Miracle-dz/UniFAR-FTUs huggingface dataset:
Is the 1-minute sit-to-stand test a good tool for the evaluation of the impact of pulmonary rehabilitation? Determination of the minimal important difference in COPD

Background The 1-minute sit-to-stand (STS) test could be valuable to assess the level of exercise tolerance in chronic obstructive pulmonary disease (COPD). There is a need to provide the minimal important difference (MID) of this test in pulmonary rehabilitation (PR). Methods COPD patients undergoing the 1-minute STS test before PR were included. The test was performed at baseline and the end of PR, as well as the 6-minute walk test, and the quadriceps maximum voluntary contraction (QMVC). Home and community-based programs were conducted as recommended. Responsiveness to PR was determined by the difference in the 1-minute STS test between baseline and the end of PR. The MID was evaluated using distribution and anchor-based methods. Results Forty-eight COPD patients were included. At baseline, the significant predictors of the number of 1-minute STS repetitions were the 6-minute walk distance (6MWD) (r=0.574; P<10−3), age (r=−0.453; P=0.001), being on long-term oxygen treatment (r=−0.454; P=0.017), and the QMVC (r=0.424; P=0.031). The multivariate analysis explained 75.8% of the variance of 1-minute STS repetitions. The improvement of the 1-minute STS repetitions at the end of PR was 3.8±4.2 (P<10−3). It was mainly correlated with the change in QMVC (r=0.572; P=0.004) and 6MWD (r=0.428; P=0.006). Using the distribution-based analysis, an MID of 1.9 (standard error of measurement method) or 3.1 (standard deviation method) was found. With the 6MWD as anchor, the receiver operating characteristic curve identified the MID for the change in 1-minute STS repetitions at 2.5 (sensibility: 80%, specificity: 60%) with area under curve of 0.716. Conclusion The 1-minute STS test is simple and sensitive to measure the efficiency of PR. An improvement of at least three repetitions is consistent with physical benefits after PR.
A Language Modeling Approach to Image Classification

Due to the recent and fast diffusion of new digital devices (digital cameras, camera cell phones, internet), the number and size of image databases is dramatically increasing. Managing such databases is an important issue, for professional databases (e.g. from photo agencies) as well as for personal collections. Image classification and retrieval are therefore becoming more and more challenging. Discriminant image descriptors and robust classifiers are needed to handle these tasks. Nowadays approaches generally rely on describing images as a set of elementary and independent image patches called visual words, then using a classical classifier such as Support Vector Machines. In this paper, we propose a more precise description of images, called visual sentences, that includes simple spatial information between visual words. We then propose a classification technique based on language modeling. This classifier can exploit the spatial information of the visual sentences. Experiments on two classical datasets show that our classification method clearly outperforms the state-of- the-art SVM classifier.
Automatic Bare Sluice Disambiguation in Dialogue

The capacity to recognise and interpret sluices—bare wh-phrases that exhibit a sentential meaning—is essential to maintaining cohesive interaction between human users and a machine interlocutor in a dialogue system. In this paper we present a machine learning approach to sluice disambiguation in dialogue. Our experiments, based on solid theoretical considerations, show that applying machine learning techniques using a compact set of features that can be automatically identified from PoS markings in a corpus can be an efficient tool to disambiguate between sluice interpretations.
A Wheelchair Platform Controlled by a Multimodal Interface

In this paper, an embedded speaker-independent isolated word speech recognition module based on STM32F103C6T6 (STM32) is designed. The speech recognition algorithm includes: voice signal pre-processing, feature extraction and recognition. In addition, a communication module is designed based on the STM32 for receiving control commands from computer by universal serial bus. The result is that we realize a multimodal interface for wheelchair control in total three types of available inputs: speech, keyboard and traditional joystick. To evaluate the performance of wheelchair platform control by speech, four subjects participated in the experiment. The results demonstrated all of them can control the wheelchair by speech efficiently with an average success rate and response time are 92% and 2.2s respectively.
Tobacco status, impulsivity, and the five-factor of the PANSS in paranoid schizophrenia

