Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating Knowledge Graphs from Unstructured Texts: Experiences in the E-commerce Field for Question Answering

Nowadays, there is a growing number of sales occurring over the Web in e-commerce stores. Customers often have questions about a product before they buy it. By answering them instantly, online stores can improve user experience, customer’s satisfaction and sales conversion rate. Eﬀective and automated customer service via computer systems requires the handling of large amounts of unstructured information like product speciﬁcations. In this paper, we deﬁne and evaluate a technique to generate knowledge graphs (KGs) by extracting relevant product information from unstructured natural language questions and answers. The knowledge encoded in the KG is used to answer new clients’ questions via SPARQL requests. Our solution is evaluated in a real world, using data from online stores in the GoBots, a leading e-commerce chatbot business in Latin America. Obtained results show the beneﬁts of exploring KGs for responding a higher spectrum of questions in real-world settings.
PolB1 Is Sufficient for DNA Replication and Repair Under Normal Growth Conditions in the Extremely Thermophilic Crenarchaeon Sulfolobus acidocaldarius

The thermophilic crenarchaeon Sulfolobus acidocaldarius has four DNA polymerases (DNAPs): PolB1, PolB2, PolB3, and Dbh (PolY). Previous in vitro studies suggested that PolB1 is the main replicative DNAP of Sulfolobales whereas PolB2 and Y-family polymerases Dpo4 (Saccharolobus solfataricus) or Dbh are involved in DNA repair and translesion DNA synthesis. On the other hand, there are various opinions about the role of PolB3, which remains to be clearly resolved. In order to examine the roles of the DNAPs of S. acidocaldarius through in vivo experiments, we constructed polB2, polB3, and dbh deletion strains and characterized their phenotypes. Efforts to construct a polB1 deletion strain were not successful; in contrast, it was possible to isolate triple gene-deletion strains lacking polB2, polB3, and dbh. The growth of these strains was nearly the same as that of the parent strains under normal growth conditions. The polB2, polB3, and dbh single-deletion strains were sensitive to some types of DNA-damaging treatments, but exhibited normal sensitivity to UV irradiation and several other damaging treatments. Overall, the genotype which exhibited the greatest sensitivity to the DNA-damaging treatments we tested was the ΔpolB2 ΔpolB3 combination, providing the first evidence of overlapping function for these two DNAPs in vivo. The results of our study strongly suggest that PolB1 is responsible for the DNA replication of both the leading and lagging strands and is sufficient to complete the repair of most DNA damage under normal growth conditions in S. acidocaldarius.
Age Differences in Preferences for Fear-Enhancing Vs. Fear-Reducing News in a Disease Outbreak

Older adults (OA) prefer positive over negative information in a lab setting, compared to young adults (YA; i.e., positivity effects). The extent to which OA avoid negative events or information relevant for their health and safety is not clear. We first investigated age differences in preferences for fear-enhancing vs. fear-reducing news articles during the Ebola Outbreak of 2014. We were able to collect data from 15 YA and 13 OA during this acute health event. Compared to YA, OA were more likely to read the fear-enhancing article, select hand-sanitizer over lip balm, and reported greater fear of Ebola. We further investigated our research question during the COVID-19 pandemic with 164 YA (18–30 years) and 171 OA (60–80 years). Participants responded to an online survey about the COVID-19 pandemic across 13 days during the initial peak of the pandemic in the United States (U.S.). Both YA and OA preferred to read positive over negative news about the coronavirus, but OA were even more likely than YA to prefer the positive news article. No age differences in the fear of contraction were found, but OA engaged in more health-protective behaviors compared to YA. Although OA may not always report greater fear than YA or seek out negative information related to a health concern, they still engage in protective health behaviors. Thus, although positivity effects were observed in attention and emotional reports (in the COVID-19 study), OA still modified their behaviors more than YA (giveaway in both studies, and health-protective behavior change in the COVID-19 study), suggesting that positivity effects did not hamper OA ability to respond to a health crisis.
Verbalizing the Evolution of Knowledge Graphs with Formal Concept Analysis

