Miracle-dz/UniFAR-FTUs huggingface dataset:
Managing Open Data Evolution through Bi-dimensional Mappings

The availability of large Open Data sources creates opportunities for data analytics on different domains. But in order to be effectively used, the data needs to be correctly extracted, formatted and integrated, which is a specially challenging task on Open Data sources, since there is usually less rigour in standardizing subsequent data releases. This means Open Data evolution must be handled. A domain specific solution, taking stock of existing approaches, but with delimited kinds of operations and mappings, would be useful for providing coarse-grained management of data evolution operations throughout time. In this paper, we present an Open Data Evolution managing solution, aiming to integrate periodically released data sets. We define a set of operations acting over the instances, schema and mappings, which are executed after each new data release. These operations rely on the existence of a time dimension in the input mappings. The approach is validated on a real-world case study, which is being currently used to integrate and access a large Brazilian educational Open Data source, with billions of records and hundreds of columns evolving over many years. The proposed solution is used to process this data source, successfully integrating more than 90 data releases from 2012 to 2018.
An Efficient Probability Estimation Decision Tree Postprocessing Method for Mining Optimal Profitable Knowledge for Enterprises with Multi-Class Customers

Enterprises often classify their customers based on the degree of profitability in decreasing order like C1, C2, . , Cn. Generally, customers representing class Cn are zero profitable since they migrate to the competitor. They are called as attritors (or churners) and are the prime reason for the huge losses of the enterprises. Nevertheless, customers of other intermediary classes are reluctant and offer an insignificant amount of profits in different degrees and lead to uncertainty. Various data mining models like decision trees, etc., which are built using the customers’ profiles, are limited to classifying the customers as attritors or non-attritors only and not providing profitable actionable knowledge. In this paper, we present an efficient algorithm for the automatic extraction of profit-maximizing knowledge for business applications with multi-class customers by postprocessing the probability estimation decision tree (PET). When the PET predicts a customer as belonging to any of the lesser profitable classes, then, our algorithm suggests the cost-sensitive actions to change her/him to a maximum possible higher profitable status. In the proposed novel approach, the PET is represented in the compressed form as a Bit patterns matrix and the postprocessing task is performed on the bit patterns by applying the bitwise AND operations. The computational performance of the proposed method is strong due to the employment of effective data structures. Substantial experiments conducted on UCI datasets, real Mobile phone service data and other benchmark datasets demonstrate that the proposed method remarkably outperforms the state-of-the-art methods.
2019 GermEval Shared Task on Offensive Tweet Detection h_da submission

This paper presents the models submitted to the 2019 GermEval Shared Task on Of-fensive Language Detection in Tweets. Our system is based on a lexicon of swear words and several rules. These rules were developed after a thorough data and error analysis. This also revealed that the detection of offensive language is far from trivial and in a lot of cases requires more than just a Tweet in isolation, but rather would require more knowledge about the context and/or the topic the Tweet is related to, which was not available in this data set.
Query-by-Blending: A Music Exploration System Blending Latent Vector Representations of Lyric Word, Song Audio, and Artist

This paper presents Query-by-Blending , a novel music exploration system that enables users to ﬁnd unfamiliar music content by ﬂexibly combining three musical aspects: lyric word, song audio, and artist. Although there are various systems for music retrieval based on the similarity between songs or artists and for music browsing based on visualized songs, it is still difﬁcult to explore unfamiliar content by ﬂexibly combining multiple musical aspects. Query-by-Blending overcomes this difﬁculty by representing each of the aspects as a latent vector representation (called a “ﬂavor” in this paper) that is a distinctive quality felt to be characteristic of a given word/song/artist. By giving a lyric word as a query, for example, a user can ﬁnd songs and artists whose ﬂavors are similar to the ﬂavor of the query word. Moreover, by giving a query combining (blending) lyric-word and song-audio ﬂavors, the user can interactively explore unfamiliar content containing the blended ﬂavor. This multi-aspect blending was achieved by constructing a novel vector space model into which all of the lyric words, song audio tracks, and artist IDs of a collection can be embedded. In our experiments, we embedded 14,505 lyric words, 433,936 songs, and 44,696 artists into the same shared vector space and found that the system can appropriately calculate similarities between different aspects and blend ﬂavors to ﬁnd related lyric words, songs, and artists.
Evaluation of a Rack-Scale Disaggregated Memory Prototype for Cloud Data Centers

