Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Methods for Extraction of Aspect Terms in Opinion Texts in Portuguese - the Challenges of Implicit Aspects

One of the challenges of aspect-based sentiment analysis is the implicit mention of aspects. These are more difficult to identify and may require world knowledge to do so. In this work, we evaluate frequency-based, hybrid, and machine learning methods, including the use of the pre-trained BERT language model, in the task of extracting aspect terms in opinionated texts in Portuguese, emphasizing the analysis of implicit aspects. Besides the comparative evaluation of methods, the differential of this work lies in the analysis’s novelty using a typology of implicit aspects that shows the knowledge needed to identify each implicit aspect term, thus allowing a mapping of the strengths and weaknesses of each method.
A Study of the Representation and Display of History in Japanese Period Architectural Heritage in Taiwan

 Abstract— The Cultural Property Act was put into force in 1982 in Taiwan. Hundreds of buildings have been listed as either monuments or historic buildings since the law was enacted. Following the Cultural Property Act, many monuments and historic buildings have been restored and either continuously used in their original functions or adaptively reused for new functions. In recent years, creating a space to display the history of the particular buildings has become a must. The historical information represented and displayed in such exhibitions may include the history of the building, the designer (architect), the features of the building, and the restoration process, as well as historic relics and documents related to the building. This paper will discuss the design of such historical spaces and their roles as educational settings, with a focus on the Japanese-Period monuments and historic Buildings in Taiwan.
Participation in the National Health Interview Survey: Exploring Reasons for Reluctance Using Contact History Process Data 1

In 2002-2003, the Census Bureau designed an automated contact history data collection system known as the Contact History Instrument (CHI). The CHI was developed as a stand-alone Blaise instrument designed to work across all Census Bureau demographic computer-assisted personal interview (CAPI) surveys as part of the case management system. Interviewers use the CHI to record the number of contact attempts, mode, date and time of attempt as well as details behind various interim outcomes (e.g., reasons for refusals and strategies attempted). Using CHI data from the 2005 National Health Interview Survey we explore the reasons why some households are reluctant to participate in the interview process. We base the analysis on 10 weeks of CHI data representing over 45,000 visit attempts to 15,000 households. We investigate the extent of reluctance, what the most frequently cited reasons are, and whether these vary by case characteristics such as region, urbanicity, or mode of contact. We also report how patterns of reluctance may change as the number of contacts increases. Finally, we examine whether the number of concerns expressed is related to refusal rates and also whether some reasons are more highly correlated with the decision to refuse.
Studying Software Architecture Design Challenges, Best Practices and Main De- cisions for Machine Learning Systems

Machine learning (ML) refers to statistical modeling techniques, which have recently sparked interest in the ML applications and service industries. The continuous usage of machine learning necessitates addressing software architecture (SA) design challenges and requires guidelines to overcome design issues. ML software system design in small and especially in big software engineering projects is a collaborative decision-making process in which software architect designers, researchers, and developers make design decisions. After considering various design alternatives, the development team handles design issues, examines the best design practices, and picks the main design decisions. In this paper, we provided the common challenges, best design practices, and major decisions for designing the software architecture of ML systems. The systematic literature approach (SLA), with snowballing, is used to extract academic papers from four databases. The inclusion/exclusion techniques helped to extract relevant articles according to research questions. Total 12 interviews were conducted from 9 countries across five continents with academic researchers and industrial professionals having a machine learning experience. Finally, SLR outcomes and interview results are mapped. The mapped and unmapped data are discussed in this study to get more insight into the software architecture design decision for machine learning systems.
The Impact of Packaging Design to Purchase Behavior through Brand Trust

: This study was conducted to identify the importance of packaging design in influencing the purchasing behavior at the same time identifying the mediating role of trust in the brand packaging design relationship with purchasing behavior. This study uses a survey approach to data collection and use of data analysis techniques Covariance-Based Structural Equation Modelling (CB-SEM). This study was conducted on 100 housewives spread in Sidoarjo, Krian and Surabaya. This study proves that packaging design has positive and significant influence on purchasing behavior and brand trust and prove that trust the brand also has a positive and significant impact on buying behavior.
Doped Polymer Nanofiber In Fabrics