Questioning Answering and Verbalization over Knowledge Graphs (KGs) are gaining momentum as they provide natural interfaces to knowledge harvested from a myriad of data sources. KGs are dynamic: new facts are added and removed over time, producing multiple versions, each representing a knowledge snapshot of a point in time. Verbalizing a report of the evolution of entities is useful in many scenarios, e.g., reporting digital twins’ evolution in manufacturing or healthcare. We envision a method to verbalize a graph summary capturing the temporal evolution of entities across different KG versions. Technically, our approach considers revisions of a graph over time and converts them into RDF molecules. Formal Concept Analysis is then performed on these RDF molecules to synthesize summary information. Finally, a verbalization pipeline generates a report in natural language.
Analysis of the Emotions Experienced by Learning Greedy Algorithms with Augmented Reality

— Students have difficulties in understanding algorithm subjects, in particular how the source code of algorithms proceeds to solve problems. This article presents an augmented reality tool intended to assist in learning greedy algorithms. Students use their smartphone’s camera to focus the source code of Dijkstra’s algorithm as written on paper, and the tool shows how the algorithm works. An experience was conducted in the classroom to assess students’ emotions and knowledge level. The results show that positive emotions experienced by students were almost twice as intense as negative ones. Despite the complexity of the task (i.e. understanding Dijkstra’s algorithm), the level of enjoyment of students was continuous during the experience. However, the anxiety experienced by students was the double than at the beginning.
A Roadmap for Quantum Software Engineering: Applying the Lessons Learned from the Classics

Quantum Computing is one of the emerging areas of computing that currently generates more expectations. However, there are many doubts about its actual future projection. On the one hand, the industry shows reluctance to invest in it. The main reasons are the high costs of the hardware, together with the fact that current commercial quantum computers offer a potential that goes little beyond experimentation. On the other hand, there is controversy in the research community about the feasibility of creating and programming powerful and reliable quantum computers. The possibility of having reliable hardware with a reasonable number of Qubits seems still distant. Finally, current quantum programming tools are still at almost logic gate level, which limits the possibility of creating real complex quantum software systems. If we look back in time, this situation is reminiscent of the Software Crisis experienced by classical computing in the 60’s. This talk starts from this analogy and, analyzing the advances and the lessons learned in the field of Software Engineering in the last 60 years, raises the directions that could help to develop the future Quantum Software Engineering.
Privacy-Preserving Monitoring System with Ultra Low-Resolution Infrared Sensor

Action monitoring systems used in households provides vital information for health monitoring particularly with aging residents. While visual inputs such as information provided by cameras can recognize the actions and position of a subject with high accuracy, they are not widely accepted due to privacy concerns. This paper proposes a posture classification method with the use of a low-resolution thermal sensor. The sensor aims to protect the subject’s privacy by capturing visual input in the infrared spectrum as well as having a low spatial resolution of 8x8 pixels. We consider a simulation which recreates the experimental environment and produces data for this postural-behavioral problem. The validity of this method is checked by considering 3 postures; standing, sitting, and laying down and examined using a classifier on simulated data. Additionally, we explore optimal position and angle of the sensor as well as the effects of color depth on accuracy. In our results we achieve over 93% classification accuracy by color conversion of the infrared array sensor image and successfully decreased loss due to displacement by DCNN. We discover higher accuracies are achieved when the sensor is located 50cm below the subject’s height with a tilt angle of ±2° .
Machine learning approaches for financial time series forecasting

