Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of environmental hazards through the feature-based fusion of optical and SAR data: a case study in southern Italy

The use of remote-sensing images is becoming common practice in the fight against environmental crimes. However, the challenge of exploiting the complementary information provided by radar and optical data, and by more conventional sources encoded in geographic information systems, is still open. In this work, we propose a new workflow for the detection of potentially hazardous cattle-breeding facilities, exploiting both synthetic aperture radar and optical multitemporal data together with geospatial analyses in the geographic information system environment. The data fusion is performed at a feature-based level. Experiments on data available for the area of Caserta, in southern Italy, show that the proposed technique provides very high detection capability, up to 95%, with a very low false alarm rate. A fast and easy-to-use system has been realized based on this approach, which is a useful tool in the hand of agencies engaged in the protection of territory.
Teaching metabolism with scientific articles: A new approach

Scientific articles have been explored as a didactic resource in the teaching of Biochemistry, as well as in other areas of science. This teaching strategy contributes to the learning and understanding of specific concepts, and development of the skills, necessary for the research training of students. The present study investigated the use of scientific articles in the Topics in Metabolism course, which was offered to Biochemistry graduate students, at the University of São Paulo. Instead of lectures and seminars, two main activities were used: directed study (DS) and discussion groups (DG). Both utilized and explored high impact publications with a recognized quality of intellectual production. In this active learning environment, the objective was to evaluate the perception of the students regarding the teaching strategy, and the level of effort each student expended to understand the course material. Data collection involved using the responses to open and closed questions, from two questionnaires. Then the Pirouette software was employed to perform the Hierarchical Cluster Analysis (HCA). The results indicated that 78% of the students had a positive perception of the teaching strategy and struggled less to understand the course material. On the other hand, 22% of the students had a less positive perception of the strategy and displayed a less intense course engagement. Ultimately, the results presented in the present study indicate that the adoption of this teaching strategy can help graduate students to develop the ability to read critically and thoroughly understand scientific materials, which are fundamental skills for future researchers. © 2018 International Union of Biochemistry and Molecular Biology, 47(1):85–92, 2018.
Scan2Mesh: From Unstructured Range Scans to 3D Meshes

We introduce Scan2Mesh, a novel data-driven generative approach which transforms an unstructured and potentially incomplete range scan into a structured 3D mesh representation. The main contribution of this work is a generative neural network architecture whose input is a range scan of a 3D object and whose output is an indexed face set conditioned on the input scan. In order to generate a 3D mesh as a set of vertices and face indices, the generative model builds on a series of proxy losses for vertices, edges, and faces. At each stage, we realize a one-to-one discrete mapping between the predicted and ground truth data points with a combination of convolutional- and graph neural network architectures. This enables our algorithm to predict a compact mesh representation similar to those created through manual artist effort using 3D modeling software. Our generated mesh results thus produce sharper, cleaner meshes with a fundamentally different structure from those generated through implicit functions, a first step in bridging the gap towards artist-created CAD models.
Automatic Stepwise Correctness Assessment of Constructed Mathematical Response Schemes

Computer-aided free-response mathematics assessments are becoming increasingly important vehicles in supporting the notion of “learning through assessment”. Hence, having a marking engine that is able to assess each step in a scheme of constructed responses (or working scheme) is a vital requirement for a mathematics computer-aided system that offers free-response assessments. Existing computational solutions for assessing the correctness of intermediate steps do so by limiting the responses to be entered stepwise, and not all at once, with the delivery of qualitative feedbacks to steps that are incorrect. In this paper, we propose our marking engine that is able to execute a stepwise assessment of responses in a working scheme that closely emulates the marking practices of a typical human. The marking engine delivers quantitative feedbacks in the form of numeric scores that indicate the degree of correctness of the responses in a working scheme and of the working scheme as well. The results of the reliability analysis on the automated correctness scores suggest that the scores are reliable indicators of responses correctness as well as working schemes correctness.
Subjective Memory Complaints in Portuguese Young Adults: Contributions from the Adaptation of the Prospective and Retrospective Memory Questionnaire