This paper focus on the synthesis and photoluminescent properties of electrospun nanofibers of europium ion doped polymethylmethacrylate nanofibers. This fibers were characterized by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDX) and photoluminescence (PL). The photoluminescence properties shows superior bright red emission spectra from the Eu and hence it is widely useful in optoelectronic devices and also specially in fabric designing in textile. All this happens because of relatively stronger hypersensitive behavior of the 5D0→ 7F2 transition. This paper present the brief discussion on the potential application of smart fabric to address challenges in human life
Antibacterial activity, free radical scavenging property and phytochemical screening of Diospyros kaki fruit of Kumaun Himalayan Region, Uttarakhand

Diospyros kaki is a deciduous tree found in the hills of Kumaun region up to an elevation of 1500 m , most of studies on the fruit of this plant have been carried out in the countries like China, Japan, Myanmar, Brazil, Turkey, Italy and South Korea very less attention has been paid to this less known wild fruit in India. This study primarily deals with total phenolic, Flavonoid, tannin content, antioxidant and antibacterial activity. Ascorbic acid was used as standard for the evaluation of antioxidant activity. The IC50 value for ethanolic extract and aqueous extract was recorded 50.43; 81.9 ppm respectively. Diospyros kaki fruit was found to contain significant amount of antioxidants. Antibacterial activity was performed by disc diffusion method against the strain Salmonella typhimurium (MTCC 3224) , Klebsiella pneumonia (MTCC 3384), Bacillus subtilis (MTCC441), Aeromonas hydrophila (MTCC 646), and Pseudomonas aeriginosa (MTCC 103). This study showed that the ethanolic extract of the fruit has significant amount of phytochemicals and antibacterial activity as compared to its aqueous extract.
Rank-constrained linear regression: a Riemannian approach

This poster presents novel algorithms for learning a linear regression model whose parameter is a real fixed-rank matrix. The focus is on the non linear nature of the search space. Because the set of fixed-rank matrices enjoys a rich Riemannian manifold structure, the theory of line-search algorithms on matrix manifolds can be applied [1]. The resulting algorithms scale to high-dimensional problems, enjoy local convergence properties, and connect with the recent contributions on learning fixed-rank matrices [3,4,5,6,10]. The proposed algorithms generalize our recent work on learning fixed-rank symmetric positive semidefinite matrices [2]. Problem formulation Given data matrix instances X ∈ R21, observations y ∈ R, and a linear regression model ŷ = Tr(WX), solve min W∈Rd1×d2 EX,y{l(ŷ, y)}, subject to rank(W) = r. The loss function is the quadratic loss l(ŷ, y) = 12(ŷ − y) . In practice, a surrogate cost function for the expectation above is
Factors affecting Customers’ Satisfaction of Online Shopping– Evidence from Kabul, Afghanistan

: The E-commerce in Afghanistan is nascent because of its fresh entry into the Afghan market.This is because ofsignificant impact of several factors (e.g. the country’s poor IT infrastructure, lack of online payment methods, and the cultural values), “delivery time” is yet another critical factor playing an important role in the operations of online retail. by far, the majority of payments are made through cash on delivery (COD) option, although, the government is planning to implement the “Afghanistan Payments Systems (APS)”, a nationwide payment system considered to be acting as a gateway between locale-retails and international payment platforms expected to help e-commerce boost(Market, 2020). Therefore, the current research aimsat investigating the critical factors that affect customers’ satisfaction among online retailersin Afghanistan. five parameters (Customer care, Speed of delivery, Price of products, Services provision, and Frequency of usage) have taken as independent variables and the overall experience of customers among online shopping as dependent variable. For data analysis and hypothesis testing the researcher has used regression analysis. findings indicate that, the e-commerce customers in Afghanistan prefer virtual shopping the basis of four critical dimensions i.e. speed of delivery, price of products, quality of services, and frequency of purchase, while sidelining the “customer care”. Which means the aforesaid four factors that affect the satisfaction level of customers of e-retails. Therefore, it can be concluded that, customers are satisfied of online shopping amid the outbreak based on the aforementioned four dimensions.
Aberrant Sialylation in Cancer Metastasis

