Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Everyday Design Behaviour: An Exploratory Experiment

People interact with diverse products for various purposes in their daily lives. For example, when people feel thirsty, they pour water into a cup. However, a cup may not only be used to hold water, but also utilised as a pencil holder or as a measuring tool when cooking. Thus, a cup is used for various purposes. Whether we use a paper napkin as a notepad or a mug as storage for pens and pencils, we can easily find things in our everyday lives that are used differently from the function intended by designers. As Desjardins and Wakkary (2013) summarized from earlier research, “. once the artifacts leave the designer’s drawing table, the design process does not stop: it can be pursued through customization, reuse, appropriation, do-it-yourself (DIY) projects and everyday design processes” (p. 253). In this paper, users’ process of adding new functions and values to existing products is termed everyday design (ED), based on the concept coined by Wakkary et al. to define family members or home dwellers reusing products for new purposes (Kim & Lee, 2014; Maestri & Wakkary, 2011; Wakkary & Maestri, 2008; Wakkary & Tanenbaum, 2009; Wakkary et al., 2015). Evidence of everyday design can be found in the “resourceful appropriation of artifacts and surroundings, the ongoing adaptation of systems and routines through design-in-use that allows emergent properties to arise and addresses individual needs” (Wakkary and Maestri, 2008, p.171). Whereas Wakkary and Maestri’s definition of everyday design is rather broad, we limit the concept to the process by which home dwellers create new uses for designed artefacts within given contexts. The phenomenon of everyday design has received fairly considerable attention in the design world, with amusing examples of the ways in which people react to a world that is not perfectly tailored to their needs. ED has been explained with reference to the concept of unselfconscious design (Alexander, 1964), by which people unconsciously design things for goodness of fit, rather than through a self-conscious process of knowledge acquisition. Other authors refer to ED as intuitive design (Suri & IDEO, 2005), as unintentional behaviour, or as unintended everyday product design (Brandes & Erlhoff, 2006; Wakkary et al., 2015). In the case of a cup that is turned into a pencil holder, what might have stimulated the person to use the cup in such a way? ORIGINAL ARTICLE
Super-resolution of Digital Elevation Models With Deep Learning Solutions

Terrain, representing features of an earth surface, plays a crucial role in many applications such as simulations, hazard prevention and mitigation planning, route planning, analysis of surface dynamics, computer graphics-based games, entertainment, films, to name a few. With recent advancements in digital technology, these applications demand the presence of high-resolution details in the terrain. However, currently available public datasets, providing terrain scans in the form of Digital Elevation Models (DEMs) have low resolution compared with the terrain information available in other modalities like aerial images. Publicly available DEM datasets for most parts of the world have a resolution of 30 m whereas the aerial images or satellite images are available at a resolution of 50 cm. The cost involved in capturing of such high-resolution DEMs (HRDEMs) turns out to be a major hurdle for making such high-resolution available in the public domain. This motivates us to provide a software solution for generating high-resolution DEM from the existing low-resolution DEMs (LRDEMs). In natural image domain, super-resolution has set up higher benchmarks by incorporating deep learning based solutions. Despite such tremendous success in image super-resolution task using deep learning solutions, there are very few works that have used these powerful systems on DEMs to generate HRDEMs. A few of them used additional modalities as aerial images or satellite images, temporal sequence of DEMs etc., to generate high-resolution terrains. However, the applicability of these methods is highly subject to the available input formats. In this research effort, we explore a new direction in DEM super-resolution by using feedback neural networks. Availing the capability of feedback neural networks to redefine the features learned by shallow layers of the network, we design DSRFB, a DEM super-resolution architecture that generates high-resolution DEM with a super-resolution factor of 8X with minimal input. Our experiments on Pyrenees and Tyrol mountain range datasets show that DSRFB can perform near to the state-of-the-art without using information from any additional modalities like aerial images. Further, by understanding the limitations of DSRFB, which primarily occur in case of highly degraded low-resolution input. In such cases, the major structures are entirely lost and the reconstruction becomes challenging. In such cases, to avail the elevation cues from alternate sources of information becomes necessary. To utilize such information from other modalities, we inherit the attention mechanism from natural language processing (NLP) domain. We integrate the attention mechanism into the feedback network to present Attentional Feedback Module (AFM). Our proposed network, Attentional Feedback
Germinal Centers and Long-Lived Humoral Immunity Roles of Bone Morphogenetic Protein Receptor 1A in

