Miracle-dz/UniFAR-FTUs huggingface dataset:
Fully-Automatic XML Clustering by Structure-Constrained Phrases

Conventional approaches to XML clustering by content and structure are generally affected by a limitation due to the adoption of the bag-of-word model for the representation of their textual contents. This choice may lead to consider structure-constrained textual items of separate XML documents as related, even though the actual meaning of such items in their respective contexts is different. To overcome such a limitation, we propose XML clustering by structure-constrained phrases. The latter is a previously unexplored method relying on the more accurate bag-of-phrase model of the XML textual content, with which to better preserve the meaning of the structure-constrained content items for improved clustering effectiveness. In order to conduct an in-depth and systematic study of the effectiveness of the proposed method, we develop a parameter-free prototypical approach to XML partitioning, which projects the XML documents into a space of XML features representing fixed-length sequences of adjacent textual items in the context of root-to-leaf paths. Feature selection without any tunable threshold is used to choose a subset of the XML features on the basis of their relevance to clustering, which is assessed through a new scoring scheme. A comparative experimentation on real-world benchmark XML corpora reveals a higher effectiveness than several state-of-the-art competitors.
On astrophysical bounds of the cosmological constant

Astrophysical bounds on the cosmological constant are examined for spherically symmetric bodies. Similar limits emerge from the hydrostatical and gravitational equilibrium and the validity of the Newtonian limit. The methods in use seem to be disjoint from the basic principles, however they have the same implication regarding the upper bounds. Therefore we will compare different inequalities and comment on the possible relationship between them. These inequalities are of relevance for the so-called coincidence problem and for the bound of the cosmological constant which comes surprisingly close to the "experimental" value.
Real-time recognition of U.S. speed signs

In this paper a camera-based system for detection, tracking, and classification of U.S. speed signs is presented. The implemented application uses multiple connected stages and iteratively reduces the number of pixels to process for recognition. Possible sign locations are detected using a fast, shape-based interest operator. Remaining objects other than speed signs are discarded using a classifier similar to the Viola-Jones detector. Classification results from tracked candidates are utilized to improve recognition accuracy. On a standard PC the system reached a detection speed of 27 fps with an accuracy of 98.8%. Including classification, speed sign recognition rates of 96.3% were achieved with a frame rate of approximately 11 fps and one false alarm every 42 s.
A single phase input switched SEPIC PFC AC-DC converter

A single phase new type AC-DC converter based on SEPIC topology has been proposed in this paper which has power factor correction and input current shaping facilities. The proposed rectifier is input switched and utilizes a bidirectional switch at the input. The rectifier gives buck-boost voltage gain without polarity reversal. The design has been made separately to operate the rectifier as a buck or a boost ac-dc input power factor corrected (PFC) converter with near sinusoidal input current having very low total harmonic distortion (THD). A simple feedback control circuitry has been used for simulated result generation to show that the proposed converter achieve the above at good conversion efficiency.
Expression and Activation of EphA4 in the Human Brain After Traumatic Injury

Abstract Glial scars that consist predominantly of reactive astrocytes create a major barrier to neuronal regeneration after traumatic brain injury (TBI). In experimental TBI, Eph receptors and their ephrin ligands are upregulated on reactive astrocytes at injury sites and inhibit axonal regeneration, but very little is known about Eph receptors in the human brain after TBI. A better understanding of the functions of glial cells and their interactions with inflammatory cells and injured axons will allow the development of treatment strategies that may promote regeneration. We analyzed EphA4 expression and activation in postmortem brain tissue from 19 patients who died after acute closed head injury and had evidence of diffuse axonal injury and 8 controls. We also examined downstream pathways that are mediated by EphA4 in human astrocyte cell cultures. Our results indicate that, after TBI in humans, EphA4 expression is upregulated and is associated with reactive astrocytes. The expression was increased shortly after the injury and remained activated for several days. EphA4 activation induced under inflammatory conditions in vitro was inhibited using unclustered EphA4 ligand. These results suggest that blocking EphA4 activation may represent a therapeutic approach for TBI and other types of brain injuries in humans.
Learning to segment temporal sequences by mixture of RNN experts with adaptive variance model

This paper proposes a novel method for learning with a ``mixture of experts'' type model, which can acquire the ability to generate desired sequences by switching experts dynamically. Our method is based on maximum likelihood estimation using gradient descent algorithm similar to conventional methods, though the likelihood function is modified by adding an adaptive mechanism of the variance at each expert. To demonstrate the capability of the proposed method, we show a numerical simulation in which the model can learn Markov chain switching among a set of nine Lissajous curves using our method, while the model using the conventional method cannot learn it. In addition, we numerically examine the generalization capability of the proposed method as compared with the conventional one. These results indicate that the proposed method improves learning performance of the model.
Near-videorealistic synthetic visual speech using non-rigid appearance models