Self-report instruments that allow to characterize the frequency of daily memory failures are essential for a comprehensive assessment of memory functioning. In this context, we aimed to provide preliminary evidence of validity and reliability for the European Portuguese adaptation of the Prospective and Retrospective Memory Questionnaire (PRMQ). A total of 1052 healthy participants completed an online survey with the PRMQ. The exploration of the construct validity suggested the tripartite model with a general memory, a prospective memory, and a retrospective memory factors to have the best adjustment to the data. Measurement invariance across age and sex groups was also verified. The questionnaire revealed good convergent validity with a general self-report measure of memory (0.778 < r < 0.853), and satisfactory values of internal consistency (0.779 < Cronbach’s alpha < 0.887) and of test-retest reliability (0.815 < r < 0.852). There were no prominent effects of sex and age in the PRMQ scores. Although the sample encompassed mainly younger and highly educated adults, this study presented the first evidence of validity and reliability for the European Portuguese version of the questionnaire.
On-the-Fly Output Compression for Join-Based Graph Mining Algorithms

Many join-based graph mining (JGM) algorithms, such as triangle listing and clique enumeration, typically output data of such a large size that it often dominates the mining cost. Despite the significance of the I/O cost, only a few research focused on reducing the size of the output data. However, those techniques have limitation that they are highly specific to their corresponding graph mining algorithms. In this paper, through careful observations of output patterns, we propose a general compression solution that can be applied to an arbitrary JGM algorithm. The proposed technique first categorizes the nodes of a set of cliques into two lists, which contain overlapping and non-overlapping nodes, respectively. Then, we remove the redundant nodes from multiple sets of cliques by creating a Clique Constructor Tree using the overlapping and non-overlapping nodes. Finally, we compress the clique constructor trees by applying a novel flag-aligned word encoding technique. Our proposed technique performs compression on-the-fly and can easily be adopted by various JGM algorithms. Experiments on real datasets show that our proposed technique, which is adopted in a triangle listing algorithm, reduces the size of the output data and running time by seven times and about four times, respectively. Experimental results also show that a maximal clique listing algorithm can reduce the size of the output data by a factor of three.
Multimodal Classification of Stressful Environments in Visually Impaired Mobility Using EEG and Peripheral Biosignals

In this study, we aim to better understand the cognitive-emotional experience of visually impaired people when navigating in unfamiliar urban environments, both outdoor and indoor. We propose a multimodal framework based on random forest classifiers, which predict the actual environment among predefined generic classes of urban settings, inferring on real-time, non-invasive, ambulatory monitoring of brain and peripheral biosignals. Model performance reached 93 for the outdoor and 87 percent for the indoor environments (expressed in weighted AUROC), demonstrating the potential of the approach. Estimating the density distributions of the most predictive biomarkers, we present a series of geographic and temporal visualizations depicting the environmental contexts in which the most intense affective and cognitive reactions take place. A linear mixed model analysis revealed significant differences between categories of vision impairment, but not between normal and impaired vision. Despite the limited size of our cohort, these findings pave the way to emotionally intelligent mobility-enhancing systems, capable of implicit adaptation not only to changing environments but also to shifts in the affective state of the user in relation to different environmental and situational factors.
Current Practice of Child Grammar Intervention: A Survey of Speech-Language Pathologists.

Purpose The aim of this study was to better understand current grammatical intervention approaches. Despite grammatical language being a common weakness among children with language impairment, relatively little is known about current grammatical intervention practices of speech-language pathologists (SLPs). Such information is needed to guide the development and evaluation of grammatical interventions and to identify areas in which the current practice is not empirically supported. Method Participants included 338 SLPs working primarily with children. Participants completed an online survey regarding their implementation of nine different grammatical intervention components, including goals, procedures, dosage, agents, contexts, goal attack strategies, service delivery models, activities, and outcome measurements. Participants also indicated how they would alter the intervention setting and dosage if resources were unlimited. Results We grouped participants based on the ages of children that represent the largest percentage of their caseload resulting in an early education group (n = 114) and an elementary group (n = 224). We aggregated responses from each question to gain an estimate of current implementation practices associated with each intervention component queried. Conclusions This study provides general guidelines of current clinical practices to help guide research on grammatical interventions for children and to promote successful translation and implementations of evidence-based treatment approaches. Results may also help clinicians and researchers better understand misalignments between empirically supported intervention approaches and current approaches for treating grammatical weaknesses.
Oddness‐based classification: A new way of exploiting neighbors