In response to T-dependent Ag, germinal centers (GC) generate bone marrow–resident plasma cells (BMPC) and memory B cells (MBC). In this study, we demonstrate that the bone morphogenetic protein receptor 1A (BMPR1A) signaling pathway, which regulates differentiation and self-renewal in multiple stem cell populations, regulates GC dynamics and resultant establishment of BMPC and MBC. Expression studies using quantitative PCR and novel Bmpr1a.IRES. EGFP reporter mice demonstrated that Bmpr1a expression is upregulated among GC B cells (GCBC) and subsets of MBC, bone marrow plasmablasts, and BMPC. In immunized mice carrying B cell–targeted Bmpr1a gene deletions, the GC response was initially diminished. Subsequently, the GCBC compartment recovered in size, concurrent with accumulation of GCBC that carried unmodified rather than deleted Bmpr1a alleles. Similarly, the resulting classswitched MBC and BMPC carried retained non-recombined alleles. Despite the strong selective pressure for leaky B cells that retained Bmpr1a, there was a permanent marked reduction in switched bone marrow Ab-forming cells (plasmablasts + plasma cells), BMPC, MBC, and Ag-specific serum IgM in mice carrying B cell–targeted Bmpr1a gene deletions. These findings demonstrate a novel role for BMPR1A in the modulation of the B cell response and in the establishment of long-term memory. ImmunoHorizons, 2021, 5:
Electronic Cigarettes Use During Pregnancy - Is it Safe or Not? A Literature Review

Recently, the use of e-cigarettes has increased highly rapidly, especially among middle school and high school students and pregnant women who are concerned about tobacco effects on baby’s health. There is little understanding of the effects of exposure to e-cigarette on human reproductive health, human development, or pregnant women. Human and animal data support that nicotine exposure during periods of development has multiple adverse health consequences, including impaired fetal brain, lung, cardiac development, and altered development of cerebral cortex and hippocampus in adolescents. However, the effects of e-cigarettes are completely unknown regarding human development. Aim: In this article, we are reviewing the most recent findings on the effects of e-cigarettes during pregnancy and early life. Method: A literature search in PMC, PubMed, Google, ResearchGate, MEDLINE and Google Scholar was carried out using the following keywords: “Electronic cigarettes”, “E-cigarettes and pregnancy”, “ENDS and pregnancy”. Study selection was in the language (English only), model (humans and animals), open accesses, and all types of studies Fortune J Health Sci 2021; 4 (2): 310-323 DOI: 10.26502/fjhs024 Fortune Journal of Health Sciences Vol. 4 No. 2 June 2021 311 were included as long as they were relevant to our study. Limitation of study: While we were collecting the information for this review, there were some limitations. Our data was primarily obtained from articles with free full access and written in English language only. This review article is a traditional review and, therefore, does not follow the Standard Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews.
Topology Optimaization of the Geometry of a Three-Outlet Channel with Specified Outlet Flow Rates