We present work towards videorealistic synthetic visual speech using non-rigid appearance models. These models are used to track a talking face enunciating a set of training sentences. The resultant parameter trajectories are used in a concatenative synthesis scheme, where samples of original data are extracted from a corpus and concatenated to form new unseen sequences. Here we explore the effect on the synthesiser output of blending several synthesis units considered similar to the desired unit. We present preliminary subjective and objective results used to judge the realism of the system.
Adaptive clustering for device free user positioning utilizing passive RFID

Context sensing is an important part of building ubiquitous smart and assistive environments. It is the major data source for intention recognition and strategy generation systems. Device-free localization systems (DFL) join the efforts of non-instrumentation of users maintaining their privacy. In recent publications we propose an innovative approach utilizing a cluster of passive Radio Frequency Identification Transponders (pRFID) for device-free radio-based positioning. Due to the point that the RFID technology is typically not designed for that purpose we have to deal with certain drawbacks. A high number of transponders typically conclude in lower measurement frame rates while generating substantially more information for accurate positioning. To fix this tradeoff this work presents a transponder clustering approach based on inherent EPC protocol based bit masking, which allows us to calculate fast coarse grained localization results and increase the precision by time, so that the user is able to adjust between localization speed and accuracy. We made simulations and conducted experiments in an indoor room DFL scenario for validation.
Lecture Notes on Language-Based Security

These lecture notes discuss language-based security, which is the term loosely used for the collection of features and mechanisms that a programming language can provide to help in building secure applications. These features include: memory-safety and typing, as offered by so-called safe programming languages; language mechanisms to enforce various forms of access control (such as sandboxing), similar to access control traditionally offered by operating systems; mechanisms that go beyond what typical operating systems do, such as information flow control.
Plant N-Glycan Processing Enzymes Employ Different Targeting Mechanisms for Their Spatial Arrangement along the Secretory Pathway[W][OA]

The processing of N-linked oligosaccharides in the secretory pathway requires the sequential action of a number of glycosidases and glycosyltransferases. We studied the spatial distribution of several type II membrane-bound enzymes from Glycine max, Arabidopsis thaliana, and Nicotiana tabacum. Glucosidase I (GCSI) localized to the endoplasmic reticulum (ER), α-1,2 mannosidase I (ManI) and N-acetylglucosaminyltransferase I (GNTI) both targeted to the ER and Golgi, and β-1,2 xylosyltransferase localized exclusively to Golgi stacks, corresponding to the order of expected function. ManI deletion constructs revealed that the ManI transmembrane domain (TMD) contains all necessary targeting information. Likewise, GNTI truncations showed that this could apply to other type II enzymes. A green fluorescent protein chimera with ManI TMD, lengthened by duplicating its last seven amino acids, localized exclusively to the Golgi and colocalized with a trans-Golgi marker (ST52-mRFP), suggesting roles for protein–lipid interactions in ManI targeting. However, the TMD lengths of other plant glycosylation enzymes indicate that this mechanism cannot apply to all enzymes in the pathway. In fact, removal of the first 11 amino acids of the GCSI cytoplasmic tail resulted in relocalization from the ER to the Golgi, suggesting a targeting mechanism relying on protein–protein interactions. We conclude that the localization of N-glycan processing enzymes corresponds to an assembly line in the early secretory pathway and depends on both TMD length and signals in the cytoplasmic tail.
A NOVEL APPROACH FOR EDGE DETECTION OF LOW CONTRAST SATELLITE IMAGES

Abstract. Discrimination between texture edges and geometrical edges is very difficult in low contrast images. Satellite images are low contrast images. It is important to extract the edges that are not clearly visible in case of Satellite images. The present work encompasses a new edge detection algorithm using newly constructed differentiator. Chebyshev polynomial based fractional order differentiator has been used for filtering operation on an image. High pass and Low pass filters are designed with the concept of Quadrature Mirror Filter (QMF). Pre-processing has been performed by using this filter. Sobel edge detection method has been applied on this pre-processed image. The algorithm has been tested with two different satellite images.
Acknowledging Challenges and Embracing Innovative Instructional Practices in Response to Cultural and Linguistic Diversity