This paper is discusses the problems of the short-term forecasting of financial time series using supervised machine learning (ML) approach. For this goal, we applied several the most powerful methods including Support Vector Machine (SVM), Multilayer Perceptron (MLP), Random Forests (RF) and Stochastic Gradient Boosting Machine (SGBM). As dataset were selected the daily close prices of two stock index: SP 500 and NASDAQ, two the most capitalized cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), and exchange rate of EUR-USD. As features we used only the past price information. To check the efficiency of these models we made out-of-sample forecast for selected time series by using one step ahead technique. The accuracy rates of the forecasted prices by using ML models were calculated. The results verify the applicability of the ML approach for the forecasting of financial time series. The best out of sample accuracy of short-term prediction daily close prices for selected time series obtained by SGBM and MLP in terms of Mean Absolute Percentage Error (MAPE) was within 0.46-3.71 %. Our results are comparable with accuracy obtained by Deep learning approaches.
Educational Game for Laterality Development in Early Childhood and the Importance of Participatory Design

—The learning process in Early Childhood Education focuses on the development of children and their different forms of expression. Learning can be established by body language, because, by movement, the child establishes symbolic relationships, and constructs his thinking. So, in Early Childhood Education a dialogue with other areas of knowledge is necessary, seeking to provide an immersion in various learning situations. In this context, the present work focuses on analyzing the development of a digital game in Scratch with the participation of students, which reflects paths and levels identified in Physical Education classes, aiming to develop laterality. The research is qualitative and used the scientific procedures of bibliographic research, participant observation, discursive textual analysis, and participatory design for the elaboration of the game. The subjects were children aged between four and six who are in Early Childhood Education. As a result, the digital game really became a strategy to assist in the development of laterality of children, as it enhanced the appropriation of psycho-motor schemes related to spatial and temporal orientation.
Comparison of Three Motor Subtype Classifications in de novo Parkinson's Disease Patients

Objective: The aims of this study were to compare the characteristics of three motor subtype classifications in patients with de novo Parkinson's disease (PD) and to find the most suitable motor subtype classification for identifying non-motor symptoms (NMSs). Methods: According to previous studies, a total of 256 patients with de novo PD were classified using the tremor-dominant/mixed/akinetic-rigid (TD/mixed/AR), TD/indeterminate/postural instability and gait disturbance (PIGD), and predominantly TD/predominantly PIGD (p-TD/p-PIGD) classification systems. Results: Among the TD/mixed/AR subgroups, the patients with the AR subtype obtained more severe motor scores than the patients with the TD subtype. Among the TD/indeterminate/PIGD subgroups and between the p-TD and p-PIGD subgroups, the patients with the PIGD/p-PIGD subtype obtained more severe scores related to activities of daily living (ADL), motor and non-motor symptoms, including depression, anxiety, and sleep impairment, than the patients with the TD/p-TD subtype. Furthermore, symptoms in the cardiovascular, gastrointestinal, and miscellaneous domains of the Non-motor Questionnaire (NMSQuest) were more prevalent in the patients with the PIGD/p-PIGD subtypes than the patients with the TD/p-TD subtypes. Conclusions: The PIGD/p-PIGD subtypes had more severe ADL, motor and non-motor symptoms than the TD/p-TD subtypes. We disclosed for the first time that the TD/indeterminate/PIGD classification seems to be the most suitable classification among the three motor subtype classifications for identifying NMSs in PD.
Using Semantic Technologies to Manage a Data Lake: Data Catalog, Provenance and Access Control

Data lake architectures enable the storage and retrieval of large amounts of data across an enterprise. At Robert Bosch GmbH, we have deployed a data lake for this expressed purpose, focused on managing automotive sensor data. Simply centralizing and storing data in a data lake, however, does not magically solve critical data management challenges such as data findabil-ity, accessibility, interoperability, and re-use. In this paper, we discuss how semantic technologies can help to resolve such challenges. More specifically, we will demonstrate the use of ontologies and knowledge graphs to provide vital data lake functions including the cataloging of data, tracking provenance, access control, and of course semantic search. Of particular importance is the development of the DCPAC Ontology (Data Catalog, Provenance, and Access Control) along with its deployment and use within a large enterprise setting to manage the huge volume and variety of data generated by current and future vehicles.
TALES: Test Set of Portuguese Lexical-Semantic Relations for AssessingWord Embeddings

