Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Mining on Educational Domain

Educational data mining (EDM) is defined as the area of scientific inquiry centered around the development of methods for making discoveries within the unique kinds of data that come from educational settings, and using those methods to better understand students and the settings which they learn in. Data mining enables organizations to use their current reporting capabilities to uncover and understand hidden patterns in vast databases. As a result of this insight, institutions are able to allocate resources and staff more effectively. In this paper, we present a real-world experiment conducted in Shree Rayeshwar Institute of Engineering and Information Technology (SRIEIT) in Goa, India. Here we found the relevant subjects in an undergraduate syllabus and the strength of their relationship. We have also focused on classification of students into different categories such as good, average, poor depending on their marks scored by them by obtaining a decision tree which will predict the performance of the students and accordingly help the weaker section of students to improve in their academics. We have also found clusters of students for helping in analyzing student's performance and also improvising the subject teaching in that particular subject.
The Compositional Nature of Verb and ArgumentRepresentations in the Human Brain

How does the human brain represent simple compositions of objects, actors, and actions? We had subjects view action sequence videos during neuroimaging (fMRI) sessions and identified lexical descriptions of those videos by decoding (SVM) the brain representations based only on their fMRI activation patterns. As a precursor to this result, we had demonstrated that we could reliably and with high probability decode action labels corresponding to one of six action videos (dig, walk, etc.), again while subjects viewed the action sequence during scanning (fMRI). This result was replicated at two different brain imaging sites with common protocols but different subjects, showing common brain areas, including areas known for episodic memory (PHG, MTL, high level visual pathways, etc., i.e., the ‘what’ and ‘where’ systems, and TPJ, i.e., ‘theory of mind’). Given these results, we were also able to successfully show a key aspect of language compositionality based on simultaneous decoding of object class and actor identity. Finally, combining these novel steps in ‘brain reading’ allowed us to accurately estimate brain representations supporting compositional decoding of a complex event composed of an actor, a verb, a direction, and an object.
Sparse basis expansions for compressive sensing of electromagnetic scattering patterns computed using iterative physical optics

Compressive sensing theory is applied to reduce the amount of data needed for reconstructing an RCS pattern generated using computational electromagnetics software. The data is generated by the iterative physical optics method for a scale-model generic tank at X-band. It is shown that the number of samples needed is considerably lower than the Nyquist requirement if the RCS pattern may be represented in a sparse basis. Here, the sparse basis is the set of point scattering functions that represent the dominant scattering centers of the target.
Inductive Learning of Pronunciation Rules by hypothesis Testing and Correction

This paper describes a system that learns the rules of pronunciation inductively. It begins with a set of 26 rules for single-letter pronunciation. Individual words are presented to it, and the system uses its rule set to hypothesise a pronunciation. This is compared with a dictionary pronunciation, and if any part of the pronunciation is incorrect new rules are created to handle the word as an exception condition. These rules are checked for similarity with others already produced, and where suitable a "general" rule is produced to deal with two or more created rules. The effect is to produce rules that are more and more general, and these approach the general pronunciation rule sets that have been produced manually by other workers.
Interactions “Candidatus Liberibacter solanacearum”—Bactericera cockerelli: Haplotype Effect on Vector Fitness and Gene Expression Analyses

“Candidatus Liberibacter solanacearum” (Lso) has emerged as a serious threat world-wide. Five Lso haplotypes have been identified so far. Haplotypes A and B are present in the Americas and/or New Zealand, where they are vectored to solanaceous plants by the potato psyllid, Bactericera cockerelli (Šulc) (Hemiptera: Triozidae). The fastidious nature of these pathogens has hindered the study of the interactions with their eukaryotic hosts (vector and plant). To understand the strategies used by these pathogens to infect their vector, the effects of each Lso haplotype (A or B) on psyllid fitness was investigated, and genome-wide transcriptomic and RT-qPCR analyses were performed to evaluate Lso gene expression in association with its vector. Results showed that psyllids infected with haplotype B had significantly lower percentage of nymphal survival compared to psyllids infected with haplotype A. Although overall gene expression across Lso genome was similar between the two Lso haplotypes, differences in the expression of key candidate genes were found. Among the 16 putative type IV effector genes tested, four of them were differentially expressed between Lso haplotypes, while no differences in gene expression were measured by qPCR or transcriptomic analysis for the rest of the genes. This study provides new information regarding the pathogenesis of Lso haplotypes in their insect vector.
The Role of Sound Changes in the Speech Recognition System: A Phonetic Analysis of the Final Nasal Shift in Mandarin