Disaggregated data centers propose a modular architecture where memory and compute resources are utilized in a finer granularity, aiming for a better optimized capacity and power consumption. In contrast to a classic compute node in a data center, memory is not required to be co-located with the processor, disaggregation can enhance hardware elasticity, improve virtual machine (VM) migration, and reduce the total cost of ownership (TCO), when compared to current data center solutions. This paper presents an evaluation of a disaggregated memory system prototype implemented on two modern FPGA SoC boards; one as a compute node and the second one as memory node. Using the PARSEC and Splash2x benchmark suites, we characterize our disaggregated prototype running on a remote memory node. In order to understand the design trade offs, we utilize data collected from AXI Performance Monitors and break down the executions into three principal stages: (i) disaggregated memory address translation, (ii) network and (iii) memory access. This helps us to better evaluate the trade offs of our prototype and to give better insight towards the next generation of disaggregated data centers.
To Act Or Not To Act - Annotating and Classifying Email Regarding Necessary Action

Every user of email is aware of the problem of reacting to emails that require a time-sensitive action by the recipient while being overwhelmed by informational emails. We deﬁne a new classiﬁcation problem to capture this distinction, creating comprehensive annotation guidelines and carrying out annotation. We carry out a proof-of-concept implementation of a classiﬁer and discuss our future research which will result in a tool that is usable in an everyday business environment.
Multi-Label Classification of Blurbs with SVM Classifier Chains

Due to the increasing digitalisation multi-label classiﬁcation gains in importance in many areas. In this paper we propose a method to classify blurbs into eight basic book genre using an ensemble of classiﬁer chains composed of radial support vector machines using word embeddings and author information as features. Five models were tested using different implementations and features, as well as different numbers of chains. The best model reached a performance of a micro average F1 of 0.841.
An Analysis of Slant in Tweets: Case Study

Determination of quality and reliability of information found in social media have been subjects of study by sever researchers. One set of solution may not work in all cases. This paper presents a method to estimate the slant of tweets related to a topic. The general approach followed is to construct labeled data from tweets and use supervised learning to build predictive models. Results obtained from two datasets are compared against OTC model and a CNN based model.
CLEO: Machine Learning for ECMP

In this paper, we propose CLEO, which is a machine learning approach to equal-cost multipath routing (ECMP) schemes to distribute and balance traffic. ECMP-based traffic load-balancing is widely practiced by datacenters, but hash collision resulting from skewed ECMP hashing makes it difficult to achieve the desired throughputs over paths. Various solutions have been proposed to overcome the performance degradation caused by hash collision, but most of these solutions require modifying packet headers or replacing switches. To solve this problem, CLEO builds a neural-network model that characterizes the ECMP scheme of a switch. The proof-of-concept evaluation shows that CLEO improves the root mean square error fourfold between the desired and real path throughputs.
Prediction of Prostate Cancer cells Based on Principal Component Analysis Technique

Amino acids, the essential building blocks of life are important to study the genetic diseases, modelling of protein structure and also in drug designing. A PCA model along with signal processing technique is used here for differentiating the prostate cancer cells from normal prostate cells. The amino acid sequence of cells is taken as input sample for the PCA technique. The model is successfully tested on 8 normal and 8 cancerous Homo sapiens prostate cells. © 2013 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of the University of Kalyani, Department of Computer Science & Engineering.
Tobacco cessation: what role can dental professionals play?

Tobacco dependence is classified as a disease by the International Classification of Diseases (ICD-10), but, medical and dental professionals have neither seriously taken this fact nor made any serious attempt to tackle this disease. Apart from supporting wider tobacco control measures, oral health professionals can help patients to stop using tobacco. This may be the single most important service dentists can provide for their patients’ overall health. Objective: This review is prepared with the object to help both clinicians and oral health professionals to scale up their involvement in tobacco control activities, including advocacy and smoking cessation programs. Literature review: Studies have shown that � �� smokers indicate that they want to quit, but a meagre 2% succeed. The dental practice setting provides a unique opportunity to assist tobacco users in achieving tobacco abstinence. Still, More than 40% of dentists do not routinely ask about tobacco use and 60% do not routinely advise tobacco users to quit, while 61.5% of dentists believe their patients do not expect tobacco cessation services. Conclusion: Interventions by dentist has been found to be effective in helping people to quit tobacco consumption. A step-wise approach and patience must be adopted while dealing with such patients.
On the Importance of Supporting Multiple Stakeholders Points of View for the Testing of Interactive Systems