Aims of the current study were to explore teachers’ background, beliefs, attitudes, and sense of self-efficacy, as well as instructional supports and innovative practices in response to cultural and linguistic diversity in classrooms serving Spanish-English speaking children of migrant workers. A total of 22 teachers participated in semi-structured interviews regarding linguistic diversity in the classroom. Their responses were transcribed and qualitative analysis procedures were utilized to deconstruct units. Individual units were then clustered by similarities and differences into themes and subcategories of themes. Teacher responses to the interview questions produced themes: awareness of cultural and linguistic diversity, issues and challenges, and innovative instructional supports and practices. Participating teachers commented on general supports designed to assist communication between English Learners (EL) and the teacher or peers, facilitate communication with parents of ELs, and embrace and promote responsiveness to cultural linguistic diversity (CLD) in the classroom. Teachers identified specific supports to intensify instruction, including (a) employing multiple modalities, (b) increasing experience and exposure, and (c) providing individualized support. Additional resources and support are warranted to identify and disseminate effective practices to provide intensified instruction and support to ELs.
Exploring Data Driven Parametric Synthesis

This paper describes our work on building a formant synthesis system based on both rule generated and database driven methods. Three parametric synthesis systems are discussed: our traditional rule based system, a speaker adapted system, and finally a gesture system. The gesture system is a further development of the adapted system in that it includes concatenated formant gestures from a data-driven unit library. The systems are evaluated technically, comparing the formant tracks with an analysed test corpus. The gesture system results in a 25% error reduction in the formant frequencies due to the inclusion of the stored gestures. Finally, a perceptual evaluation shows a clear advantage in naturalness for the gesture system compared to both the traditional system and the speaker adapted system.
Physical Modeling Using Digital Waveguides

Historically, physical models have led to prohibitively expensive synthesis algorithms, and commercially available synthesizers do not yet appear to make use of them. These days, most synthesizers use either processed digital recordings (\sampling synthesis") or an abstract algorithm such as Frequency Modulation (FM). However, as computers become faster and cheaper, and as algorithms based on physical models become more e cient, we may expect to hear more from them.
Detecting mild cognitive impairment in narratives in Brazilian Portuguese: first steps towards a fully automated system

In recent years, Mild Cognitive Impairment (MCI) has received a great deal of attention, as it may represent a pre-clinical state of Alzheimer´s disease (AD). In the distinction between healthy elderly (CTL) and MCI patients, automated discourse analysis tools have been applied to narrative transcripts in English and in Brazilian Portuguese. However, the absence of sentence boundary segmentation in transcripts prevents the direct application of methods that rely on these marks for the correct use of tools, such as taggers and parsers. To our knowledge, there are only a few studies evaluating automatic sentence segmentation in transcripts of neuropsychological tests. The purpose of this study is to investigate the impact ofthe automatic sentence segmentation method DeepBond on nine syntactic complexity metrics extracted of transcripts of CTL and MCI patients. ***Detecção de comprometimento cognitivo leve em narrativas em Português Brasileiro: primeiros passos para um sistema automatizado***Nos últimos anos, o Comprometimento Cognitivo Leve (CCL) tem recebido bastante atenção, uma vez que pode representar um estado pré-clínico da Doença de Alzheimer (DA). Na distinção entre idosos saudáveis (CTL) e pacientes com CCL, ferramentas de análise automática do discurso têm sido aplicadas a transcrições de narrativas em inglês e em português brasileiro. No entanto, a ausência da segmentação dos limites da sentença em transcrições impede a aplicação direta de métodos que empregam essas pontuações para o uso correto de ferramentas, como taggers e parsers. Segundo nosso conhecimento, há poucos estudos avaliando a segmentação automática de sentenças em transcrições de testes neuropsicológicos. O propósito deste estudo é investigar o impacto do método DeepBond para segmentação automática de sentenças em nove métricas de complexidade sintática extraídas de transcrições de CTL e de pacientes com CCL.
Comparing successful aging, resilience, and holistic wellness as predictors of the good life

ABSTRACT Objectives: The purpose of the study was to compare the relative contributions of Rowe and Kahn’s definition of successful aging (SA), resilience, and the holistic wellness paradigm for predicting happiness, life satisfaction, and self-rated physical health in late life. Method: A cross-sectional research design was used to survey 200 residents across 12 senior housing sites. Criteria with strong psychometric properties representing the three constructs were operationalized using hierarchical regression within the context of relevant control variables to compare the relative strengths of the three paradigms for predicting measures of quality of life. Results: In this study, 8.5% of the sample met modified criteria for SA and were used as a comparison group with those who did not meet the criteria. Overall, holistic wellness and resilience predicted happiness, life satisfaction, and physical health better than SA alone. When predicting happiness and life satisfaction, race and holistic wellness were significant predictors. Age and holistic wellness were the best predictors of self-rated physical health. Conclusion: The criteria underlying SA poorly predicted happiness, life satisfaction, and self-rated physical health compared to the resilience and holistic wellness models. The results suggest that definitions of aging well are complex and require greater nuance. The findings have important implications for clinicians seeking translatable theoretical models that are amenable to practice with older adults, especially for those living in independent senior housing communities.
South Asians and Suicide: Beliefs About Suicide in a U.S. Community Sample