Over the past decade, computational linguists have been striving to design a speech recognition system that is able to identify standard speeches and to accommodate sound variables caused by different individual accents. Furthermore, some speech recognition programs have been able to learn and identify distinctive sound frequencies due to the user’s age and gender. Nevertheless, regular sound alterations that occur in different varieties of a language have never been seriously considered in the design of the speech recognition system. Accordingly, this study proposes to incorporate the socio-phonological information about regular sound modifications to enhance the performance of Automatic Speech Recognition. To illustrate this point, this study investigates and analyzes the acoustic variation of the syllable-final nasal shift from the velar to the dental, which has been discovered to be one of the distinctive sound features that makes the variety of Mandarin spoken in Taiwan differ from that spoken in China. Following the phonetic analysis, this study discusses the effect of the nasal merger on the development of phonology-dependent speech technologies. It concludes by proposing a preliminary resolution to the identification of syllable-final nasals for the design of Automatic Speech Recognition. 關鍵詞:中文語音學,語音變化,鼻音,語音辨識系統
The Construction of Meaning

We argue that word meanings are not stored in a mental lexicon but are generated in the context of working memory from long-term memory traces that record our experience with words. Current statistical models of semantics, such as latent semantic analysis and the Topic model, describe what is stored in long-term memory. The CI-2 model describes how this information is used to construct sentence meanings. This model is a dual-memory model, in that it distinguishes between a gist level and an explicit level. It also incorporates syntactic information about how words are used, derived from dependency grammar. The construction of meaning is conceptualized as feature sampling from the explicit memory traces, with the constraint that the sampling must be contextually relevant both semantically and syntactically. Semantic relevance is achieved by sampling topically relevant features; local syntactic constraints as expressed by dependency relations ensure syntactic relevance.
SPATIAL PORTALS : ADDING VALUE TO SPATIAL DATA INFRASTRUCTURES

This paper examines how well designed spatial portals bring value and return on investment to Spatial Data Infrastructure (SDI). Spatial portals are generally the visible front-end to SDI, they are the gateways (or brokers) through which users access the geographic services made available through SDI. Until recently the geographic information (GI) community focused effort on the practicalities of building infrastructure. These include: the development and implementation of the data, metadata, policies, standards, interpretations, networks, skills, database and application resources. SDI would not exist without this essential work. However, such emphasis has perhaps distracted attention from the spatial portal – the final, critical bridge between the user and the spatial resources offered through a data infrastructure. Over the last three or four years this beginning to change with greater attention being given to the design and functionality of spatial portals. This paper highlights some of these developments and argues that spatial portals play an important role in the success and continued viability of SDI.
Antibodies Targeted to the Brain with Image-Guided Focused Ultrasound Reduces Amyloid-β Plaque Load in the TgCRND8 Mouse Model of Alzheimer's Disease

Immunotherapy for Alzheimer's disease (AD) relies on antibodies directed against toxic amyloid-beta peptide (Aβ), which circulate in the bloodstream and remove Aβ from the brain [1], [2]. In mouse models of AD, the administration of anti-Aβ antibodies directly into the brain, in comparison to the bloodstream, was shown to be more efficient at reducing Aβ plaque pathology [3], [4]. Therefore, delivering anti-Aβ antibodies to the brain of AD patients may also improve treatment efficiency. Transcranial focused ultrasound (FUS) is known to transiently-enhance the permeability of the blood-brain barrier (BBB) [5], allowing intravenously administered therapeutics to enter the brain [6]–[8]. Our goal was to establish that anti-Aβ antibodies delivered to the brain using magnetic resonance imaging-guided FUS (MRIgFUS) [9] can reduce plaque pathology. To test this, TgCRND8 mice [10] received intravenous injections of MRI and FUS contrast agents, as well as anti-Aβ antibody, BAM-10. MRIgFUS was then applied transcranially. Within minutes, the MRI contrast agent entered the brain, and BAM-10 was later found bound to Aβ plaques in targeted cortical areas. Four days post-treatment, Aβ pathology was significantly reduced in TgCRND8 mice. In conclusion, this is the first report to demonstrate that MRIgFUS delivery of anti-Aβ antibodies provides the combined advantages of using a low dose of antibody and rapidly reducing plaque pathology.
Artificial general intelligence: an organism and level based position statement

Do simple animals, even single-celled animals, display general intelligence? If we take this to mean that they can cope with their environment, even when it is dynamic, the answer would have to be yes. What is it that gives them this easy capability, yet makes it hard for us to design systems which have it? We suggest that it is from the non-determinism which arises from the lowest levels of their operation, and from the way in which the different levels of their operation interact. We propose techniques whereby these might be incorporated into artificial systems.
Bulk content distribution using peer -to -peer overlay: Design and analysis

