compares the effect of watermarking on the YIQ and YCbCr colour transforms used in processing digital coloured images and video in recent times. Using a new spread spectrum watermarking algorithm, it was found that YIQ has better distortion performance than YCbCr in the order of 3dB while YCbCr had lower BER for accurate watermark retrieval and tamper detection in the order of 1.3 x 10-3.
Produção, purificação e caracterização de uma lacase de Phlebia rufa (Pers.) M. P. Christ.

O objectivo deste trabalho produzir, purificar e caracterizar a lacase segregada pelo fungo Phlebia rufa. Os seguintes suplementos foram adicionados ao meio basal liquido de Eggert et al. (1996) e testados: trigo, malte, acido ferulico e concentracao adicional de sulfatos. A enzima foi produzida neste meio liquido com trigo por 24 dias. Durante a incubacao a actividade da lacase foi determinada usando acido 2,2’–azino-bis (3-etilbenzotiazolina-6-sulfonato) (ABTS) como substrato e foi verificada a existencia de dois picos com a actividade maxima da lacase depois de 24 dias (3,1 Uml-1). O primeiro pico obtido depois de 10 dias foi recolhido e concentrado por ultrafiltracao. O extracto bruto da lacase foi purificado por cromatografia liquida de proteina rapida (FPLC) com um factor de purificacao de 8. O rendimento obtido foi de 6%, e a actividade total de 149 U foi determinada. Depois disto a lacase foi caracterizada e encontrado que o pH optimo depende dos diferentes substratos usados no ensaio.Com ABTS, siringaldazina (SGZ) e 2,6-dimetoxifenol (DMP) o pH optimo determinado foi de 3,0; 5,0; e 3,5 respectivamente. A massa molecular obtida para a lacase foi de 59 kDa e a energia de activacao de 19,6 kj mol-1. Com ABTS como substrato a temperatura optima foi obtida a 65oC. O tempo de meia vida (t1/2) da lacase determinada a 50oC, 60oC e 65oC e os valores obtidos de 5 h, 11 min e 2,5 min respectivamente. A especificidade de 12 substratos foi estudada e os resultados apontam que o acido ferulico e o ABTS mostraram uma elevada actividade enzimatica enquanto o acido p-cumarico e o acido protocatequico mostraram baixa actividade (apenas 10% e 9% respectivamente, comparado com acido ferulico). As constantes cineticas obtidas mostraram elevada afinidade da enzima para a SGZ (Km = 9,9 μM) seguida pelo ABTS (Km = 23,1 μM) e uma baixa afinidade para o DMP (Km = 555,4 μM). A eficiencia catalitica (kcat/Km) tem a mesma ordem de grandeza (107) para o ABTS e SGZ enquanto o DMP tem baixa eficiencia catalitica (105). O IC50 mostra que NaN3 e um inibidor mais potente que o NaF e NaCl com ambos os substratos (ABTS e DMP). O inibidor NaF evidenciou uma inibicao nao competitiva com ABTS (Kic = Kiu = 46,9 μM) e inibicao competitiva (Kic = 2,6 μM) com DMP. Com ABTS a inibicao mista foi obtida com azida de sodio (Kic = 5,6 μM e Kiu = 3,4 μM) no entanto com DMP, a inibicao nao competitiva foi encontrada (Kic = Kiu = 2,5 μM). A inibicao obtida com Cl- foi competitiva para ambos os substratos, ABTS (Kic = 1407,5 μM) e DMP (Kic = 375,1 μM). %%%%The aim of this work is to produce, purify and characterize a laccase secreted by the fungus Phlebia rufa. The following supplements added to the basal liquid medium of Eggert et al. (1996) were tested: wheat, malt, ferulic acid and additional concentration of sulfates. The higher laccase activity was obtained with wheat grain supplement. The enzyme was produced in this liquid medium with wheat grain for 24 days. During incubation the laccase activity was determined using 2,2'…
The Use of Methylphenidate in the Postoperative Management of Older Adults after Coronary Artery Bypass Surgery

ABSTRACT Objective: Apathy and depression are common in older adult patients following open-heart surgery and contribute to delayed early mobility. The stimulant methylphenidate has not been widely studied in the older adult population. The aim of this study was to investigate differences in short-term outcomes between older adults who received methylphenidate following coronary artery bypass (CABG). Methods: In this retrospective cohort study, patients who underwent isolated CABG at a single community hospital were reviewed. All patients ≥70 years old were included. Groups were defined by whether or not they received postoperative methylphenidate (PM). Univariate and multivariate analysis was conducted. A generalized linear model predicting hospital length of stay (LOS) was constructed.Results: Of the 150 patients reviewed, 50 were included. Median age was 75.5 years (IQR 72–79). Seven patients were women. Twenty-seven patients received PM; 23 patients did not (NPM). Both groups had similar baseline and clinical characteristics. PM had shorter LOS (4 [3–5] days vs. 6 [4–7] days, p < 0.001). The association of methylphenidate with shorter LOS remained significant in multivariate analysis (IRR 0.74 [95% CI 0.62–0.87], p = 0.001). Mortality at 30-days was zero in both groups.Conclusions: PM following CABG may be associated with shorter LOS in the older adult population.
Survey on ontology Based Semantic Web Usage Mining for Enhanced Recommendation Model