In the present paper, the optimal geometry of a two-dimensional multi-channel with one inlet and three outlets is investigated at =10. In this study, the topology optimization based on a porosity method using lattice Boltzmann simulation is adopted to find the optimal layout by computing the sensitivity analysis of an objective function. In contrary to previous studies of the channel with constant width of the outlets, the average velocity of the outlets is maintained as the same while the energy dissipation is reduced by 26.04%, showing the adavntages of changing in width of ducts rather than the change in their average velocities.Therefore, the geometry of the inlet duct including width and position of the duct, play an important role in the final design as the main goal of this research. Assuming that the duct width in the outlets changes linearly, the numerical results showed that the equality of inlet width with the largest outlet caused the lowest power loss for the flow. The inlet being parallel to the space between the two outlets with medium and large widths, made a reduction of 23.18% of loss rather than the inlet was set in front of the duct with the smallest size.
Tackling the OnForumS Challenge

In this paper we describe the participation of the Joint Research Centre in the OnFo-rumS shared task. We experimented with distributional similarity to measure the distance between comments and sentences from the corresponding articles. Using distributional term similarity for English resulted in a small improvent with respect to the lexical-based baseline (0.03). For detecting sentiment and attitude we used statistical ngram models, which gave relatively good performance in both languages - English and Italian.
Personalization for Web-based Book Shop System using Hybrid Data Mining Approaches

Personalization is a new system development approach for designing information systems that change configurations based on each user's needs and preferences. If it is possible to recommend products to customers’ liking at the time they are visiting the specific web site, it would reduce the hassle customers experience in searching for products from a large information base. An effective personalization technique has to be customized to meet the specific needs of every particular domain and deliver quality recommendations. This paper presents a hybrid personalization system for web-based book-shop system. It combines the Bayesian classification method with association rule mining to model individual customer’s behavior. While Bayesian classifier is for effective customer profiles, Association rule presents item to item association based on user transactions.
Comparing the effect of Seven isolated Bacillus thuringiensis against The Indian mealmoth (Plodia interpunctella), infesting during storage

Seven bacterial isolated strains, Bacillus thuringiensis B.T Dendrolimus , B.t thuringiensis , Bt Sotto 4A/4B , BT IP thurizide, Bt Toloworthi, Bt HD 210 and Bt HD 128 tested aginst the Indian meal moth (Plodia interpunctella, the LC50 of the different bacterial strains , 103X104, 98 X104, 44 X104, 40 X104, 42 X104, 110 X104 and 177 X104 after treated with different concentrations of bacterial strains, Bacillus thuringiensis B.T Dendrolimus , B.t thuringiensis , Bt Sotto 4A/4B , BT IP thurizide, Bt Toloworthi, Bt HD 210 and Bt HD 128,. The effect of the number of eggs laid/ female of (Plodia interpunctella were significantly decreased to 10.1±1.6 after treated with Bt Toloworthi as compared to 98.4±4.9 in the control after 120 days of the storage . The percentage of adult emergence significantly decreased to 10 after Bt Toloworthi treatments after 120 days as compared to 99% in the control.
Effect of Quality and Competence on Satisfaction of Civil Servants for E-Kinerja Users through Perceptions of Ease of Use as an Intervening Variable at the City Government of Banda Aceh in 2020

This study aims to determine effect of quality and competence on satisfaction of civil servants for e-kinerja users through perceptions of ease of use as an intervening variable at the City Government of Banda Aceh in 2020. This type of research is a type of quantitative descriptive research. The entire State Civil Apparatus (ASN) in the five agencies in the Banda Aceh City Government is 236 people. The characteristics of the sample in this study were the civil servants who used the e-kinerja application from all levels of positions and groups who were randomly selected. So the number of samples taken was 149 respondents. The data analysis method uses descriptive statistics and path analysis. The results showed application quality has a significant effect on user satisfaction because the resulting significance value is 0.049<0.05. User competence has no significant effect on user satisfaction because the resulting significance value is 0.061>0.05. Application quality has no significant effect on perceptions of ease of use because the resulting significance value is 0.323>0.05. User competence has a significant effect on perceptions of ease of use because it has a significant value of 0.000<0.05. Perceptions of ease of use has a significant effect on user satisfaction because it has a significance value of 0.000<0.05 with a beta value of 0.551. Perceptions of ease of use cannot be used as an intervening variable in the influence of application quality on user satisfaction, but perceptions of ease of use can be used as an intervening variable in the influence of user competence on user satisfaction.
ASSOCIATION OF PHYTOPLASMA AND BEGOMOVIRUS WITH SOME ORNAMENTAL PLANTS: A REVIEW