Koo, Gar Man Simon Paul. Ph.D., Purdue University, August, 2005. Bulk Content Distribution Using Peer-to-Peer Overlay: Design and Analysis. Major Professors: C. S. George Lee and Karthik Kannan. The research focus on Peer-to-Peer (P2P) networks had been on low-complexity content mapping and efficient search mechanisms, and many of the existing P2P applications provide an efficient substrate for sharing or distributing small files like MP3 or images because searching efficiency is essential to the overall performance. However, for bulk contents like multimedia documents or scientific data sets, the data transfer efficiency becomes critical to the overall distribution efficiency. This research explores the use of P2P overlays on distributing bulk contents, and the goal is to improve the distribution efficiency and make P2P overlay a cost-effective alternative to Content Distribution Networks. We consider a hybrid P2P model and propose a genetic-algorithm-based neighbor-selection strategy. The proposed strategy increases the content availability of peers from their immediate neighbors, and improves system performance without trading off users’ satisfaction. Simulation results show that the proposed strategy does not provide incentives for peers to deviate from truthfully revealing their downloading progresses and is suitable for real-time deployment. To improve the efficiency of a P2P system, it is also important to provide incentives for the peers to participate and contribute their resources. We address the incentive provisioning problem and present a “seeing-is-believing” incentive-compatible mechanism in which a peer will decide how much resource to be assigned to which neighbor based on what it has experienced. The protocol applies a utility-based resource-trading concept where peers will maximize their contributions for a fair
Practical application of the geometric wavefront sensor for adaptive optics

Wavefront sensors measure specific properties, either in the optical pupil plane or indirectly at the image plane; the former uses incident light in the pupil of an optical system, whereas the later employs one or more focal plane sensors to recover the wave front. Phase perturbations, in the form of low-order Zernike coefficients, e.g., defocus (Z5) in this case, are introduced using a bimorph deformable mirror. We investigate the practical application of the geometric wave front sensor (WFS), using a versatile optical test-bench to emulate the effects of wave front propagation in an open-loop configuration. In this paper, the estimation of residual wave front error, for a user-generated aberration, is further verified using simulation results.
Vector Quantization based Multiple Watermarking using Spread Transform

Multiple watermarking is used for embedding multiple watermarks into the host single multimedia object (image) to provide authentication without bearing on the optical quality of the image. It is an embranchment of digital watermarking, which gives some particular applications, such as transaction tracking. Some digital watermarking techniques ensure the host-signal noise while associating the watermark in chronological sequence on experienced image data. The cover work is projected to multiple orthogonal projection vectors to be sure that different watermark signals do not mutually interfere in which they are embedded. Many multiple watermarking techniques suffer from host-signal interference when correlating the watermark sequence with received image data. To avoid the interference of one watermark from other watermark, N orthogonal projection vectors are chosen corresponding to N different watermarks having same dimension as the cover image. The cover work is projected to these orthogonal projection vectors to get the different orientations in which the different watermark signals are embedded. Experimental results demonstrate how the spread spectrum method enables the embedding of multiple watermarks into host image and reports its robustness to realistic attacks such as cropping, filtering, lossy compression, scaling, rotation and analog-digital and digital analog conversion. Keywords––Digital Watermarking, Spread transform, Dither modulation.
Effect of Probiotics on Symptoms in Korean Adults with Irritable Bowel Syndrome

Background/Aims Irritable bowel syndrome (IBS) is a troublesome disease. Some strains of probiotics reportedly exert remarkable immunomodulatory effects, and so we designed a prospective double-blind randomized placebo-controlled clinical study to assess their effects in Korean adults with IBS. Methods IBS patients who met Rome III criteria were randomly assigned to receive composite probiotics or placebo. A total of 20 billion lyophilized bacteria were administered twice daily for 8 weeks. Primary outcome variables were symptom scores consisting of abdominal pain, flatulence, defecation discomfort, and sum of symptom scores. A visual analogue scale was used to quantify the severity. Secondary outcome variables consisted of the quality of life and bowel habits including defecation frequency and stool form. Results Thirty-six and 34 patients were randomized to the probiotics and placebo groups, respectively. Intention-to-treat analysis showed significant reductions in pain after 8 weeks of treatment: -31.9 and -17.7 in the probiotics and placebo groups, respectively (p=0.045). The reductions in abdominal pain, defecation discomfort, and sum of scores were more significant in 58 patients with a score of at least 3 on the baseline stool-form scale. Conclusions Composite probiotics containing Bifidobacterium bifidum BGN4, Lactobacillus acidophilus AD031, and other species are safe and effective, especially in patients who excrete normal or loose stools.
Prevalence and Correlates of Vitamin D Deficiency and Insufficiency in Luxembourg Adults: Evidence from the Observation of Cardiovascular Risk Factors (ORISCAV-LUX) Study