Testing is the activity meant to demonstrate that systems are fit for purpose and to detect their defects. On interactive systems, checking the fitness for purpose requires proper knowledge of the users’ activities and profiles as well as of the context of use. Moreover, defects may be present in software, input/output device hardware or in the way interaction techniques are handled. Comprehensively testing interactive systems thus requires a large set of skills provided by usability experts, software engineers, human-factor specialists, etc. So far, these stakeholders conduct testing activities using processes from their respective areas of expertise that do not take advantage of others stakeholders’ expertise effectively. This paper discusses the contribution of each stakeholders in current testing activities and highlights that a common view of the interactive system under test can serve as a mediating tool for each stakeholder to share information and identify/execute more relevant test suites.
Development of the human placenta

ABSTRACT The placenta is essential for normal in utero development in mammals. In humans, defective placental formation underpins common pregnancy disorders such as pre-eclampsia and fetal growth restriction. The great variation in placental types across mammals means that animal models have been of limited use in understanding human placental development. However, new tools for studying human placental development, including 3D organoids, stem cell culture systems and single cell RNA sequencing, have brought new insights into this field. Here, we review the morphological, molecular and functional aspects of human placental formation, with a focus on the defining cell of the placenta – the trophoblast. Summary: A broad review encompassing basic science to clinical relevance of human placental development.
Crucial role of serum response factor in renal tubular epithelial cell epithelial-mesenchymal transition in hyperuricemic nephropathy

Objective: To explore the regulation and function of serum response factor (SRF) in epithelial-mesenchymal transition (EMT) in renal tubular epithelial cells (TECs) in hyperuricemic nephropathy (HN). Results: In NRK-52E cells treated with UA and renal medulla tissue samples from hyperuricemic rats, SRF, fibronectin, α-SMA and FSP-1 expression was upregulated, while ZO-1 and E-cadherin expression was downregulated. SRF upregulation in NRK-52E cells increased slug expression. Blockade of SRF by an SRF-specific siRNA or CCG-1423 reduced slug induction and protected TECs from undergoing EMT both in vitro and in vivo. Conclusion: Increased SRF activity promotes EMT and dysfunction in TECs in HN. Targeting SRF with CCG-1423 may be an attractive therapeutic strategy in HN. Methods: The expression of SRF, mesenchymal markers (fibronectin, α-SMA, and FSP-1), epithelial markers (ZO-1 and E-cadherin) and was examined in rat renal TECs (NRK-52E cells) or renal medulla tissue samples following uric acid (UA) treatment. SRF overexpressed with pcDNA-SRF plasmid and suppressed by CCG-1423 (a small molecule inhibitor of SRF) to study how SRF influences EMT in TECs in HN. Oxonic acid (OA) was used to establish HN in rats.
Anterior tooth-use behaviors among early modern humans and Neandertals

Early modern humans (EMH) are often touted as behaviorally advanced to Neandertals, with more sophisticated technologies, expanded resource exploitation, and more complex clothing production. However, recent analyses have indicated that Neandertals were more nuanced in their behavioral adaptations, with the production of the Châtelperronian technocomplex, the processing and cooking of plant foods, and differences in behavioral adaptations according to habitat. This study adds to this debate by addressing the behavioral strategies of EMH (n = 30) within the context of non-dietary anterior tooth-use behaviors to glean possible differences between them and their Neandertal (n = 45) counterparts. High-resolution casts of permanent anterior teeth were used to collect microwear textures of fossil and comparative bioarchaeological samples using a Sensofar white-light confocal profiler with a 100x objective lens. Labial surfaces were scanned, totaling a work envelope of 204 x 276 μm for each individual. The microwear textures were examined for post-mortem damage and uploaded to SSFA software packages for surface characterization. Statistical analyses were performed to examine differences in central tendencies and distributions of anisotropy and textural fill volume variables among the EMH sample itself by habitat, location, and time interval, and between the EMH and Neandertal samples by habitat and location. Descriptive statistics for the EMH sample were compared to seven bioarchaeological samples (n = 156) that utilized different tooth-use behaviors to better elucidate specific activities that may have been performed by EMH. Results show no significant differences between the means within the EMH sample by habitat, location, or time interval. Furthermore, there are no significant differences found here between EMH and Neandertals. Comparisons to the bioarchaeological samples suggest both fossil groups participated in clamping and grasping activities. These results indicate that EMH and Neandertals were similar in their non-dietary anterior tooth-use behaviors and provide additional evidence for overlapping behavioral strategies employed by these two hominins.
Protein Carbonyl Content Is a Predictive Biomarker of Eccentric Left Ventricular Hypertrophy in Hemodialysis Patients