Abstract There is consistent evidence that impulsivity is linked to tobacco consumption and to symptomatology in schizophrenia. In the current study, we propose a new integrative model of the relationship between impulsivity, psychopathological symptoms, and tobacco status in patients with paranoid schizophrenia. We investigated 33 paranoid schizophrenia patients and 37 healthy controls using a battery of psychopathological scales included the Structured Clinical Interview for DSM-IV (SCID), the five-factor model of the Positive and Negative Symptom Scale (PANSS), the Barratt Impulsiveness Scale (BIS-10), and the UPPS Impulsive Behavior scale (UPPS). Step-wise regression analysis revealed that positive factor of the PANSS and tobacco status contributed positively and significantly to the explained variance of impulsivity. In addition, non-planning impulsivity and sensation-seeking emerged as significant predictors of tobacco status, while smoking predicted non-planning impulsivity and sensation-seeking. Our results suggest that the relationship between sensation-seeking and tobacco use is reciprocal and proposes a new integrative model of the relationship between impulsivity, positive symptoms and tobacco status in patients with paranoid schizophrenia. However, the exact mechanisms for these relationships deserve further investigation.
Web 2.0 dictionary

How might we benefit from the billions of tagged multimedia files (e.g. image, video, audio) available on the Internet? This paper presents a new concept called Web 2.0 Dictionary, a dynamic dictionary that takes advantage of, and is in fact built from, the huge database of tags available on the Web. The Web 2.0 Dictionary distinguishes itself from the traditional dictionary in six main ways: (1) it is fully automatic because it downloads tags from the Web and inserts this new information into the dictionary; (2) it is dynamic because each time a new shared image/video is uploaded, a "bag-of-tags" corresponding to the image/video will be downloaded, thus updating Web 2.0 Dictionary. The Web 2.0 Dictionary is literally updating every second, which is not true of the traditional dictionary; (3) it integrates all kinds of languages (e.g. English, Chinese), as long as the images/videos are tagged with words from such languages; (4) it is built by distilling a small amount of useful information from a massive and noisy tag database maintained by the entire Internet community, therefore the relatively small amount of noise present in the database will not affect it; (5) it truly reflects the most prevalent and relevant explanations in the world, unaffected by majoritarian views and political leanings. It is a real, free dictionary. Unlike Wikipedia" [5] which can be easily revised by even a single person, the Web 2.0 Dictionary is very stable because its contents are informed by a whole community of users that upload photo/videos; (6) it provides a correlation value between every two words ranging from 0 to 1. The correlation values stored in the dictionary have wide applications. We demonstrate the effectiveness of the Web 2.0 Dictionary for image/video understanding and retrieval, object categorization, tagging recommendation, etc, in this paper.
Mapping the spatial variability of plankton metabolism using nitrate and oxygen sensors on an autonomous underwater vehicle

It is now possible to make highly resolved vertical (<1 m) and horizontal (3 m at constant depth and ~400 m when undulating from surface to 100 m) measurements of nitrate concentration in the upper ocean using sensors deployed on propeller‐driven autonomous underwater vehicles (AUVs). The ability to make such highly resolved measurements opens the possibility of detecting small‐scale anomalies in nutrient fields that are created by locally high rates of primary production. We employed an in situ ultraviolet spectrophotometer optical nitrate sensor and Sea‐Bird oxygen sensor mounted on the Monterey Bay Aquarium Research Institute Dorado AUV to investigate the spatial variability of nitrate and oxygen in Monterey Bay, California. The Dorado conducts missions up to 100 km in length. A very simplified diffusion and reaction model for nitrate, ignoring horizontal processes and advection and assuming steady state, would require that consumption rate = KZ × δ2NO3−/δZ2 (KZ = vertical eddy diffusion coefficient). Water column regions exhibiting elevated nitrate uptake rates are identified from anomalies in the second derivatives of the vertical concentration profiles relative to the second derivative of conservative properties. The relationship between chlorophyll a fluorescence and the inferred productivity rates exhibits coherent spatial variability across the bay.
Segmentation through DWT and adaptive morphological closing

Object segmentation is an essential task in computer vision and object recognitions. In this paper, we present an image segmentation technique that extract edge information from wavelet coefficients and uses mathematical morphology to segment the image. We threshold the image to get its binary version and get a high-pass image by the inverse DWT of its high frequency subbands from the wavelet domain. This is followed by an adaptive morphological closing operation that dynamically adjusts the structuring element according to the local orientation of edges. The ensued holes are, subsequently, filled by a morphological fill operation. For comparison, we are relying on the well-established Canny's method and show that, for images with low-textured background, our method performs better.
AIR/X - A rule-based multistage indexing system for Iarge subject fields