Research from the United Kingdom has suggested differential risk factors for suicide among South Asians when compared with the general population. Studies in the United Kingdom have found higher risk related to gender, marital status, and religion. It has been unknown how such demographic variables, acculturative stress, and lower rates of help-seeking influence rates of suicide among South Asians in the United States. In this study, a community sample of South Asians in the United States (n = 524) were asked to complete a demographic questionnaire and validated measures for levels of acculturation, help-seeking, and beliefs about reasons for living via web-based survey. South Asians’ scores on the Reasons for Living Inventory (RFL) were significantly higher than the normative population across all subscales except the Survival and Coping Beliefs subscales. Demographic variables significantly related to RFL scores included gender (men with higher RFL scores), age (younger individuals with lower RFL scores), country of origin (those from India with lower RFL scores), education level (lower RFL scores in those with higher education levels), and income (those with lower incomes having lower RFL scores). Help-seeking attitudes were also significantly related to RFL scores. As a whole, the findings are consistent with U.K.-based studies and provide valuable comparative data on a U.S.-based sample.
Using conditional restricted Boltzmann machines for spectral envelope modeling in speech bandwidth extension

In this paper, we present a conditional restricted Boltzmann machine (CRBM) based speech bandwidth extension (BWE) method. A CRBM is employed to obtain time information and model deep non-linear relationships between the spectral envelope features of low frequency (LF) and high frequency (HF). Two exclusive CRBMs are adopted to model the distribution of LF's and HF's spectral envelope features. respectively. A neural network (NN) is then used to model the joint distribution of hidden variables extracted from the two CRBMs. The proposed method takes advantage of the strong ability of CRBM in discovering the temporal correlation between adjacent frames and modeling deep non-linear relationships between input and output. Both the objective and subjective evaluations indicate that our proposed method outperforms the conventional Gaussian mixture model based methods and other NN based methods.
Joint range estimation and spectral classification for 3D scene reconstruction using multispectral Lidar waveforms

This paper presents a new Bayesian classification method to analyse remote scenes sensed via multispectral Lidar measurements. To a first approximation, each Lidar waveform mainly consists of the temporal signature of the observed target, which depends on the wavelength of the laser source considered and which is corrupted by Poisson noise. By sensing the scene at several wavelengths, we expect a more accurate target range estimation and a more efficient spectral analysis of the scene. Thanks to its spectral classification capability, the proposed hierarchical Bayesian model, coupled with an efficient Markov chain Monte Carlo algorithm, allows the estimation of depth images together with reflectivity-based scene segmentation images. The proposed methodology is illustrated via experiments conducted with real multispectral Lidar data.
New challenges in CSCL: Towards adaptive script support

: Scaffolding learners, i.e. helping learners to attain tasks they could not accomplish without support, entails the notion of fading, i.e. reducing the scaffolding for learners to become more and more self-regulated. Fading implies to tailor support for collaboration, such as collaboration scripts, to the particular needs of the specific collaborators. In computer supported collaborative learning (CSCL) settings, support can be designed in a very restrictive and inflexible fashion; at the same time computerized settings open new possibilities for the realization of adaptive support as they enable automation of analysis and feedback mechanisms. In this symposium we present new technical approaches and latest empirical research on possibilities and limitations of adaptive support for learners in CSCL settings.
Stemming Versus Light Stemming as Feature Selection Techniques for Arabic Text Categorization

This paper compares and contrasts two feature selection techniques when applied to Arabic corpus; in particular; stemming, and light stemming were employed. With stemming, words are reduced to their stems. With light stemming, words are reduced to their light stems. Stemming is aggressive in the sense that it reduces words to their 3-letters roots. This affects the semantics as several words with different meanings might have the same root. Light stemming, by comparison, removes frequently used prefixes and suffixes in Arabic words. Light stemming doesn't produce the root and therefore doesn't affect the semantics of words; it maps several words, which have the same meaning to a common syntactical form. The effectiveness of above two feature selection techniques was assessed in a text categorization exercise for Arabic corpus. This corpus consists of 15000 documents that fall into three categories. The K-nearest neighbors (KNN) classifier was used in this work. Several experiments were carried out using two different representations of the same corpus; the first version uses stem- vectors; and the second uses light stem-vectors as representatives of documents. These two representations were assessed in terms of size, time and accuracy. The light stem representation was superior in terms of classifier accuracy when compared with stemming.