In recent years, the development of the floriculture sector has received rising attention, particularly for the benefit of small-scale agricultural enterprises producing domestic seedlings of perennial ornamental plants and for export to neighbouring countries. Plant diseases, including those associated with phytoplasma infections and plant viruses, are affected by this industry, as are other sectors of the agricultural economy.In a number of commercial cut flowers and ornamental plants, phytoplasma and plant virus infection causes diseases, causing major economic losses globally.Therefore, phytoplasma and plant virus diseases are the key constraints in the production of lucrative ornamental plants and lower their quantum and quality, gaining international importance due to unspecific symptoms, different losses and complex epidemiology around the world. These disease epidemics forced the removal of several varieties of floricultural plants such as gladiolus, lily, chrysanthemum and rose from cultivation. In various ornamental plants in botanical gardens and various floriculture farms, symptoms of general yellowing as well as plant stunting, shoot proliferation, phyllody, virescence, lower cost of flowers and reddening of leaves were observed. The prevalent mode of distribution of plant viruses is vector transmission, vegetative propagation or seed, although in some cases, viruses are transmitted by mechanical contact. Begomoviruses in economically important ornamental plants, especially in the tropical and subtropical regions, are among the most dangerous epidemic-causing pathogens, but phytoplasmas of ornamental plants have been widely distributed geographically. Information on phytoplasma and begomovirus infecting ornamental plants has been addressed in this study.
Deep learning Enabled Molecule Design

The advent of modern machine learning and deep learning methods have revolutionized various fields such as computer vision, natural language processing, speech recognition, etc. In the the past decade these methods have made a profound impact in the field of natural science. Particularly, the computations involved in the field of molecular science have been greatly transformed with the advent of machine learning. These changes have resulted in tremendous reduction of time and cost involved in tasks such as molecular generation and property prediction, which are two crucial steps involved in the process of molecular design. In this thesis, two studies on in each of these is reported. First part of the thesis presents a deep learning based inorganic material generator (DING) framework consisting of a generator module and a predictor module. The generator module is developed based upon conditional variational autoencoders (CVAE) and the predictor module consists of three deep neural networks trained for predicting enthalpy of formation, volume per atom and energy per atom chosen to demonstrate the proposed method. The predictor and generator modules have been developed using a one-hot key representation of the material composition. A series of tests were done to examine the robustness of the predictor models, to demonstrate the continuity of the latent material space, and its ability to generate materials exhibiting target property values. The DING architecture proposed in this paper can be extended to other properties based on which the chemical space can be efficiently explored for interesting materials/molecules. Second part of the thesis presents an interpretable deep learning method based on graph networks that accurately predict solvation free energies of small organic molecules. The proposed model CIGIN, comprising of three phases, namely message passing, interaction and prediction is able to predict solvation free energies in any generic organic solvent with a mean absolute error of 0.16 kcal/mol. In terms of the accuracy, the CIGIN model outperforms all the proposed machine learning
A Hybrid Approach for Dropout Prediction of MOOC Students using Machine Learning