AIR/X is a rule-based system for indexing with terms (descriptors) from a prescribed vocabulary. For this task, an indexing dictionary with rules for mapping terms from the text onto descriptors is required, which can be derived automatically from a set of manually indexed documents. Based on the Darmstadt Indexing Approach, the indexing task is divided into a description step and a decision step. First, terms (single words or phrases) are identiied in the document text. With term-descriptor rules from the dictionary, descriptor indications are formed. The set of all indications from a document leading to the same descriptor is called a relevance description. A probabilistic classiication procedure computes indexing weights for each relevance description. Since the whole system is rule-based, it can be adapted to diierent subject elds by appropriate modiications of the rule bases. A major application of AIR/X is the AIR/PHYS system developed for a large physics database. This application is described in more detail along with experimental results.
Fragile Families and Child Wellbeing Study

For the past decade, a research team based at Princeton and Columbia universities has been engaged in a national, long-term study of the consequences of childbearing outside marriage and has shared the resulting data and findings widely with the academic and policy-making communities. The Fragile Families and Child Wellbeing Study was supported by the Robert Wood Johnson Foundation (RWJF) from February 1, 1998 through December 31, 2011, and the study continues with support from other grants.
Colour-based texture image classification using the complex wavelet transform

In this paper we present an experimental analysis of colour-based texture image classification in order to evaluate whether colour and texture information should be used jointly or separately. Various colour spaces are used for colour information extraction. The complex wavelet transform is used to extract texture information. Results show that colour and texture information should be treated separately. As well, they evidence that colour is highly important when present in textured images.
A novel approach to detect most effective compression technique based on compression ratio and time complexity with high image data load for cloud migration

Digital image compression technology is of special interest for the fast transmission and real-time processing of digital image. However image compression is a trend of research grown for a long time and there are multiple sophisticated approaches which significantly improve the compression rate and down grade computation time, a basic comparison with the aspect of storage on cloud environment is required. This work analyzes about the background of image compression, including when image compression is needed, categories of techniques and their properties. However compression uses many algorithms that store an exact representation or an approximation of the original image in a smaller number of bytes that can be expanded back to its uncompressed form with a corresponding decompression algorithm. This work majorly focuses on analyzing the impact of generic image compression techniques for high data loads on cloud storage services.
Why certificates don ’ t meet e-business needs ?

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A Personalized Recommender Algorithm Based on Semantic Tree

Most of today's content-based personalized recommender algorithms make recommendations with respect to the match-making between the user and the item profiles, which are generally represented with eigenvectors. In conventional methods, the semantic relationships between the terms are missing, with only the frequency of the terms being taken into account. This would be a key factor that causes the poor recommendation results. To cope with this drawback, we in this paper propose a novel recommender algorithm, in which the user and the item profiles are both denoted as semantic trees, so as to incorporate the semantic information between terms when evaluating the similarity between the profiles. By taking the semantic similarity into account, the experimental tests illustrate that the similarity measure is more accurate with the proposed method and more reliable recommendations can then be made.
Lauryl-poly-L-lysine: A New Antimicrobial Agent?

The development of multiple antibiotic resistance is a global problem. It is necessary to find new tools whose mechanisms of action differ from those of currently used antibiotics. It is known that fatty acids and cationic polypeptides are able to fight bacteria. Here, we describe the synthesis of fatty acids linked to a polypeptide with antibacterial activity. The linkage of fatty acids to a polypeptide is reported to increase the antibacterial effect of the linked fatty acid in comparison with free fatty acids (FA) or free poly-L-lysine (PLL) or a mixture of both (FA free + PLL free). A number of C6–C18 fatty acids were linked to PLL to obtain new synthetic products. These compounds were assessed in vitro to evaluate their antibacterial activity. Some fatty acid-PLLs showed a good ability to fight bacteria. Their bactericidal activity was evaluated, and, lauryl linked to PLL was found to be the most active product against both Gram-positive and Gram-negative bacteria. This new active component showed a good degree of specificity and reproducibility and its minimum inhibitory concentration (MIC) was comparatively good. The antibacterial activity of the lauryl-PLL compound suggests that it is a new and promising antimicrobial agent.
Towards the development of a sustainable soya bean‐based feedstock for aquaculture