Static word embeddings, like word2vec or GloVe, are often assessed when solving syntactic and semantic analogies. Among the latter, we are interested in relations that one would ﬁnd in lexical-semantic knowledge bases like WordNet, also covered in analogy test sets for English. This paper describes the creation of a new test for assessing Portuguese word embeddings, dubbed TALES, with an exclusive focus on lexical-semantic relations, acquired from lexical resources in Portuguese. It further reports on the performance of methods previously used for solving analogies, with pre-trained Portuguese word embeddings, when applied to the created dataset, an experiment that revealed that TALES is challenging to solve. Results achieved are brieﬂy discussed, with conclusions that may be useful for developing new approaches for this problem, possibly new embeddings, as well as future versions of TALES.
Exploring the Use of Topic Analysis in Latvian Legal Documents

The large number of legislative documents produced every day makes it difficult to follow each and every document. However, it is important for enterprises to comply with all current legislative acts. In this paper we demonstrate the application of different topic analysis algorithms and stop word filtering approaches to the corpus of legal texts of the Republic of Latvia. This is done for the purpose of supporting the discovery of expressive and meaningful legal topics and marking respective documents according to those topics. Topic models produced in this work are intended to be used as an aid for experts, enabling faster document browsing possibilities.
Generating Grammars from Lemon Lexica for Questions Answering over Linked Data: a Preliminary Analysis

Most approaches to question answering over linked data (QALD) frame the task as a machine learning problem, consisting in learning a mapping from natural language questions into SPARQL queries by parametrizing a model from training data given in the form of pairs of natural language (NL) question and SPARQL query. In this prelimi-nary work we present an alternative approach to developing a QA sys-tem using machine learning that relies on the automatic generation of a QA grammar from a lemon lexicon. This model-based approach comes with a number of advantages compared to a machine learning approach. First, our approach gives maximum control over the QA interface to the developer of the system as every entry added to the lexicon increases the coverage of the grammar and thus of the QA system in a predictable way. This is in contrast to machine learning approaches where the impact of the addition of a single training example is difﬁcult to predict. A further advantage of our approach is that the QA system operates on the basis of a symbolic grammar that can be used to provide guidance and auto-completion functionality to users. Our system is indeed intended to be used in the context of an auto-completion interface that allows users to ask only questions that the grammar can cover. We present very preliminary results showing that a large percentage of the questions of the training set of QALD-7 can be rephrased in terms of questions that our grammar can parse. We show that with a hand-crafted lexicon, we can in principle get very high micro-F1 scores of 62.5% on the training data of QALD-7 when questions are manually rephrased to ﬁt our grammar. Although these preliminary results do not constitute a proper evaluation of our approach, they hint at the fact that an approach as we propose seems feasible.
An Integrated Inspection and Visualization Tool for Accurate Android Collusive Malware Detection

Collusive malwares in Android exploit the Inter Component Communication (ICC) scheme in Android architecture. Several collusive malware detection and analysis tools have been developed since the beginning of Android. These tools are mostly based on static code analysis and dynamic behavior analysis. Although such approaches can point out ICC paths and risk associated with them pretty well, an addition of visual component can provide ease of perception on collusive malware behaviors. We reviewed the state-of-the-art approaches and developed a tool, AndroCap which integrates the static code analysis process of a state-of-the-art tool to our system, visualizes the ICC paths and risks associated with them found from static code analysis results and dynamically collects data from apps running on Android environment to observe malicious communication among app components. We further test AndroCap on a set malwares and present the results. Our results show that the developed tool can successfully visualize ICC paths found from static code analysis and mark suspicious ones using contrasting colors. Further, malicious communication among components can be dynamically observed by executing apps on Android environment.
Attacks on Health Workers during COVID-19 Pandemic - Data Exploration and News Article Detection using NLP and GRU model