The classification of a new item may be viewed as a matter of associating it with the class where it is the least at odds w.r. t. the elements already in the class. An oddness measure of an item with respect to a multiset, applicable to Boolean features as well as to numerical ones, has been recently proposed. It has been shown that cumulating this measure over pairs or triples (rather than larger subsets) of elements in a class could provide an accurate estimate of the global oddness of an item with respect to a class. This idea is confirmed and refined in the present paper. Rather than considering all the pairs in a class, one can only deal with the pairs whose an element is one of the nearest neighbors of the item, in the target class. The oddness evaluation computed on this basis still leads to good results in terms of accuracy. One can take a step further and choose the second element in the pair also as another nearest neighbor in the class. Although the method relies on the notion of neighbors, the resulting algorithm is far from being a variant of the classical k ‐nearest neighbors approach. The oddness with respect to a class computed only on the basis of pairs made of two nearest neighbors leads to a low complexity algorithm. Experiments on a set of UCI benchmarks show that the classifier obtained can compete with other well‐known approaches.
Decomposition-based analysis of the multi-physical coupling structure in LED System-in-Package design

1. Abstract LED System-in-Package aims to reduce manufacturing costs of LED lighting products through integration of components into one single package, based on semiconductor technology. This causes tight multi-physical coupling which complicates the design process. We present a method to analyze the coupling structure of the LED SiP. A specification language is used to specify the input-output (binary) relationships between design variables, physical responses, design objectives and constraints. An adapted design structure matrix (DSM), representing the linkage, is automatically generated from the specification. The rows and columns of the adapted DSM matrix are subsequently re-ordered using partitioning and sequencing algorithms to provide insight in the coupling structure. A LED SiP prototype is used as illustration. 2.
Finding Dutch natives in online forums

Abstract Law enforcement agencies have a restricted area in which their powers apply, which is called their jurisdiction. These restrictions also apply to the Internet. However, on the Internet, the physical borders of the jurisdiction, typically country borders, are hard to discover. In our case, it is hard to establish whether someone involved in criminal online behavior is indeed a Dutch citizen. We propose a way to overcome the arduous task of manually investigating whether a user on an Internet forum is Dutch or not. More precisely, we aim to detect that a given English text is written by a Dutch native author. To develop a detector, we follow a machine learning approach. Therefore, we need to prepare a specific training corpus. To obtain a corpus that is representative for online forums, we collected a large amount of English forum posts from Dutch and non-Dutch authors on Reddit. To learn a detection model, we used a bag-of-words representation to capture potential misspellings, grammatical errors or unusual turns of phrases that are characteristic of the mother tongue of the authors. For this learning task, we compare the linear support vector machine and regularized logistic regression using the appropriate performance metrics f1 score, precision, and average precision. Our results show logistic regression with frequency-based feature selection performs best at predicting Dutch natives. Further study should be directed to the general applicability of the results that is to find out if the developed models are applicable to other forums with comparable high performance.
Generating Attention from Classifier Activations for Fine-grained Recognition

Recent advances in fine-grained recognition utilize attention maps to localize objects of interest. Although there are many ways to generate attention maps, most of them rely on sophisticated loss functions or complex training processes. In this work, we propose a simple and straightforward attention generation model based on the output activations of classifiers. The advantage of our model is that it can be easily trained with image level labels and softmax loss functions. More specifically, multiple linear local classifiers are firstly adopted to perform fine-grained classification at each location of high level CNN feature maps. The attention map is generated by aggregating and max-pooling the output activations. Then the attention map serves as a surrogate target object mask to train those local classifiers, similar to training models for semantic segmentation. Our model achieves state-of-the-art results on three heavily benchmarked datasets, i.e. 87.9% on CUB-200-2011 dataset, 94.1% on Stanford Cars dataset and 92.1% on FGVC-Aircraft dataset, demonstrating its effectiveness on fine-grained recognition tasks.
Synergistic anticancer action of quercetin and curcumin against triple‐negative breast cancer cell lines

Women with the breast cancer type 1 susceptibility protein (BRCA1) mutation and loss of BRCA1 expression are reported to have an increased risk of triple‐negative breast cancer (TNBC). Targeting BRCA1 modulation might offer a therapeutic option to treat TNBC patients. Our studies detected that BRCA1 is poorly expressed in TNBC cell lines and highly expressed in ER+ breast cancer cell lines. To modulate BRCA1 expression, we tested two different dietary components to find out if any would induce tumor suppressor genes. We detected that quercetin and curcumin dose‐dependently enhanced the BRCA1 expression. Further, a synergistic action of quercetin and curcumin was observed in modulating the BRCA1 level and in inhibiting the cell survival and migration of TNBC cell lines. Quercetin and curcumin appeared to induce BRCA1 promoter histone acetylation. Furthermore, BRCA1 knockdown induced cell survival and cell migration in ER + cells were significantly decreased by the combined treatment of quercetin and curcumin. Our present study concluded that the combination treatment of quercetin and curcumin acts synergistically to induce anticancer activity against TNBC cells by modulating tumor suppressor genes.
Comparison of microwave scattering models for leaf