Summary Soya bean (Glycine max (L.) Merr. ) is sought after for both its oil and protein components. Genetic approaches to add value to either component are ongoing efforts in soya bean breeding and molecular biology programmes. The former is the primary vegetable oil consumed in the world. Hence, its primary usage is in direct human consumption. As a means to increase its utility in feed applications, thereby expanding the market of soya bean coproducts, we investigated the simultaneous displacement of marine ingredients in aquafeeds with soya bean‐based protein and a high Omega‐3 fatty acid soya bean oil, enriched with alpha‐linolenic and stearidonic acids, in both steelhead trout (Oncorhynchus mykiss) and Kampachi (Seriola rivoliana). Communicated herein are aquafeed formulations with major reduction in marine ingredients that translates to more total Omega‐3 fatty acids in harvested flesh. Building off of these findings, subsequent efforts were directed towards a genetic strategy that would translate to a prototype design of an optimal identity‐preserved soya bean‐based feedstock for aquaculture, whereby a multigene stack approach for the targeted synthesis of two value‐added output traits, eicosapentaenoic acid and the ketocarotenoid, astaxanthin, were introduced into the crop. To this end, the systematic introduction of seven transgenic cassettes into soya bean, and the molecular and phenotypic evaluation of the derived novel events are described.
Output-constraint specialization

In component-based software development, gluing of two software components are usually achieved by defining an interface specification, and creating wrappers on the components to support the interface. We believe that interface specification provides useful information for specializing components. However, an interface may define constraints on a component's inputs, as well as on its outputs. In this paper, we propose a novel concept of program specialization with respect to output constraints. We define how an ideal specialized program should behave after such specialization, and consider a variant of partial evaluation to achieve it. In the process, we translate an output constraint into a characterization function for a component's input, and define a specializer that uses this characterization to guide the specialization process. We believe this work will broaden the scope of program specialization, and provide a framework for building more generic and versatile program adaptation techniques.
An optimal feedback approach to particle source identification in tokamaks

A new identification technique is proposed to study the plasma phenomena taking place in the so-called scrape-off layer and their correlation with the lower hybrid radio frequency antenna in the experimental nuclear fusion tokamak reactor Tore Supra. A deeper knowledge of the plasma behavior in this region would contribute to the achievement of steady-state controlled thermonuclear fusion for power generation. The proposed approach relies on the design of a feedback/feedforward optimized architecture to solve a blind identification problem. While our paper is mostly focused on experimental results for the studied application, it can provide valuable insights on input estimation and model validation for transport phenomena described by partial differential equations.
Face recognition CAPTCHA made difficult

A CAPTCHA is a Turing test to distinguish human users from automated scripts to defend against internet adversarial attacks. As text-based CAPTCHAs (TBC) have become increasingly difficult to solve, image-based CAPTCHAs, and particularly face recognition CAPTCHAs (FRC), offer a chance to overcome TBC limitations. In this paper, we systematically design and implement a practical FRC, informed by psychological findings. We use gray-scale and binary images, which are computationally inexpensive to generate and deploy. Furthermore, our FRC complies with CAPTCHA design guidelines, thereby ensuring its robustness.
Factors Affecting Attitudes Toward Homosexuals Among Undergraduate Students in Malaysia

Homosexuals faced sexual prejudice and discrimination by the society due to their sexual orientation, especially in Malaysia where homosexuality is illegal. Therefore, this study aimed to examine the relationship between intrinsic religiosity, gender role belief and the attitudes toward homosexuals among heterosexual undergraduates in Malaysia. Quantitative, cross-sectional research design was used in this correlation study. Four hundred sixty undergraduate students were recruited through convenient sampling. Both online and paper and pencil survey method were used in data collection. Attitudes Toward Lesbians and Gay Men Scale Revised 5-Item version (ATLG-R), Duke University Religion Index (DUREL), and Liberal Feminist Attitude and Ideology Scale (LFAIS): Gender Role subscale were used to measure the variables in this study. Result of the study revealed that those who had higher score in intrinsic religiosity showed more prejudicial attitudes toward homosexuality while those who held more feminist gender role belief had more positive attitudes toward homosexuality. Moreover, findings also indicated that males held more unfavorable attitudes toward both gay men and lesbians as compared to females. Furthermore, results of multiple regression revealed that gender role belief is the strongest predictor for the attitudes toward homosexuals, followed by intrinsic religiosity. The findings of the study are important for future studies as well as being a source of reference in planning awareness campaign and public health discourse.