A major less spoken impact of COVID-19 is the irrational behavior from people. We are experiencing abnormal behaviour from individuals all over the world. It ranges from “absurd conspiracy theories” to “panic buying of tissue paper”. Likewise, one such example would be attack on health workers - the front line defenders. An outrageous surge is visible regarding attacks on health workers (e.g., doctors, nurses, security personnel) during COVID-19 pervasive. This kind of unsocial behaviour is unexpected and should be prohibited. In this paper, we observe the news articles related to attacks on health workers. We explore data from numerous direction and inspect the relationship between attack-news and various aspects of countries such as literacy rate, GDP, and etc. Furthermore, we apply topic modeling in order to find the bag-of-words that describe best these sorts of news. We reveal masked-emotions within words through emotion lexicon. In addition, we find a way to detect attack-news instantly using Natural Language Processing (NLP) with the help of deep-learning techniques. Gated Recurrent Unit (GRU), a deep learning model works best for our purpose with accuracy up to 94%. Finally, an architecture is proposed for timely vigilance. Henceforth, we believe that our proposed solution will help the policy makers immensely to design their strategies accordingly.
The Two‐Channel Maximum‐Entropy Method Applied to the Charge Density of a Molecular Crystal: α‐Glycine

A two-channel maximum-entropy method (MEM), first used to enhance magnetization densities from phased polarized neutron data by Papoular & Gillon [(1990). Europhys. Lett. 13, 429–434], has been applied to the electron deformation density. The resulting entropic densities are compared with standard deformation densities and with dynamic and static deformation maps obtained from multipole refinements. The procedure is illustrated with simulated and real single-crystal X-ray data sets on the molecular crystal of α-glycine. Both a uniform prior and a prior equal to the MEM-enhanced dynamic model deformation density are used in the MEM procedure, the result of which does not depend on the starting density. The method is judged by the appearance of the resulting maps and the values of the molecular dipole moment before and after the MEM. Compared with the conventional deformation density, the MEM procedure sharpens the peaks in the bond but flattens the weaker features, especially when a uniform prior is used. The dipole-moment criterion shows the non-uniform prior to be preferable to the uniform prior in reproducing electrostatic properties. The usefulness of the MEM in charge-density analysis remains open to discussion.
Identification of Rice Varieties with High Tolerance or Sensitivity to Copper

ABSTRACT To study tolerance of rice (Oryza sativa L.) to excessive copper (Cu), 167 varieties were screened for Cu tolerance or sensitivity. Based on the elongation of the root, No. 1139 and No. 1195 were chosen as Cu-tolerant and Cu-sensitive variety. Compared with the control (0.32 μ mol L− 1 Cu), treatments with 5 and 8 μ mol L− 1 Cu for 10 days had no significant effect on the root and shoot dry masses of Cu-tolerant variety No. 1139, but significantly decreased root and shoot dry masses of variety No. 1195. Copper at 5 and 8 μ mol L− 1 also significantly decreased contents of chlorophyll and carotenoid and the ratio of variable to maximal chlorophyll fluorescence (Fv/Fm) in the leaves of the Cu-sensitive variety No. 1195. Roots and shoots of variety No. 1139 contained significantly higher the concentration of Cu than variety No. 1195 exposed to 5 and 8 μ mol L− 1 Cu. However, variety No. 1139 had higher Cu proportions on the cell walls in shoots and roots than variety No. 1195.
A Routine Method for Oxide and Hydroxide Interference Corrections in ICP‐MS Chemical Analysis of Environmental and Geological Samples

Inductively coupled plasma-mass spectrometry has revolutionized the field of multi-elemental analysis because of its high sensitivity and speed of analysis. However, problems with polyatomic ion interferences during routine analysis can give rise to inaccuracies. We propose a method that systematically corrects for oxide and hydroxide interferences in routine analysis, for a wide range of materials. Oxide and hydroxide production for a specified element is calculated from measurements on each sample, as levels may depend on sample composition and may also vary with time. We show that oxide and hydroxide interferences can then be calculated for other elements, by reference to a set of oxide and hydroxide ratios measured on standard solutions. Validity of correction is controlled by internal tests involving two isotopes of one element with respect to experimental errors. The limits and validity of this approach are demonstrated with the analysis of selected reference materials.