Evidence on vitamin D status and related risk factors in Luxembourg adults is lacking. This study aimed to determine the prevalence of vitamin D deficiency and insufficiency and related risk factors among healthy adults in Luxembourg. Based on clinicians’ observations, it was hypothesized that vitamin D deficiency and insufficiency might be common in our population, constituting a significant public health concern. A nationally representative random sample of 1432 adults was enrolled in the ORISCAV-LUX study, 2007–2008. The participants were divided into four categories according to their serum concentrations of 25-hydroxyvitamin D [25(OH)D]. Descriptive, univariate and multivariate statistical analyses used weighted methods to account for the stratified sampling scheme. Only 17.1% of the population had a “desirable” serum 25(OH)D level ≥75 nmol/L, whereas 27.1% had “inadequate” [serum 25(OH)D level 50–74 nmol/L], 40.4% had “insufficient” [serum 25(OH)D level 25–49 nmol/L], and 15.5% had “deficient” [serum 25(OH)D level <25 nmol/L)]. The prevalence of vitamin D deficiency was greater among current smokers, obese subjects, those having reduced HDL-cholesterol level and fair/poor self-perception of health, compared to their counterparts. The prevalence of vitamin D insufficiency was additionally higher among nondrinkers of alcohol, Portuguese and subjects from non-European countries. The final multivariate logistic regression analyses revealed that smoking status and obesity were independent correlates of vitamin D deficiency and insufficiency, respectively. Inadequate vitamin D status is highly prevalent among adults in Luxembourg and is associated with specific lifestyle factors. Along with the effect of vitamin D deficiency and insufficiency on the risk of several diseases, cancer and mortality, our findings have practical implications for public health dietary recommendations, and of particular importance for healthcare practitioners and policy makers.
Linear Models and Biomarker Search with Microarray Data

High throughput biological experiments such as DNA Microarrays are very powerful tools to understand and characterize multiple illnesses. These types of experiments, however, have also been described as large, complex, expensive and hard to analyze. For these reasons, analyses with linear assumptions are frequently bypassed for more sophisticated procedures with higher complexity. In this work, a search procedure for potential biomarkers using data from microarray experiments is proposed under purely linear assumptions. The method shows a high discrimination rate and does not require the adjustment of parameters by the user, thus preserving analysis objectivity and repeatability. A case study in the identification of potential biomarkers for cervix cancer is presented to illustrate the application of the proposed procedure.
Automated labeling of ink stroke data

Labeled ink stroke data is essential to the development and evaluation of sketch recognizers. Manually labeling strokes is a tedious, time-consuming, and error prone task; and very few tools are available to facilitate this. We propose a new and intuitive method of automatic labeling for single stroke primitives. This involves building a recognizer from a partially labeled dataset. This recognizer is then used to identify and automatically label the remaining data, therefore reducing the amount of manual labeling required by researchers. An evaluation comparing manual labeling against our new auto labeling method shows that users are able to label significantly faster and produce less errors using auto labeling. Furthermore, users found auto labeling easier and more preferable.
Systematic Deciphering of Cancer Genome Networks

When growth regulatory genes are damaged in a cell, it may become cancerous. Current technological advances in the last decade have allowed the characterization of the whole genome of these cells by directly or indirectly measuring DNA changes. Complementary analyses were developed to make sense of the massive amounts of data generated. A large majority of these analyses were developed to construct interaction networks between genes from, primarily, expression array data. We review the current technologies and analyses that have developed in the last decade. We further argue that as cancer genomics evolves from single gene validations to gene network inferences, new analyses must be developed for the different technological platforms.
Democracy and Foreign Education

Do foreign educated individuals play a role in promoting democracy in their home country? Despite the large amount of private and public resources spent on foreign education, there is no systematic evidence that foreign educated individuals foster democracy in their home countries. Using a unique panel dataset on foreign students starting from 1950, I show that indeed foreign-educated individuals promote democracy in their home country, but only if the foreign education is acquired in democratic countries. The results are robust to reverse causality, country-specific omitted variables, and inclusion of a variety of control variables. The results are stronger for small countries.
Stressful life events and the onset of asthma

The status of stressful life events as a risk factor for asthma is unclear and may be dependent on pre-existing allergic rhinitis. This study examined whether exposure to stressful life events predicted the onset of asthma in adults. This is a prospective, population-based cohort study of 16,881 males and females, aged 20–54 yrs and free of diagnosed asthma at the beginning of the follow-up (January 1, 2004). Data about stressful life events were gathered with a postal survey. The onset of asthma was ascertained through national registers until December 31, 2005. During the follow-up period, 192 incident cases of asthma were identified. High total exposure to stressful life events, as indicated by a cumulative severity score, predicted the onset of asthma (hazard ratio 1.96, 95% CI 1.22–3.13). This association was robust to adjustment for demographics, smoking and having a cat/dog at home and it was observed both among those with and without allergic rhinitis at baseline. Of the 10 most stressful life events, the illness of a family member, marital problems, divorce or separation and conflicts with a supervisor were associated with the onset of asthma. Our study suggests that stressful life events may increase the onset of asthma.