The Generalized Rayleigh-Gans (GRG) approach is used for the scattering cross section computation of leaves at low frequencies, while the Physical Optics (PO) approach is employed at high frequencies. In this paper, the magnitude of the scattering amplitude of the leaf is calculated using the two analytical methods (GRG PO) at different frequencies (i.e., L, C and X bands) and with different leaf shapes (circular and elliptical). In general, the difference between these models is of the order of several dB.
Cabazitaxel liposomes with aptamer modification enhance tumor-targeting efficacy in nude mice

The present study investigated the feasibility of improving the tumor-targeting efficacy and decreasing the toxicity of liposomal cabazitaxel (Cab) with aptamer modification. The process involved preparing aptamer (TLS1c)-modified liposomes and studying the behavior of the liposomes in vitro and in vivo. TLS1c as an aptamer, which has high specificity for BNL 1ME A.7R. 1 (MEAR) cells, was conjugated with Cab liposomes (Cab/lipo) to enhance MEAR tumor tissue targeting. Confocal laser scanning microscopy and flow cytometry analyses demonstrated that the fluorescence of the liposomes modified with the aptamer was notably stronger compared with that of the unmodified liposomes. Furthermore, the biodistribution data of the modified liposomes tested in tumor-bearing mice revealed high specificity of biotinylated TLS1c-modified Cab/lipo (BioTL-Cab/lipo) for tumor tissues. Furthermore, the modified liposomes demonstrated decreased cytotoxicity and simultaneously retained potent inhibition against tumor growth. It is likely that the specific binding of the aptamer (TLS1c) to the targeted cells (MEAR) facilitates the binding of the liposomes to the targeted cells. Therefore, BioTL-Cab/lipo may be considered as a promising delivery system to improve cell targeting and reduce drug toxicity in the treatment of cancer.
Software Measurement for E-Commerce CMS by Using OO Metrics and Quality Factor Parameters Based on JAVA

Software measurement has been used to analyse software quality in order to provide information for improving software processes and products. The objective of this research is to measure e-commerce Content Management System (CMS) based open source: Magento, OsCommerce, Prestashop, Zencart, and Opencart. The measurement uses two parameters, Object Oriented Metrics (OO Metrics) esspecially in CK Metrics Suite and Quality Factor with the Analytical Hierarchy Process method as the completion step of the parameters used. By using OO Metrics and Quality Factor as a benchmark this will give a ranking in form of CMS e-commerce recommendations which have the best software quality criteria. The CK Metrics Suite that is implemented consists of Weighted Method per Class (WMC), Depth of Inheritance Tree (DIT), Number of Children (NOC), and Coupling Between Objects (CBO). The Quality Factor parameter consists of Maintainability, Efficiency, Understandability, and Reusability. The system development uses is the Rapid Application Development (RAD) method by building JAVA applications. The result found that Magento is the best CMS based on Object Oriented Metrics and Quality Factor parameters.
Microsatellite markers for Corybas (Orchidaceae) species in New Zealand

Premise of the Study Microsatellite markers were developed for New Zealand species of Corybas (Orchidaceae) to investigate population genetics and species delimitation. Methods and Results From sequencing a total genomic DNA library (using Illumina MiSeq), we developed 22 microsatellite markers for C. obscurus. The di‐ and trinucleotide repeat loci were initially trialed on individuals representing seven Corybas taxa (C. “rimutaka,” C. confusus, C. hypogaeus, C. macranthus, C. obscurus, C. trilobus, and C. walliae) and had one to eight alleles per locus. Twelve polymorphic markers were further tested on six Corybas populations from three of the seven taxa (C. obscurus, C. “rimutaka,” and C. trilobus). Observed and expected heterozygosities ranged from 0–1 and 0–0.859, respectively. The utility of these 12 loci was further validated in five related Corybas species (C. hypogaeus, C. obscurus, C. vitreus, C. walliae, and C. “rimutaka”; 38 individuals) representing populations from across the North and South Islands. The average value for genetic diversity among populations (FST) of 0.439 shows differentiation among species. Conclusions These markers will be useful for future studies aimed at delimiting species boundaries and examining the genetic diversity of the New Zealand Corybas species.