Neoplasm metastasis is a multi-step and multi-level pathological feature causing 90% of cancer mortality due to a shortage of effective drugs and clinical therapeutics. To change this scenario, new drug targets and developments are required. Aberrant tumor sialylation as a putative drug target candidate has been evolving over the past half century. More recently, some specific agents and clinical events showed some promising therapeutic potentiality and benefits against neoplasm metastasis in a number of cellular and animal tumor models. Since neoplasm tissues often contain higher levels and diverse sialic acids (sia) analogues and antigens, sia-related tumor biology/pathology are emerging as cancer diagnostic categories and a series of useful agents until now. Previously some compounds enabling to inhibit sia-related pathologic pathways were reported to exhibit therapeutic activity in cellular or animal tumor models. These types of cancer treatment agents can provide excellent therapeutic outcomes by glycome/metabolomics diagnostics first. Taking together of these experimental/clinical discoveries, the “central dogma” of glycobiology might be found out via all these principle discoveries. In addition, mathematic- or physics-majored talents might render new therapeutic discoveries by computational analysis of experimental and clinical data. In this article, a line of patho-ther-apeutic events and relations is documented on this topic. Overall, therapeutic targets/benefits in the clinic should be pursuit with earnest attitude.
Vulnerability of Banana-Based Farming Communities of Apayao to Climate Change Hazards

: The study was conducted to assess the vulnerability of banana-based farming communities of Apayao to climate change hazards. The results of the study showed that typhoon, torrential rainfall, drought, increased incidence of pest and diseases, and soil erosion were the main climate change hazards affecting banana-based farming communities in the areas covered by the study. In Buluan, Conner, the community is vulnerable to typhoon, torrential rainfall and increased incidence of pests and diseases. Exposures to these climate hazards are medium to high; but adaptive capacities of the community are moderate. The potential impact outweighs the adaptive capacities of the community. On the other hand, the community is moderately resilient to drought and erosion. The overall vulnerability index showed that the banana-based farming communities of Buluan, Conner and Atok, Flora are vulnerable to climate change hazards. Comparison of vulnerability indices of communities with similar crop commodities located in upper and lower elevations did not differ significantly. In conclusion, typhoon, torrential rainfall, drought, increased incidence of pest and diseases, and soil erosion were the main climate-related hazards affecting banana-based productions in the areas covered by the study. With high exposures and sensitivities to climate change hazards, high dependency of farmers to climate sensitive sources of income and moderate to low adaptive capacities, generally, the banana-based production communities were rated vulnerable. on like to fruit based bearing Comparison of Vulnerability Indices of Crop-base Farming Communities in Apayao: The t-test for independent variables was used to compare the vulnerability indices of banana-based farming in different elevations and determine if there is a significant difference between their indices.
Survival of patients with acute leukemia after hematopoietic cell transplantation: A 8-years single-center study in Mexico