Summary Massive Open Online Courses (MOOC) is an extensive way of providing online education to the students all over the world. Based on the statistics, this education system have millions of students attending hundreds of courses in different offered programs. Since, MOOC started, it has been facing a challenging concerns, which is also a major difference between the traditional teaching and MOOC, known as “student dropout ratio”. With this fact, the overall performance of MOOC is negatively impacted the real purpose of distance learning. Whereas the difference between course registration and course completion ratio in MOOC is quite large. On the better side, the emerging technologies has created several opportunities for the students to get education online, but due to multiple factors the dropout ratio of online students is high as compare to traditional school learning process. This research is focusing on the issue to understand and predict the MOOC dropout ratio. The multiple models and evaluation metrics generating variety of results as extracted from literature review. To tackle this problem, the experiment conducted in this study using KDD MOOC dataset by implementing hybrid approach of machine learning algorithms. The results suggested the appropriate improvements in the dropout accuracy ratio. Based on the final results, the maximum accuracy recorded as 90% that measured through random forest model. Finally, the model can help and assist the online education system to understand the early dropout prediction and to do necessary arrangements.
Physics-based height map optimization conveying real-measured flow speed for virtual soap bubble rendering

In this paper, we propose a method to generate and optimize the height map that is suitable to render a soap bubble. The height map represents the flow speed of soap bubbles. To this end, we have analyzed the flow of the soap bubble surface through experiment, derived the moving speed value for each section. Some image filters have been used for optimization that reflects the parameters of the derived height map. In addition, in order to verify the results of the study, actual data measuring the surface flow speed of soap bubbles, the speed of the initial height map, and the optimized height map speed have been compared and tested. Through this study, we reach the issue that it is possible to express the variable flow speed of soap bubbles with the optimized height map, and it will help to express various fluids.
Various Attribute Based Encryption Algorithms in Clouds-A Survey

— Attribute Based Encryption method (ABE) ensures that authorized access to the data. Due to the huge user base and often unknown identities of target recipients the User based access control models are too restrictive. The ABE is highly efficient and flexible since it allows a content provider to specify an access policy and encrypt multiple messages within one ciphertext. In particular, only the users whose attributes satisfy the access policy can decrypt the ciphertext. The ABE method provides security for appropriate users when accessing the data’s and the encryptions and decryptions are done by the user based access control attributes. The ABE authority will assign the access rights for various users. Attribute-Based Encryption is a promising cryptographic primitive which greatly enhances the versatility of access control mechanisms. The computational complexities of ABE key-issuing and decryption are getting prohibitively high because of the high expressiveness of ABE policies. This paper focuses on various concepts for ABE encryption and decryption methods for providing Security.
Can Fake News Detection be Accountable? The Adversarial Examples Challenge (Extended Abstract)

Automated fake news detection is an important challenge in view of the increasing ability of statistical language models to generate large amounts of (possibly fake) articles, so that recognizing them manually becomes unrealistic. Yet, the reliable deployment of such automated detection tools would require ensuring that they are accountable. Algorithmic accountability is known to be diﬃcult to reach, especially when adversarial behaviors aim to make algorithms deviate from their expected mode of operation. In this paper, we illustrate with a case study that this challenge is further ampliﬁed in contexts where the labeling of the articles is prone to errors, which is the case of fake news detection.
Adaptive RNN with CSOA controlled based MMC-DSTATCOM for PQ enhancement in Distribution System

In this paper, a hybrid convergence method is used to evaluate the development of PQ in a distribution device. For PQ analysis, a D-STATCOM-based Modular Multilevel Converter device is being analyzed. This proposed hybrid adapter is named as the Adaptive Recurrent Neural Network with a Crow Search Optimization Algorithm (ARNN-CSOA), which is used for Modular Multilevel Converter (MMC) optimization based on the device D-STATCOM. The proposed D-STATCOM advanced method will now go by providing a fast watt controller with invisible power to compensate for loads, modern day imbalances, flicker power reductions and voltage regulation. The proposed hybrid control strategy is to maximize the power of participation through the RNN approach. By using the proposed hybrid adapter method, the PI controller barriers are identified in advance to deliver appropriate MMC based DSTATCOM action. The proposed process learns all types of switches for mechanical problems such as DC power, real and active power. Based on the proposed procedure, the appropriate MM-based D-STATCOM cones are produced and obtain the correct results. The proposed method employed in the MATLAB / Simulink platform and is associated with different PWM methods such as SVM and ANN process.