High prevalence of left ventricular hypertrophy (LVH) and elevated oxidative stress are associated with poor outcomes in chronic hemodialysis patients. Abnormal left ventriculаr geomеtry and different geometric patterns play an important role as well. Our study analyzed the role of oxidative stress on myocardial remodeling in these patients. Plasma malondialdehyde (MDA), protein carbonyl (PC) content, and total antioxidative capacity (TAC) were investigated in 104 hemodialysis patients together with transthoracic echocardiography. Compared to patients with normal ventricular geometry, patients with LVH had increased MDA and PC plasma concentration. Multivariate analysis demonstrated that protein carbonyls, as biomarkers of oxidative protein modification, were an independent predictor of eccentric hypertrophy (eLVH), including higher LV end-diastolic diameter and LV end-diastolic volume, (β = 0.32 and β = 0.28, p < 0.001 for both). The incidence of eLVH increased progressively from the lowest to the highest baseline PC tertile (p < 0.001 for the trend) and the subjects in the former group showed a 76% greater risk of developing eLVH compared to their counterparts. After further adjustment for the potential mediators, PCs carried eLVH odds (95% confidence interval (CI)) of 1.256 (0.998–1.514), per standard deviation increase. High plasma protein carbonyls levels are a significant independent predictor of eccentric LVH in chronic hemodialysis patients.
Multiple innate antibacterial immune defense elements are correlated in diverse ungulate species

In this study, we aimed to evaluate to what extent different assays of innate immunity reveal similar patterns of variation across ungulate species. We compared several measures of innate antibacterial immune function across seven different ungulate species using blood samples obtained from captive animals maintained in a zoological park. We measured mRNA expression of two receptors involved in innate pathogen detection, toll-like receptors 2 and 5 (TLR2 and 5), the bactericidal capacity of plasma, as well as the number of neutrophils and lymphocytes. Species examined included aoudad (Ammotragus lervia), American bison (Bison bison bison), yak (Bos grunniens), Roosevelt elk (Cervus canadensis roosevelti), fallow deer (Dama dama), sika deer (Cervus nippon), and Damara zebra (Equus quagga burchellii). Innate immunity varied among ungulate species. However, we detected strong, positive correlations between the different measures of innate immunity–specifically, TLR2 and TLR5 were correlated, and the neutrophil to lymphocyte ratio was positively associated with TLR2, TLR5, and bacterial killing ability. Our results suggest that ecoimmunological study results may be quite robust to the choice of assays, at least for antibacterial innate immunity; and that, despite the complexity of the immune system, important sources of variation in immunity in natural populations may be discoverable with comparatively simple tools.
Efficient and stable low-bandgap perovskite solar cells enabled by CsPbBr3-cluster assisted bottom-up crystallization approach.

Recently, low-bandgap formamidinium lead iodide FAPbI3-based perovskites are of particular interest for high performance perovskite solar cells (PSCs) due to their broad spectral response and high photocurrent output. However, to inhibit the spontaneous α-to-δ phase transition, 15-17% (molar ratio) of bromide and cesium or methylammonium incorporated into the FAPbI3 are indispensable to achieve efficient PSCs. In return, the high bromide content will increase bandgap and narrow the spectral response region. If simply reducing the bromide content, the corresponding PSCs exhibit inferior operational stability due to α-to-δ phase transition, interface degradation and halide migration. Herein, we report a CsPbBr3-cluster assisted vertically bottom-up crystallization approach to fabricate low-bromide (1%~6%), α-phase pure and MA-free FAPbI3-based PSCs. The clusters, in the size of several nanometers, could act as nuclei to facilitate vertical growth of high quality α-FAPbI3 perovskite crystals. Moreover, these clusters can be further intake by perovskite after thermal annealing, which improves the phase homogeneity of the as-prepared perovskite films. As a result, the corresponding mesoporous PSCs deliver a champion efficiency of 21.78% with photo-response extended to 830 nm. Moreover, these devices show remarkably improved operational stability, retaining ~82% of the initial efficiency after 1,000 h of maximum power point tracking under 1 sun condition.
Citrus species: A Review of Traditional Uses, Phytochemistry and Pharmacology.

The Citrus species from family Rutaceae have worldwide applications such as cardiovascular and gastrointestinal problems. Phytochemical investigations have showed that these plants have constituents including flavonoids, limonoids and carotenoids. There are many reports on a wide range of activities such as anti-inflammatory, anti-oxidant, immunomodulatory, metabolic, cardiovascular and neuroprotective effects. In the current review, we discuss information regarding botany, phytochemistry, ethnobotany uses, traditional knowledge and pharmacological aspects of the Citrus species.