Introduction: Hematopoietic stem cell transplantation (HSCT) is the treatment for acute leukemia in children and is the most common type of cancer in children. The objective of the present study was to determine the overall and disease-free survival in a group of patients undergoing HSCT and to explore the risk factors for pediatric patients with acute leukemia. Methodology: This observational study included all pediatric patients diagnosed with acute myeloid leukemia (AML) or lymphoid leukemia (ALL) undergoing HSCT from March 2011 to March 2018 at the Federico Gómez Children's Hospital. Kaplan ‒ Meier curves were con-structed for overall survival by subgroups according to the type of leukemia and disease-free status, as well as a multivariable study to measure risk factors. Results: Fifty-three patients were included in the analysis. Five patients (11%) had primary graft failure. Overall survival was 28% at 24 months. Thirty patients (67%) died. The median overall survival was 11 months. For AML, it was 8.9 months, and for ALL, it was 12.4 months. One of the risk factors was age >10 years at the time of transplant (OR 5.2 (1.07-25.12), P=0.04) and the number of relapses prior to transplant (OR 4.3 (1.2-15.07), P =0.025). Conclusion: Patients who survived one year free of the disease had a better prognosis. In studies related to HSCT, it has not been reported that there is an age range of transplant recipients related to higher mortality, which is why it is a significant and independent risk factor.
SOFTWARE VULNERABILITY CLASSIFICATION MODEL USING NEURAL NETWORK

Security risks are caused mainly due to software vulnerabilities. If any vulnerability is exploited due to a malicious attack, it will greatly compromise the system’s safety. It may even create catastrophic losses. So, automatic classification methods are enviable for effective management of vulnerability in software, thereby improving security performance of the system. It will reduce the risk of system being attacked and spoiled. In this study, a new model has been proposed named automatic vulnerability classification model (IGTF-DNN) Information Gain based on Term Frequency - Deep Neural Network. The model is built using information gain (IG) which is based on frequency-inverse document frequency (TF-IDF), and deep neural network (DNN): TF-IDF is used for calculating frequency/weight of words prepared from vulnerability description; Information Gain is used to select features for gathering optimal set of feature words. Then deep neural network model is used to construct an automatic vulnerability classifier to achieve effective vulnerability classification. The National Vulnerability Database of the United States has been used to test proposed model’s effectiveness. Compared to KNN, the TFI-DNN model has achieved better performance in evaluation indexes which includes precision and recall measures.
Supplementary Information for "A 15.65 solar mass black hole in an eclipsing binary in the nearby spiral galaxy Messier 33"

This Supplementary Information provides details about the spectral extraction (crowding issues and the removal of nebular lines), a discussion about the distance to M33, a model for the O-star wind and the measurement of the true photospheric X-ray eclipse width Theta, and details about ellipsoidal modelling. It also contains five related figures, one related table, and additional references.
Synergistically Employing User Stories and Use Cases in the Practice and Teaching of Systems Analysis and Design

Over the past three decades, user stories and use cases have become increasingly dominant systems analysis and design (SA&D) requirements techniques. Both support articulating functional requirements for software projects, although they evolved within different software development approaches—user stories from agile development and use cases from traditional software engineering—and they differ significantly in terms of their grammar, syntax, and the level of detail they can capture. As such, user stories and use cases are neither synonyms nor mutually exclusive alternatives. Rather, they can and should be complementary in the systems requirements process. Unfortunately, this mix of similarities and differences—coupled with a lack of formal standards for either—make understanding and synergistically employing user stories with use cases confusing and challenging for practitioners and students alike. To address this, this paper first provides a descriptive overview of the evolution of user stories, use cases, and their interrelationship. Second, it fills a gap in the literature by providing a prescriptive, detailed approach to employing user stories and use cases together. This prescriptive approach is illustrated via a comprehensive tutorial example, providing practitioners with actionable skills and SA&D teachers and students with a new pedagogical tool.
GENERAL PRINCIPLES OF SOL - GEL