Ms.R. RoobaM.Sc(CT). ,M.Phil., Dr.V. VallimayilPh.D Abstract: Web Usage Mining is the process of extracting useful knowledge such as browsing pattern from weblog. The predicted behavioural pattern from the weblog is used to construct personalized recommendation system for website engineers. Integration of semantic information into web usage miningphases will support intelligent process in the web so as to prune the search space.Ontology plays a pivotal role in integrating semantic information into web usage mining. The deployment of ontology captures the domain of interest which deals machine understandable data on the current human-readable web. This paper discusses various methodologies to create domain ontologies and semantic enhanced web usage mining techniques which add semantic information into weblog data. The paper also discusses the framework of Enhanced Recommendation Model for Semantic Web Usage Mining which includes semantic metadata of web pages.
Percentis para o perímetro de cintura de adolescentes do município do Rio de Janeiro

OBJECTIVE: To determine waist circumference and present waist circumference critical values to classify the risk and high risk posed by excessive abdominal fat according to age and gender. METHODS: A probabilistic sample of adolescent students (n=3,175) from public schools in Rio de Janeiro was analyzed. Waist circumference measurements were performed at midpoint between the last rib and the top of the iliac crest. Waist circumference percentiles were estimated using the LMS (Lambda, Mu, Sigma) method proposed by Cole (1990). The cut-points used to determine the risk and high-risk posed by excessive abdominal fat were proposed by Jolliffe & Janssen (2007). RESULTS: Comparing the values estimated, it was observed that the girls had higher waist circumference than the boys at all percentiles (except for P90) and ages. The estimated percentiles were lower in girls and increased with age in both sexes. At all ages, (except for 17 year old boys) and both genders, the estimated values were lower than the reference values used. CONCLUSION: The results obtained confirm the importance of measuring waist circumference in adolescents. This study presents waist circumference critical values for the anthropometric classification of the group studied, which has not yet been reported in the literature with data of Brazilian adolescents.
Establishing the protocol validity of an electronic standardised measuring instrument

Title: Establishing the protocol validity of an electronic standardised measuring instrument Key terms: Protocol validity, item response theory, neural networks, well-being instruments Over the past few decades, the nature of work has undergone remarkable changes, resulting in a shift from manual demands to mental and emotional demands on employees. In order to manage these demands and optimise employee performance, organisations use well-being surveys to guide their interventions. Because these interventions have a drastic financial implication it is important to ensure the validity and reliability of the results. However, even if a validated measuring instrument is used, the problem remains that wellness audits might be reliable, valid and equivalent when the results of a group of people are analysed, but cannot be guaranteed for each individual. It is therefore important to determine the validity and reliability of individual measurements (i.e. protocol validity). However, little information exists concerning the efficiency ofdifferent methods to evaluate protocol validity. The general objective of this study was to establish an efficient, real-time method/indicator for determining protocol validity in web-based instruments. The study sample consisted of 14 592 participants from several industries in South Africa and was extracted from a work­ related well-being survey archive. A protocol validity indicator that detects random responses was developed and evaluated. It was also investigated whether Item Response Theory CIRT) fit statistics have the potential to serve as protocol validity indicators and this was compared to the newly developed protocol validity indicator. The developed protocol validity indicator makes use of neural networks to predict whether cases have protocol validity. A neural network was trained on a large non-random sample and a computer-generated random sample. The neural network was then cross-validated to see whether posterior cases can be accurately classified as belonging to the random or non­ random sample. The neural network proved to be effective in detecting 86,39% ofthe random responses and 85,85% of the non-random responses correctly. Analyses on the misclassified cases demonstrated that the neural network was accurate because non-random classified cases were in fact valid and reliable, while random classified cases showed a problematic factor
High-Throughput Re-configurable content-addressable memory on FPGAs

Content-addressable memory (CAM) is a searching memory which provides the address of the search key in a single clock cycle. High speed lookup operation of CAM makes it extremely attractive in security, in-memory computing, distributed systems and networking applications. Field programmable gate arrays (FPGAs) are famous for hardware-like performance and software-like re-configurability, but it does not have built-in CAM. Researchers have used different hardware components, like Block random-access memory (RAM), distributed RAM, flip-flops etc., to emulate the functionality of CAM inside FPGAs. This paper presents the design space exploration of gate-based area efficient ternary content addressable memory (G-AETCAM) which uses flip-flops as memory elements on FPGA and can be configured as binary as well as ternary CAM. By configuring G-AETCAM as binary CAM, due to the redundancy of masking bits, half of the resources on FPGA are saved. We have implemented different sizes of G-AETCAM on Xilinx Virtex-5, Virtex-6 and Virtex-7 FPGAs and provided the results in detail for the designers to choose according to the application's requirements. Throughput, as a critical performance metric, is improved by 28\% compared to other FPGA-based TCAM counterparts. Moreover, scalability is shown as a trade-off of G-AETCAM due to the routing complexity of the architecture and the factors limiting the largest possible size on each FPGA device are reported in this work.
“Get Firefighters Moving”: Marketing a Physical Fitness Intervention to Reduce Sudden Cardiac Death Risk in Full-Time Firefighters

Firefighters as an occupational group are at increased risk for a cardiac-related injury, with the largest proportion (44%) of firefighter on-duty mortality due to sudden cardiac death. As poor physical fitness behaviors are a contributing factor to this risk, a social marketing approach was used to design and market a competitive team-sports intervention to improve physical fitness and reduce cardiac risk factors in a group of full-time firefighters. This article describes how social marketing principles were used in the design and implementation of the “Get Firefighters Moving” physical fitness intervention. This includes strategic development of the marketing plan, with discussion of audience segmentation and the marketing mix used to implement the intervention. Preliminary findings are also presented, as they suggest improvement in cardiac risk factors including blood pressure, cardiovascular class, flexibility, and percent body fat.