The sol–gel synthesis based on the hydrolysis and condensation of molecular precursors is used to prepare a wide range of inorganic materials. The sol-gel technique is a very convenient method of obtaining both inorganic and hybrid organic-inorganic polymers. Basic advantage of this technique is carrying out the whole process at very gentle conditions. In contrast to solid-state processes, sol- gel processes offer the possibility to control the reaction pathways on a molecular level during the transformation of the precursor species to the final product. In this way, sol-gel processes enable the synthesis of nanoparticles with well-defined and uniform crystal morphologies and with superior purity and homogeneity. Sol-gel chemistry is a quite complex process, on the one hand due to the high reactivity of the metal oxide precursors towards water and the double role of water as ligand and solvent, and, on the other hand, due to the large number of reaction parameters that have to be strictly controlled (hydrolysis and condensation rate of the metal oxide precursors, pH, temperature, method of mixing, rate of oxidation, etc.) in order to provide good reproducibility of the synthesis protocol. Therefore, the main target of the present review is to explain the basic principles of sol-gel process and to evaluate the parameters that are effective in the sol- gel processes.
Heat-Shock Proteins and their Role in the Adaptation Mechanisms of Living Organisms to the Environment Temperature

The adaptation mechanisms of animal and plant organisms to the temperature are considered. The polyamines, heat-shock proteins and their synthesizing genes actively participate in it, by means of which as a result of their aggregation the endurance of living organisms against the temperature is formed, which prevents the damage of the proteins in the nucleus and other organelles of the cell. The adaptation to high or low temperatures promotes normal functioning of the peripheral and central nervous systems. In connection with this problem, we studied the thermo-stabilizing proteins of rats brain and found that this is neorolectin pH3(40), the content of which was measured in the rat brain cerebral cortex, as well as its dependence on the temperature to which the animals were exposed to outdoors during 1 year. In May, the specific activity of neurolectin pH3(40) in the cerebral cortex was 4 units, whereas in December, it increased more than 37-times. The perspective of therapeutic use of heat-shock proteins in the treatment of malignant tumors is also considered. © 2017 Bull. Georg. Natl. Acad. Sci.
Exploration of Knowledge of TB Patients Regarding Their Disease in Quetta

: Objective: This study aimed to assess and explore the knowledge of TB patients regarding TB in Quetta. Method: A questionnaire based, cross sectional analysis was conducted in Fatima Jinnah chest hospital with 280 TB patients. Knowledge was assessed by using a pre-validated self-administered questionnaire containing 22 disease related questions. Convenience sampling technique was used for data collection. Descriptive analysis was used to demonstrate the characteristics of the study population. Inferential statistics (Mann-Whitney U test and Kruskal Wallis tests, p< 0.05) were used to assess the significance among study variables. Results: Mean age of respondents was 40.99 ± 18.10. Study was dominated by 168 (60.00%) of females. Two hundred (71.40%) were married. One hundred sixteen (41.40%) had no any education. One hundred sixty-eight (60.00%) were Pashtun. One hundred thirty-two (47.10%) having income less than 10000 PKR and ranges between 10000 to 18000 PKR respectively. One hundred fifty-six (55.70%) were having rural residency. One hundred fifty-six (58.10%) having no any co-morbidity. Mean score of knowledge was 11.23 ± 3.616. Conclusion: Study concluded that as knowledge is a key factor for the prevention and control of TB, it is obvious to plan and apply appropriate health education programs, seminars and interventions regardless to the level of education of population to propagate the knowledge and information about causes, transmission and duration of treatment of tuberculosis in the general population and TB patients to coup with further disease progression in Pakistan.
A Comment on Dealing with Deep Uncertainty in the Energy Transition Cases from the electricity and transportation sectors*

We demonstrate how deep uncertainty (“unknown unknowns”) delays investment in the energy transition. Three cases resembling different characteristics of the energy transition are examined: offshore wind, electric vehicles, and Datteln 4 (a new coal fired power plant). Deep uncertainty typically arises in close connection with – and therefore ampli-fying – the “usual” statistical risk context. Sources or drivers of deep uncertainty vary with technology or market maturity and may originate from practically all levels, starting with the broader macro-economic environment via the sectoral, firm, project or technology level. There are some approaches to coping with deep uncertainty in the literature, but they appear to be rarely used in practice, particularly with a view to investment decisions. All cases have in common, that long-term credible policy signals would strongly contribute to reducing deep uncertainty, despite all the challenges that come with sending such signals.